Tag: OpenAI

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

  1. 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.
  2. ChatGPT-Like Interface: With a user-friendly, chatbot-like interface, users can have interactive programming sessions, making the coding experience more intuitive.
  3. 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.
  4. 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.
  5. 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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Accessibility: For people with disabilities, communicating with a computer using natural language could make computing more accessible, especially if paired with voice recognition software.
  7. 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.
  8. 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.

  1. First, you have to install Python; in my case, I have Python 3.11.2 installed.
  2. 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.
  3. 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 here 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/

Feature Spotlight: A Closer Look at BotGPT’s Integration with Google Bard

In the world of chatbots, two names that are often mentioned in the same breath are BotGPT and Google Bard. Both are powerful AI-powered chatbots that can hold conversations with humans in a natural and engaging way. But what happens when you combine the two?

That’s the question that BotGPT is answering with its new integration with Google Bard. This integration gives BotGPT access to Google Bard’s vast knowledge and language processing capabilities, making it even more powerful and versatile.

In this blog post, we’ll take a closer look at the BotGPT-Google Bard integration. We’ll discuss what it is, how it works, and what it means for the future of chatbots.

What is Google Bard?

Google Bard is a large language model (LLM) chatbot developed by Google AI. It is trained on a massive dataset of text and code, which allows it to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

Google Bard is set to rival Bing’s offering that incorporates ChatGPT integration. Like OpenAI’s ChatGPT, Bard employs AI technology to interpret natural language, delivering human-like responses to intricate inquiries.

Google Bard is still under development, but it has already learned to perform many kinds of tasks, including:

  • Answering your questions in a comprehensive and informative way, even if they are open-ended, challenging, or strange.
  • Generating different creative text formats of text content, like poems, code, scripts, musical pieces, emails, letters, etc.
  • Translating languages accurately and fluently.
  • Writing different kinds of creative content, like blog posts, articles, stories, and poems.

This is the official website for Google’s Bard.

What is BotGPT?

BotGPT is a virtual assistant that connects different artificial intelligences to your Whatsapp:

  • Chat GPT 3.5 and GPT 4
  • Google Bard
  • Dall-e 2
  • Midjourney
  • Whisper
  • Azure Cognitive Services

BotGPT is a new service product that leverages the power of artificial intelligence to provide a personalized chat experience to customers through WhatsApp. Powered by the large language models, ChatGPT and Google Bard, BotGPT is designed to understand natural language and provide relevant responses to customer inquiries in real time.

One of the key benefits of using BotGPT is its ability to provide personalized recommendations to customers based on their preferences and past interactions. BotGPT can suggest products, services, or solutions tailored to each customer’s needs by analyzing customer data. This personalized approach helps to enhance the overall customer experience, leading to increased customer satisfaction and loyalty.

You can use BotGPT for a variety of tasks, such as:

Text generation: writing stories, poems, and scripts.
Answer questions.
Translate language.
Summarize texts.
General chatting.
Generating creative ideas.
Create realistic images and art from instructions by text.

Learn all the commands before using:

help to get instructions on how to use the BotGPT
prompts to get a list of example prompts
create image to create realistic images using Dall-e 2
midjourney to create realistic images using Midjourney
midselect to select one of the 4 Midjourney images
gpt-3 to change the ChatGPT model to GPT-3
gpt-4 to change the ChatGPT model to GPT-4
bard to change to the Google Bard model
send to send the last response you received from BotGPT to your email
send audio to send the last response as audio
give me a document for you to receive the last BotGPT response as a Word document
list user first instruction to display the first prompt. The first prompt sets the direction for BotGPT
alter user first instruction to change the first prompt
onlyvoice to configure the BotGPT to respond through audio only
onlytext to configure the BotGPT to respond through text only
voicetext to configure the BotGPT to respond through text + audio
clear to clear the memory buffer and start new questions
buffer displays the set of prompts currently in BotGPT’s memory
save to save a sequence of prompts
load to load a previously saved sequence of prompts
del to delete a saved sequence of prompts
list to list all saved prompt sequences
list <name>to display the saved sequence of a single prompt sequence
status to get your expiration date, ChatGPT model, and selected language
cancel to manage or cancel your subscription

How to sign up?

Signing up for BotGPT is a straightforward process:

  1. Navigate to the official BotGPT website.
  2. Look for the “Try BotGPT” or “Register” button, usually located at the top right corner.
  3. Follow the prompted steps, enter your details, and click here to enter.
  4. Once registered, you can explore and customize your chatbot based on your requirements.

