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

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

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

What is Data Wrangler?

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

Advantages of Data Wrangler

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

How to use Data Wrangler?

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

You have to select the Notebook button.

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

Python
import pandas as pd

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

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

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

In the end this code will be generate.

Python
# Code generated by Data Wrangler for pandas DataFrame

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

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

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

Data Wrangler Extension for Visual Studio Code

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

This document will cover how to:

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

Setting up your environment

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

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

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

NameMinimum required versionAutomatically installed
Python3.8No
pandas0.25.2Yes
regex*2020.11.13Yes

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

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

Connecting to a Python kernel

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

1. Connect using a local Python interpreter

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

2. Connect using Jupyter URL and token

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

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

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

Launching Data Wrangler

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

Launching Data Wrangler from a Jupyter Notebook

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

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

  • df
  • df.head()
  • df.tail()

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

image

Launching Data Wrangler directly from a CSV file

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

image

Using Data Wrangler

image

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

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

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

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

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

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

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

Example: Filtering a column

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

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

image

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

image

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

image

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

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

Editing and exporting code

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

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

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

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

Data Wrangler operations

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

OperationDescription
Sort valuesSort column(s) ascending or descending
FilterFilter rows based on one or more conditions
Calculate text lengthCreate new column with values equal to the length of each string value in a text column
One-hot encodeSplit categorical data into a new column for each category
Multi-label binarizerSplit categorical data into a new column for each category using a delimiter
Create column from formulaCreate a column using a custom Python formula
Change column typeChange the data type of a column
Drop columnDelete one or more columns
Select columnChoose one or more columns to keep and delete the rest
Rename columnRename one or more columns
Drop missing valuesRemove rows with missing values
Drop duplicate rowsDrops all rows that have duplicate values in one or more columns
Fill missing valuesReplace cells with missing values with a new value
Find and replaceSplit a column into several columns based on a user-defined delimiter
Group by column and aggregateGroup by columns and aggregate results
Strip whitespaceCapitalize the first character of a string with the option to apply to all words.
Split textRemove whitespace from the beginning and end of the text
Convert text to capital caseAutomatically create a column when a pattern is detected from your examples.
Convert text to lowercaseConvert text to lowercase
Convert text to uppercaseConvert text to UPPERCASE
String transform by exampleAutomatically perform string transformations when a pattern is detected from the examples you provide
DateTime formatting by exampleAutomatically perform DateTime formatting when a pattern is detected from the examples you provide
New column by exampleAutomatically create a column when a pattern is detected from the examples you provide.
Scale min/max valuesScale a numerical column between a minimum and maximum value
Custom operationAutomatically create a new column based on examples and the derivation of existing column(s)

Limitations

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

Conclusion

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

That’s it for today!

Sources:

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

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

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

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

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

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:

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

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

The Future of Data Analytics: An Introduction to Microsoft Fabric

Microsoft Fabric, launched on May 24-25 of 2023 at the Microsoft Build event, is an end-to-end data and analytics platform that combines Microsoft’s OneLake data lake, Power BI, Azure Synapse, and Azure Data Factory into a unified software as a service (SaaS) platform. It’s a one-stop solution designed to serve various data professionals including data engineers, data warehousing professionals, data scientists, data analysts, and business users, enabling them to collaborate effectively within the platform to foster a healthy data culture across their organizations​​.

What are the Microsoft Fabric key features?


Data Factory – Microsoft’s Azure Data Factory is a powerful tool that combines the simplicity of Power Query with Azure Data Factory’s scale. It provides over 200 native connectors for data linkage from on-premises and cloud-based sources. Data Factory enables the scheduling and orchestration of notebooks and Spark jobs.

Data Engineering – Leveraging the extensive capabilities of Spark, data engineering in Microsoft Fabric provides premier authoring experiences and facilitates large-scale data transformations. It plays a crucial role in democratizing data through the lakehouse model. Moreover, integration with

Data Science – The data science capability in Microsoft Fabric aids in building, deploying, and operationalizing machine learning models within the Fabric framework. It interacts with Azure Machine Learning for built-in experiment tracking and model registry, empowering data scientists to enhance organizational data with predictions that business analysts can incorporate into their BI reports, thereby transitioning from descriptive to predictive insights.

Data Warehouse – The data warehousing component of Microsoft Fabric offers top-tier SQL performance and scalability. It features a full separation of computing and storage for independent scaling and native data storage in the open Delta Lake format.

Real-Time Analytics – Observational data, acquired from diverse sources like apps, IoT devices, human interactions, and more, represents the fastest-growing data category. This semi-structured, high-volume data, often in JSON or Text format with varying schemas, presents challenges for conventional data warehousing platforms. However, Microsoft Fabric’s Real-Time Analytics offers a superior solution for analyzing such data.

Power BI – Recognised as a leading Business Intelligence platform worldwide, Power BI in Microsoft Fabric enables business owners to access all Fabric data swiftly and intuitively for data-driven decision-making.

