In today’s digital age, data is more valuable than ever. Businesses of all sizes are constantly looking for ways to make sense of the vast amounts of data they collect, and that’s where ChatGPT and Power BI come in. These powerful tools can help businesses make data-driven decisions, improve their operations, and ultimately increase their bottom line. If you’re skilled in using these tools, you may be wondering how you can turn that skill into a profit. In this blog post, we’ll explore the different ways you can earn money by using ChatGPT and Power BI. Whether you’re a freelancer, a consultant, or an entrepreneur, there are many opportunities out there for those who know how to use these tools effectively. So, let’s dive in and see how you can monetize your knowledge and skills in ChatGPT and Power BI!
What is chatGPT?
ChatGPT, or Generative Pre-trained Transformer, is a state-of-the-art language generation model developed by OpenAI that has the ability to generate human-like text. It is capable of completing tasks such as writing articles, generating code, and even composing poetry.
How can chatGPT be used to create content?
One of the ways that ChatGPT can be used is to create content for businesses and individuals. By providing ChatGPT with a prompt, it can generate high-quality, unique content that can be used for blogs, social media, and other marketing materials.
How to make money with chatGPT and Power BI?
Power BI is a data visualization tool that allows businesses to analyze and communicate data in an interactive and visually appealing way. Combining the use of ChatGPT and Power BI can help businesses to create engaging and informative content that can lead to increased revenue and improved efficiency. Here are three real examples of how businesses are using ChatGPT and Power BI to increase revenue:
A financial services company is using ChatGPT to generate financial reports and Power BI to visualize the data. By using this combination, the company can create informative and visually appealing reports that help clients to understand their financial information and make better investment decisions.
A marketing agency is using ChatGPT to generate social media posts, and Power BI to analyze the data on engagement, reach, and conversion. By using this combination, the agency can create effective and engaging social media campaigns that help to increase revenue for their clients.
A consulting firm is using ChatGPT to generate client reports and Power BI to visualize the data. By using this combination, the firm can create informative and visually appealing reports that help clients to understand their business information and make better decisions.
Why most people will not succeed?
While the potential for making money with ChatGPT and Power BI is great, most people will not succeed in doing so. This is because it requires a deep understanding of data analysis and the ability to communicate insights effectively to others. Additionally, it requires a significant investment of time and resources to develop the necessary skills and tools to succeed.
Importance of human creativity and putting to work
The importance of human creativity and input cannot be overstated when it comes to using ChatGPT and Power BI. While technology can automate certain tasks, it is not a replacement for human creativity and critical thinking. To truly succeed, businesses must combine the power of technology with the creativity and insight of their human employees.
Conclusion
ChatGPT and Power BI can be powerful tools for businesses looking to increase revenue and improve efficiency. However, it requires a deep understanding of data analysis and the ability to communicate insights effectively to others. Additionally, it requires a significant investment of time and resources to develop the necessary skills and tools to succeed. The importance of human creativity and input cannot be overstated when it comes to using ChatGPT and Power BI. To truly succeed, businesses must combine the power of technology with the creativity and insight of their human employees.
Impressive what we can do with ChatGPT, this post was entirely created by ChatGPT, using the prompt below.
this picture was extracted from ChatGPTThe introduction is also created with ChatGPT
This is just the beginning, ChatGPT is based in GPT-3 and I can already imagine how far we will go once GPT-4 is released.
GPT-4 is a hypothetical model that refers to the next iteration of the GPT series, following GPT-3. The GPT series are large language models that are trained on massive amounts of text data and have the ability to generate human-like text, complete a wide range of language tasks, and even compose poetry. While there is no official release of GPT-4 yet, OpenAI has been actively researching and developing new models in the GPT series, so it is possible that a GPT-4 model will be released in this year.
Some potential improvements that could be made in GPT-4 include:
Increased model size: GPT-4 could have even more parameters than GPT-3, which would allow it to have an even greater capacity for understanding and generating text.
Improved training data: GPT-4 could be trained on even more diverse and extensive text data, which would allow it to have an even greater understanding of language and a wider range of knowledge.
Advanced capabilities: GPT-4 could have even more advanced capabilities than GPT-3, such as the ability to perform more complex language tasks, like writing a book or composing poetry.
Improved performance: GPT-4 could have even more accurate and natural language generation than GPT-3, making it even more powerful for various applications.
Finally, ChatGPT is a powerful language generation model that can be used for a wide range of natural language processing (NLP) tasks. From text generation to question answering, language translation, chatbot development, text completion and sentiment analysis, ChatGPT can help businesses and organizations make sense of their data and improve their operations.
One of the key advantages of ChatGPT is its ability to generate human-like text. This can be incredibly valuable for businesses that need to produce large amounts of high-quality content, such as articles, stories, and blog posts, in a short amount of time. Additionally, its ability to answer a wide range of questions can be useful for businesses that want to provide quick and accurate customer service.
Another advantage of ChatGPT is its ability to translate text and perform text summarization, this feature can be used by businesses that operate in multiple languages, or work with international partners.
ChatGPT can also be used to develop chatbots that can engage in natural language conversations with users. This can be incredibly valuable for businesses that want to improve their customer service or provide 24/7 support.
In short, ChatGPT is a versatile and powerful tool that can be used for a wide variety of NLP tasks. Businesses and organizations in many different industries can benefit from its ability to generate human-like text, answer questions, translate text, develop chatbots and perform sentiment analysis. So, if you’re looking to make sense of your data, improve your operations, or simply save time and effort, ChatGPT is definitely worth considering.
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 pdimport openaiimport pyodbcopenai.api_key = "YOU HAVE TO INSERT HERE YOUR OPEN AI KEY"# Define the funcion to ask the question and extract the informationdefOpenAI_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']defExtract_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.