Once you register you will have 2 days free to use. For more information go to the BotGPT website.

How does the new integration with Google Bard work?

Setting up Google Bard is straightforward. Simply type “bard” on WhatsApp to begin. If you prefer to switch back to GPT-4, just type “gpt 4”, and you’re all set.

Feel free to ask any questions. If Google Bard provides an image in response, you’ll receive it via WhatsApp.

For further details, visit the BotGPT website, or don’t hesitate to reach out to me here.

Conclusion

The convergence of two powerful technologies – BotGPT and Google Bard – heralds a new era in the realm of chatbots. For businesses, this presents an opportunity to elevate customer interactions, offer more personalized experiences, and harness the full power of AI-driven conversational platforms. As these technologies continue to evolve, one can only imagine the innovative ways they’ll transform our digital interactions.

That’s it for today.

Sources:

✨Unleash the Power of GPT-4 with BotGPT: Your Inspiring Ally on WhatsApp🌟 – Tech News & Insights (lawrence.eti.br)

English | BOT GPT (botgpt-ai.com)

Bard (google.com)

What is Google Bard? Here’s everything you need to know | ZDNET

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:

  1. 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.
  2. 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.
  3. 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).
  4. 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.
  5. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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).
  7. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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:

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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:

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):

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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:

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!

OpenAI has unveiled a groundbreaking new feature, the Code Interpreter, accessible to all ChatGPT Plus users. Check out my experiments using the 2739 edition of BRPTO’s Patent Gazette

Code Interpreter is an innovative extension of ChatGPT, now available to all subscribers of the ChatGPT Plus service. This tool boasts the ability to execute code, work with uploaded files, analyze data, create charts, edit files, and carry out mathematical computations. The implications of this are profound, not just for academics and coders, but for anyone looking to streamline their research processes. Code Interpreter transcends the traditional scope of AI assistants, which have primarily been limited to generating text responses. It leverages large language models, the AI technology underpinning ChatGPT, to provide a general-purpose toolbox for problem-solving.

What is the Code Interpreter?

The Code Interpreter Plugin for ChatGPT is a multifaceted addition that provides the AI chatbot with the capacity to handle data and perform a broad range of tasks. This plugin equips ChatGPT with the ability to generate and implement code in natural language, thereby streamlining data evaluation, file conversions, and more. Pioneering users have experienced its effectiveness in activities like generating GIFs and examining musical preferences. The potential of the Code Interpreter Plugin is enormous, having the capability to revolutionize coding processes and unearth novel uses. By capitalizing on ChatGPT’s capabilities, users can harness the power of this plugin, sparking a voyage of discovery and creativity.

Professor Ethan Mollick from the Wharton School of the University of Pennsylvania shares his experiences with using the Code Interpreter

Artificial intelligence is rapidly revolutionizing every aspect of our lives, particularly in the world of data analytics and computational tasks. This transition was recently illuminated by Wharton Professor Ethan Mollick who commented, “Things that took me weeks to master in my PhD were completed in seconds by the AI.” This is not just a statement about time saved or operational efficiency, but it speaks volumes about the growing capabilities of AI technologies, specifically OpenAI’s new tool for ChatGPT – Code Interpreter.

Mollick, an early adopter of AI and an esteemed academic at the Wharton School of the University of Pennsylvania lauded Code Interpreter as the most significant application of AI in the sphere of complex knowledge work. Not only does it complete intricate tasks in record time, but Mollick also noticed fewer errors than those typically expected from human analysts.

One might argue that Code Interpreter transcends the traditional scope of AI assistants, which have primarily been limited to generating text responses. It leverages large language models, the AI technology underpinning ChatGPT, to provide a general-purpose toolbox for problem-solving.

Mollick commended Code Interpreter’s use of Python, a versatile programming language known for its application in software building and data analysis. He pointed out that it closes some of the gaps in language models as the output is not entirely text-based. The code is processed through Python, which promptly flags any errors.

In practice, when given a dataset on superheroes, Code Interpreter could clean and merge the data seamlessly, with an admirable effort to maintain accuracy. This process would have been an arduous task otherwise. Additionally, it allows a back-and-forth interaction during data visualization, accommodating various alterations and enhancements.

Remarkably, Code Interpreter doesn’t just perform pre-set analyses but recommends pertinent analytical approaches. For instance, it conducted predictive modeling to anticipate a hero’s potential powers based on other factors. Mollick was struck by the AI’s human-like reasoning about data, noting the AI’s observation that the powers were often visually noticeable as they derived from the comic book medium.

