Open Interpreter’s ChatGPT-Like Interface: The Future of Natural Language Code Execution

There’s an ever-increasing need for practical, adaptable, and user-friendly tools in the dynamic world of software development and programming. Developers are looking for solutions that allow them to operate effortlessly in their local development setups without being hampered by the confines of online hosted platforms. This article delves into a revolutionary answer to these demands – Open Interpreter.

While the Code Interpreter from OpenAI, based on the GPT-4 architecture, is undeniably transformative, it has limitations. Being a hosted solution, OpenAI’s offering runs in a distant, regulated environment, restricting developers from harnessing the full capabilities of their local configurations. Among the challenges faced are no access to the internet, a confined list of pre-loaded packages, a cap on uploads at 100 MB, and a runtime restriction of 120 seconds. Moreover, if the session terminates, any produced files or links vanish, creating obstacles for extended assignments.

With Open Interpreter, developers can fully utilize their computer’s resources, side-stepping the limitations of online services.

Understanding Open Interpreter

At its core, Open Interpreter is a bridge between human language and computer code. Instead of manually writing out every line of code, users can describe their intentions in natural language, and Open Interpreter translates that into executable code. This offers an entirely new programming approach, simplifying and streamlining the process.

Features of Open Interpreter

Code Interpretation from Natural Language: The primary selling point is that this feature allows users to describe a task in English, and the tool will convert it into executable code. It significantly reduces barriers to programming for non-developers.
ChatGPT-Like Interface: With a user-friendly, chatbot-like interface, users can have interactive programming sessions, making the coding experience more intuitive.
Data Handling Capabilities: Beyond simple code interpretation, Open Interpreter is a powerhouse for data-related tasks. It provides a comprehensive toolset for data professionals, from data visualization to in-depth analysis.
Open-Source Nature: Open Interpreter is a gift to the developer community. As an open-source project, developers can contribute to its growth, refining its features and expanding its capabilities.
Local Execution: In an age where data privacy is paramount, Open Interpreter allows for local code execution, ensuring that sensitive data remains on the user’s machine.

What can I do with Open Interpreter?

Educational Purposes: This can revolutionize teaching programming and other computer-related tasks. Students can communicate with their machines more intuitively, eliminating the steep learning curve associated with traditional programming.
Data Science & Analysis: As it can plot, clean, and analyze large datasets, researchers and data scientists can streamline their workflow using natural language to manipulate and visualize their data.
Automation: For individuals who aren’t traditionally programmers, Open Interpreter might offer a more accessible avenue for automating tasks on their computers, from simple file manipulations to more complex functions like web scraping.
Content Creation: The ability to create and edit photos, videos, and PDFs using natural language commands could revolutionize how content creators work. Imagine telling your computer how to edit a video or photo without manually going through the process.
Integration with Other Tools: Mention of it working with Code Llama or GPT-4 by using an OpenAI API key suggests that it can be integrated with other tools, increasing its utility.
Accessibility: For people with disabilities, communicating with a computer using natural language could make computing more accessible, especially if paired with voice recognition software.
Security: As with any powerful tool, there are potential risks. Running code or commands without understanding the implications can lead to unintended consequences. Users should be educated about the risks, especially when executing commands that can modify their system or data.
Open Source Community: The rapid growth in stars on GitHub indicates a strong community interest. This suggests that developers worldwide might contribute to its development, making the tool even more robust and versatile.

How do you install and use the Open Interpreter?

At this moment, you have to install it inside a Python environment.

First, you have to install Python; in my case, I have Python 3.11.2 installed.
In this step, it’s not necessary, but I recommend you install the Visual Studio Code. Press shift+control+p and create a Python virtual environment.
I created the “requirements.txt file” with the ChatGPT Code Interpreter libraries.

Python

pandas
numpy
scipy
xarray
#Machine Learning:
scikit-learn
xgboost
keras
torch
#Natural Language Processing:
nltk
spacy
textblob
gensim
#Data Visualization:
matplotlib
seaborn
plotly
bokeh
#Web Scraping and Network:
requests
urllib3
aiohttp
beautifulsoup4
#Deep Learning:
keras
torch
theano
#Image Processing:
pillow
imageio
opencv-python
scikit-image
#Audio Processing:
librosa
pyaudio
soundfile
#File Format:
openpyxl
xlrd
pyPDF2
python-docx
#Database:
sqlalchemy
psycopg2
mysql-connector-python
#Web Development:
flask
django
tornado
quart
#Others:
pytest
joblib
pytz
pyyaml

python-docx
pdfkit
wkhtmltopdf
pandoc 
pdfplumber  
reportlab
open-interpreter

Type pip install -r requirements.txt to install the most used Python libraries.

PowerShell

pip install -r requirements.txt

After that, install the Open Interpreter.

PowerShell

pip install open-interpreter

After installation, run interpreter:

PowerShell

interpreter -y

Now, you are free to use and test the Code Interpreter. If you want more detailed information, go to the GitHub oficial page.

Insert the prompt:

create a csv file with a year from 2000 to 2023 and total of sales amount column with ramdom numbers

Note that a file called sales_data.csv was automatically created in the folder.

Now type this prompt: create a line visual to show this sales_data.csv and plot on the screen.

You can do whatever you want. You also can create a code to use Open Interpreter, like the example below.

Python

import interpreter

interpreter.chat("Plot AAPL and META's normalized stock prices") # Executes a single command
interpreter.chat() # Starts an interactive chat

Type py teste.py to execute this code.

Python

PS D:\interpreter> & d:/interpreter/.venv/Scripts/Activate.ps1
(.venv) PS D:\interpreter> py teste.py

▌ Model set to GPT-4
Tip: To run locally, use interpreter --local

Open Interpreter will require approval before running code. Use interpreter -y to bypass this.

Press CTRL-C to exit.

  To plot AAPL and META's normalized stock prices, we will need to follow these steps:

   1 Install necessary packages. We will need pandas, yfinance, and matplotlib for Python.
   2 Download the stock prices for AAPL and META using yfinance.
   3 Normalize the stock prices.
   4 Plot the normalized stock prices.

  Let's start with the first step, which is installing the necessary packages.

  import pkg_resources                                                                                                                                                          
                                                                                                                                                                                
  REQUIRED_PACKAGES = ['pandas', 'yfinance', 'matplotlib']                                                                                                                      
                                                                                                                                                                                
  for package in REQUIRED_PACKAGES:                                                                                                                                             
      try:                                                                                                                                                                      
          dist = pkg_resources.get_distribution(package)                                                                                                                        
          print('{} ({}) is installed'.format(dist.key, dist.version))                                                                                                          
      except pkg_resources.DistributionNotFound:                                                                                                                                
          print('{} is NOT installed'.format(package))                                                                                                                          

  import pkg_resources                                                                                                                                                          
                                                                                                                                                                                
  REQUIRED_PACKAGES = ['pandas', 'yfinance', 'matplotlib']                                                                                                                      
                                                                                                                                                                                
  for package in REQUIRED_PACKAGES:                                                                                                                                             
          dist = pkg_resources.get_distribution(package)                                                                                                                        
          print('{} ({}) is installed'.format(dist.key, dist.version))                                                                                                          
      except pkg_resources.DistributionNotFound:                                                                                                                                
          print('{} is NOT installed'.format(package))                                                                                                                          


  pandas (2.1.0) is installed
  yfinance (0.2.28) is installed
  matplotlib (3.8.0) is installed

  Great! All the necessary packages are already installed.

  Now, let's move on to the next step: downloading the stock prices for AAPL (Apple Inc.) and META (Meta Platforms Inc., formerly Facebook Inc.) using the yfinance package.    
  We will download the last 5 years of daily closing prices.

  import yfinance as yf                                                                                                                                                         
                                                                                                                                                                                
  # Define the ticker symbols                                                                                                                                                   
  symbols = ['AAPL', 'META']                                                                                                                                                    
                                                                                                                                                                                
  # Download the stock data                                                                                                                                                     
  stock_data = yf.download(symbols, start='2017-01-01', end='2022-12-31')['Close']                                                                                              
                                                                                                                                                                                
  # Print the first few rows of the data                                                                                                                                        
  stock_data.head()                                                                                                                                                             

  import yfinance as yf                                                                                                                                                         
                                                                                                                                                                                
  # Define the ticker symbols                                                                                                                                                   
  symbols = ['AAPL', 'META']                                                                                                                                                    
                                                                                                                                                                                
  stock_data = yf.download(symbols, start='2017-01-01', end='2022-12-31')['Close']                                                                                              
                                                                                                                                                                                
  # Print the first few rows of the data                                                                                                                                        
  stock_data.head()                                                                                                                                                             

  [                       0%%                      ]
  [*********************100%%**********************]  2 of 2 completed
  AAPL        META
  Date
  2017-01-03  29.037500  116.860001
  2017-01-04  29.004999  118.690002
  2017-01-05  29.152500  120.669998
  2017-01-06  29.477501  123.410004
  2017-01-09  29.747499  124.900002

  We have successfully downloaded the stock prices for AAPL and META.

