System

Abstract

An object of the system according to the embodiment is to efficiently check omission and contradiction of necessary items and search for a similar matter in creation of a patent application document and a contract.SOLUTION: A system according to an embodiment includes a document analysis unit, a proposal unit, and a search unit. The document analysis unit analyzes the content of the document by using the generated AI. The proposal unit makes an appropriate correction or proposal based on the content of the document analyzed by the document analysis unit. The search unit searches for a past similar case based on the content of the document analyzed by the document analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, when preparing patent application documents or contracts, checking for missing or inconsistent information and searching for similar cases was a time-consuming and labor-intensive process.

[0005] The system according to the embodiment aims to efficiently check for missing or inconsistent required items and search for similar cases when preparing patent application documents and contracts. [Means for solving the problem]

[0006] The system according to the embodiment includes a document analysis unit, a suggestion unit, and a search unit. The document analysis unit analyzes the content of a document using a generative AI. The suggestion unit makes appropriate corrections and suggestions based on the content of the document analyzed by the document analysis unit. The search unit searches for similar past cases based on the content of the document analyzed by the document analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently check for missing or inconsistent required items and search for similar cases when preparing patent application documents or contracts. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The document creation support system according to an embodiment of the present invention is a system that supports the creation of documents that require specialized and unique wording, such as patent application documents and contracts. This system is useful for checking for omissions and inconsistencies in required items, searching for similar cases, and so on. As a result, the document creation support system can analyze the content of documents, make appropriate corrections and suggestions, and search for similar cases in the past, thereby providing efficient and accurate document creation support.

[0029] A document creation support system according to an embodiment includes a document analysis unit, a suggestion unit, and a search unit. The document analysis unit analyzes the content of a document using a generation AI. For example, the generation AI analyzes the content of a document using natural language processing techniques such as GPT-3 and BERT. The document analysis unit can also perform detailed analysis of the content of a document using text mining techniques. The suggestion unit makes appropriate corrections and suggestions based on the content of the document analyzed by the document analysis unit. For example, the suggestion unit suggests grammatical corrections and content improvements. The suggestion unit can also present specific examples of writing based on the content of the document. The search unit searches for similar past cases based on the content of the document analyzed by the document analysis unit. For example, the search unit searches for similar cases using a patent database or a contract database. The search unit can also search for similar cases using IPC classifications or keyword searches. This allows the document creation support system to analyze the content of a document, make appropriate corrections and suggestions, and search for similar past cases, thereby providing efficient and accurate document creation support.

[0030] The document analysis unit can point out any deficiencies in the detailed description of the invention or the claims and suggest appropriate examples of how to write them. For example, the document analysis unit uses a generation AI to analyze the contents of a document and point out any deficiencies in the detailed description of the invention or the claims. For example, if the description requirements under the Patent Act are not met, the generation AI will point out those deficiencies and suggest specific revisions. The document analysis unit can also suggest examples of how to write them based on the Patent Office guidelines. For example, the generation AI can suggest appropriate examples of how to write them based on the Patent Office guidelines, improving the quality of the document. This improves the quality of the document by pointing out any deficiencies in the detailed description of the invention or the claims and suggesting appropriate examples of how to write them.

[0031] The document analysis unit can point out any inconsistencies between clauses and suggest corrections. For example, the generation AI analyzes the contents of a document and points out any inconsistencies between clauses. For example, if a contract does not meet logical or legal consistency, the generation AI points out those parts and suggests specific corrections. The document analysis unit can also suggest corrections based on legal requirements. For example, the generation AI suggests appropriate corrections based on legal requirements to maintain the consistency of the document. This makes it possible to point out any inconsistencies between clauses and suggest corrections, thereby maintaining the consistency of the document.

[0032] The search unit can search for past patents with similar technical fields or invention content and use their content as a reference. For example, the generation AI analyzes the content of a document and searches for past patents with similar technical fields or invention content. For example, it can search for similar cases using a patent database and use their content as a reference. The search unit can also search for similar cases using IPC classification or keyword search. For example, the generation AI can search for patents in a similar technical field based on IPC classification and use their content as a reference. It can also search for patents with similar invention content using keyword search and use their content as a reference. In this way, by searching for past patents with similar technical fields or invention content and using their content as a reference, the quality of the document can be improved.