Beyond its technical capabilities, Code Interpreter democratizes access to complex data analysis, making it accessible to more people, thereby transforming the future of work. It saves time and reduces the tedium of repetitive tasks, enabling individuals to focus on more fulfilling, in-depth work.

Here are 10 examples of how you can use Code Interpreter for data analysis:

  1. Analyzing customer feedback data to identify trends and patterns.
  2. Creating interactive dashboards and reports for business intelligence purposes.
  3. Cleaning and transforming datasets for machine learning models.
  4. Extracting insights from social media data to inform marketing strategies.
  5. Generating charts and graphs to visualize sales data.
  6. Analyzing website traffic data to optimize the user experience.
  7. Creating custom functions and scripts for specific data analysis tasks.
  8. Performing statistical analysis on survey data.
  9. Automating repetitive data analysis tasks with Python scripts.
  10. Creating custom visualizations for presentations and reports.

How to use it? Follow my own experiments

Initially, you must upload a file of your choice. Following this, you are permitted to pose queries concerning it. The interpreter for the code will generate and run a Python script to address nearly all inquiries you have. The entire process is incredibly swift. I conducted a few trials using the XML file from BRTPTO’s 2739 Patent Gazette. The experience was truly incredible. There’s no need for any understanding of coding or Python. The code interpreter simply provides the results. If you wish, you can view the Python script.

By selecting the “Show Work” option, the Python script will become visible to you.

He executed the work flawlessly!

This is the screen of the CSV file.

Here is the Python code generated by the Code Interpreter to create the CSV file:

Python
import pandas as pd
import xml.etree.ElementTree as ET

# Load the XML file
tree = ET.parse("/mnt/data/Patente_2739_04072023.xml")
root = tree.getroot()

# Function to extract data from XML into a dictionary
def extract_data(root):
    data = []
    for despacho in root.findall('despacho'):
        row = {}
        row['despacho_codigo'] = despacho.find('codigo').text
        row['despacho_titulo'] = despacho.find('titulo').text
        row['despacho_comentario'] = despacho.find('comentario').text if despacho.find('comentario') is not None else ""
        processo_patente = despacho.find('processo-patente')
        row['processo_numero'] = processo_patente.find('numero').text
        row['processo_data_deposito'] = processo_patente.find('data-deposito').text if processo_patente.find('data-deposito') is not None else ""
        row['processo_concessao'] = processo_patente.find('concessao').text if processo_patente.find('concessao') is not None else ""
        titulars = processo_patente.findall('titular-lista/titular')
        row['titular_nome_completo'] = "; ".join([titular.find('nome-completo').text for titular in titulars])
        row['titular_endereco_pais'] = "; ".join([titular.find('endereco/pais/sigla').text for titular in titulars])
        data.append(row)
    return data

# Extract data from XML
data = extract_data(root)

# Convert to DataFrame
df = pd.DataFrame(data)

# Save DataFrame to CSV
df.to_csv('/mnt/data/patente.csv', index=False)

df.head()  # Display the first few rows of the DataFrame

# Extract distinct application/patent numbers
distinct_application_numbers = df['processo_numero'].unique()

# Convert to DataFrame
df_distinct_application_numbers = pd.DataFrame(distinct_application_numbers, columns=['processo_numero'])

# Save DataFrame to CSV
df_distinct_application_numbers.to_csv('/mnt/data/distinct_application_numbers.csv', index=False)

df_distinct_application_numbers.head()  # Display the first few rows of the DataFrame

This video can demonstrate the capabilities of the Code Interpreter.

You can find more information on the official Open AI site by clicking here.

Conclusion

Code Interpreter is a powerful tool that is making data analysis accessible for everyone with ChatGPT Plus. By allowing users to run code snippets within their chat sessions, it enables them to perform a wide range of data analysis tasks quickly and easily. Whether you’re analyzing customer feedback data or creating custom visualizations for presentations and reports, Code Interpreter has something to offer everyone.

Code Interpreter invites us to consider how we can leverage such advancements across various sectors impacted by AI. Indeed, Code Interpreter signifies the dawn of a new era in artificial intelligence and computational capabilities. So why not give it a try today?

That’s it for today!

Sources:

Wharton professor sees future of work in new ChatGPT tool | Fortune

https://openai.com/blog/chatgpt-plugins#code-interpreter

https://www.searchenginejournal.com/code-interpreter-chatgpt-plus/490980/#close

https://www.gov.br/inpi/pt-br