Open AI released this week the new ChatGPT API

OpenAI has introduced two new APIs to its suite of powerful language models this week. ChatGPT has been making waves in the market these past few months since its release to the public in November 2022 by Open AI. Now, any company can incorporate ChatGPT features into their applications. Using the API is very simple and will revolutionize how we know artificial intelligence today.

What is ChatGPT?

ChatGPT is an API (Application Programming Interface) developed by OpenAI, which is designed to facilitate the creation of chatbots that can engage in natural language conversations with users. ChatGPT is based on the GPT (Generative Pre-trained Transformer) family of language models, which have been pre-trained on vast amounts of text data and can generate high-quality text that closely mimics human writing.

ChatGPT aims to make it easier for developers to create chatbots that can understand and respond to natural language queries. The API can be fine-tuned for specific use cases, such as customer service or sales, and developers can integrate it into their applications with just a few lines of code.

ChatGPT works by taking in user input, such as a question or statement, and generating a response designed to mimic natural language conversation. The API uses machine learning to process and understand the input, allowing it to respond in a relevant and engaging way.

Overall, ChatGPT represents a significant step forward in developing conversational AI. By providing developers with a powerful and flexible tool for creating chatbots, OpenAI is making it easier for businesses and organizations to engage with their customers and users more naturally and intuitively.

What is the ChatGPT API?

The ChaGPT API is an extension of the GPT (Generative Pre-trained Transformer) family of language models. The GPT models are pre-trained on massive amounts of text data, allowing them to generate high-quality text with a natural language understanding often indistinguishable from the human-written text.

The ChaGPT API is designed to handle conversations, making it an excellent tool for building chatbots. With ChaGPT, developers can create chatbots that can respond to user input in a natural, conversational way. The API is flexible and can be fine-tuned for specific use cases, such as customer service or sales.

How to use the API?

You can integrate the ChatGPT features into your solution with a few lines. Follow an example code:

Python
import openai
openai.api_key = "PUT YOUR OPEN AI KEY HERE"

def ChatGPT(question):
 response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[
         {"role": "system", "content": "You are a helpful assistant."},
         {"role": "user", "content": question}
    ]
)
 return response["choices"][0]["message"]["content"]
 
print( ChatGPT("How to create a python code to analyze the increasing of the population arround the world?") )

Take a look at the ChatGPT API result:

One approach to analyzing the increase of population around the world in Python is to use data from the United Nations World Population Prospects. Here's an example of how to load and analyze this data in Python:

First, you will need to install the pandas package:

```python
!pip install pandas
```

Next, you can load the data into a pandas DataFrame:

```python
import pandas as pd

# Load the data from a CSV file
data = pd.read_csv('https://population.un.org/wpp/DVD/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_TotalPopulationBySex.csv')

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

This will load the data into a pandas DataFrame and print the first few rows of the data:

```
   LocID     Location  VarID Variant  ...  PopFemale  PopTotal  PopDensity  PopPyramid
0      4  Afghanistan      2  Medium  ...  19594.381  38928.35      54.422        0-24
1      4  Afghanistan      2  Medium  ...  20179.675  40341.77      56.937        0-24
2      4  Afghanistan      2  Medium  ...  20778.593  41754.96      59.661        0-24
3      4  Afghanistan      2  Medium  ...  21415.724  43203.14      62.529        0-24
4      4  Afghanistan      2  Medium  ...  22099.928  44701.23      65.484        0-24

[5 rows x 14 columns]
```

The `data` DataFrame contains the population data for all countries and regions of the world from 1950 to 2100, broken down by sex and age group.

To analyze the increase of population over time, you can use pandas' grouping and aggregation functions. For example, to calculate the total population by year, you can group the data by year and sum the population columns:

```python
# Group the data by year and sum the population columns
pop_by_year = data.groupby('Time')[['PopTotal', 'PopMale', 'PopFemale']].sum()

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

This will output:

```
         PopTotal       PopMale     PopFemale
Time                                         
1950  2.536542e+09  1.278728e+09  1.257814e+09
1951  2.584034e+09  1.303461e+09  1.280572e+09
1952  2.630584e+09  1.328990e+09  1.301594e+09
1953  2.676380e+09  1.354295e+09  1.322086e+09
1954  2.721384e+09  1.379313e+09  1.342071e+09
```

You can then plot the total population over time using the matplotlib package:

```python
import matplotlib.pyplot as plt

# Create a line plot of total population over time
plt.plot(pop_by_year.index, pop_by_year['PopTotal'])

# Set the plot title and axis labels
plt.title('World Population')
plt.xlabel('Year')
plt.ylabel('Population (billions)')

# Show the plot
plt.show()
```

This will plot the total population over time:

![World Population plot](https://i.imgur.com/XXcRVit.png)

You can also calculate the percentage increase in population from year to year using pandas' `pct_change()` function:

```python
# Calculate the percentage increase in population from year to year
pop_growth = pop_by_year.pct_change() * 100

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

This will output:

```
      PopTotal    PopMale  PopFemale
Time                                
1950       NaN        NaN        NaN
1951  1.854079   1.934514   1.814192
1952  1.798119   1.952587   1.658901
1953  1.735834   1.900590   1.556621
1954  1.686583   1.845293   1.512103
```

You can then plot the percentage increase in population over time:

```python
# Create a line plot of percentage population growth over time
plt.plot(pop_growth.index, pop_growth['PopTotal'])

# Set the plot title and axis labels
plt.title('World Population Growth')
plt.xlabel('Year')
plt.ylabel('Population growth (%)')