[0033] The search unit can search for past contracts with similar contract terms and clauses and use their contents as a reference. For example, the generation AI analyzes the content of a document and searches for past contracts with similar contract terms and clauses. For example, it uses a contract database to search for similar cases and uses their contents as a reference. The search unit can also use a keyword search to search for contracts with similar contract terms and clauses. For example, the generation AI searches for contracts with similar contract terms and clauses based on a keyword search and uses their contents as a reference. In this way, the quality of documents can be improved by searching for past contracts with similar contract terms and clauses and using their contents as a reference.

[0034] The document analysis unit can point out insufficient technical descriptions and suggest specific revisions. For example, the generation AI analyzes the contents of a document and points out insufficient technical descriptions. For example, if there is a lack of technical details or examples, the generation AI points out those parts and suggests specific revisions. The document analysis unit can also suggest adding technical details or completing examples. For example, the generation AI suggests specific revisions to add technical details and improve the quality of the document. In this way, by pointing out insufficient technical descriptions and suggesting specific revisions, the quality of the document can be improved.

[0035] The document analysis unit can point out ambiguous clauses and suggest clearer examples. For example, the generation AI analyzes the contents of a document and points out ambiguous clauses. For example, if there is unclear legal interpretation or unclear wording, the generation AI points out those parts and suggests specific revisions. The document analysis unit can also suggest clear examples based on legal requirements. For example, the generation AI can suggest appropriate examples based on legal requirements to improve the quality of the document. This makes it possible to improve the quality of the document by pointing out ambiguous clauses and suggesting clearer examples.

[0036] The document analysis unit can analyze the frequency and context of technical terms and suggest optimal term selection. For example, the document analysis unit uses a generation AI to analyze the content of a document and analyze the frequency and context of technical terms. For example, the document analysis unit makes suggestions for selecting appropriate technical terms in patent application documents. The document analysis unit can also make suggestions for selecting appropriate technical terms in contracts. For example, the generation AI makes suggestions for selecting appropriate technical terms in patent application documents and contracts. This allows the document to be both technical and easy to understand by analyzing the frequency and context of technical terms and suggesting optimal term selection.

[0037] The document analysis unit can refer to past success stories and failure stories and make suggestions to minimize risk. For example, the generation AI analyzes the contents of a document and refers to past success stories and failure stories. For example, the document analysis unit makes specific suggestions to minimize risk in patent application documents. The document analysis unit can also make specific suggestions to minimize risk in contracts. For example, the generation AI makes specific suggestions to minimize risk in patent application documents and contracts. In this way, by referring to past success stories and failure stories and making suggestions to minimize risk, the reliability of the document can be increased.

[0038] The document analysis unit can document what a user dictates in real time using voice input and check for omissions or inconsistencies in required fields. For example, the document analysis unit uses a generation AI to document what a user dictates in real time using voice input. For example, in patent application documents, the document analysis unit can check for omissions or inconsistencies in required fields. The document analysis unit can also document what a user dictates in real time using voice input and have the generation AI analyze the content. For example, in contracts, the document analysis unit can check for omissions or inconsistencies in required fields. The document analysis unit can also document what a user dictates in real time using voice input and analyze the content. For example, in patent application documents and contracts, the document analysis unit can check for omissions or inconsistencies in required fields. This can improve the efficiency and accuracy of document creation by documenting what a user dictates in real time using voice input and checking for omissions or inconsistencies in required fields.

[0039] The document analysis unit supports document creation in different languages ​​and can automatically generate documents in multiple languages. For example, the document analysis unit uses a generation AI to analyze the content of a document and support document creation in different languages. For example, it automatically generates patent application documents in multiple languages. The document analysis unit can also automatically generate contracts in multiple languages. For example, the generation AI uses machine translation technology to automatically generate patent application documents and contracts in multiple languages. This supports document creation in different languages ​​and automatically generates documents in multiple languages, making it possible to support international document creation.

[0040] The search unit displays search results in chronological order, allowing users to visually understand the evolution of technology and contract terms. In the search unit, for example, the generation AI searches for similar cases and displays the search results in chronological order. For example, the search unit allows users to visually understand the evolution of technology and contract terms. The search unit can also build a system that displays search results in chronological order, allowing users to visually understand the evolution of technology and contract terms. For example, the generation AI displays search results using timeline display or time-series data visualization technology, deepening the user's understanding. This allows users to visually understand the evolution of technology and contract terms, deepening their understanding.

