System

Abstract

An object of the system according to the embodiment is to analyze the content of a legal document and notify a user of an unfair condition.SOLUTION: A system includes a legal document acquisition part, an analysis part, and a notification part. The legal document acquisition section acquires a legal document from a user. The analysis section analyzes the legal document acquired by the legal document acquisition section. The notification unit notifies the user of a result analyzed by the 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] Conventional techniques have had the problem that it is difficult for ordinary people to understand the contents of legal documents and identify unfair terms.

[0005] The system according to the embodiment aims to analyze the contents of legal documents and notify users of unfair conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a legal document acquisition unit, an analysis unit, and a notification unit. The legal document acquisition unit acquires a legal document from a user. The analysis unit analyzes the legal document acquired by the legal document acquisition unit. The notification unit notifies the user of the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the content of legal documents and notify users of unfair terms. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A legal document analysis system according to an embodiment of the present invention is a system that uses AI technology to analyze legal documents and clearly indicate to users any deviations from standard contracts or unfair terms. This enables ordinary people without legal expertise to enter into fair and transparent contracts while protecting their rights.

[0029] A legal document analysis system according to an embodiment includes a legal document acquisition unit, an analysis unit, and a notification unit. The legal document acquisition unit acquires legal documents from a user. For example, a user can upload legal documents electronically. The legal document acquisition unit can also receive manually entered legal documents. The legal document acquisition unit can also acquire legal documents from other systems through an API. For example, a user can upload a contract in PDF format, and the system acquires its contents. Manually entered legal documents are acquired by the user entering the contract contents into a text box. When acquired through an API, contract data is automatically acquired from other systems. The analysis unit analyzes the legal documents acquired by the legal document acquisition unit. For example, the generation AI analyzes legal documents using a text generation AI (e.g., LLM). The generation AI can also analyze the contents of legal documents using a multimodal generation AI. The generation AI compares each clause of a document with common contract standards to identify deviations or unfair terms. For example, the text generation AI has learned a large amount of legal document data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI analyzes each clause of a contract and compares it with a typical contract to identify deviations. The notification unit notifies the user of the analysis results. For example, the notification unit may send the analysis results to the user via email. The notification unit may also notify the user of the analysis results via push notification. The notification unit may also display the analysis results on a dashboard. For example, email notification sends the analysis results to an email address specified by the user. Push notification notifies the analysis results in real time via a mobile application. Dashboard display allows the user to view the analysis results on a web application. This allows the legal document analysis system according to the embodiment to analyze legal documents and identify problems. For example, the user can identify deviations or unfair terms in the contract in advance and take appropriate measures. Based on the analysis results, the user can renegotiate the contract terms.By checking the analysis results on the dashboard, users can intuitively understand the problem.

[0030] The analysis unit can perform analysis based on the historical background of legal documents and the impact of legal changes. For example, when the generation AI analyzes a legal document, the analysis unit considers the document's historical background by referencing the time the document was created and information on legal changes from that time. For example, it retrieves the history of past legal changes from a database and analyzes whether each clause of the document is affected by them. The analysis unit also refers to related legal precedents and laws to consider the document's historical background. For example, it analyzes whether each clause of the document is affected based on legal precedents from the time the document was created. The analysis unit also analyzes the content and scope of legal changes to consider the impact of legal changes. For example, it identifies clauses that have been changed by legal changes and evaluates their impact. This enables more precise analysis by considering the historical background of legal documents and the impact of legal changes. For example, users can understand the historical background of a document and evaluate the validity of contract content. By considering the impact of legal changes, users can confirm contract content based on the latest laws and regulations.

[0031] The analysis unit can infer the intent or purpose of the document's author and interpret the document based on that intent. For example, when a generative AI analyzes a legal document, the analysis unit refers to the document's context and related documents to infer the document's author's intent or purpose. For example, it analyzes other documents written by the same author to find common intent. The analysis unit also analyzes the document's context to infer the document's author's intent. For example, it analyzes the sentences and paragraphs before and after the document to identify the author's intent. The analysis unit also analyzes the situation and purpose in which the document is used to infer the document's purpose. For example, it interprets the document based on the type and purpose of the contract in which the document is used. This enables more accurate analysis by inferring the intent or purpose of the document's author and interpreting the document based on that intent. For example, users can understand the document's author's intent and grasp the background of the contract's contents. By understanding the document's purpose, users can evaluate the appropriateness of the contract's contents.