  The next step is to normalize these stock prices. Normalizing the stock prices allows us to compare the two stocks on the same scale. We will normalize the prices such that  
  the first price is 1 and all subsequent prices are relative to the first price.

  normalized_data = stock_data / stock_data.iloc[0]                                                                                                                             
                                                                                                                                                                                
  # Print the first few rows of the normalized data                                                                                                                             
  normalized_data.head()                                                                                                                                                        

  normalized_data = stock_data / stock_data.iloc[0]                                                                                                                             
                                                                                                                                                                                
  # Print the first few rows of the normalized data                                                                                                                             
  normalized_data.head()                                                                                                                                                        

  AAPL      META
  Date
  2017-01-03  1.000000  1.000000
  2017-01-04  0.998881  1.015660
  2017-01-05  1.003960  1.032603
  2017-01-06  1.015153  1.056050


  The stock prices for AAPL and META have been successfully normalized.

  Now, let's move on to the final step: plotting the normalized stock prices. We will use the matplotlib package for this.

  import matplotlib.pyplot as plt                                                                                                                                               
                                                                                                                                                                                
  # Create a new figure                                                                                                                                                         
  plt.figure(figsize=(14, 7))                                                                                                                                                   
                                                                                                                                                                                
  # Plot the normalized stock prices                                                                                                                                            
  for symbol in symbols:                                                                                                                                                        
      plt.plot(normalized_data.index, normalized_data[symbol], label=symbol)                                                                                                    
                                                                                                                                                                                
  # Add a title and labels                                                                                                                                                      
  plt.title('Normalized Stock Prices: AAPL vs META')                                                                                                                            
  plt.xlabel('Date')                                                                                                                                                            
  plt.ylabel('Normalized Price')                                                                                                                                                
  plt.legend()                                                                                                                                                                  
                                                                                                                                                                                
  # Save the plot as an image                                                                                                                                                   
  plt.savefig('normalized_stock_prices.png')                                                                                                                                    
                                                                                                                                                                                
  # Close the plot                                                                                                                                                              
  plt.close()                                                                                                                                                                   

  import matplotlib.pyplot as plt                                                                                                                                               
                                                                                                                                                                                
  # Create a new figure                                                                                                                                                         
  plt.figure(figsize=(14, 7))                                                                                                                                                   
                                                                                                                                                                                
  # Plot the normalized stock prices                                                                                                                                            
  for symbol in symbols:                                                                                                                                                        
      plt.plot(normalized_data.index, normalized_data[symbol], label=symbol)                                                                                                    
                                                                                                                                                                                
  # Add a title and labels                                                                                                                                                      
  plt.title('Normalized Stock Prices: AAPL vs META')                                                                                                                            
  plt.xlabel('Date')                                                                                                                                                            
  plt.ylabel('Normalized Price')                                                                                                                                                
  plt.legend()                                                                                                                                                                  
                                                                                                                                                                                
  # Save the plot as an image                                                                                                                                                   
  plt.savefig('normalized_stock_prices.png')                                                                                                                                    
                                                                                                                                                                                
  # Close the plot                                                                                                                                                              
  plt.close()█                                                                                                                                                                  

  <Figure size 1400x700 with 0 Axes>
  [<matplotlib.lines.Line2D object at 0x0000024DE75D0B90>]
  [<matplotlib.lines.Line2D object at 0x0000024DE7604990>]
  Text(0.5, 1.0, 'Normalized Stock Prices: AAPL vs META')
  Text(0.5, 0, 'Date')
  Text(0, 0.5, 'Normalized Price')
  <matplotlib.legend.Legend object at 0x0000024DE75DAB50>

Let’s try asking for a search on the internet: What are the last 10 BBC news headlines?

Python

(.venv) PS D:\interpreter> interpreter -y
> What are the last 10 BBC news headlines?

  To get the last 10 BBC news headlines, we can use web scraping techniques. Python has several libraries for this purpose, such as BeautifulSoup and requests. Here is the     
  plan:

   1 Install necessary Python packages (requests and BeautifulSoup) if they are not already installed.
   2 Fetch the BBC News homepage HTML using requests.
   3 Parse the HTML using BeautifulSoup to find the news headlines.
   4 Extract and return the last 10 news headlines.

  Let's start with the first step, which is to check if the necessary Python packages are installed. If not, we will install them.



  import pkg_resources                                                                                                                                                          
                                                                                                                                                                                
  REQUIRED_PACKAGES = ['beautifulsoup4', 'requests']                                                                                                                            
                                                                                                                                                                                
  for package in REQUIRED_PACKAGES:                                                                                                                                             
      try:                                                                                                                                                                      
          dist = pkg_resources.get_distribution(package)                                                                                                                        
          print('{} ({}) is installed'.format(dist.key, dist.version))                                                                                                          
      except pkg_resources.DistributionNotFound:                                                                                                                                
          print('{} is NOT installed'.format(package))                                                                                                                          


  beautifulsoup4 (4.12.2) is installed
  requests (2.31.0) is installed


  Great! Both 'beautifulsoup4' and 'requests' packages are already installed.

  Now, let's move to the next step of the plan: Fetch the BBC News homepage HTML using requests. We will send a GET request to the BBC News homepage URL and print the status   
  of the response to ensure we are able to connect to the page.

  import requests                                                                                                                                                               
                                                                                                                                                                                
  url = 'https://www.bbc.co.uk/news'                                                                                                                                            
  response = requests.get(url)                                                                                                                                                  
                                                                                                                                                                                
  print('Status code:', response.status_code)                                                                                                                                   

  Status code: 200


  The status code 200 indicates that we successfully fetched the HTML of the BBC News homepage.

  Now, let's move to the next step of the plan: Parse the HTML using BeautifulSoup to find the news headlines. We will parse the HTML and find the elements that contain the    
  news headlines. For this, we need to know the structure of the BBC News homepage. The headlines are usually contained in HTML elements with specific class names. We will     
  try to find these elements and print some of them to verify that we are correctly extracting the headlines.

  from bs4 import BeautifulSoup                                                                                                                                                 
                                                                                                                                                                                
  soup = BeautifulSoup(response.text, 'html.parser')                                                                                                                            
                                                                                                                                                                                
  # BBC News headlines are usually in h3 elements with class 'gs-c-promo-heading__title'                                                                                        
                                                                                                                                                                                
  # Print the first 5 headlines to verify                                                                                                                                       
  for headline in headlines[:5]:                                                                                                                                                
      print(headline.text.strip())                                                                                                                                              

  Russell Brand accused of rape and sexual assault
  Russell Brand accused of rape and sexual assault
  A barren wasteland with a lingering smell of death
  Diamonds and diplomacy: How a US envoy fell from grace
  'Everything has just been completely ripped away'


  We have successfully extracted the news headlines from the BBC News homepage. The headlines printed above are a sample of the headlines we found.

  Now, let's move to the final step of the plan: Extract and return the last 10 news headlines. We will extract the text from the last 10 headline elements and return them.    

  # Get the last 10 headlines                                                                                                                                                   
  last_10_headlines = [headline.text.strip() for headline in headlines[-10:]]                                                                                                   
                                                                                                                                                                                
  last_10_headlines                                                                                                                                                             


  ['How old is too old to lead?', 'The companies staying fully remote', "A 'beach towel revolt' sweeping Greece", 'Sexton breaks record as eight-try Ireland beat Tonga',       
  'Wales struggle past Portugal at World Cup', 'Brighton beat Man Utd as fans boo Ten Hag', 'Barcelona crush Betis to go top', 'Sainz on Singapore pole as angry Verstappen     
  struggles', "'We have a good vibe' - Ten Hag avoids Sancho questioning", 'Newcastle beat Brentford to ease pressure on Howe']

  Here are the last 10 BBC news headlines:

    1 'How old is too old to lead?'
    2 'The companies staying fully remote'
    3 "A 'beach towel revolt' sweeping Greece"
    4 'Sexton breaks record as eight-try Ireland beat Tonga'
    5 'Wales struggle past Portugal at World Cup'
    6 'Brighton beat Man Utd as fans boo Ten Hag'
    7 'Barcelona crush Betis to go top'
    8 'Sainz on Singapore pole as angry Verstappen struggles'
    9 "'We have a good vibe' - Ten Hag avoids Sancho questioning"
   10 'Newcastle beat Brentford to ease pressure on Howe'

Can you create a PDF file with these 10 BBC news headlines to finish the test?