[0041] The search unit can simultaneously search for related legal precedents and regulatory information, thereby providing comprehensive information. For example, the generation AI searches for similar cases and simultaneously searches for related legal precedents and regulatory information. For example, comprehensive information is provided using a laws and regulations database and a precedent database. The search unit can also build a system for simultaneously searching for related legal precedents and regulatory information and providing comprehensive information. For example, the generation AI can search for related information using a laws and regulations database and a precedent database to support user decision-making. This allows the generation AI to simultaneously search for related legal precedents and regulatory information and provide comprehensive information, thereby supporting user decision-making.

[0042] The search unit can search including visual data such as images and drawings, and provide visual reference materials. For example, the generation AI searches for similar cases, including visual data such as images and drawings. For example, the search unit can provide visual reference materials. The search unit can also build a system for searching including visual data such as images and drawings, and providing visual reference materials. For example, the generation AI can provide visual reference materials using image data and drawing data, deepening the user's understanding. In this way, the user's understanding can be deepened by searching including visual data such as images and drawings, and providing visual reference materials.

[0043] The search unit can also search for similar cases in different industries or fields, incorporating knowledge from different fields. For example, the generation AI searches for similar cases, and also searches for similar cases in different industries or fields. For example, incorporating knowledge from different fields. The search unit can also build a system to search for similar cases in different industries or fields, incorporating knowledge from different fields. For example, the generation AI can search for similar cases in different fields using industry classifications or field-specific databases, and incorporate that knowledge. This allows the search for similar cases in different industries or fields, incorporating knowledge from different fields, thereby improving the quality of document creation.

[0044] The document analysis unit can refer to past success stories and propose the most effective document structure. For example, when the generation AI automatically generates a document, the document analysis unit refers to past success stories. For example, it proposes the most effective document structure for patent application documents. The document analysis unit can also propose the most effective document structure for contracts. For example, the generation AI proposes the most effective document structure for patent application documents and contracts based on past success stories. In this way, by referring to past success stories and proposing the most effective document structure, the quality of the document can be improved.

[0045] The document analysis unit can predict future risks based on user input and automatically add clauses to avoid them. For example, when a generation AI automatically generates a document, the document analysis unit predicts future risks based on user input. For example, in patent application documents, clauses to avoid risks are automatically added. The document analysis unit can also automatically add clauses to avoid risks in contracts. For example, in patent application documents or contracts, the generation AI predicts future risks based on user input and automatically adds clauses to avoid them. In this way, by predicting future risks based on user input and automatically adding clauses to avoid them, the reliability of the document can be improved.

[0046] The document analysis unit can use voice input to document what a user dictates in real time and instantly generate a draft. For example, the document analysis unit allows a generation AI to document what a user dictates in real time using voice input. For example, a draft of a patent application document can be instantly generated. The document analysis unit can also document what a user dictates in real time using voice input, and the generation AI can generate a draft based on that content. For example, a draft of a contract can be instantly generated. The document analysis unit can also document what a user dictates in real time using voice input and generate a draft based on that content. For example, a draft of a patent application document or a contract can be instantly generated. This allows the efficiency and accuracy of document creation to be improved by documenting what a user dictates in real time using voice input and instantly generating a draft.

[0047] The document analysis unit can simultaneously generate documents in different languages ​​and provide documents in multiple languages. For example, when the generation AI automatically generates a document, the document analysis unit simultaneously generates documents in different languages. For example, it provides patent application documents in multiple languages. The document analysis unit can also provide contracts in multiple languages. For example, the generation AI uses machine translation technology to provide patent application documents and contracts in multiple languages. This makes it possible to simultaneously generate documents in different languages ​​and provide documents in multiple languages, thereby supporting international document creation.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The document analysis unit can predict future risks based on user input and automatically add clauses to avoid them. For example, clauses to avoid risks are automatically added to patent application documents. The document analysis unit can also automatically add clauses to avoid risks in contracts. For example, the generative AI can predict future risks in patent application documents or contracts based on user input and automatically add clauses to avoid them. This makes it possible to improve the reliability of documents by predicting future risks based on user input and automatically adding clauses to avoid them.

[0050] The document analysis unit can simultaneously generate documents in different languages ​​and provide documents in multiple languages. For example, it can provide patent application documents in multiple languages. The document analysis unit can also provide contracts in multiple languages. For example, the generation AI can use machine translation technology to provide patent application documents and contracts in multiple languages. This allows documents in different languages ​​to be generated simultaneously and provided in multiple languages, thereby supporting international document creation.