# Show the plot
plt.show()
```

This will plot the percentage increase in population over time:

![World Population Growth plot](https://i.imgur.com/E18reRf.png)

This analysis can be extended in many ways depending on the specific questions you are interested in.

They also released the Whisper API. Whisper can transcribe speech to text in any language.

Follow an example:

Let’s test to extract the text with Wisper from this video:

First, you have to extract only the audio:

Python
!pip install pytube

import pytube as pt

yt = pt.YouTube("https://www.youtube.com/watch?v=XxIfSkkyAaQ")
stream = yt.streams.filter(only_audio=True)[0]
stream.download(filename="audio_ChatGPTAPI.mp3")

Now, you have to use the API to transcribe the audio:

Python
import openai

file = open("/path/to/file/audio_ChatGPTAPI.mp3", "rb")
transcription = openai.Audio.transcribe("whisper-1", file)

print(transcription)

Take a look at the result of the Whisper API result:

{
  "text": "OpenAI recently released the API of chatgpt. This is an API that calls gpt 3.5 turbo, which is the same model used in the chatgpt product. If you already know how to use the OpenAI API in Python, learning how to use the chatgpt API should be simple, but there are still some concepts that are exclusive to this API, and we'll learn these concepts in this video. Okay, let's explore all the things we can do with the chatgpt API in Python. Before we start with this video, I'd like to thank Medium for supporting me as a content creator. Medium is a platform where you can find Python tutorials, data science guides, and more. You can get unlimited access to every guide on Medium for $5 a month using the link in the description. All right, to start working with the chatgpt API, we have to go to our OpenAI account and create a new secret key. So first, we have to go to this website that I'm going to leave the link on the description, and then we have to go to the view API keys option. And here, what we have to do is create a new secret key in case you don't have one. So in this case, I have one, and I'm going to copy the key I have, and then we can start working with the API. So now I'm going here to Jupyter Notebooks, and we can start working with this API. And the first thing we have to do is install the OpenAI API. So chatgpt, the API of chatgpt or the endpoint, is inside of this library, and we have to install it. So we write pip install OpenAI, and then we get, in my case, a requirement already satisfied because I already have this library. But in your case, you're going to install this library. And then what we have to do is go to the documentation of chatgpt API, which I'm going to leave in the description, and we have to copy the code snippet that is here. So you can copy from my GitHub that I'm going to leave also in the description, or you can go to the documentation. So this is going to be our starting point. And before you run this code, you have to make sure that here in this variable OpenAI.API underscore key, you type your secret key that we generated before. So you type here your key, and well, you're good to go. And here's something important you need to know is that the main input is the messages parameter. So this one. And this messages parameter must be an array of message objects where each object has a role. You can see here in this case, the role is the user. And also we have the content. And this content is basically the content of the message. Okay. There are three roles. There are besides user, we have also the admin role and also the assistant role. And we're going to see that later. And now I'm going to test this with a simple message here in the content. Here I'm going to leave the role as user as it was by default. And here I'm going to change that content of the message. So I don't want to write hello, but I want to type this. So tell the world about the chatgpt API in the style of a pirate. So if I run this, we can see that we're going to get something similar that we'll get with chatgpt. But before running this, I'm going to delete this, this quote. And now I'm going to run and we're going to get a message similar to chatgpt. So here we have a dictionary with two elements, the content and the role. And here I only want the content. This is the text that we're going to get. We will get if we were using chatgpt. And if I write content, I'm going to get only the content. So only the text. So here's the text. So this is an introduction to the chatgpt API in the style of a pirate. And well, this is the message or the response. And if we go to the website to chatgpt, we're going to see that we're going to get something similar. So if I go here, and I go to chatgpt, and I write to the world about the chatgpt API in the style of a pirate, we can see we get this message in the style of a pirate. So we get this ahojder and then all the things that a pirate will say. And we get here the same. So we get a similar message. So basically, this response is what we will get with chatgpt, but without all this fancy interface. So we're only getting the text. Okay, now to interact with this API, as if we were working with chatgpt, we can make some modifications to the code. For example, we can use that input function to interact with with this API in a different way, as if we were working with chatgpt, like in the website. So here I can use that input. And I can, I can write, for example, users. So we are the users. And this is what we're going to ask chatgpt. And this is going to be my content. So here content. And instead of writing this, I'm going just to write content equal to content. And this is going to be the message that is going to change based on the input we insert, then instead of just printing this message, I'm going to create a variable called chat underscore response. And this is going to be my response, but we're gonna put it like in a chatgpt style. So here, I'm going to print this. And with this, we can recognize which is the user request and which is that chatgpt response. So let's try it out. Here, I'm going to press Ctrl Enter to run this. Okay, and here I'm going to type who was the first man on the moon. So if I press Enter, we get here the answer. And well, this is like in a chatgpt style, we get an input where we can type any question or request we have. And then we get the answer by chatgpt. And now let's see the roles that are going to change the way we interact with chatgpt. Okay, now let's see