Python

> can you crate a pdf file with this 10 BBC news headlines?

  Sure, we can create a PDF file with these headlines. We will use the Python library reportlab for this purpose. Here is the plan:

   1 Install the necessary Python package (reportlab) if it is not already installed.
   2 Create a PDF file using reportlab and add the headlines to it.
   3 Save the PDF file.

  Let's start with the first step, which is to check if the necessary Python package is installed. If not, we will install it.


  REQUIRED_PACKAGES = ['reportlab']                                                                                                                                             
                                                                                                                                                                                
  for package in REQUIRED_PACKAGES:                                                                                                                                             
      try:                                                                                                                                                                      
          dist = pkg_resources.get_distribution(package)                                                                                                                        
          print('{} ({}) is installed'.format(dist.key, dist.version))                                                                                                          
      except pkg_resources.DistributionNotFound:                                                                                                                                
          print('{} is NOT installed'.format(package))                                                                                                                          


  reportlab (4.0.4) is installed


  Great! The 'reportlab' package is already installed.

  Now, let's move to the next step of the plan: Create a PDF file using reportlab and add the headlines to it. We will create a new PDF file, add each headline as a separate   
  paragraph, and then save the PDF file.



  from reportlab.lib.pagesizes import letter                                                                                                                                    
  from reportlab.pdfgen import canvas                                                                                                                                           
  from reportlab.lib.units import inch                                                                                                                                          
                                                                                                                                                                                
  # Create a new PDF file                                                                                                                                                       
  c = canvas.Canvas('BBC_headlines.pdf', pagesize=letter)                                                                                                                       
                                                                                                                                                                                
  # Set the font and size                                                                                                                                                       
  c.setFont('Helvetica', 12)                                                                                                                                                    
                                                                                                                                                                                
  # Add each headline as a separate paragraph                                                                                                                                   
  y = 750  # Start from the top of the page                                                                                                                                     
  for i, headline in enumerate(last_10_headlines, 1):                                                                                                                           
      c.drawString(50, y, f'{i}. {headline}')                                                                                                                                   
      y -= 25  # Move down for the next headline                                                                                                                                
                                                                                                                                                                                
  # Save the PDF file                                                                                                                                                           
  c.save()                                                                                                                                                                      

  The PDF file 'BBC_headlines.pdf' with the last 10 BBC news headlines has been created successfully. You can find it in the current working directory (D:\interpreter).

It’s fantastic. Can you imagine how many things you can do with this?

They also have a Google Colab version with many examples of use.

I created a GitHub repository with this project. If you want, you can click h e re to download or clone in this hyperlink: https://github.com/LawrenceTeixeira/OpenInterpreter.git

Follow bellow the official Open Interpreter website.

https://openinterpreter.com/

Open Interpreter 🖥️ ChatGPT Code Interpreter You Can Run LOCALLY!

How Does Open Interpreter Stand Out?

While numerous code interpreters are available, Open Interpreter sets itself apart in its approach to bridging human language and computer code. The emphasis on natural language processing combined with the power of local execution ensures that it’s both accessible to newcomers and powerful enough for seasoned developers.

Conclusion

Open Interpreter stands as a monumental advancement in local development platforms. Its superior features equip developers to operate productively and with adaptability, unfettered by the confines of online services. With Open Interpreter by your side, step into the next era of software development and discover unparalleled avenues in your local development odyssey. It is more than just a tool; it’s a step forward in the evolution of programming. Combining the powers of natural language processing, AI, and code interpretation paves the way for a more inclusive and streamlined coding environment. As the world embraces digital solutions, tools like Open Interpreter make technology accessible to everyone.

That’s it for today!

Sources:

The Open Interpreter Project

KillianLucas/open-interpreter: OpenAI’s Code Interpreter in your terminal, running locally (github.com)

https://beebom.com/how-run-open-interpreter-run-chatgpt-code-interpreter-locally/

https://openaimaster.com/what-is-open-interpreter/

Data Wrangler in Microsoft Fabric: A New Tool for Accelerating Data Preparation. Experience the Power Query Feel but with Python Code Output

In the modern digital era, the importance of streamlined data preparation cannot be emphasized enough. For data scientists and analysts, a large portion of time is dedicated to data cleansing and preparation, often termed ‘wrangling.’ Microsoft’s introduction of Data Wrangler in its Fabric suite seems like an answer to this age-old challenge. It promises Power Query’s intuitiveness and Python code outputs’ flexibility. Dive in to uncover the magic of this new tool.

Data preparation is a time-consuming and error-prone task. It often involves cleaning, transforming, and merging data from multiple sources. This can be a daunting task, even for experienced data scientists.

What is Data Wrangler?

Data Wrangler is a state-of-the-art tool Microsoft offers in its Fabric suite explicitly designed for data professionals. At its core, it aims to simplify the data preparation process by automating tedious tasks. Much like Power Query, it offers a user-friendly interface, but what sets it apart is that it can generate Python code as an output. As users interact with the GUI, Python code snippets are generated behind the scenes, making integrating various data science workflows easier.

Advantages of Data Wrangler

User-Friendly Interface: Offers an intuitive GUI for those not comfortable with coding.
Python Code Output: Generates Python code in real-time, allowing flexibility and easy integration.
Time-Saving: Reduces the time spent on data preparation dramatically.
Replicability: Since Python code is generated, it ensures replicable data processing steps.
Integration with Fabric Suite: Can be effortlessly integrated with other tools within the Microsoft Fabric suite.
No-code to Low-code Transition: Ideal for those wanting to transition from a no-code environment to a more code-centric one.

How to use Data Wrangler?

You have to click on Data Science inside the Power BI Service.

You have to select the Notebook button.

You have to insert this code above after the upload of the CSV file in the LakeHouse.

Python

import pandas as pd

# Read a CSV into a Pandas DataFrame from e.g. a public blob store
df = pd.read_csv("/lakehouse/default/Files/Top_1000_Companies_Dataset.csv")

You have to click in the Lauch Data Wrangler and then select the data frame “df”.

On this screen, you can do all transformations you need.

In the end this code will be generate.

Python

# Code generated by Data Wrangler for pandas DataFrame

def clean_data(df):
    # Drop columns: 'company_name', 'url' and 6 other columns
    df = df.drop(columns=['company_name', 'url', 'city', 'state', 'country', 'employees', 'linkedin_url', 'founded'])
    # Drop columns: 'GrowjoRanking', 'Previous Ranking' and 10 other columns
    df = df.drop(columns=['GrowjoRanking', 'Previous Ranking', 'job_openings', 'keywords', 'LeadInvestors', 'Accelerator', 'valuation', 'btype', 'total_funding', 'product_url', 'growth_percentage', 'contact_info'])
    # Drop column: 'indeed_url'
    df = df.drop(columns=['indeed_url'])
    # Performed 1 aggregation grouped on column: 'Industry'
    df = df.groupby(['Industry']).agg(estimated_revenues_sum=('estimated_revenues', 'sum')).reset_index()
    # Sort by column: 'estimated_revenues_sum' (descending)
    df = df.sort_values(['estimated_revenues_sum'], ascending=[False])
    return df

df_clean = clean_data(df.copy())
df_clean.head()

After that, you can create or add to a pipeline or schedule a moment to execute this transformation automatically.

Data Wrangler Extension for Visual Studio Code

Data Wrangler is a code-centric data cleaning tool integrated into VS Code and Jupyter Notebooks. Data Wrangler aims to increase the productivity of data scientists doing data cleaning by providing a rich user interface that automatically generates Pandas code and shows insightful column statistics and visualizations.

This document will cover how to:

Install and setup Data Wrangler
Launch Data Wrangler from a notebook
Use Data Wrangler to explore your data
Perform operations on your data
Edit and export code for data wrangling to a notebook
Troubleshooting and providing feedback

Setting up your environment

If you have not already done so, install Python.
IMPORTANT: Data Wrangler only supports Python version 3.8 or higher.
Install Visual Studio Code.
Install the Data Wrangler extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Data Wrangler extension is named Data Wrangler, and Microsoft publishes it.

When you launch Data Wrangler for the first time, it will ask you which Python kernel you would like to connect to. It will also check your machine and environment to see if any required Python packages are installed (e.g., Pandas).

Here is a list of the required versions for Python and Python packages, along with whether they are automatically installed by Data Wrangler:

Name Minimum required version Automatically installed
Python 3.8 No
pandas 0.25.2 Yes
regex* 2020.11.13 Yes

* We use the open-source regex package to be able to use Unicode properties (for example, /\p{Lowercase_Letter}/), which aren’t supported by Python’s built-in regex module (re). Unicode properties make it easier and cleaner to support foreign characters in regular expressions.