[0051] The document analysis unit can use voice input to document what a user dictates in real time and instantly generate a draft. For example, a draft of a patent application document can be instantly generated. The document analysis unit can also use voice input to document what a user dictates in real time, and the generation AI can generate a draft based on that content. For example, a draft of a contract can be instantly generated. The document analysis unit can also use voice input to document what a user dictates in real time and generate a draft based on that content. For example, a draft of a patent application document or a contract can be instantly generated. This can improve the efficiency and accuracy of document creation by documenting what a user dictates in real time and instantly generating a draft using voice input.

[0052] The document analysis unit can refer to past success stories and propose the most effective document structure. For example, it proposes the most effective document structure for patent application documents. The document analysis unit can also propose the most effective document structure for contracts. For example, the generative AI proposes the most effective document structure for patent application documents and contracts based on past success stories. This makes it possible to improve the quality of documents by referring to past success stories and proposing the most effective document structure.

[0053] The document analysis unit can predict future risks based on user input and automatically add clauses to avoid them. For example, clauses to avoid risks are automatically added to patent application documents. The document analysis unit can also automatically add clauses to avoid risks in contracts. For example, the generative AI can predict future risks in patent application documents or contracts based on user input and automatically add clauses to avoid them. This makes it possible to improve the reliability of documents by predicting future risks based on user input and automatically adding clauses to avoid them.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The document analysis unit uses the generative AI to analyze the content of the document. For example, the generative AI can use natural language processing techniques such as GPT-3 or BERT to analyze the content of the document. The document analysis unit can also use text mining techniques to perform a detailed analysis of the content of the document. Step 2: The suggestion unit makes appropriate corrections and suggestions based on the content of the document analyzed by the document analysis unit. For example, the suggestion unit makes suggestions for grammatical corrections and content improvements. The suggestion unit can also provide specific examples of writing based on the content of the document. Step 3: The search unit searches for similar past cases based on the content of the documents analyzed by the document analysis unit. For example, the search unit may search for similar cases using a patent database or a contract database. The search unit may also search for similar cases using IPC classifications or keyword searches.

[0056] (Example 2) The document creation support system according to an embodiment of the present invention is a system that supports the creation of documents that require specialized and unique wording, such as patent application documents and contracts. This system is useful for checking for omissions and inconsistencies in required items, searching for similar cases, and so on. As a result, the document creation support system can analyze the content of documents, make appropriate corrections and suggestions, and search for similar cases in the past, thereby providing efficient and accurate document creation support.

[0057] A document creation support system according to an embodiment includes a document analysis unit, a suggestion unit, and a search unit. The document analysis unit analyzes the content of a document using a generation AI. For example, the generation AI analyzes the content of a document using natural language processing techniques such as GPT-3 and BERT. The document analysis unit can also perform detailed analysis of the content of a document using text mining techniques. The suggestion unit makes appropriate corrections and suggestions based on the content of the document analyzed by the document analysis unit. For example, the suggestion unit suggests grammatical corrections and content improvements. The suggestion unit can also present specific examples of writing based on the content of the document. The search unit searches for similar past cases based on the content of the document analyzed by the document analysis unit. For example, the search unit searches for similar cases using a patent database or a contract database. The search unit can also search for similar cases using IPC classifications or keyword searches. This allows the document creation support system to analyze the content of a document, make appropriate corrections and suggestions, and search for similar past cases, thereby providing efficient and accurate document creation support.

[0058] The document analysis unit can point out any deficiencies in the detailed description of the invention or the claims and suggest appropriate examples of how to write them. For example, the document analysis unit uses a generation AI to analyze the contents of a document and point out any deficiencies in the detailed description of the invention or the claims. For example, if the description requirements under the Patent Act are not met, the generation AI will point out those deficiencies and suggest specific revisions. The document analysis unit can also suggest examples of how to write them based on the Patent Office guidelines. For example, the generation AI can suggest appropriate examples of how to write them based on the Patent Office guidelines, improving the quality of the document. This improves the quality of the document by pointing out any deficiencies in the detailed description of the invention or the claims and suggesting appropriate examples of how to write them.

[0059] The document analysis unit can point out any inconsistencies between clauses and suggest corrections. For example, the generation AI analyzes the contents of a document and points out any inconsistencies between clauses. For example, if a contract does not meet logical or legal consistency, the generation AI points out those parts and suggests specific corrections. The document analysis unit can also suggest corrections based on legal requirements. For example, the generation AI suggests appropriate corrections based on legal requirements to maintain the consistency of the document. This makes it possible to point out any inconsistencies between clauses and suggest corrections, thereby maintaining the consistency of the document.