the system role. The system role helps set the behavior of the system. And this is different from that user role, because in the user role, we only give instructions to the system. But here, in the system role, we can control how the system behaves. For example, here, I add two different behaviors. And to do this, first, we have to use the messages object. It is the same messages object we had before. This is the same that we had here. But in this case, this is for the system role. And here I added two just to show you different ways to use this, this role. But usually you only have only one behavior for the system, or sorry for the system. And well, here in the first one, I'm saying you're a kind, helpful assistant. And well, in this case, we're telling the system to be as helpful as possible. And in the second one, is something I came up with. And it's something like you're a recruiter who asks tough interview questions. So for example, this second role, we can interact with chat GPT as if it was a job interview. So it's something like chat GPT is going to be the recruiter who asks questions, and we're going to be the candidate who answers all the questions. So let's use this, this second content. And now let's include this system role in our code. So to do this, I'm going to copy the code I had before, and I'm going to paste it here. And as you can see, we have two messages variable, one with a system role and the other with that user role. And what I'm going to do is just append one list into the other. So to do this, I'm going to create or write messages that append. And then I'm going to put this dictionary inside my variables. So here I write append, and now I put this inside. And after doing this, I'm just have to delete this and write messages equal to messages. And with this, we have that system role and also that user role in our code. Now I only have to put this content equal to input at the beginning. And with this, everything is ready. And now we can run this code. So first, I'm going to run the messages here, the list I have, and then I'm going to run the code we have before. And here is asking me to insert something. So here, I'm going to write just hi. And after this, we're going to see that the behavior of chat GPT change. So now is telling us Hello, welcome to the interview. Are you ready to get started? And this happened because we changed the behavior of the system. Now the behavior is set to you're a recruiter who asks tough interview questions. And well, here the conversation finished because this doesn't have a while loop. But here, I'm going to add a while loop. So I'm going to write while true. And then I'm going to run again. So here, I'm going to run again. And let's see how the conversation goes. So first, I write hi. And then this is going to give me the answer that well, welcome to the interview. And then can you tell me about a work related challenge that you overcame? So here, I can say, I had problems in public presentations. And I overcame it with practice. So I'm going to write this. And let's see how the conversation goes. And now it's asking me to add some specific actions I did to improve my presentation skills. So now you can see that chatgp is acting like a recruiter in a job interview. And this is thanks to this behavior we added in the system role. And well, now something that you need to know is that there is another role, which is the assistant role. And this role is very important. And it's important because sometimes here, for example, in this chat that is still on, if we write no, what we're going to see is that chatgpt is not able to remember the conversation we had. So it cannot read that preview responses. So here, for example, I type no. And what we got is thanks for sharing that. And actually, I didn't share anything. I just wrote no. And well, it's telling me to continue with something else. But as you can see, chatgpt is not able to remember what we said before. And if we add an assistant role, with this, we can make sure that we build a conversation history where chatgpt is able to remember the previous responses. So now let's do this. Let's create an assistant role. Okay, as I mentioned before, the system role is used to store prior responses. So by storing prior responses, we can build a conversation history that will come in handy when user instructions refer to prior messages. So here, to create this assistant role, we have to create again this dictionary. And then in the role, we have to type assistant, as you can see here. And then in the content, we have to introduce that chat response. And to understand this much better, I'm going to copy the previous code, and I'm going to paste it here. So here, the chat response is this one, this chat response that has that content of the response given by chatgpt. So here, I'm going to copy this code, and I'm going to paste it here. And what I'm going to do here to include this assistant role is to append this into that messages list. So here, I'm going to write messages.append() and then the parentheses. And with this, we integrated that assistant role to our little script. And here, for you to see the big picture of all of this, I'm going to copy also that assistant role. And well, it's here, the assistant role. I'm going to delete this first line of code. And well, this is the big picture. So we have the assistant role. This sets the behavior of the assistant. Then we have the user, which sets the instructions. And finally, we have the assistant, which stores all the responses. And with this, we can have a proper conversation with chatgpt. Here, before I run this code, I'm going to customize a little bit more the behavior of the assistant in the assistant role. And here, I'm going to type this. So it's basically the same, but here I'm adding, you ask one question or one new question after my response. So to simulate a job interview. And well, now that this is ready, here, I'm going to make sure that everything is right. And well, everything is perfect now. So here, I'm going to run these two blocks. And then I'm going to type hi, so we can start with that interview. So are you ready for that interview? Yes. So here, it's going to ask me a question. Let's get started. Can you tell me about your previous work experience? And well, I worked at Google, I'm going to say and well, now it tells me that's great. Can you tell me your role and responsibilities? And I can say, I was a