Name	Minimum required version	Automatically installed
Python	3.8	No
pandas	0.25.2	Yes
regex*	2020.11.13	Yes

If they are not found in your environment, Data Wrangler will attempt to install them for you via pip. If Data Wrangler cannot install dependencies, the easiest workaround is to run the pip install and then relaunch Data Wrangler manually. These dependencies are required for Data Wrangler such that it can generate Python and Pandas code.

Connecting to a Python kernel

There are currently two ways to connect to a Python kernel, as shown in the quick pick below.

1. Connect using a local Python interpreter

If this option is selected, the kernel connection is created using the Jupyter and Python extensions. We recommend this option for a simple setup and a quick way to start with Data Wrangler.

2. Connect using Jupyter URL and token

A kernel connection is created using JupyterLab APIs if this option is selected. Note that this option has performance benefits since it bypasses some initialization and kernel discovery processes. However, it will also require separate Jupyter Notebook server user management. We recommend this option generally in two cases: 1) if there are blocking issues in the first method and 2) for power users who would like to reduce the cold-start time of Data Wrangler.

To set up a Jupyter Notebook server and use it with this option, follow the steps below:

Install Jupyter. We recommend installing the accessible version of Anaconda with Jupyter installed. Alternatively, follow the official instructions to install it.
In the appropriate environment (e.g., in an Anaconda prompt if Anaconda is used), launch the server with the following command (replace the jupyter token with your secure token):
jupyter notebook --no-browser --NotebookApp.token='<your-jupyter-token>'
In Data Wrangler, connect using the address of the spawned server. E.g., http://localhost:8888, and pass in the token used in the previous step. Once configured, this information is cached locally and can automatically be reused for future connections.

Launching Data Wrangler

Once Data Wrangler has been successfully installed, there are 2 ways to launch it in VS Code.

Launching Data Wrangler from a Jupyter Notebook

If you are in a Jupyter Notebook working with Pandas data frames, you’ll now see a “Launch Data Wrangler” button appear after running specific operations on your data frame, such as df.head(). Clicking the button will open a new tab in VS Code with the Data Wrangler interface in a sandboxed environment.

Important note:
We currently only accept the following formats for launching:

df
df.head()
df.tail()

Where df is the name of the data frame variable. The code above should appear at the end of a cell without any comments or other code after it.

Launching Data Wrangler directly from a CSV file

You can also launch Data Wrangler directly from a local CSV file. To do so, open any VS Code folder with the CSV dataset you’d like to explore. In the File Explorer panel, right-click the. CSV dataset and click “Open in Data Wrangler.”

Using Data Wrangler

The Data Wrangler interface is divided into 6 components, described below.

The Quick Insights header lets you quickly see valuable information about each column. Depending on the column’s datatype, Quick Insights will show the distribution of the data, the frequency of data points, and missing and unique values.

The Data Grid gives you a scrollable pane to view your entire dataset. Additionally, when selecting an operation to perform, a preview will be illustrated in the data grid, highlighting the modified columns.

The Operations Panel is where you can search through Data Wrangler’s built-in data operations. The operations are organized by their top-level category.

The Summary Panel shows detailed summary statistics for your dataset or a specific column if one is selected. Depending on the data type, it will show information such as min, max values, datatype of the column, skew, and more.

The Operation History Panel shows a human-readable list of all the operations previously applied in the current Data Wrangling session. It enables users to undo specific operations or edit the most recent operation. Selecting a step will highlight the data grid changes and show the generated code associated with that operation.

The Code Preview section will show the Python and Pandas code that Data Wrangler has generated when an operation is selected. It will remain blank when no operation is selected. The code can even be edited by the user, and the data grid will highlight the effect on the data.

Example: Filtering a column

Let’s go through a simple example using Data Wrangler with the Titanic dataset to filter adult passengers on the ship.

We’ll start by looking at the quick insights of the Age column, and we’ll notice the distribution of the ages and that the minimum age is 0.42. For more information, we can glance at the Summary panel to see that the datatype is a float, along with additional statistics such as the passengers’ mean and median age.

To filter for only adult passengers, we can go to the Operation Panel and search for the keyword “Filter” to find the Filter operation. (You can also expand the “Sort and filter” category to find it.)

Once we select an operation, we are brought into the Operation Preview state, where parameters can be modified to see how they affect the underlying dataset before applying the operation. In this example, we want to filter the dataset only to include adults, so we’ll want to filter the Age column to only include values greater than or equal to 18.

Once the parameters are entered in the operation panel, we can see a preview of what will happen to the data. We’ll notice that the minimum value in age is now 18 in the Quick Insights, along with a visual preview of the rows that are being removed, highlighted in red. Finally, we’ll also notice the Code Preview section automatically shows the code that Data Wrangler produced to execute this Filter operation. We can edit this code by changing the filtered age to 21, and the data grid will automatically update accordingly.

After confirming that the operation has the intended effect, we can click Apply.

Editing and exporting code

Each step of the generated code can be modified. Changes to the data will be highlighted in the grid view as you make changes.

Once you’re done with your data cleaning steps in Data Wrangler, there are 3 ways to export your cleaned dataset from Data Wrangler.

Export code back to Notebook and exit: This creates a new cell in your Jupyter Notebook with all the data cleaning code you generated packaged into a clean Python function.
Export data as CSV: This saves the cleaned dataset as a new CSV file onto your machine.
Copy code to clipboard: This copies all the code generated by Data Wrangler for the data cleaning operations.

Note: If you launched Data Wrangler directly from a CSV, the first export option will be to export the code into a new Jupyter Notebook.

Data Wrangler operations

These are the Data Wrangler operations currently supported in the initial launch of Data Wrangler (with many more to be added soon).

Operation	Description
Sort values	Sort column(s) ascending or descending
Filter	Filter rows based on one or more conditions
Calculate text length	Create new column with values equal to the length of each string value in a text column
One-hot encode	Split categorical data into a new column for each category
Multi-label binarizer	Split categorical data into a new column for each category using a delimiter
Create column from formula	Create a column using a custom Python formula
Change column type	Change the data type of a column
Drop column	Delete one or more columns
Select column	Choose one or more columns to keep and delete the rest
Rename column	Rename one or more columns
Drop missing values	Remove rows with missing values
Drop duplicate rows	Drops all rows that have duplicate values in one or more columns
Fill missing values	Replace cells with missing values with a new value
Find and replace	Split a column into several columns based on a user-defined delimiter
Group by column and aggregate	Group by columns and aggregate results
Strip whitespace	Capitalize the first character of a string with the option to apply to all words.
Split text	Remove whitespace from the beginning and end of the text
Convert text to capital case	Automatically create a column when a pattern is detected from your examples.
Convert text to lowercase	Convert text to lowercase
Convert text to uppercase	Convert text to UPPERCASE
String transform by example	Automatically perform string transformations when a pattern is detected from the examples you provide
DateTime formatting by example	Automatically perform DateTime formatting when a pattern is detected from the examples you provide
New column by example	Automatically create a column when a pattern is detected from the examples you provide.
Scale min/max values	Scale a numerical column between a minimum and maximum value
Custom operation	Automatically create a new column based on examples and the derivation of existing column(s)

Limitations

Data Wrangler currently supports only Pandas DataFrames. Support for Spark DataFrames is in progress.
Data Wrangler’s display works better on large monitors, although different interface portions can be minimized or hidden to accommodate smaller screens.

Conclusion

Data Wrangler in Microsoft Fabric is undeniably a game-changer in data preparation. It combines the best of both worlds by offering the simplicity of Power Query with the robustness and flexibility of Python. As data continues to grow in importance, tools like Data Wrangler that simplify and expedite the data preparation process will be indispensable for organizations aiming to stay ahead.

That’s it for today!

Sources:

https://medium.com/towards-data-engineering/data-wrangler-in-fabric-simplifying-data-prep-with-no-code-ab4fe7429b49

https://radacad.com/fabric-data-wrangler-a-tool-for-data-scientist

https://learn.microsoft.com/en-us/fabric/data-science/data-wrangler

https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler

https://github.com/microsoft/vscode-data-wrangler

Presenting the First Edition of the New ChatBot for Making Questions about Trademarks, Patents, and More from the Brazilian Patent and Trademark Office (BRPTO)

It is with great delight that I introduce the innovative ChatBot developed with artificial intelligence to answer queries concerning the official manuals provided by the National Institute of Industrial Property (BRPTO) on trademarks, patents, industrial designs, and more.