[0032] The analysis unit can also analyze contracts from different legal systems and cultural spheres and compare them from an international perspective. For example, the analysis unit analyzes contracts from different legal systems and cultural spheres using the generation AI and compares them from an international perspective. For example, it analyzes American, European, and Asian contracts to identify similarities and differences. The analysis unit also references the laws and precedents of each country to analyze contracts from different legal systems. For example, when analyzing an American contract, it performs analysis based on American laws and precedents. The analysis unit also takes cultural backgrounds and customs into account to analyze contracts from different cultural spheres. For example, when analyzing an Asian contract, it performs analysis based on Asian cultural backgrounds and customs. This enables more extensive analysis by analyzing contracts from different legal systems and cultural spheres and comparing them from an international perspective. For example, users can compare contracts from different legal systems and cultural spheres and evaluate the validity of their content. By comparing from an international perspective, users can understand the content of global contracts and take appropriate measures.

[0033] The analysis unit can expand the types of documents analyzed by the generative AI to include insurance contracts or rental contracts. For example, the analysis unit analyzes each clause of an insurance contract to identify deviations compared to a typical insurance contract. The analysis unit also analyzes each clause of a rental contract to identify deviations compared to a typical rental contract. For example, when analyzing insurance contracts, the analysis unit analyzes each clause of life insurance and non-life insurance contracts. When analyzing rental contracts, the analysis unit analyzes each clause of residential and commercial rental contracts. This expands the types of documents analyzed, enabling the analysis of a wider variety of legal documents. For example, users can identify deviations in insurance contracts and rental contracts in advance and take appropriate measures. Based on the analysis results of insurance contracts, users can evaluate the appropriateness of insurance coverage. Based on the analysis results of rental contracts, users can evaluate the appropriateness of rental terms.

[0034] The notification unit can propose a specific action plan to the user in response to unfair terms. For example, when the generation AI identifies unfair terms, the notification unit proposes a specific action plan to the user. For example, it presents a method for renegotiating unfair terms. The notification unit also refers to relevant laws and precedents to propose an action plan to the user. For example, it proposes specific measures to be taken by the user based on past precedents. The notification unit also generates visual infographics and charts to propose an action plan. For example, it displays the renegotiation steps in graphs and charts. This allows the user to take appropriate measures by proposing a specific action plan to the user in response to unfair terms. For example, the user can understand how to renegotiate unfair terms and take appropriate measures. Through the visual display, the user can intuitively understand the action plan and renegotiate the contract terms.

[0035] The notification unit can generate infographics and charts to visually display deviations and unfair terms. For example, when the generation AI identifies deviations and unfair terms, the notification unit generates infographics to visually display them. For example, each clause of the contract is displayed in a graph or chart. The notification unit also collects related data and visually displays it to generate infographics and charts. For example, it displays a risk assessment for each clause of the contract in a graph or chart. The notification unit also enables users to intuitively understand the information through visual displays. For example, each clause of the contract is displayed in a color-coded manner to highlight high-risk clauses. This allows users to intuitively understand deviations and unfair terms by visually displaying them. For example, users can visually identify deviations and unfair terms in the contract and take appropriate measures. Through visual displays, users can intuitively understand the risks and renegotiate the contract terms.

[0036] The notification unit can provide a platform for users to consult with other experts regarding unfair conditions. For example, when the generation AI indicates unfair conditions, the notification unit provides a platform for users to consult with other experts (such as lawyers). For example, a function for booking a consultation with a lawyer is added. The notification unit also collects and visually displays related data to provide a platform for users to consult with experts. For example, it displays the expert's profile and ratings. The notification unit also provides online consultation and video conferencing functions to provide a platform for users to consult with experts. For example, it allows users to consult with a lawyer online. By providing a platform for users to consult with other experts regarding unfair conditions, the user can obtain appropriate advice. For example, the user can understand specific measures to address unfair conditions and take appropriate measures. Based on the expert's advice, the user can renegotiate the terms of the contract.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The legal document analysis system can further include a history reference unit that references the user's past contract history. The history reference unit retrieves contracts previously concluded by the user from a database and compares them with the current contract. For example, it compares the clauses of the past contract with those of the current contract to identify changes or newly added clauses. The history reference unit also identifies problems or unfair conditions that the user has experienced in the past based on the results of analyzing the past contract and checks whether similar problems exist in the current contract. This allows the user to utilize past experience to evaluate the validity of the current contract. For example, the user can identify clauses that have caused problems in the past and take appropriate measures. The user can renegotiate the content of the current contract based on the past contract history.