software engineer. And well, now that conversation is going to keep going. And chatgpt is going to ask me more and more questions. And in this case, it remembers the previous responses I gave. So for example, I said I worked at Google. And here it's telling me the responsibilities I had at Google. And in the next response is also mentioning Google again. And I think if I mentioned the project that is asking here, for example, if I write, I had a credit card fraud detection project, and I overcame it with teamwork, I don't know, something like this, then it's going to ask me about this project. So now it mentions teamwork, which I said in my previous response. And now it's asking me more about this project. So with this, we can see that our assistant is storing our previous responses. And with this, we're building a conversation history that keeps the conversation going without losing quality in the responses. And that's pretty much it. Those are the three those are the three modes that you have to know to work with the chatgpt API. And in case you wonder about the pricing of the chatgpt API, well, it's priced at 0.002 per 1000 tokens, which is 10 times cheaper than the other models like gpt 3.5. And well, it is another reason why I wouldn't pay $20 for a chatgpt plus subscription. And well, in case you're interested why I am going to cancel my chatgpt plus subscription, you can watch this video where I explain why I regret paying $20 for a chatgpt plus subscription. And well, that's it. I'll see you on the next video."
}

OpenAI’s ChaGPT and Whisper APIs are a significant step forward for conversational AI. By making it easy for developers to build chatbots and voice assistants, these APIs have the potential to revolutionize the way we interact with technology. With the power of these language models at their fingertips, developers can create more intuitive and engaging user experiences than ever before.

Follow the official ChatGPT API post:

https://openai.com/blog/introducing-chatgpt-and-whisper-apis

Regarding ChatGPT, I would like to share the project I’m developing using the official ChatGPT API. It’s just the beginning!

BotGPT

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 model, ChatGPT, 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.

Unleash the potential of GPT-4 with BotGPT today by clicking this link and embarking on a seven days, cost-free journey into conversational AI without any payment information. Begin your adventure by clicking here. And finally, to make the monthly subscription after seven days, click here.

Once subscribed after seven days, you can manage or cancel your subscription anytime via this link.

Should you encounter any obstacles, you can directly add the BotGPT WhatsApp: +1 (205) 754-6921 number to your phone.

If you have any questions or suggestions, please get in touch using this link.

That’s it for today!

Extracting information from unstructured text is easy with Open AI. All you need to do is give the instructions.

How does one successfully extract information from the unstructured text? Through natural language processing, or NLP. You may be wondering what that even means or how it can facilitate the extraction of information. All you need to do is give the instructions. This article will discuss how NLP facilitates the extraction process and how it is done – supervised and unsupervised learning.

What is Open AI?

Open AI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. Founded in December 2015, with initial funding of $1 billion from Sam Altman and several other investors, OpenAI has the stated goal of promoting friendly artificial intelligence to benefit humanity as a whole.

How does Open AI Work?

There are many different ways to extract information from unstructured text. The most common way is to use a keyword or keyphrase. This is where you give a specific word or phrase to the Open AI software, which will locate all instances of that word or phrase in the text. It will then return the results to you in an easily readable format.

Another way to extract information from unstructured text is to use a concept search. This is where you give a general concept or topic to the software, and it will locate all instances of that concept in the text. It will then return the results to you in an easily readable format.

The last way to extract information from unstructured text is to use a natural language processing model. This is where you provide the software with a large amount of text, and it will analyze the text’s grammar, syntax, and meaning. It will then return the results to you in an easily readable format.

Creating a System for Extracting Information from Unstructured Text with Open AI

If you have a lot of unstructured text and you need to extract information from it, Open AI can help. All you need to do is give the instructions to the software, and it will do the rest.

Open AI is especially useful for extracting information from unstructured text because it can handle various formats. For example, if you have a PDF document, Open AI can convert it into text that can be further processed.

 Open AI is also good at dealing with multiple languages. For example, if you have a document in English and another in Portuguese, Open AI can usually translate between the two languages and extract the desired information.

Putting it to work:

Open AI makes extracting information from unstructured text easy. All you need to do is give the instructions. Let’s go to the example. I selected the sub-judice patent publications extracted from the 10 latest BRPTO Brazilian gazettes. Note that everything is written in Brazilian Portuguese. If you want the dataset I used, you can click here to download it.

Python
import pandas as pd
import openai
import pyodbc

openai.api_key = "YOU HAVE TO INSERT HERE YOUR OPEN AI KEY"

# Define the funcion to ask the question and extract the information
def OpenAI_Question(question_type, openai_response ):
    response = openai.Completion.create(
      engine="text-davinci-003",
      prompt= question_type + chr(10) + openai_response + chr(10),
      temperature=0.7,
      max_tokens=256,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0
    )
    return response['choices'] [0]['text']
    
def Extract_Process_Information( Text ):
    Resultado = OpenAI_Question("Extrair do texto o número do processo udicial, tipo da ação, tribunal, interessados, autor e réus:", Text)
          