[0060] The search unit can search for past patents with similar technical fields or invention content and use their content as a reference. For example, the generation AI analyzes the content of a document and searches for past patents with similar technical fields or invention content. For example, it can search for similar cases using a patent database and use their content as a reference. The search unit can also search for similar cases using IPC classification or keyword search. For example, the generation AI can search for patents in a similar technical field based on IPC classification and use their content as a reference. It can also search for patents with similar invention content using keyword search and use their content as a reference. In this way, by searching for past patents with similar technical fields or invention content and using their content as a reference, the quality of the document can be improved.

[0061] The search unit can search for past contracts with similar contract terms and clauses and use their contents as a reference. For example, the generation AI analyzes the content of a document and searches for past contracts with similar contract terms and clauses. For example, it uses a contract database to search for similar cases and uses their contents as a reference. The search unit can also use a keyword search to search for contracts with similar contract terms and clauses. For example, the generation AI searches for contracts with similar contract terms and clauses based on a keyword search and uses their contents as a reference. In this way, the quality of documents can be improved by searching for past contracts with similar contract terms and clauses and using their contents as a reference.

[0062] The document analysis unit can point out insufficient technical descriptions and suggest specific revisions. For example, the generation AI analyzes the contents of a document and points out insufficient technical descriptions. For example, if there is a lack of technical details or examples, the generation AI points out those parts and suggests specific revisions. The document analysis unit can also suggest adding technical details or completing examples. For example, the generation AI suggests specific revisions to add technical details and improve the quality of the document. In this way, by pointing out insufficient technical descriptions and suggesting specific revisions, the quality of the document can be improved.

[0063] The document analysis unit can point out ambiguous clauses and suggest clearer examples. For example, the generation AI analyzes the contents of a document and points out ambiguous clauses. For example, if there is unclear legal interpretation or unclear wording, the generation AI points out those parts and suggests specific revisions. The document analysis unit can also suggest clear examples based on legal requirements. For example, the generation AI can suggest appropriate examples based on legal requirements to improve the quality of the document. This makes it possible to improve the quality of the document by pointing out ambiguous clauses and suggesting clearer examples.

[0064] The document analysis unit can use the emotion estimation function to estimate the user's emotions, identify areas where the user feels anxious, and make suggestions for improvement. For example, the document analysis unit uses the generation AI to analyze the content of a document and monitor the user's emotions in real time using the emotion estimation function. For example, it can identify areas where the user feels anxious and make specific suggestions for improving those areas. The document analysis unit can also use the emotion estimation function to estimate the user's emotions and suggest revisions to explain those areas more clearly. For example, the generation AI can identify areas where the user feels anxious and make specific suggestions for improving those areas. In this way, by estimating the user's emotions, identifying areas where the user feels anxious, and making suggestions for improvement, it is possible to increase the user's sense of security.

[0065] The document analysis unit can analyze the frequency and context of technical terms and suggest optimal term selection. For example, the document analysis unit uses a generation AI to analyze the content of a document and analyze the frequency and context of technical terms. For example, the document analysis unit makes suggestions for selecting appropriate technical terms in patent application documents. The document analysis unit can also make suggestions for selecting appropriate technical terms in contracts. For example, the generation AI makes suggestions for selecting appropriate technical terms in patent application documents and contracts. This allows the document to be both technical and easy to understand by analyzing the frequency and context of technical terms and suggesting optimal term selection.

[0066] The document analysis unit can refer to past success stories and failure stories and make suggestions to minimize risk. For example, the generation AI analyzes the contents of a document and refers to past success stories and failure stories. For example, the document analysis unit makes specific suggestions to minimize risk in patent application documents. The document analysis unit can also make specific suggestions to minimize risk in contracts. For example, the generation AI makes specific suggestions to minimize risk in patent application documents and contracts. In this way, by referring to past success stories and failure stories and making suggestions to minimize risk, the reliability of the document can be increased.

[0067] The document analysis unit can document what a user dictates in real time using voice input and check for omissions or inconsistencies in required fields. For example, the document analysis unit uses a generation AI to document what a user dictates in real time using voice input. For example, in patent application documents, the document analysis unit can check for omissions or inconsistencies in required fields. The document analysis unit can also document what a user dictates in real time using voice input and have the generation AI analyze the content. For example, in contracts, the document analysis unit can check for omissions or inconsistencies in required fields. The document analysis unit can also document what a user dictates in real time using voice input and analyze the content. For example, in patent application documents and contracts, the document analysis unit can check for omissions or inconsistencies in required fields. This can improve the efficiency and accuracy of document creation by documenting what a user dictates in real time using voice input and checking for omissions or inconsistencies in required fields.