What are the Advantages of Microsoft Fabric?

Unified Platform: Microsoft Fabric provides a unified platform for different data analytics workloads such as data integration, engineering, warehousing, data science, real-time analytics, and business intelligence. This can foster a well-functioning data culture across the organization as data engineers, warehousing professionals, data scientists, data analysts, and business users can collaborate within Fabric​​.

Multi-cloud Support: Fabric is designed with a multi-cloud approach in mind, with support for data in Amazon S3 and (soon) Google Cloud Platform. This means that users are not restricted to using data only from Microsoft’s ecosystem, providing flexibility​.

Accessibility: Microsoft Fabric is currently available in public preview, and anyone can try the service without providing their credit card information. Starting from July 1, Fabric will be enabled for all Power BI tenants​.

AI Integration: The private preview of Copilot in Power BI will combine advanced generative AI with data, enabling users to simply describe the insights they need or ask a question about their data, and Copilot will analyze and pull the correct data into a report, turning data into actionable insights instantly​​.

Microsoft Fabric – Licensing and Pricing

Microsoft Fabric capacities are available for purchase in the Azure portal. These capacities provide the compute resources for all the experiences in Fabric from the Data Factory to ingest and transform to Data Engineering, Data Science, Data Warehouse, Real-Time Analytics, and all the way to Power BI for data visualization. A single capacity can power all workloads concurrently and does not need to be pre-allocated across the workloads. Moreover, a single capacity can be shared among multiple users and projects, without any limitations on the number of workspaces or creators that can utilize it.

To gain access to Microsoft Fabric, you have three options:

  1. Leverage your existing Power BI Premium subscription by turning on the Fabric preview switch. All Power BI Premium capacities can instantly power all the Fabric workloads with no additional action required. If you already have a Power BI Premium subscription, you can simply turn on the Fabric preview switch. This means you can enable Microsoft Fabric’s capabilities as part of your existing Power BI Premium subscription without having to do anything else. All the capacities you have with your Power BI Premium subscription can be used to power the full range of workloads in Microsoft Fabric. In other words, you can use your existing Power BI Premium resources to run all of the data and analytics tasks that Microsoft Fabric can handle.
  2. Start a Fabric trial if your tenant supports trials. If you’re not sure about committing to Microsoft Fabric yet, you can start a trial if your tenant (an instance of Azure Active Directory) supports it. A trial allows you to test the service before deciding to purchase. During the trial period, you can explore the full capabilities of Microsoft Fabric, such as data ingestion, data transformation, data engineering, data science, data warehouse operations, real-time analytics, and data visualization with Power BI.
  3. Purchase a Fabric pay-as-you-go capacity from the Azure portal. If you decide that Microsoft Fabric suits your needs and you don’t have a Power BI Premium subscription, you can directly purchase a Fabric capacity on a pay-as-you-go basis from the Azure portal. The pay-as-you-go model is flexible because it allows you to pay for only the compute and storage resources you use. Microsoft Fabric capacities come in different sizes, from F2 to F2048, representing 2 – 2048 Capacity Units (CU). Your bill will be determined by the amount of computing you provision (i.e., the size of the capacity you choose) and the amount of storage you use in OneLake, the data lake built into Microsoft Fabric. This model also allows you to easily scale your capacities up and down to adjust their computing power, and even pause your capacities when not in use to save on your bills​​.

Microsoft Fabric is a unified product for all your data and analytics workloads. Rather than provisioning and managing separate compute for each workload, with Fabric, your bill is determined by two variables: the amount of compute you provision and the amount of storage you use.

Follow the capacities that you can buy in the Azure portal:

Check out this video from Guy and Cube which breaks down the details on pricing and licensing.

How to activate the Microsoft Fabric Trial version?

Step 1

Login to Microsoft Power BI with your Developer Account

You will observe that asides from the OneLake icon at the top left, everything looks normal if you are familiar with Power BI Service.

Step 2

Enable Microsoft Fabric for your Tenant

Your Screen will Look like this

So far, we’ve only enabled Microsoft Fabric at the tenant level. This doesn’t give full access to Fabric resources as can be seen in the illustration below

So, Let’s upgrade the Power BI License to Microsoft Fabric Trial

For a smoother experience, You should create a new Workspace and add Microsoft Fabric Trial License as can be seen below

As you can see, while creating a new Workspace, you can now Assign Fabric Trial License to it. Upon creation, we are able to take full advantage of Microsoft Fabric

This video by Guy and Cube explains the steps for getting the Microsoft Fabric Trial.

Conclusion

Microsoft Fabric is currently in preview but already represents a significant advancement in the field of data and analytics, offering a unified platform that brings together various tools and services. It enables a smooth and collaborative experience for a variety of data professionals, fostering a data-driven culture within organizations. Let´s wait for the next steps from Microsoft.

That’s it for today!