    return Resultado
    
# Connect to my experiment database to get the complement of the sub-judice patent publications 
server = 'dbserverlaw.database.windows.net' 
database = 'db_lawrence_experiments' 
username = 'YOUR HAVE TO PUT YOU USER HERE' 
password = 'YOU HAVE TO PUT YOUT PASSWORD HERE'  
cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password)
cursor = cnxn.cursor()

# select 20 rows from SQL table to insert in dataframe.
query = "select top 20 Complemento from Patentes_SubJudce;"
df = pd.read_sql(query, cnxn)

# Show the results. Here you can do everything you want with the extract information.
print( "Question asked from OpenAI model text-davinci-003: Extrair do texto o número do processo judicial sem ser INPI, tipo da ação, tribunal, interessados, autor e réus.", chr(10))

for i in df.index:
    Extract = Extract_Process_Information(df['Complemento'][i])
    
    print ("Text ", i+1, ":")
    print( df['Complemento'][i], chr(10) )
    print( "Information extracted from the text ",i+1, ":")
    print( Extract.strip(), chr(10) )    

Let’s show the results of this Python script:

Question asked from OpenAI model text-davinci-003: “Extrair do texto o número do processo judicial sem ser INPI, tipo da ação, tribunal, interessados, autor e réus.

Text 1 :
Processo SEI Nº: 52402.011406/2022-11 NUP PRINCIPAL: 01032.546858/2021-44 NUP REMISSIVO: 00848.001324/2022-17 PROCESSO Nº: 5019398-85.2021.4.03.0000 AUTOR: ARTIPE PRODUTOS ORTOPEDICOS E ESPORTIVOS LTDA ? ME Acórdão: A Primeira Turma, por unanimidade, deu provimento ao agravo de instrumento para determinar a suspensão dos efeitos da patente de invenção discutida nos autos de origem.

Information extracted from the text 1 :
Número do processo judicial:
5019398-85.2021.4.03.0000
Tipo da ação: Agravo de Instrumento
Tribunal: Primeira Turma
Interessados: ARTIPE PRODUTOS ORTOPEDICOS E ESPORTIVOS LTDA ? ME
Autor: ARTIPE PRODUTOS ORTOPEDICOS E ESPORTIVOS LTDA ? ME
Réus: Não especificado

Text 2 :
Processo INPI nº 52400.000958/2008-57 NUP PRINCIPAL: 00408.005736/2017-48 NUP REMISSIVO: 00848.001319/2022-12 Origem : TRIBUNAL REGIONAL FEDERAL DA 2ª REGIÃO AGRAVANTE : BMZAK BENEFICIAMENTO METAL MECANICO LTDA – ME AGRAVADO : MUNDIAL S.A. – PRODUTOS DE CONSUMO INTERESSADO : INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL Decisão: 1) julgo PROCEDENTE o pedido autoral, resolvendo o mérito, nos termos do art.269, I, do CPC, para decretar a nulidade da patente de modelo de utilidade MU7801576-6 para ?disposição em botão metálico?; 2) reconheço a litispendência e julgo extinto o pedido reconvencional, sem resolução de mérito, nos termos do art.267, V, penúltima figura, do CPC. Deverá o INPI publicar a presente decisão na próxima RPI e em seu site oficial. Trânsito em julgado.

Information extracted from the text 2 :
Número do processo judicial:
00408.005736/2017-48
Tipo da ação: Ação de nulidade de patente
Tribunal: Tribunal Regional Federal da 2ª Região
Interessados: BMZAK Beneficiamento Metal Mecânico Ltda – ME; Mundial S.A – Produtos de Consumo; Instituto Nacional da Propriedade Industrial
Autor: BMZAK Beneficiamento Metal Mecânico Ltda – ME
Réus: Mundial S.A – Produtos de Consumo

Text 3 :
Processo INPI nº 52402.001592/2021-91 13ª Vara Federal do Rio de Janeiro PROCEDIMENTO COMUM Nº 5007472-60.2021.4.02.5101/RJ AUTOR: OTTA SUSHI COMERCIO DE ALIMENTOS LTDA RÉU: INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL RÉU: LKD ALIMENTOS SAUDÁVEIS LTDA. Sentença: Ante o exposto, Julgo improcedente o pedido de nulidade da patente de modelo de utilidade MU 8900712-3 para ?disposição construtiva introduzida em embalagem para acondicionamento de alimentos?, resolvendo o mérito (CPC/2015, art. 487, inciso I). Trânsito em julgado.

Information extracted from the text 3 :
Processo: 5007472-60.2021.4.02.5101/RJ
Tipo da Ação: Procedimento Comum
Tribunal: 13ª Vara Federal do Rio de Janeiro
Interessados: Otta Sushi Comercio de Alimentos Ltda e LKD Alimentos Saudáveis Ltda
Autor: Otta Sushi Comercio de Alimentos Ltda
Réus: INPI-Instituto Nacional da Propriedade Industrial e LKD Alimentos Saudáveis Ltda.

Text 4 :
Processo INPI nº 52402.005814/2019-20 9ª Vara Federal do Rio de Janeiro NUP: 00408.036343/2019-48 (REF. 5025815-75.2019.4.02.5101) EXEQUENTE: IMPLANTICA PATENT LTD (SOCIEDADE) EXECUTADO: INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL Decisão: Isto posto, julgo procedente o pedido, para decretar a nulidade dos atos administrativos do INPI que extinguiram as patentes de invenção PI0108142-0 e PI0108309-0 com base no art. 13 da Resolução INPI n. 113/2013 e determinar a consequente restauração das mesmas, nos moldes da fundamentação acima.

Information extracted from the text 4 :
Número do processo judicial:
00408.036343/2019-48
Tipo da ação: Execução
Tribunal: 9ª Vara Federal do Rio de Janeiro
Interessados: Implantaica Patent Ltd (Sociedade) e INPI-Instituto Nacional da Propriedade Industrial
Autor: Implantaica Patent Ltd (Sociedade)
Réus: INPI-Instituto Nacional da Propriedade Industrial

Text 5 :
Processo INPI nº 52402.005814/2019-20 9ª Vara Federal do Rio de Janeiro NUP: 00408.036343/2019-48 (REF. 5025815-75.2019.4.02.5101) EXEQUENTE: IMPLANTICA PATENT LTD (SOCIEDADE) EXECUTADO: INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL Decisão: Isto posto, julgo procedente o pedido, para decretar a nulidade dos atos administrativos do INPI que extinguiram as patentes de invenção PI0108142-0 e PI0108309-0 com base no art. 13 da Resolução INPI n. 113/2013 e determinar a consequente restauração das mesmas, nos moldes da fundamentação acima.

Information extracted from the text 5 :
Número do processo judicial:
5025815-75.2019.4.02.5101
Tipo da ação: Execução
Tribunal: 9ª Vara Federal do Rio de Janeiro
Interessados: Implantaica Patent LTD (Sociedade) e INPI – Instituto Nacional da Propriedade Industrial
Autor: Implantaica Patent LTD (Sociedade)
Réus: INPI – Instituto Nacional da Propriedade Industrial

Text 6 :
Processo INPI nº 52402.004535/2022-44 21ª Vara Federal Cível da SJDF PROCESSO JUDICIAL: 1006097-47.2022.4.01.3400 NUP: 00424.125631/2022-73 (REF. 1006097-47.2022.4.01.3400) INTERESSADOS: AGÊNCIA NACIONAL DE VIGILÂNCIA SANITÁRIA – ANVISA E OUTROS Decisão: Pelo exposto, DEFIRO o pedido de tutela provisória de urgência para determinar a suspensão dos efeitos do despacho 16.3 (publicado na RPI nº 2.629 de 25/05/21), que reduziu o prazo de vigência das patentes PI0212733-4 e BR 12 2012 023120 7, de modo que estas permaneçam vigentes até a prolação de sentença de mérito ? limitada a compensação de prazo requerida no pedido, qual seja, 663 (seiscentos e sessenta e três) dias para a PI0212733-4 e 1.594 (mil quinhentos e noventa e quatro) dias para a BR 12 2012 023120 7, bem como que o INPI publique, na primeira edição da RPI subsequente a sua intimação, a informação acerca da tutela concedida.