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:
OpenAI Whisper is a new artificial intelligence system that can achieves human level performance in speech recognition. This system was developed by OpenAI, an artificial intelligence research lab. The goal of this system is to improve the quality of speech-to-text systems. With a 1.6 billion parameters AI model that can transcribe and translate speech audio from 97 languages. Whisper was trained on 680,000 hours of audio data collected from the web and showed robust zero-shot performance on a wide range of automated speech recognition (ASR) tasks. This will benefit many applications, such as virtual assistants, smart speakers, and more.
This video can help you understand the benefits of the Whisper.
OpenAI introduced Whisper on September 21, 2022, in this article. This will accelerate the use of artificial intelligence in applications that need to make use of technology. Here are some examples:
You record in any language, and the API extracts the text.
Click on the image to open the app
In this example, the API extracts text from a YouTube video.
Click on the image to open the app
Let’s experiment using the OpenAI Whisper API in Python to extract the text from the YouTube video.
Python
# Author: Lawrence Teixeira# Date: 02/11/2022# Requirements to run this script:#pip install git+https://github.com/openai/whisper.git#pip install pytube# import the necessary packagesimport pytube as ptimport whisper# download mp3 from youtube video (Indroductrion to Whisper: The speech recognition)yt = pt.YouTube("https://www.youtube.com/watch?v=Bf6Z5bjlHcI")stream = yt.streams.filter(only_audio=True)[0]stream.download(filename="audio.mp3")# load the modelmodel = whisper.load_model("medium")# transcribe the audio fileresult = model.transcribe("audio.mp3")# print the text extracted from the videoprint(result["text"])
Text extracted from the video “Introduction to Whisper: The speech recognition.”
“Whisper is an open source deep learning model for speech recognition that was released by Oppenai last week. Oppenai’s tests of Whisper show that it can do a good job of transcribing not just English audio, but also audio in a number of other languages. Developers and researchers who have worked with Whisper and seen what it can do are also impressed by it. But the release of Whisper may be just as important for what it tells us about how artificial intelligence AI research is changing, and what kinds of applications we can expect in the future. Whisper from Oppenai is open to all kinds of data. One of the most important things about Whisper is that it was trained with many different kinds of data. Whisper was trained on 680,000 hours of data from the web that was supervised by people who spoke different languages and did different tasks. A third of the training data is made up of audio examples that are not in English. Whisper can reliably transcribe English speech and perform at a state-of-the-art level with about 10 languages, an Oppenai representative told VentraBeat in written comments. It can also translate from those languages into English. Even though the lab’s analysis of languages other than English isn’t complete, people who have used it say it gives good results. Again, the AI research community has become more interested in different kinds of data. This year, Bloom was the first language model to work with 59 different languages. Meta is also working on a model that can translate between 200 different languages. By moving toward more data and language diversity, more people will be able to use and benefit from deep learning’s progress. Make your own test since Whisper is open source. Developers and users can choose to run it on their laptop, desktop workstation, mobile device, or a cloud server. OpenAI made Whisper in five different sizes. Each size traded accuracy for speed in a proportional way, with the smallest model being about 60 times faster than the largest. Developers who have used Whisper and seen what it can do are happy with it, and it can make cloud-based ASR services, which have been the main choice until now, less appealing. And Lobs expert Noah Giff told VentraBeat, At first glance, Whisper seems to be much more accurate than other SaaS products. Since it is free and can be programmed, it will probably be a very big problem for services that only do transcription. Whisper was released as an open source model that was already trained, and that anyone can download and run on any computer platform they want. In the past few months, commercial AI research labs have been moving in the direction of being more open to the public. You can make your own apps. There are already a number of ways to make it easier for people who don’t know how to set up and run machine learning models to use Whisper. One example is a project by journalist Peter Stern and GitHub engineer Christina Warren to make a free, secure, and easy to use transcription app for journalists based on Whisper. In the cloud, open source models like Whisper are making new things possible. Platforms like Hugging Face are used by developers to host Whisper and make it accessible through API calls. Jeff Bootyer, growth and product manager at Hugging Face, told VentraBeat, It takes a company 10 minutes to create their own transcription service powered by Whisper and start transcribing calls or audio content, even at a large scale. Hugging Face already has a number of services based on Whisper, such as an app that translates YouTube videos. Or, you can tweak existing apps to fit your needs. And fine-tuning, which is the process of taking a model that has already been trained and making it work best for a new application, is another benefit of open source models like Whisper. For example, Whisper can be tweaked to make ASR work better in a language that the current model doesn’t do as well with. Or, it can be tweaked to understand medical or technical terms better. Another interesting idea would be to fine-tune the model for tasks other than ASR, like verifying the speaker, finding sound events, and finding keywords. Hugging Face’s technical lead, Philip Schmidt, told VentraBeat that people have already told them that Whisper can be used as a plug-and-play service to get better results than before. When you put this together with fine-tuning the model, the performance will get even better. Fine-tuning for languages that were not well represented in the pre-training dataset can make a big difference in how well the system works.”
As you can see, the text is exactly what was spoken. Note that in this example, we use the intermediate model. Here are the models that we can use to increase the accuracy.
Available models and languages
There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.
For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.
Whisper’s performance varies widely depending on the language. The figure below shows a WER breakdown by languages of Fleur’s dataset using the large model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D of the paper.
The image is taken from the official Whisper documentation.
Conclusion: Although there is still some controversy around how well AI Whisper works, the concept behind it is something to think about. With more and more businesses moving towards automated marketing and customer service, AI Whisper could be a valuable tool for those looking to get ahead in the industry. Have you tried using AI Whisper or any other similar tools? Let us know in the comments!