[0039] The analysis unit may include a risk assessment unit that performs a risk assessment for each clause in a legal document. The risk assessment unit assesses the risk level of each clause and notifies the user. For example, the risk assessment unit calculates a risk score based on the content of the clause and past legal precedents, and highlights clauses with high risk. The risk assessment unit also generates infographics and charts to visually display the results of the risk assessment. For example, the risk assessment for each clause in a contract may be displayed in a graph or chart, allowing the user to intuitively understand. This allows the user to grasp the risks of the contract in advance and take appropriate measures. The user may renegotiate the contract content based on the results of the risk assessment.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The legal document acquisition unit acquires legal documents from the user. For example, the user can upload legal documents electronically. The legal document acquisition unit can also receive manually entered legal documents. Furthermore, the legal document acquisition unit can acquire legal documents from other systems through APIs. For example, the user can upload a contract in PDF format, and the system acquires its contents. Manually entered legal documents are acquired by the user entering the contract contents into a text box. When acquired through APIs, contract data is automatically acquired from other systems. Step 2: The analysis unit analyzes the legal documents acquired by the legal document acquisition unit. For example, the generation AI analyzes the legal documents using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to analyze the contents of the legal documents. The generation AI also compares each clause of the document with the standards of general contracts to identify deviations or unfair conditions. For example, the text generation AI has trained on large amounts of legal document data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI analyzes each clause of the contract and identifies deviations from the standards of general contracts. Step 3: The notification unit notifies the user of the results of the analysis performed by the analysis unit. For example, the notification unit sends the analysis results to the user via email. The notification unit can also notify the user of the analysis results via push notification. The notification unit can also display the analysis results on a dashboard. For example, email notification sends the analysis results to an email address specified by the user. Push notification notifies the analysis results in real time via a mobile application. Dashboard display allows the user to check the analysis results on a web application.

[0042] (Example 2) A legal document analysis system according to an embodiment of the present invention is a system that uses AI technology to analyze legal documents and clearly indicate to users any deviations from standard contracts or unfair terms. This enables ordinary people without legal expertise to enter into fair and transparent contracts while protecting their rights.

[0043] A legal document analysis system according to an embodiment includes a legal document acquisition unit, an analysis unit, and a notification unit. The legal document acquisition unit acquires legal documents from a user. For example, a user can upload legal documents electronically. The legal document acquisition unit can also receive manually entered legal documents. The legal document acquisition unit can also acquire legal documents from other systems through an API. For example, a user can upload a contract in PDF format, and the system acquires its contents. Manually entered legal documents are acquired by the user entering the contract contents into a text box. When acquired through an API, contract data is automatically acquired from other systems. The analysis unit analyzes the legal documents acquired by the legal document acquisition unit. For example, the generation AI analyzes legal documents using a text generation AI (e.g., LLM). The generation AI can also analyze the contents of legal documents using a multimodal generation AI. The generation AI compares each clause of a document with common contract standards to identify deviations or unfair terms. For example, the text generation AI has learned a large amount of legal document data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI analyzes each clause of a contract and compares it with a typical contract to identify deviations. The notification unit notifies the user of the analysis results. For example, the notification unit may send the analysis results to the user via email. The notification unit may also notify the user of the analysis results via push notification. The notification unit may also display the analysis results on a dashboard. For example, email notification sends the analysis results to an email address specified by the user. Push notification notifies the analysis results in real time via a mobile application. Dashboard display allows the user to view the analysis results on a web application. This allows the legal document analysis system according to the embodiment to analyze legal documents and identify problems. For example, the user can identify deviations or unfair terms in the contract in advance and take appropriate measures. Based on the analysis results, the user can renegotiate the contract terms.By checking the analysis results on the dashboard, users can intuitively understand the problem.