Information extracted from the text 6 :
Processo judicial:
1006097-47.2022.4.01.3400
Tipo da ação: Tutela provisória de urgência
Tribunal: 21ª Vara Federal Cível da SJDF
Interessados: Agência Nacional de Vigilância Sanitária – Anvisa e outros
Autor: Agência Nacional de Vigilância Sanitária – Anvisa e outros
Réus: INPI

Text 7 :
Processo INPI nº 52402.004535/2022-44 21ª Vara Federal Cível da SJDF PROCESSO JUDICIAL: 1006097-47.2022.4.01.3400 NUP: 00424.125631/2022-73 (REF. 1006097-47.2022.4.01.3400) INTERESSADOS: AGÊNCIA NACIONAL DE VIGILÂNCIA SANITÁRIA – ANVISA E OUTROS Decisão: Pelo exposto, DEFIRO o pedido de tutela provisória de urgência para determinar a suspensão dos efeitos do despacho 16.3 (publicado na RPI nº 2.629 de 25/05/21), que reduziu o prazo de vigência das patentes PI0212733-4 e BR 12 2012 023120 7, de modo que estas permaneçam vigentes até a prolação de sentença de mérito ? limitada a compensação de prazo requerida no pedido, qual seja, 663 (seiscentos e sessenta e três) dias para a PI0212733-4 e 1.594 (mil quinhentos e noventa e quatro) dias para a BR 12 2012 023120 7, bem como que o INPI publique, na primeira edição da RPI subsequente a sua intimação, a informação acerca da tutela concedida.

Information extracted from the text 7 :
Processo judicial:
1006097-47.2022.4.01.3400
Tipo da ação: Tutela provisória de urgência
Tribunal: 21ª Vara Federal Cível da SJDF
Interessados: Agência Nacional de Vigilância Sanitária – Anvisa e outros
Autor: Agência Nacional de Vigilância Sanitária – Anvisa e outros
Réus: INPI

Text 8 :
Processo INPI nº 52400.003545/2022-39 NUP: 00408.078470/2022-10 (REF. 0017246-69.2002.4.02.5101) Autor: Formax Quimiplan Componentes Para Calçados Ltda. Reús: Giulini Chemie GmbH e Instituto Nacional da Propriedade Industrial- INPI Sentença: Isto posto, JULGO IMPROCEDENTE o pedido de nulidade da patente de invenção PI 8506015-1, bem como o pedido de nulidade do privilégio decorrente da reivindicação n’ 1 da patente em tela, formulado alternativamente. Trânsito em julgado.

Information extracted from the text 8 :
Número do processo:
00408.078470/2022-10
Tipo da ação: Nulidade de patente
Tribunal: Tribunal Regional Federal da 2ª Região
Interessados: Formax Quimiplan Componentes Para Calçados Ltda. e Giulini Chemie GmbH
Autor:
Formax Quimiplan Componentes Para Calçados Ltda.
Réus: Giulini Chemie GmbH e Instituto Nacional da Propriedade Industrial- INPI

Text 9 :
Processo INPI nº 52402.005638/2020-60 13ª Vara Federal do Rio de PROCEDIMENTO COMUM Nº 5029675-50.2020.4.02.5101/RJ AUTOR: LIBBS FARMACEUTICA LTDA AUTOR: MABXIENCE RESEARCH SL RÉU: GENENTECH, INC RÉU: INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL Decisão: Isto posto, homologo a renúncia ao direito sobre o qual se funda a ação, extinguindo o processo com resolução de mérito (CPC/2015, art. 487, III, ‘c’). Tendo em vista a manifesta ausência de interesse recursal das partes litigantes, o que deriva da própria preclusão lógica inerente à renúncia, certifique-se, desde logo, o trânsito em julgado.

Information extracted from the text 9 :
Número do processo judicial:
5029675-50.2020.4.02.5101/RJ
Tipo da ação: Procedimento comum
Tribunal: 13ª Vara Federal do Rio de Janeiro
Interessados: Libbs Farmacêutica Ltda, Mabxience Research SL, Genentech, Inc. e INPI-Instituto Nacional da Propriedade Industrial
Autor: Libbs Farmacêutica Ltda
Réus: Mabxience Research SL, Genentech, Inc. e INPI-Instituto Nacional da Propriedade Industrial

Text 10 :
INPI nº 52402.011824/2022-08 Origem: JUÍZO FEDERAL DA 9ª VF DO RIO DE JANEIRO (TRF2) Processo Nº: 5076666-16.2022.4.02.5101 NULIDADE DA PATENTE DE MODELO DE UTILIDADE com pedido de Antecipação de Tutela Autor: M.A. ROSSINI LOPES – ME. Réu(s): ANDRÉ LOPES e INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL ? INPI

Information extracted from the text 10 :
Processo Nº: 5076666-16.2022.4.02.5101
Tipo da ação: NULIDADE DE PATENTE DE MODELO DE UTILIDADE com pedido de Antecipação de Tutela
Tribunal: JUÍZO FEDERAL DA 9ª VF DO RIO DE JANEIRO (TRF2)
Interessados: M.A. ROSSINI LOPES – ME., ANDRÉ LOPES e INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL ? INPI
Autor: M.A. ROSSINI LOPES – ME.
Réus: ANDRÉ LOPES e INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL ? INPI

Text 11 :
INPI nº 52402.011451/2022-67 Origem: 25ª Vara Federal do Rio de Janeiro Processo Nº: 5071020-25.2022.4.02.5101/RJ SUBJUDICE com pedido de Antecipação de Tutela Autor: OURO FINO SAUDE ANIMAL LTDA Réu(s): ZOETIS SERVICES LLC e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Information extracted from the text 11 :
Número do processo judicial:
5071020-25.2022.4.02.5101/RJ
Tipo da ação: SUBJUDICE com pedido de Antecipação de Tutela
Tribunal: 25ª Vara Federal do Rio de Janeiro
Interessados: OURO FINO SAUDE ANIMAL LTDA, ZOETIS SERVICES LLC e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL
Autor: OURO FINO SAUDE ANIMAL LTDA
Réus: ZOETIS SERVICES LLC e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Text 12 :
INPI nº 52402.011991/2022-41 Origem: 22ª VARA FEDERAL CÍVEL DA SJDF (TRF1) Processo Nº: 1047948-66.2022.4.01.3400 AÇÃO DE PROCEDIMENTO COMUM Autor: EUSA Pharma (UK) Limited Réu(s): INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Information extracted from the text 12 :
Processo Nº:
1047948-66.2022.4.01.3400
Tipo da Ação: Ação de Procedimento Comum
Tribunal: 22ª Vara Federal Cível da SJDF (TRF1)
Interessados: EUSA Pharma (UK) Limited e Instituto Nacional da Propriedade Industrial
Autor: EUSA Pharma (UK) Limited
Réus: Instituto Nacional da Propriedade Industrial

Text 13 :
INPI nº 52402.010443/2022-01 Origem: 25ª Vara Federal do Rio de Janeiro Processo Nº: 5052162-43.2022.4.02.5101/RJ NULIDADE DA PATENTE DE INVENÇÃO com pedido de Antecipação de Tutela Autor: KOMATSU BRASIL INTERNATIONAL LTDA Réu(s): ESCO GROUP LLC e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Information extracted from the text 13 :
Número do processo judicial:
5052162-43.2022.4.02.5101/RJ
Tipo da ação: NULIDADE DA PATENTE DE INVENÇÃO com pedido de Antecipação de Tutela
Tribunal: 25ª Vara Federal do Rio de Janeiro