These manuals, presented in Brazilian Portuguese PDF format, are readily accessible in the respective sections of the INPI website. What’s unique about this initiative is the inclusivity that transcends language barriers. In the app, you can ask questions in any language, and the ChatBot will comprehend and respond in the language used in the query.

A vital feature of this AI-powered ChatBot is its ability to provide the exact source of the information extracted. Each time the ChatBot responds to a query, it will present the hyperlink to the PDF from where the information was derived. This enhances transparency and allows users to delve into more details if they wish.

Moreover, within the left-side options in the app, you can access the complete manuals utilized to create the AI model. This provides direct access to the information and serves as a testament to the quality and reliability of the data used.

How do you get to the BRPTO’s Chatbot app?

To try out the app, click here to reach out to me. Please include “BRPTO Chatbot” in your message.

Here are a few examples of the questions you can ask:

What is a patent?
What is a trademark?
What are the fees I need to pay to file a patent?
What is a contract?
What is copyright?
What are the rules of geographical indications?
How was the intellectual protection of circuit topology established?
What are the filing procedures for an industrial design?

What are the limitations?

If the National Institute of Industrial Property (INPI) makes any updates to a manual, it will be necessary to reprocess the Artificial Intelligence model and roll out a new update for the app.
The chatbot is only limited to answering questions related to the manuals specified on the left side of the app.
Any questions outside the context of these manuals will not be answered.
As we are using a free version of the vector database (Pinecone), there might be some confusion in the responses.

How the app was created?

The app was created based on the guidelines provided in my previous post that I copy below.

Asking questions via chat to the BRPTO’s Basic Manual for Patent Protection PDF, using LangChain, Pinecone, and Open AI

Conclusion

The AI ChatBot is an innovative solution that aims to democratize access to information and make it more convenient for the public to learn and understand essential aspects of industrial property rights. So feel free to explore, ask, and learn!

That’s it for today!

Exploring Patent Data with Artificial Intelligence: An Automated Approach Using Langchain, Open AI, Google Search API, and Browserless

Navigating the Challenges of Data Extraction in Intellectual Property: Overcoming Obstacles with the Help of AI for Enhanced Analyses. With the rapid advancement of generative AI, let’s discuss how it’s possible to automate formerly complex tasks such as analyzing a 2023 Excel file from WIPO’s official patent gazette, identifying the top 10 applicants, and generating detailed summaries. Ultimately, we’ll integrate these insights into our Data Lake, but Data Warehouse or database options are also feasible.

Increasingly, we face a range of challenges in extracting data for Intellectual Property analyses, from a lack of standardization to the technological limitations of official bodies. Often, we find ourselves resorting to manual methods to collect specific information that can be integrated into our Data Lake for comprehensive data analysis.

However, thanks to the ongoing advancements of generative Artificial Intelligence (AI), particularly following the popularization of ChatGPT in November 2022, we’re witnessing the growing ease of automating tasks previously considered unreachable through traditional programming.

In this article, I’ll demonstrate how it’s possible to read an Excel file from a 2023 official publication of the World Intellectual Property Organization (OMPI), look for the top ten applicants, and employ a robot to search for these applicants on the internet. The AI will create a summary of each of these applicants, clarifying the type of company, their business lines, global presence, and websites, among others.

The obtained information will be saved in an Excel file. However, it’s worth noting that this data can be easily transferred to a Data Lake, Data Warehouse, or any other database system you prefer to use for your data analysis needs.

What is Google Search API?

Google Search API is a tool that allows developers to create programs that can search the internet and return results. It is like a library of code that developers can use to build their own search engines or other applications that use Google’s search technology. It is an important tool for people who want to build websites or apps that use search functionality.

Website: SerpApi: Google Search API

What is Browserless?

Browserless is a cloud-based platform that allows you to automate web-browser tasks. It uses open-source libraries and REST APIs to collect data, automate sites without APIs, produce PDFs, or run synthetic testing. In other words, it is a browser-as-a-service where you can use all the power of headless Chrome, hassle-free¹. It offers first-class integrations for Puppeteer, Playwright, Selenium’s WebDriver, and a slew of handy REST APIs for doing more common work.

Website: Browserless – #1 Web Automation & Headless Browser Automation Tool

I have created an application meant to test your search for other applicants on the web. Feel free to access it here.

For accessing the OMPI’s official patent gazette file, click here, and to access the Applications_informations file generated automatically by the Python code, click here.

To learn more about Langchain, click on the link to another post provided below:

Asking questions via chat to the BRPTO’s Basic Manual for Patent Protection PDF, using LangChain, Pinecone, and Open AI

To learn more about ChatGPT API, click on the link to another post provided below:

Open AI released this week the new ChatGPT API

Take a look at the Python script that extracts patent applicant information from the web. It’s organized into two distinct sections: the functions section and the app section.

1 – functions.py

Python

import os
from langchain import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
from typing import Type
from bs4 import BeautifulSoup
import requests
import json
from langchain.schema import SystemMessage

from dotenv import load_dotenv

load_dotenv()

brwoserless_api_key = os.getenv("BROWSERLESS_API_KEY")
serper_api_key = os.getenv("SERP_API_KEY")

# 1. Tool for search

def search(query):
    url = "https://google.serper.dev/search"

    payload = json.dumps({
        "q": query
    })

    headers = {
        'X-API-KEY': serper_api_key,
        'Content-Type': 'application/json'
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    print(response.text)

    return response.text


# 2. Tool for scraping
def scrape_website(objective: str, url: str):
    # scrape website, and also will summarize the content based on objective if the content is too large
    # objective is the original objective & task that user give to the agent, url is the url of the website to be scraped

    print("Scraping website...")
    # Define the headers for the request
    headers = {
        'Cache-Control': 'no-cache',
        'Content-Type': 'application/json',
    }

    # Define the data to be sent in the request
    data = {
        "url": url
    }

    # Convert Python object to JSON string
    data_json = json.dumps(data)

    # Send the POST request
    post_url = f"https://chrome.browserless.io/content?token={brwoserless_api_key}"
    response = requests.post(post_url, headers=headers, data=data_json)

    # Check the response status code
    if response.status_code == 200:
        soup = BeautifulSoup(response.content, "html.parser")
        text = soup.get_text()
        print("CONTENTTTTTT:", text)

        if len(text) > 10000:
            output = summary(objective, text)
            return output
        else:
            return text
    else:
        print(f"HTTP request failed with status code {response.status_code}")


def summary(objective, content):
    llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")

    text_splitter = RecursiveCharacterTextSplitter(
        separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500)
    docs = text_splitter.create_documents([content])
    map_prompt = """
    Write a summary of the following text for {objective}:
    "{text}"
    SUMMARY:
    """
    map_prompt_template = PromptTemplate(
        template=map_prompt, input_variables=["text", "objective"])

    summary_chain = load_summarize_chain(
        llm=llm,
        chain_type='map_reduce',
        map_prompt=map_prompt_template,
        combine_prompt=map_prompt_template,
        verbose=True
    )

    output = summary_chain.run(input_documents=docs, objective=objective)

    return output


class ScrapeWebsiteInput(BaseModel):
    """Inputs for scrape_website"""
    objective: str = Field(
        description="The objective & task that users give to the agent")
    url: str = Field(description="The url of the website to be scraped")


class ScrapeWebsiteTool(BaseTool):
    name = "scrape_website"
    description = "useful when you need to get data from a website url, passing both url and objective to the function; DO NOT make up any url, the url should only be from the search results"
    args_schema: Type[BaseModel] = ScrapeWebsiteInput

    def _run(self, objective: str, url: str):
        return scrape_website(objective, url)

    def _arun(self, url: str):
        raise NotImplementedError("error here")

2 – extract.py

Python

import pandas as pd
from functions import search, ScrapeWebsiteTool
from langchain.agents import initialize_agent, Tool
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.prompts import MessagesPlaceholder
from langchain.memory import ConversationSummaryBufferMemory
from langchain.schema import SystemMessage

# 3. Create langchain agent with the tools above
tools = [
    Tool(
        name="Search",
        func=search,
        description="useful for when you need to answer questions about current events, data. You should ask targeted questions"
    ),
    ScrapeWebsiteTool(),
]

system_message = SystemMessage(
    content="""You are a world class researcher, who can do detailed research on any topic and produce facts based results; 
            you do not make things up, you will try as hard as possible to gather facts & data to back up the research
            