[0068] The document analysis unit supports document creation in different languages ​​and can automatically generate documents in multiple languages. For example, the document analysis unit uses a generation AI to analyze the content of a document and support document creation in different languages. For example, it automatically generates patent application documents in multiple languages. The document analysis unit can also automatically generate contracts in multiple languages. For example, the generation AI uses machine translation technology to automatically generate patent application documents and contracts in multiple languages. This supports document creation in different languages ​​and automatically generates documents in multiple languages, making it possible to support international document creation.

[0069] The document analysis unit can use the emotion estimation function to monitor the user's emotions in real time and make suggestions that elicit positive emotions. For example, the document analysis unit uses the generation AI to analyze the content of a document and monitor the user's emotions in real time using the emotion estimation function. For example, it makes suggestions that will make the user feel positive emotions. The document analysis unit can also use the emotion estimation function to monitor the user's emotions in real time and provide feedback according to those emotions. For example, the generation AI can make suggestions that will make the user feel positive emotions, improving user satisfaction. In this way, by monitoring the user's emotions in real time and making suggestions that will elicit positive emotions, it is possible to improve user satisfaction.

[0070] The search unit can use the emotion estimation function to analyze the user's emotional response to past cases and prioritize presenting cases that received the most positive response. For example, the search unit uses the generation AI to search for similar cases and analyze the user's emotional response to past cases using the emotion estimation function. For example, the search unit can prioritize presenting cases that received the most positive response. The search unit can also use the emotion estimation function to analyze the user's emotional response to past cases and present the most appropriate case based on the results. For example, the generation AI can prioritize presenting cases that received the most positive response based on the user's emotional response, improving user satisfaction. In this way, by analyzing the user's emotional response to past cases and prioritize presenting cases that received the most positive response, user satisfaction can be improved.

[0071] The search unit displays search results in chronological order, allowing users to visually understand the evolution of technology and contract terms. In the search unit, for example, the generation AI searches for similar cases and displays the search results in chronological order. For example, the search unit allows users to visually understand the evolution of technology and contract terms. The search unit can also build a system that displays search results in chronological order, allowing users to visually understand the evolution of technology and contract terms. For example, the generation AI displays search results using timeline display or time-series data visualization technology, deepening the user's understanding. This allows users to visually understand the evolution of technology and contract terms, deepening their understanding.

[0072] The search unit can simultaneously search for related legal precedents and regulatory information, thereby providing comprehensive information. For example, the generation AI searches for similar cases and simultaneously searches for related legal precedents and regulatory information. For example, comprehensive information is provided using a laws and regulations database and a precedent database. The search unit can also build a system for simultaneously searching for related legal precedents and regulatory information and providing comprehensive information. For example, the generation AI can search for related information using a laws and regulations database and a precedent database to support user decision-making. This allows the generation AI to simultaneously search for related legal precedents and regulatory information and provide comprehensive information, thereby supporting user decision-making.

[0073] The search unit can search including visual data such as images and drawings, and provide visual reference materials. For example, the generation AI searches for similar cases, including visual data such as images and drawings. For example, the search unit can provide visual reference materials. The search unit can also build a system for searching including visual data such as images and drawings, and providing visual reference materials. For example, the generation AI can provide visual reference materials using image data and drawing data, deepening the user's understanding. In this way, the user's understanding can be deepened by searching including visual data such as images and drawings, and providing visual reference materials.

[0074] The search unit can also search for similar cases in different industries or fields, incorporating knowledge from different fields. For example, the generation AI searches for similar cases, and also searches for similar cases in different industries or fields. For example, incorporating knowledge from different fields. The search unit can also build a system to search for similar cases in different industries or fields, incorporating knowledge from different fields. For example, the generation AI can search for similar cases in different fields using industry classifications or field-specific databases, and incorporate that knowledge. This allows the search for similar cases in different industries or fields, incorporating knowledge from different fields, thereby improving the quality of document creation.

[0075] The search unit can use the emotion estimation function to monitor the user's emotions in real time and present optimal results based on their emotional reactions to the search results. For example, the search unit uses the generation AI to search for similar cases and monitors the user's emotions in real time using the emotion estimation function. For example, the search unit presents optimal results based on their emotional reactions to the search results. The search unit can also build a system that uses the emotion estimation function to monitor the user's emotions in real time and presents optimal results based on those emotional reactions. For example, the generation AI presents optimal search results based on the user's emotional reactions, improving user satisfaction. This makes it possible to improve user satisfaction by monitoring the user's emotions in real time and presenting optimal results based on their emotional reactions to the search results.