[0044] The analysis unit can perform analysis based on the historical background of legal documents and the impact of legal changes. For example, when the generation AI analyzes a legal document, the analysis unit considers the document's historical background by referencing the time the document was created and information on legal changes from that time. For example, it retrieves the history of past legal changes from a database and analyzes whether each clause of the document is affected by them. The analysis unit also refers to related legal precedents and laws to consider the document's historical background. For example, it analyzes whether each clause of the document is affected based on legal precedents from the time the document was created. The analysis unit also analyzes the content and scope of legal changes to consider the impact of legal changes. For example, it identifies clauses that have been changed by legal changes and evaluates their impact. This enables more precise analysis by considering the historical background of legal documents and the impact of legal changes. For example, users can understand the historical background of a document and evaluate the validity of contract content. By considering the impact of legal changes, users can confirm contract content based on the latest laws and regulations.

[0045] The analysis unit can infer the intent or purpose of the document's author and interpret the document based on that intent. For example, when a generative AI analyzes a legal document, the analysis unit refers to the document's context and related documents to infer the document's author's intent or purpose. For example, it analyzes other documents written by the same author to find common intent. The analysis unit also analyzes the document's context to infer the document's author's intent. For example, it analyzes the sentences and paragraphs before and after the document to identify the author's intent. The analysis unit also analyzes the situation and purpose in which the document is used to infer the document's purpose. For example, it interprets the document based on the type and purpose of the contract in which the document is used. This enables more accurate analysis by inferring the intent or purpose of the document's author and interpreting the document based on that intent. For example, users can understand the document's author's intent and grasp the background of the contract's contents. By understanding the document's purpose, users can evaluate the appropriateness of the contract's contents.

[0046] The analysis unit uses the emotion estimation function to analyze the user's emotional response to each clause in a document and can identify parts that make the user feel anxious. For example, when the generative AI analyzes a legal document, the analysis unit uses the emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the emotion estimation function to identify parts that make the user feel anxious, allowing the user to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0047] The analysis unit can also analyze contracts from different legal systems and cultural spheres and compare them from an international perspective. For example, the analysis unit analyzes contracts from different legal systems and cultural spheres using the generation AI and compares them from an international perspective. For example, it analyzes American, European, and Asian contracts to identify similarities and differences. The analysis unit also references the laws and precedents of each country to analyze contracts from different legal systems. For example, when analyzing an American contract, it performs analysis based on American laws and precedents. The analysis unit also takes cultural backgrounds and customs into account to analyze contracts from different cultural spheres. For example, when analyzing an Asian contract, it performs analysis based on Asian cultural backgrounds and customs. This enables more extensive analysis by analyzing contracts from different legal systems and cultural spheres and comparing them from an international perspective. For example, users can compare contracts from different legal systems and cultural spheres and evaluate the validity of their content. By comparing from an international perspective, users can understand the content of global contracts and take appropriate measures.

[0048] The analysis unit can expand the types of documents analyzed by the generative AI to include insurance contracts or rental contracts. For example, the analysis unit analyzes each clause of an insurance contract to identify deviations compared to a typical insurance contract. The analysis unit also analyzes each clause of a rental contract to identify deviations compared to a typical rental contract. For example, when analyzing insurance contracts, the analysis unit analyzes each clause of life insurance and non-life insurance contracts. When analyzing rental contracts, the analysis unit analyzes each clause of residential and commercial rental contracts. This expands the types of documents analyzed, enabling the analysis of a wider variety of legal documents. For example, users can identify deviations in insurance contracts and rental contracts in advance and take appropriate measures. Based on the analysis results of insurance contracts, users can evaluate the appropriateness of insurance coverage. Based on the analysis results of rental contracts, users can evaluate the appropriateness of rental terms.