            Please make sure you complete the objective above with the following rules:
            1/ You should do enough research to gather as much information as possible about the objective
            2/ If there are url of relevant links & articles, you will scrape it to gather more information
            3/ After scraping & search, you should think "is there any new things i should search & scraping based on the data I collected to increase research quality?" If answer is yes, continue; But don't do this more than 3 iteratins
            4/ You should not make things up, you should only write facts & data that you have gathered
            5/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research
            6/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research"""
)

agent_kwargs = {
    "extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
    "system_message": system_message,
}

llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
memory = ConversationSummaryBufferMemory(
    memory_key="memory", return_messages=True, llm=llm, max_token_limit=1000)

agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.OPENAI_FUNCTIONS,
    verbose=True,
    agent_kwargs=agent_kwargs,
    memory=memory,
)

# Read the excel file using pandas
data = pd.read_excel('https://lawrence.eti.br/wp-content/uploads/2023/07/2023.xlsx')

# Print the first few rows
print(data.head())

# Assuming 'Applicant' is a column in your excel file
top_applicants = data['Applicant'].value_counts().nlargest(10)
print(top_applicants)

# Prepare an empty list to store the results
results = []

# Iterate over each applicant and their count
for applicant_name, count in top_applicants.items():
    first_word = str(applicant_name).split()[0]
    print('First word of applicant: ', first_word)
    
    # You can now use first_word in your agent function
    result = agent({"input": first_word})
    print('Applicant :', applicant_name, 'Information: ',result['output'])

    # Append the result into the results list
    results.append({'Applicant': applicant_name, 'Information': result['output']})

# Convert the results list into a DataFrame
results_df = pd.DataFrame(results)

# Save the DataFrame into an Excel file
results_df.to_excel("Applicants_Informations.xlsx", index=False)

Upon executing this Python script, you’ll observe the following in the console:

PowerShell

(.venv) PS D:\researcher\researcher-gpt> & d:/researcher/researcher-gpt/.venv/Scripts/python.exe d:/researcher/researcher-gpt/extract.py

  Publication Number Publication Date  ...                     Applicant                                                Url
0     WO/2023/272317       2023-01-05  ...  INNIO JENBACHER GMBH & CO OG  http://patentscope.wipo.int/search/en/WO202327...
1     WO/2023/272318       2023-01-05  ...                  STIRTEC GMBH  http://patentscope.wipo.int/search/en/WO202327...
2     WO/2023/272319       2023-01-05  ...                 SENDANCE GMBH  http://patentscope.wipo.int/search/en/WO202327...
3     WO/2023/272320       2023-01-05  ...                  HOMER, Alois  http://patentscope.wipo.int/search/en/WO202327...
4     WO/2023/272321       2023-01-05  ...      TGW LOGISTICS GROUP GMBH  http://patentscope.wipo.int/search/en/WO202327...
[5 rows x 8 columns]

Applicant
HUAWEI TECHNOLOGIES CO., LTD.                           3863
SAMSUNG ELECTRONICS CO., LTD.                           2502
QUALCOMM INCORPORATED                                   1908
GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., LTD.    1186
LG ELECTRONICS INC.                                     1180
ZTE CORPORATION                                         1134
TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)                  1039
CONTEMPORARY AMPEREX TECHNOLOGY CO., LIMITED             987
LG ENERGY SOLUTION, LTD.                                 967
NIPPON TELEGRAPH AND TELEPHONE CORPORATION               946

Entering new AgentExecutor chain…
Huawei is a Chinese multinational technology company that specializes in telecommunications equipment and consumer electronics. It was founded in 1987 by Ren Zhengfei and is headquartered in Shenzhen, Guangdong, China. Huawei is one of the largest telecommunications equipment manufacturers in the world and is also a leading provider of smartphones and other consumer devices.

Here are some key points about Huawei:

Telecommunications Equipment: Huawei is a major player in the telecommunications industry, providing a wide range of equipment and solutions for network infrastructure, including 5G technology, mobile networks, broadband networks, and optical networks. The company offers products such as base stations, routers, switches, and optical transmission systems.
Consumer Devices: Huawei is known for its smartphones, tablets, smartwatches, and other consumer electronics. The company’s smartphone lineup includes flagship models under the Huawei brand, as well as budget-friendly devices under the Honor brand. Huawei smartphones are known for their advanced camera technology and innovative features.
Research and Development: Huawei invests heavily in research and development (R&D) to drive innovation and technological advancements. The company has established numerous R&D centers worldwide and collaborates with universities and research institutions to develop cutting-edge technologies. Huawei is particularly focused on areas such as 5G, artificial intelligence (AI), cloud computing, and Internet of Things (IoT).
Global Presence: Huawei operates in over 170 countries and serves more than three billion people worldwide. The company has established a strong presence in both developed and emerging markets, offering its products and services to telecommunications operators, enterprises, and consumers.
Controversies: Huawei has faced several controversies and challenges in recent years. The company has been accused by the United States government of posing a national security threat due to concerns over its alleged ties to the Chinese government. As a result, Huawei has faced restrictions and bans in some countries, limiting its access to certain markets.

For more detailed information about Huawei, you can refer to the following sources:

Huawei Official Website: https://www.huawei.com/
Wikipedia: https://en.wikipedia.org/wiki/Huawei

Please let me know if there is anything specific you would like to know about Huawei.

Finished chain.
Applicant : HUAWEI TECHNOLOGIES CO., LTD. Information: Huawei is a Chinese multinational technology company that specializes in telecommunications equipment and consumer electronics. It was founded in 1987 by Ren Zhengfei and is headquartered in Shenzhen, Guangdong, China. Huawei is one of the largest telecommunications equipment manufacturers in the world and is also a leading provider of smartphones and other consumer devices.

Entering new AgentExecutor chain…
Samsung is a South Korean multinational conglomerate that operates in various industries, including electronics, shipbuilding, construction, and more. It was founded in 1938 by Lee Byung-chul and is headquartered in Samsung Town, Seoul, South Korea. Samsung is one of the largest and most well-known technology companies in the world.

Here are some key points about Samsung:

Electronics: Samsung Electronics is the most prominent subsidiary of the Samsung Group and is known for its wide range of consumer electronics products. The company manufactures and sells smartphones, tablets, televisions, home appliances, wearable devices, and other electronic gadgets. Samsung is particularly renowned for its flagship Galaxy smartphones and QLED televisions.
Semiconductor: Samsung is a major player in the semiconductor industry. The company designs and manufactures memory chips, including DRAM (Dynamic Random Access Memory) and NAND flash memory, which are widely used in various electronic devices. Samsung is one of the leading suppliers of memory chips globally.
Display Technology: Samsung is a leader in display technology and is known for its high-quality screens. The company produces a variety of displays, including OLED (Organic Light Emitting Diode) panels, LCD (Liquid Crystal Display) panels, and AMOLED (Active Matrix Organic Light Emitting Diode) panels. Samsung’s displays are used in smartphones, televisions, monitors, and other devices.
Home Appliances: Samsung manufactures a range of home appliances, including refrigerators, washing machines, air conditioners, vacuum cleaners, and kitchen appliances. The company focuses on incorporating innovative features and smart technology into its appliances to enhance user experience and energy efficiency.
Global Presence: Samsung has a strong global presence and operates in numerous countries around the world. The company has manufacturing facilities, research centers, and sales offices in various locations, allowing it to cater to a wide customer base.
Research and Development: Samsung invests heavily in research and development to drive innovation and stay at the forefront of technology. The company has established multiple R&D centers globally and collaborates with universities and research institutions to develop new technologies and products.

For more detailed information about Samsung, you can refer to the following sources:

Samsung Official Website: https://www.samsung.com/
Wikipedia: https://en.wikipedia.org/wiki/Samsung

Please let me know if there is anything specific you would like to know about Samsung.

Finished chain.
Applicant : SAMSUNG ELECTRONICS CO., LTD. Information: Samsung is a South Korean multinational conglomerate that operates in various industries, including electronics, shipbuilding, construction, and more. It was founded in 1938 by Lee Byung-chul and is headquartered in Samsung Town, Seoul, South Korea. Samsung is one of the largest and most well-known technology companies in the world.

Entering new AgentExecutor chain…
Qualcomm Incorporated, commonly known as Qualcomm, is an American multinational semiconductor and telecommunications equipment company. It was founded in 1985 by Irwin M. Jacobs, Andrew Viterbi, Harvey White, and Franklin Antonio. The company is headquartered in San Diego, California, United States.