[0076] The document analysis unit can refer to past success stories and propose the most effective document structure. For example, when the generation AI automatically generates a document, the document analysis unit refers to past success stories. For example, it proposes the most effective document structure for patent application documents. The document analysis unit can also propose the most effective document structure for contracts. For example, the generation AI proposes the most effective document structure for patent application documents and contracts based on past success stories. In this way, by referring to past success stories and proposing the most effective document structure, the quality of the document can be improved.

[0077] The document analysis unit can predict future risks based on user input and automatically add clauses to avoid them. For example, when a generation AI automatically generates a document, the document analysis unit predicts future risks based on user input. For example, in patent application documents, clauses to avoid risks are automatically added. The document analysis unit can also automatically add clauses to avoid risks in contracts. For example, in patent application documents or contracts, the generation AI predicts future risks based on user input and automatically adds clauses to avoid them. In this way, by predicting future risks based on user input and automatically adding clauses to avoid them, the reliability of the document can be improved.

[0078] The document analysis unit can use voice input to document what a user dictates in real time and instantly generate a draft. For example, the document analysis unit allows a generation AI to document what a user dictates in real time using voice input. For example, a draft of a patent application document can be instantly generated. The document analysis unit can also document what a user dictates in real time using voice input, and the generation AI can generate a draft based on that content. For example, a draft of a contract can be instantly generated. The document analysis unit can also document what a user dictates in real time using voice input and generate a draft based on that content. For example, a draft of a patent application document or a contract can be instantly generated. This allows the efficiency and accuracy of document creation to be improved by documenting what a user dictates in real time using voice input and instantly generating a draft.

[0079] The document analysis unit can simultaneously generate documents in different languages ​​and provide documents in multiple languages. For example, when the generation AI automatically generates a document, the document analysis unit simultaneously generates documents in different languages. For example, it provides patent application documents in multiple languages. The document analysis unit can also provide contracts in multiple languages. For example, the generation AI uses machine translation technology to provide patent application documents and contracts in multiple languages. This makes it possible to simultaneously generate documents in different languages ​​and provide documents in multiple languages, thereby supporting international document creation.

[0080] The document analysis unit can use the emotion estimation function to monitor user emotions in real time and generate documents that elicit positive emotions. For example, when the generation AI automatically generates a document, the document analysis unit uses the emotion estimation function to monitor user emotions in real time. For example, it generates documents that evoke positive emotions in the user. The document analysis unit can also use the emotion estimation function to monitor user emotions in real time and generate documents that correspond to those emotions. For example, the generation AI generates documents that evoke positive emotions in the user, improving user satisfaction. In this way, by monitoring user emotions in real time and generating documents that evoke positive emotions, user satisfaction can be improved.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The document analysis unit can estimate the user's emotions and adjust the tone of the document based on the estimated emotions. For example, if the user is feeling stressed, the document analysis unit can suggest creating a document with a more relaxed tone. If the user is in a hurry, the document analysis unit can also suggest creating a document that is concise and to the point. Furthermore, if the user is feeling anxious, the document analysis unit can suggest creating a document that gives a sense of security. In this way, adjusting the tone of the document according to the user's emotions can improve user satisfaction.

[0083] The document analysis unit can estimate the user's emotions and customize the content of the document based on the estimated emotions. For example, if the user is excited, the document analysis unit can suggest creating a document that includes many detailed explanations and specific examples. If the user is tired, the document analysis unit can also suggest creating a concise and easy-to-read document. Furthermore, if the user is feeling anxious, the document analysis unit can suggest creating a document that gives a sense of security. In this way, by customizing the content of the document according to the user's emotions, user satisfaction can be improved.

[0084] The document analysis unit can estimate the user's emotions and adjust the layout of the document based on the estimated emotions. For example, if the user is feeling stressed, the document analysis unit can suggest a simple and easy-to-read layout. If the user is feeling excited, the document analysis unit can also suggest a visually appealing layout. Furthermore, if the user is feeling anxious, the document analysis unit can suggest a layout that gives a sense of security. In this way, adjusting the document layout according to the user's emotions can improve user satisfaction.