Interessados: KOMATSU BRASIL INTERNATIONAL LTDA, ESCO GROUP LLC e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL
Autor: KOMATSU BRASIL INTERNATIONAL LTDA
Réus: ESCO GROUP LLC e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Text 14 :
INPI nº 52402.013111/2022-71 Origem: a 22ª VARA CÍVEL FEDERAL DE SÃO PAULO Processo Nº: 5007277-58.2021.4.03.6100 SUBJUDICE com pedido de Antecipação de Tutela Autor: SYNGENTA SEEDS LTDA, SYNGENTA PARTICIPATIONS AG Réu(s): SEMPRE SEMENTES EIRELI, MINISTÉRIO DA AGRICULTURA, PECUÁRIA E ABASTECIMENTO ? MAPA, INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Information extracted from the text 14 :
Processo nº
5007277-58.2021.4.03.6100
Tipo da Ação: Pedido de Antecipação de Tutela
Tribunal: 22ª Vara Cível Federal de São Paulo
Interessados: Syngenta Seeds Ltda., Syngenta Participations AG, Sempre Sementes Eireli, Ministério da Agricultura, Pecuária e Abastecimento ? MAPA, Instituto Nacional da Propriedade Industrial
Autor: Syngenta Seeds Ltda., Syngenta Participations AG
Réus: Sempre Sementes Eireli, Ministério da Agricultura, Pecuária e Abastecimento ? MAPA, Instituto Nacional da Propriedade Industrial

Text 15 :
“INPI nº 52402.011780/2022-16 Origem: 13ª Vara Federal do Rio de Janeiro Processo Nº: 5047067-32.2022.4.02.5101/RJ NULIDADE DA PATENTE DE INVENÇÃO Autor: COMPANHIA NITRO QUÍMICA BRASILEIRA Réu(s): ICL AMERICA DO SUL S.A. (nova denominação de COMPASS MINERALS AMÉRICA
DO SUL INDÚSTRIA E COMÉRCIO LTDA.) e Instituto Nacional da Propriedade Industrial ? INPI”

Information extracted from the text 15 :
Processo Nº:
5047067-32.2022.4.02.5101/RJ
Tipo da ação: NULIDADE DA PATENTE DE INVENÇÃO
Tribunal: 13ª Vara Federal do Rio de Janeiro
Interessados: Companhia Nitro Química Brasileira, ICL America do Sul S.A. (nova denominação de Compass Minerals América do Sul Indústria e Comércio Ltda.) e Instituto Nacional da Propriedade Industrial (INPI).
Autor: Companhia Nitro Química Brasileira
Réus: ICL America do Sul S.A. (nova denominação de Compass Minerals América do Sul Indústria e Comércio Ltda.) e Instituto Nacional da Propriedade Industrial (INPI).

Text 16 :
INPI nº 52402.012620/2022-86 Origem: 1ª Vara Federal de Curitiba Processo Nº: 5061501-95.2022.4.04.7000/PR NULIDADE DA PATENTE DE INVENÇÃO com pedido de Antecipação de Tutela Autor: S. Almeida Eventos Ltda. Réu(s): HOLMES PEDRO GIACOMET JUNIOR E Instituto Nacional da Propriedade Industrial – INPI

Information extracted from the text 16 :
Número do processo judicial:
5061501-95.2022.4.04.7000/PR
Ação: NULIDADE DA PATENTE DE INVENÇÃO
Tribunal: 1ª Vara Federal de Curitiba
Interessados: S. Almeida Eventos Ltda., HOLMES PEDRO GIACOMET JUNIOR E Instituto Nacional da Propriedade Industrial – INPI
Autor: S. Almeida Eventos Ltda.
Réus: HOLMES PEDRO GIACOMET JUNIOR E Instituto Nacional da Propriedade Industrial – INPI

Text 17 :
INPI nº 52402.012852/2022-34 Origem: 2ª Vara Federal de Blumenau Processo Nº: 5021248-32.2022.4.04.7205 NULIDADE DA PATENTE DE INVENÇÃO Autor: PRATIMIX INDUSTRIA E COMERCIO DE ACESSORIOS ELETRICOS E HIDRAULICOS LTDA, Réu(s): LORENZETTI SA INDÚSTRIAS BRASILEIRAS ELETROMETALURGICAS e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Information extracted from the text 17 :
Processo Nº:
5021248-32.2022.4.04.7205
Tipo da ação: NULIDADE DA PATENTE DE INVENÇÃO
Tribunal: 2ª Vara Federal de Blumenau
Interessados: PRATIMIX INDUSTRIA E COMERCIO DE ACESSORIOS ELETRICOS E HIDRAULICOS LTDA, LORENZETTI SA INDÚSTRIAS BRASILEIRAS ELETROMETALURGICAS e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL
Autor: PRATIMIX INDUSTRIA E COMERCIO DE ACESSORIOS ELETRICOS E HIDRAULICOS LTDA,
Réus: LORENZETTI SA INDÚSTRIAS BRASILEIRAS ELETROMETALURGICAS e INPI-INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Text 18 :
INPI nº 52402.009647/2022-91 Origem: 31ª Vara Federal do Rio de Janeiro Processo Nº: 5059924-13.2022.4.02.5101 SUBJUDICE com pedido de Antecipação de Tutela Autor: EMERSON CORDEIRO DE OLIVEIRA Réu(s): MODULARE BRASIL ARTEFATOS PLÁSTICOS LTDA, MARIANAAZAMBUJA SOARES MUNARI e INSTITUTO NACIONAL DA PROPRIEDADEINDUSTRIAL ? INPI.

Information extracted from the text 18 :
Número do processo judicial:
5059924-13.2022.4.02.5101
Tipo da ação: SUBJUDICE com pedido de Antecipação de Tutela
Tribunal: 31ª Vara Federal do Rio de Janeiro
Interessados: Emerson Cordeiro de Oliveira, Modulare Brasil Artefatos Plásticos Ltda., Mariana Azambuja Soares Munari e Instituto Nacional da Propriedade Industrial – INPI.
Autor: Emerson Cordeiro de Oliveira
Réus: Modulare Brasil Artefatos Plásticos Ltda., Mariana Azambuja Soares Munari e Instituto Nacional da Propriedade Industrial – INPI.

Text 19 :
INPI nº 52402.011352/2022-85 Origem: JUÍZO FEDERAL DA 25ª VF DO RIO DE JANEIRO (TRF2) Processo Nº: 5036388-70.2022.4.02.5101 NULIDADE DA PATENTE DE INVENÇÃO com pedido de Antecipação de Tutela Autor: FALCON DISTRIBUICAO, ARMAZENAMENTO E TRANSPORTES S.A. Réu(s): DRYLOCK TECHNOLOGIES NV e INPI – INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL

Information extracted from the text 19 :
Número do processo judicial:
5036388-70.2022.4.02.5101
Tipo da ação: Nulidade da patente de invenção com pedido de antecipação de tutela
Tribunal: Juízo Federal da 25ª VF do Rio de Janeiro (TRF2)
Interessados: Falcon Distribuição, Armazenamento e Transportes S.A.
Autor: Falcon Distribuição, Armazenamento e Transportes S.A.
Réus: Drylock Technologies NV e INPI – Instituto Nacional da Propriedade Industrial.

Text 20 :
INPI nº 52402.011631/2022-49Origem: Justiça Federal – 9ª Vara Federal do Rio de JaneiroProcedimento Comum nº 5098088-81.2021.4.02.5101/RJembargos de declaração opostos contra alegado equívoco na decisão proferidaautor: Adama Brasil S/Aréus: United Phosphorus Limited e INPI – Instituto Nacional da Propriedade Industrial

Information extracted from the text 20 :
Processo judicial:
5098088-81.2021.4.02.5101/RJ
Tipo da ação: Embargos de Declaração
Tribunal: Justiça Federal – 9ª Vara Federal do Rio de Janeiro
Interessados: Adama Brasil S/A, United Phosphorus Limited e INPI – Instituto Nacional da Propriedade Industrial
Autor: Adama Brasil S/A
Réus: United Phosphorus Limited e INPI – Instituto Nacional da Propriedade Industrial

As you can see, with a few instructions, you can easily and quickly perform many tasks efficiently compared to the traditional way of using an algorithm created by you in a programming language. By the traditional method, you would need to consider all the variations in the text.