Here are some key points about Qualcomm:

Semiconductors: Qualcomm is a leading provider of semiconductors and system-on-chip (SoC) solutions for various industries, including mobile devices, automotive, networking, and IoT (Internet of Things). The company designs and manufactures processors, modems, and other semiconductor components that power smartphones, tablets, wearables, and other electronic devices.
Mobile Technologies: Qualcomm is widely recognized for its contributions to mobile technologies. The company has developed numerous innovations in wireless communication, including CDMA (Code Division Multiple Access) technology, which has been widely adopted in mobile networks worldwide. Qualcomm’s Snapdragon processors are widely used in smartphones and tablets, offering high performance and power efficiency.
5G Technology: Qualcomm is at the forefront of 5G technology development. The company has been instrumental in driving the adoption and commercialization of 5G networks and devices. Qualcomm’s 5G modems and SoCs enable faster data speeds, lower latency, and enhanced connectivity for a wide range of applications.
Licensing and Intellectual Property: Qualcomm holds a significant portfolio of patents related to wireless communication technologies. The company licenses its intellectual property to other manufacturers, generating a substantial portion of its revenue through licensing fees. Qualcomm’s licensing practices have been the subject of legal disputes and regulatory scrutiny in various jurisdictions.
Automotive and IoT: In addition to mobile devices, Qualcomm provides solutions for the automotive industry and IoT applications. The company offers connectivity solutions, processors, and software platforms for connected cars, telematics, and smart home devices. Qualcomm’s technologies enable advanced features such as vehicle-to-vehicle communication, infotainment systems, and autonomous driving capabilities.
Research and Development: Qualcomm invests heavily in research and development to drive innovation and stay competitive in the rapidly evolving technology landscape. The company has research centers and collaborations with academic institutions worldwide, focusing on areas such as wireless communication, AI (Artificial Intelligence), and IoT.

For more detailed information about Qualcomm, you can refer to the following sources:

Qualcomm Official Website: https://www.qualcomm.com/
Wikipedia: https://en.wikipedia.org/wiki/Qualcomm

Please let me know if there is anything specific you would like to know about Qualcomm.

Finished chain.
Applicant : QUALCOMM INCORPORATED Information: Qualcomm Incorporated, commonly known as Qualcomm, is an American multinational semiconductor and telecommunications equipment company. It was founded in 1985 by Irwin M. Jacobs, Andrew Viterbi, Harvey White, and Franklin Antonio. The company is headquartered in San Diego, California, United States.

Entering new AgentExecutor chain…
Guangdong is a province located in the southern part of China. It is one of the most populous and economically prosperous provinces in the country. Here are some key points about Guangdong:

Location and Geography: Guangdong is situated on the southern coast of China, bordering the South China Sea. It is adjacent to Hong Kong and Macau, two Special Administrative Regions of China. The province covers an area of approximately 180,000 square kilometers (69,500 square miles) and has a diverse landscape, including mountains, plains, and coastline.
Population: Guangdong has a large population, making it the most populous province in China. As of 2020, the estimated population of Guangdong was over 115 million people. The province is known for its cultural diversity, with various ethnic groups residing there, including Han Chinese, Cantonese, Hakka, and others.
Economy: Guangdong is one of the economic powerhouses of China. It has a highly developed and diversified economy, contributing significantly to the country’s GDP. The province is known for its manufacturing and export-oriented industries, including electronics, textiles, garments, toys, furniture, and more. Guangdong is home to many multinational corporations and industrial zones, attracting foreign investment and driving economic growth.
Trade and Ports: Guangdong has several major ports that play a crucial role in international trade. The Port of Guangzhou, Port of Shenzhen, and Port of Zhuhai are among the busiest and most important ports in China. These ports facilitate the import and export of goods, connecting Guangdong with global markets.
Tourism: Guangdong offers a rich cultural heritage and natural attractions, attracting tourists from both within China and abroad. The province is known for its historical sites, such as the Chen Clan Ancestral Hall, Kaiping Diaolou and Villages, and the Mausoleum of the Nanyue King. Guangdong also has popular tourist destinations like Shenzhen, Guangzhou, Zhuhai, and the scenic areas of the Pearl River Delta.
Cuisine: Guangdong cuisine, also known as Cantonese cuisine, is renowned worldwide. It is one of the eight major culinary traditions in China. Guangdong dishes are characterized by their freshness, delicate flavors, and emphasis on seafood. Dim sum, roast goose, sweet and sour dishes, and various types of noodles are popular examples of Guangdong cuisine.

For more detailed information about Guangdong, you can refer to the following sources:

Guangdong Government Official Website: http://www.gd.gov.cn/
Wikipedia: https://en.wikipedia.org/wiki/Guangdong

Please let me know if there is anything specific you would like to know about Guangdong.

Finished chain.
Applicant : GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., LTD. Information: Guangdong is a province located in the southern part of China. It is one of the most populous and economically prosperous provinces in the country. Here are some key points about Guangdong:

Entering new AgentExecutor chain…
LG Corporation, formerly known as Lucky-Goldstar, is a multinational conglomerate based in South Korea. It is one of the largest and most well-known companies in the country. Here are some key points about LG:

Company Overview: LG Corporation is a diversified company with operations in various industries, including electronics, chemicals, telecommunications, and more. It was founded in 1947 and has its headquarters in Seoul, South Korea. LG operates through numerous subsidiaries and affiliates, with a global presence in over 80 countries.
Electronics: LG is widely recognized for its consumer electronics products. The company manufactures and sells a wide range of electronic devices, including televisions, refrigerators, washing machines, air conditioners, smartphones, and home appliances. LG’s electronics division is known for its innovative designs, advanced technologies, and high-quality products.
LG Electronics: LG Electronics is a subsidiary of LG Corporation and focuses on the development, production, and sale of consumer electronics. It is one of the leading manufacturers of televisions and smartphones globally. LG’s OLED TVs are highly regarded for their picture quality, and the company’s smartphones have gained popularity for their features and design.
Chemicals: LG also has a significant presence in the chemical industry. The company produces a wide range of chemical products, including petrochemicals, industrial materials, and specialty chemicals. LG Chem, a subsidiary of LG Corporation, is one of the largest chemical companies in the world and is involved in the production of batteries for electric vehicles and energy storage systems.
Home Appliances: LG is a major player in the home appliance market. The company offers a comprehensive range of home appliances, including refrigerators, washing machines, dishwashers, vacuum cleaners, and air purifiers. LG’s home appliances are known for their energy efficiency, smart features, and innovative technologies.
Telecommunications: LG has a presence in the telecommunications industry through its subsidiary, LG Electronics. The company manufactures and sells smartphones, tablets, and other mobile devices. LG smartphones have gained recognition for their unique features, such as dual screens and high-quality cameras.
Research and Development: LG places a strong emphasis on research and development (R&D) to drive innovation and technological advancements. The company invests a significant amount in R&D activities across its various business sectors, focusing on areas such as artificial intelligence, 5G technology, and smart home solutions.

For more detailed information about LG Corporation, you can refer to the following sources:

LG Corporation Official Website: https://www.lgcorp.com/
LG Electronics Official Website: https://www.lg.com/

Please let me know if there is anything specific you would like to know about LG.

Finished chain.
Applicant : LG ELECTRONICS INC. Information: LG Corporation, formerly known as Lucky-Goldstar, is a multinational conglomerate based in South Korea. It is one of the largest and most well-known companies in the country. Here are some key points about LG:

Entering new AgentExecutor chain…
ZTE Corporation is a Chinese multinational telecommunications equipment and systems company. It is one of the largest telecommunications equipment manufacturers in the world. Here are some key points about ZTE:

Company Overview: ZTE Corporation was founded in 1985 and is headquartered in Shenzhen, Guangdong, China. It operates in three main business segments: Carrier Networks, Consumer Business, and Government and Corporate Business. ZTE provides a wide range of products and solutions for telecommunications operators, businesses, and consumers.
Telecommunications Equipment: ZTE is primarily known for its telecommunications equipment and solutions. The company offers a comprehensive portfolio of products, including wireless networks, fixed-line networks, optical transmission, data communication, and mobile devices. ZTE’s equipment is used by telecommunications operators worldwide to build and upgrade their networks.
5G Technology: ZTE has been actively involved in the development and deployment of 5G technology. The company has made significant contributions to the advancement of 5G networks and has been a key player in the global 5G market. ZTE provides end-to-end 5G solutions, including infrastructure equipment, devices, and software.
Mobile Devices: In addition to its telecommunications equipment business, ZTE also manufactures and sells mobile devices. The company offers a range of smartphones, tablets, and other mobile devices under its own brand. ZTE smartphones are known for their competitive features and affordability.
International Presence: ZTE has a global presence and operates in over 160 countries. The company has established partnerships with telecommunications operators and businesses worldwide, enabling it to expand its reach and market share. ZTE’s international operations contribute significantly to its revenue and growth.
Research and Development: ZTE places a strong emphasis on research and development (R&D) to drive innovation and technological advancements. The company invests a significant amount in R&D activities, focusing on areas such as 5G, artificial intelligence, cloud computing, and Internet of Things (IoT).
Corporate Social Responsibility: ZTE is committed to corporate social responsibility and sustainability. The company actively participates in various social and environmental initiatives, including education, poverty alleviation, disaster relief, and environmental protection.