[0085] The document analysis unit can estimate the user's emotions and adjust the format of the document based on the estimated emotions. For example, if the user is feeling stressed, the document analysis unit can suggest a concise and to-the-point format. If the user is feeling excited, the document analysis unit can also suggest a format that includes detailed explanations and many concrete examples. Furthermore, if the user is feeling anxious, the document analysis unit can suggest a format that gives a sense of security. In this way, by adjusting the format of the document according to the user's emotions, user satisfaction can be improved.

[0086] The document analysis unit can estimate the user's emotions and adjust the content of the document based on the estimated emotions. For example, if the user is feeling stressed, the document analysis unit can suggest creating a document with a relaxed tone. If the user is feeling excited, the document analysis unit can also suggest creating a document that includes many detailed explanations and concrete examples. Furthermore, if the user is feeling anxious, the document analysis unit can suggest creating a document that gives a sense of security. In this way, adjusting the content of the document according to the user's emotions can improve user satisfaction.

[0087] The document analysis unit can predict future risks based on user input and automatically add clauses to avoid them. For example, clauses to avoid risks are automatically added to patent application documents. The document analysis unit can also automatically add clauses to avoid risks in contracts. For example, the generative AI can predict future risks in patent application documents or contracts based on user input and automatically add clauses to avoid them. This makes it possible to improve the reliability of documents by predicting future risks based on user input and automatically adding clauses to avoid them.

[0088] The document analysis unit can simultaneously generate documents in different languages ​​and provide documents in multiple languages. For example, it can provide patent application documents in multiple languages. The document analysis unit can also provide contracts in multiple languages. For example, the generation AI can use machine translation technology to provide patent application documents and contracts in multiple languages. This allows documents in different languages ​​to be generated simultaneously and provided in multiple languages, thereby supporting international document creation.

[0089] The document analysis unit can use voice input to document what a user dictates in real time and instantly generate a draft. For example, a draft of a patent application document can be instantly generated. The document analysis unit can also use voice input to document what a user dictates in real time, and the generation AI can generate a draft based on that content. For example, a draft of a contract can be instantly generated. The document analysis unit can also use voice input to document what a user dictates in real time and generate a draft based on that content. For example, a draft of a patent application document or a contract can be instantly generated. This can improve the efficiency and accuracy of document creation by documenting what a user dictates in real time and instantly generating a draft using voice input.

[0090] The document analysis unit can refer to past success stories and propose the most effective document structure. For example, it proposes the most effective document structure for patent application documents. The document analysis unit can also propose the most effective document structure for contracts. For example, the generative AI proposes the most effective document structure for patent application documents and contracts based on past success stories. This makes it possible to improve the quality of documents by referring to past success stories and proposing the most effective document structure.

[0091] The document analysis unit can predict future risks based on user input and automatically add clauses to avoid them. For example, clauses to avoid risks are automatically added to patent application documents. The document analysis unit can also automatically add clauses to avoid risks in contracts. For example, the generative AI can predict future risks in patent application documents or contracts based on user input and automatically add clauses to avoid them. This makes it possible to improve the reliability of documents by predicting future risks based on user input and automatically adding clauses to avoid them.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The document analysis unit uses the generative AI to analyze the content of the document. For example, the generative AI can use natural language processing techniques such as GPT-3 or BERT to analyze the content of the document. The document analysis unit can also use text mining techniques to perform a detailed analysis of the content of the document. Step 2: The suggestion unit makes appropriate corrections and suggestions based on the content of the document analyzed by the document analysis unit. For example, the suggestion unit makes suggestions for grammatical corrections and content improvements. The suggestion unit can also provide specific examples of writing based on the content of the document. Step 3: The search unit searches for similar past cases based on the content of the documents analyzed by the document analysis unit. For example, the search unit may search for similar cases using a patent database or a contract database. The search unit may also search for similar cases using IPC classifications or keyword searches.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a document analysis unit that analyzes the content of documents using generative AI; a suggestion unit that makes appropriate corrections and suggestions based on the content of the document analyzed by the document analysis unit; a search unit that searches for similar past cases based on the content of the document analyzed by the document analysis unit. A system characterized by:

2. The document analysis unit If the detailed description of the invention or the claims are insufficient, we will point out the missing parts and suggest appropriate examples.

2. The system of claim 1.

3. The document analysis unit If there are any inconsistencies between clauses, point out the inconsistencies and propose amendments 2. The system of claim 1.

4. The search unit Search for past patents with similar technical fields or inventions and refer to their contents 2. The system of claim 1.

5. The search unit Search for and reference past contracts with similar terms and conditions 2. The system of claim 1.

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