[0049] The analysis unit can use the emotion estimation function to monitor changes in emotions in real time as a user reads a legal document and reconstruct the document to make it easier for the user to understand. For example, the analysis unit uses the emotion estimation function to monitor changes in emotions in real time as a user reads a legal document and identify parts that make the user feel anxious. For example, the analysis unit concisely reconstructs parts that make the user feel anxious. The analysis unit also analyzes the user's facial expressions and voice to monitor changes in the user's emotions. For example, it captures changes in the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This helps the user understand the document by using the emotion estimation function to reconstruct it to make it easier for the user to understand. For example, the reconstructed document makes it easier for the user to understand parts that make them feel anxious. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0050] The notification unit can propose a specific action plan to the user in response to unfair terms. For example, when the generation AI identifies unfair terms, the notification unit proposes a specific action plan to the user. For example, it presents a method for renegotiating unfair terms. The notification unit also refers to relevant laws and precedents to propose an action plan to the user. For example, it proposes specific measures to be taken by the user based on past precedents. The notification unit also generates visual infographics and charts to propose an action plan. For example, it displays the renegotiation steps in graphs and charts. This allows the user to take appropriate measures by proposing a specific action plan to the user in response to unfair terms. For example, the user can understand how to renegotiate unfair terms and take appropriate measures. Through the visual display, the user can intuitively understand the action plan and renegotiate the contract terms.

[0051] The notification unit can use the emotion estimation function to analyze the emotions a user feels in response to unfair conditions and provide appropriate advice based on those emotions. For example, the notification unit can use the emotion estimation function to analyze the emotions a user feels in response to unfair conditions and provide appropriate advice based on those emotions. For example, the notification unit can suggest a renegotiation method to a user who feels anxious. The notification unit can also analyze the user's facial expressions and voice to analyze the user's emotions. For example, the notification unit can capture changes in the user's facial expressions with a camera and analyze the emotions using an emotion estimation algorithm. The notification unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. The notification unit can also collect the user's biometric data and analyze the emotions using an emotion estimation algorithm. For example, the notification unit can collect heart rate and electrodermal activity with a sensor and calculate an emotion score. This allows the emotion estimation function to analyze the user's emotions in response to unfair conditions and provide appropriate advice based on those emotions, allowing the user to take measures with peace of mind. For example, the user can understand specific measures to address conditions that make them anxious and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0052] The notification unit can generate infographics and charts to visually display deviations and unfair terms. For example, when the generation AI identifies deviations and unfair terms, the notification unit generates infographics to visually display them. For example, each clause of the contract is displayed in a graph or chart. The notification unit also collects related data and visually displays it to generate infographics and charts. For example, it displays a risk assessment for each clause of the contract in a graph or chart. The notification unit also enables users to intuitively understand the information through visual displays. For example, each clause of the contract is displayed in a color-coded manner to highlight high-risk clauses. This allows users to intuitively understand deviations and unfair terms by visually displaying them. For example, users can visually identify deviations and unfair terms in the contract and take appropriate measures. Through visual displays, users can intuitively understand the risks and renegotiate the contract terms.

[0053] The notification unit can provide a platform for users to consult with other experts regarding unfair conditions. For example, when the generation AI indicates unfair conditions, the notification unit provides a platform for users to consult with other experts (such as lawyers). For example, a function for booking a consultation with a lawyer is added. The notification unit also collects and visually displays related data to provide a platform for users to consult with experts. For example, it displays the expert's profile and ratings. The notification unit also provides online consultation and video conferencing functions to provide a platform for users to consult with experts. For example, it allows users to consult with a lawyer online. By providing a platform for users to consult with other experts regarding unfair conditions, the user can obtain appropriate advice. For example, the user can understand specific measures to address unfair conditions and take appropriate measures. Based on the expert's advice, the user can renegotiate the terms of the contract.

