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

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

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

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

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

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

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

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

  1. What is a patent?
  2. What is a trademark?
  3. What are the fees I need to pay to file a patent?
  4. What is a contract?
  5. What is copyright?
  6. What are the rules of geographical indications?
  7. How was the intellectual protection of circuit topology established?
  8. What are the filing procedures for an industrial design?

What are the limitations?

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

How the app was created?

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


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

That’s it for today!

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:

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])
    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.


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!


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