For more detailed information about ZTE Corporation, you can refer to the following sources:

ZTE Corporation Official Website: https://www.zte.com.cn/global/

Please let me know if there is anything specific you would like to know about ZTE.

Finished chain.
Applicant : ZTE CORPORATION Information: ZTE Corporation is a Chinese multinational telecommunications equipment and systems company. It is one of the largest telecommunications equipment manufacturers in the world. Here are some key points about ZTE:

Entering new AgentExecutor chain…
Telefonaktiebolaget LM Ericsson, commonly known as Ericsson, is a Swedish multinational telecommunications company. Here are some key points about Ericsson:

Company Overview: Ericsson was founded in 1876 and is headquartered in Stockholm, Sweden. It is one of the leading providers of telecommunications equipment and services globally. The company operates in four main business areas: Networks, Digital Services, Managed Services, and Emerging Business.
Networks: Ericsson’s Networks business focuses on providing infrastructure solutions for mobile and fixed networks. The company offers a wide range of products and services, including radio access networks, core networks, transport solutions, and network management systems. Ericsson’s network equipment is used by telecommunications operators worldwide to build and operate their networks.
Digital Services: Ericsson’s Digital Services business provides software and services for the digital transformation of telecommunications operators. This includes solutions for cloud infrastructure, digital business support systems, and network functions virtualization. Ericsson helps operators evolve their networks and services to meet the demands of the digital era.
Managed Services: Ericsson offers managed services to telecommunications operators, helping them optimize their network operations and improve efficiency. The company provides services such as network design and optimization, network rollout, and network operations and maintenance. Ericsson’s managed services enable operators to focus on their core business while leveraging Ericsson’s expertise.
Emerging Business: Ericsson’s Emerging Business focuses on exploring new business opportunities and technologies. This includes areas such as Internet of Things (IoT), 5G applications, and industry digitalization. Ericsson collaborates with partners and customers to develop innovative solutions and drive digital transformation in various industries.
Global Presence: Ericsson has a global presence and operates in more than 180 countries. The company works closely with telecommunications operators, enterprises, and governments worldwide to deliver advanced communication solutions. Ericsson’s global reach enables it to serve a diverse range of customers and markets.
Research and Development: Ericsson invests heavily in research and development (R&D) to drive innovation and stay at the forefront of technology. The company has research centers and innovation hubs around the world, focusing on areas such as 5G, IoT, artificial intelligence, and cloud computing. Ericsson’s R&D efforts contribute to the development of cutting-edge telecommunications solutions.

For more detailed information about Ericsson, you can refer to the following sources:

Ericsson Official Website: https://www.ericsson.com/

Please let me know if there is anything specific you would like to know about Ericsson.

Finished chain.
Applicant : TELEFONAKTIEBOLAGET LM ERICSSON (PUBL) Information: Telefonaktiebolaget LM Ericsson, commonly known as Ericsson, is a Swedish multinational telecommunications company. Here are some key points about Ericsson:

Entering new AgentExecutor chain…
LG Corporation, formerly known as Lucky-Goldstar, is a South Korean multinational conglomerate. Here are some key points about LG:

Company Overview: LG Corporation was founded in 1947 and is headquartered in Seoul, South Korea. It is one of the largest and most well-known conglomerates in South Korea. LG operates in various industries, including electronics, chemicals, telecommunications, and services.
Electronics: LG Electronics is a subsidiary of LG Corporation and is known for its wide range of consumer electronics products. This includes televisions, home appliances (such as refrigerators, washing machines, and air conditioners), smartphones, audio and video equipment, and computer products. LG Electronics is recognized for its innovative designs and advanced technologies.
Chemicals: LG Chem is another subsidiary of LG Corporation and is involved in the production of various chemical products. It manufactures and supplies a range of products, including petrochemicals, industrial materials, and high-performance materials. LG Chem is known for its focus on sustainability and environmentally friendly solutions.
Telecommunications: LG Corporation has a presence in the telecommunications industry through its subsidiary LG Uplus. LG Uplus is a major telecommunications provider in South Korea, offering mobile, internet, and IPTV services. The company has been actively involved in the development and deployment of 5G technology.
Research and Development: LG Corporation places a strong emphasis on research and development (R&D) to drive innovation and technological advancements. The company invests significant resources in R&D activities across its various business sectors. LG’s R&D efforts have led to the development of cutting-edge products and technologies.
Global Presence: LG Corporation has a global presence and operates in numerous countries worldwide. The company has manufacturing facilities, sales offices, and research centers in various regions, including North America, Europe, Asia, and Latin America. LG’s global reach enables it to cater to a diverse customer base and expand its market share.

For more detailed information about LG Corporation, you can refer to the following sources:

LG Corporation Official Website: https://www.lgcorp.com/

Please let me know if there is anything specific you would like to know about LG.

Finished chain.
Applicant : LG ENERGY SOLUTION, LTD. Information: LG Corporation, formerly known as Lucky-Goldstar, is a South Korean multinational conglomerate. Here are some key points about LG:

Entering new AgentExecutor chain…
“Nippon” is the Japanese word for Japan. It is often used to refer to the country in a more traditional or formal context. Here are some key points about Japan (Nippon):

Location and Geography: Japan is an island country located in East Asia. It is situated in the Pacific Ocean and consists of four main islands: Honshu, Hokkaido, Kyushu, and Shikoku. Japan is known for its diverse geography, including mountains, volcanoes, and coastal areas.
Population: Japan has a population of approximately 126 million people. It is the 11th most populous country in the world. The capital city of Japan is Tokyo, which is one of the most populous cities globally.
Economy: Japan has the third-largest economy in the world by nominal GDP. It is known for its advanced technology, automotive industry, electronics, and manufacturing sectors. Major Japanese companies include Toyota, Honda, Sony, Panasonic, and Nintendo.
Culture and Traditions: Japan has a rich cultural heritage and is known for its traditional arts, such as tea ceremonies, calligraphy, and flower arranging (ikebana). The country is also famous for its cuisine, including sushi, ramen, tempura, and matcha tea. Traditional Japanese clothing includes the kimono and yukata.
Technology and Innovation: Japan is renowned for its technological advancements and innovation. It is a global leader in areas such as robotics, electronics, and high-speed rail. Japanese companies have made significant contributions to the development of consumer electronics and automotive technology.
Tourism: Japan attracts millions of tourists each year who come to experience its unique culture, historical sites, and natural beauty. Popular tourist destinations include Tokyo, Kyoto, Osaka, Hiroshima, Mount Fuji, and the ancient temples and shrines of Nara.

For more detailed information about Japan (Nippon), you can refer to the following sources:

Japan National Tourism Organization: https://www.japan.travel/en/
Ministry of Foreign Affairs of Japan: https://www.mofa.go.jp/

Please let me know if there is anything specific you would like to know about Japan.

Finished chain.
Applicant : NIPPON TELEGRAPH AND TELEPHONE CORPORATION Information: “Nippon” is the Japanese word for Japan. It is often used to refer to the country in a more traditional or formal context. Here are some key points about Japan (Nippon):

Conclusion

The challenges of data extraction in Intellectual Property have always been a roadblock to effective and efficient analyses. However, with the advent of advanced generative AI models, we’re now able to automate complex tasks that used to require manual effort. From analyzing extensive patent gazette files to identifying top applicants and generating comprehensive summaries, AI is revolutionizing the way we handle data extraction in this field.

The integration of tools such as the Google Search API and Browserless illustrates the growing potential of AI to not only enhance the accuracy of our data but also to significantly reduce the time taken for these tasks. Our discussions have shown that whether the data is to be integrated into a Data Lake, Data Warehouse, or other database options, AI capabilities make it all possible and increasingly convenient.

However, it’s important to remember that as we continue to navigate the changing landscape of Intellectual Property, staying adaptive to technological advancements is crucial. AI will continue to evolve, and as it does, the ability to utilize it to its full potential will become an invaluable asset in our field. The challenge, therefore, is not just in overcoming the obstacles of data extraction but also in keeping pace with the rapid evolution of technology, and the many benefits it brings to Intellectual Property analyses.

As we look to the future, the promise of AI in overcoming challenges and enhancing analyses in Intellectual Property is incredibly promising. While we have made significant progress, this is only the beginning of the journey. The full potential of AI in this area is yet to be completely unlocked, and its future applications may very well reshape the entire field of Intellectual Property as we know it today. This rapid evolution of technology is not something to be feared, but rather, it’s an exciting opportunity that we must embrace, and I look forward to witnessing where this journey takes us.

That’s it for today!