You can test your experiment direct from Open AI Playground.

Screen extract from Open AI Playground

Conclusion

More and more, these AI models are getting more advanced. This example we used was done using GPT-3. The Open AI is working in GPT-4. The Open AI GPT-4 is considerably larger than the GPT-3 in terms of parameters and performance. While the GPT-3 only has 8 million parameters, the GPT-4 has 1.5 billion. This increase in size allows the GPT-4 to learn much faster and achieve better results on various tasks.

Follow the information about new Novembre 2022 update in GPT-3:

https://arstechnica.com/information-technology/2022/11/openai-conquers-rhyming-poetry-with-new-gpt-3-update/

Follow articles about the new GPT-4 still being created by Open AI:

https://towardsdatascience.com/gpt-4-is-coming-soon-heres-what-we-know-about-it-64db058cfd45
https://www.datacamp.com/blog/what-we-know-gpt4
https://www.technologyreview.com/2022/11/30/1063878/openai-still-fixing-gpt3-ai-large-language-model/

That’s it for today!

The Future of Legal Content Interpreting: How NLP Can Help?

Legal content is dense, complicated, and often less than straightforward. As such, it’s crucial to understand what you’re reading and make decisions based on that information. That’s where natural language processing (NLP) comes in.

What is NLP?

NLP, or natural language processing, is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, mainly how to program computers to process and analyze large amounts of natural language data.

NLP interprets legal content through algorithms that identify relevant information from unstructured text. This process can extract key phrases, concepts, and entities from a document for further analysis. Additionally, NLP can generate summaries of legal documents or identify critical issues within a document.

Why Use NLP for Interpreting Legal Content?

There are many reasons to use natural language processing (NLP) when interpreting legal content. First, NLP can help you to identify the essential information in a document. This is especially helpful when dealing with long and complex legal documents. Second, NLP can help you understand the text’s meaning by extracting key concepts and ideas. This is extremely helpful in understanding the implications of a legal document. Finally, NLP can help you to identify relationships between different concepts in the text. This can help identify issues that may not be immediately apparent.
Overall, NLP can be a very helpful tool when interpreting legal content. It can help you to identify the most crucial information, understand the meaning of the text, and identify relationships between different concepts.

Applications of NLP in the Legal Field

There are many potential applications for NLP in the legal field. Here are a few examples:

  1. Automated contract analysis: NLP can automatically analyze contracts and identify critical provisions, such as parties, obligations, and termination clauses. This can save time and improve accuracy compared to manual contract review.
  2. Legal research: NLP can quickly search large volumes of legal documents (e.g., court opinions) for relevant information. This can save time and improve accuracy compared to traditional keyword search methods.
  3. Sentiment analysis of legal documents: NLP can be used to analyze the sentiment of legal documents, such as court opinions, to identify positive or negative feelings towards specific individuals or entities. This information could be helpful for lawyers when making strategic decisions about cases.
  4. Predictive analytics for litigation: NLP can predict the outcome of litigation based on past cases with similar facts and circumstances. This information could be helpful for lawyers when deciding whether to settle a claim or take it to trial.
  5. Automated document summarization: NLP can automatically summarize legal documents, such as court opinions, to save time and improve accuracy. This information could be helpful for lawyers who need a quick case overview.
  6. Entity extraction from legal documents: NLP can automatically extract entities, such as names of people and organizations, from legal documents. This information could be helpful for lawyers when they need to find information about specific individuals or entities quickly.

Let’s explore some real examples extracted from John Snow LABS.

What is John Snow LABS company?

John Snow Labs, an AI and NLP for a healthcare, legal, and finance company, provides state-of-the-art software, models, and data to help healthcare, legal, and life science organizations build, deploy, and operate AI projects. Click here to go to their LinkedIn page.

They have a model called “Spark NLP for Legal” to work on Legal documents. Let’s deeply in.

Introducing Spark NLP for Legal

What’s in the Spark NLP for Legal?

State-of-the-art software + pre-trained legal-specific models

One of the most common uses of NLP is Entity Recognition. Let’s try using a Portuguese document.

If you want yourself try this example, click here. Also, you can look at the Python code on Google Colab here.

Another exciting use of NLP technology is to extract relations between parties in an agreement. Look at the example below.

This model returns something like this to organize and save the insights.

Identified relations
Identified chunks

If you want yourself try this example, click here.

You can save this information with each document and use it to analyze and predict insights. This tool is so powerful and is available to work in multiple languages. In addition, you can look at other attractive models in John Snow LABS in healthcare and finance.

Conclusion

NLP is a powerful tool that can be used for various tasks, including the interpretation of legal content. In this article, we’ve looked at how NLP can be used to interpret legal documents and how it can be used to improve the accuracy of translations. We hope this has given you a better understanding of how NLP can be used in the legal industry and how it can benefit your business.
If you found this article helpful, please share it with your network! And if you have any questions or comments, please feel free to leave them below.

Let’s share more articles talking about Spark NLP For Legal:

Spark NLP For Legal 1.0.0: Over 300+ new state-of-the-art models in multiple languages!

Legal NLP 1.1.0 for Spark NLP has been released

Legal NLP 1.2.0 for Spark NLP has been released!

That’s it for today!