[0054] The notification unit can use the emotion estimation function to track changes in the user's emotions after they understand the unfair conditions and provide follow-up to reassure the user. For example, the notification unit can use the emotion estimation function to track changes in the user's emotions after they understand the unfair conditions in real time and provide follow-up to reassure the user. For example, if the user feels anxious, the notification unit can provide specific measures or advice. The notification unit can also analyze the user's facial expressions and voice to track changes in the user's emotions. For example, the notification unit can capture changes in the user's facial expressions with a camera and analyze the emotions using an emotion estimation algorithm. The notification unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. The notification unit can also collect biometric data of the user and analyze the emotions using an emotion estimation algorithm. For example, the notification unit can collect heart rate and electrodermal activity with a sensor and calculate an emotion score. This allows the emotion estimation function to track changes in the user's emotions after they understand the unfair conditions and provide follow-up to reassure the user, thereby increasing the user's sense of security. For example, users can understand specific countermeasures for conditions that make them feel uneasy and take appropriate measures. Based on the emotion score, users can re-examine areas that make them feel uneasy and renegotiate the terms of the contract.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The legal document analysis system can further include a history reference unit that references the user's past contract history. The history reference unit retrieves contracts previously concluded by the user from a database and compares them with the current contract. For example, it compares the clauses of the past contract with those of the current contract to identify changes or newly added clauses. The history reference unit also identifies problems or unfair conditions that the user has experienced in the past based on the results of analyzing the past contract and checks whether similar problems exist in the current contract. This allows the user to utilize past experience to evaluate the validity of the current contract. For example, the user can identify clauses that have caused problems in the past and take appropriate measures. The user can renegotiate the content of the current contract based on the past contract history.

[0057] The analysis unit may include a risk assessment unit that performs a risk assessment for each clause in a legal document. The risk assessment unit assesses the risk level of each clause and notifies the user. For example, the risk assessment unit calculates a risk score based on the content of the clause and past legal precedents, and highlights clauses with high risk. The risk assessment unit also generates infographics and charts to visually display the results of the risk assessment. For example, the risk assessment for each clause in a contract may be displayed in a graph or chart, allowing the user to intuitively understand. This allows the user to grasp the risks of the contract in advance and take appropriate measures. The user may renegotiate the contract content based on the results of the risk assessment.

[0058] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0059] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0060] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0061] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0062] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0063] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0064] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0065] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0066] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0067] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0068] The analysis unit can analyze the user's emotional response to each clause in a legal document and identify parts that make the user feel anxious. For example, when the generation AI analyzes a legal document, the analysis unit uses an emotion estimation function to collect the user's emotional response in real time and calculate an emotion score for each clause in the document. For example, it identifies clauses that make the user feel anxious and highlights those parts. The analysis unit also analyzes the user's facial expressions and voice to analyze the user's emotional response. For example, it captures changes in the user's facial expression with a camera and analyzes their emotions using an emotion estimation algorithm. It also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also collects the user's biometric data and analyzes their emotions using an emotion estimation algorithm. For example, it collects heart rate and electrodermal activity with a sensor and calculates an emotion score. This allows the user to use the emotion estimation function to identify parts that make the user feel anxious, allowing them to understand the document with peace of mind. For example, the user can identify clauses that make them feel anxious in advance and take appropriate measures. Based on the sentiment score, users can reassess any areas of concern and renegotiate the terms of the contract.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The legal document acquisition unit acquires legal documents from the user. For example, the user can upload legal documents electronically. The legal document acquisition unit can also receive manually entered legal documents. Furthermore, the legal document acquisition unit can acquire legal documents from other systems through APIs. For example, the user can upload a contract in PDF format, and the system acquires its contents. Manually entered legal documents are acquired by the user entering the contract contents into a text box. When acquired through APIs, contract data is automatically acquired from other systems. Step 2: The analysis unit analyzes the legal documents acquired by the legal document acquisition unit. For example, the generation AI analyzes the legal documents using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to analyze the contents of the legal documents. The generation AI also compares each clause of the document with the standards of general contracts to identify deviations or unfair conditions. For example, the text generation AI has trained on large amounts of legal document data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI analyzes each clause of the contract and identifies deviations from the standards of general contracts. Step 3: The notification unit notifies the user of the results of the analysis performed by the analysis unit. For example, the notification unit sends the analysis results to the user via email. The notification unit can also notify the user of the analysis results via push notification. The notification unit can also display the analysis results on a dashboard. For example, email notification sends the analysis results to an email address specified by the user. Push notification notifies the analysis results in real time via a mobile application. Dashboard display allows the user to check the analysis results on a web application.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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 legal document acquisition unit for acquiring a legal document from a user; an analysis unit that analyzes the legal document acquired by the legal document acquisition unit; a notification unit that notifies a user of the results of the analysis performed by the analysis unit. A system characterized by:

2. The analysis unit Inferring the intent or purpose of the document's author and interpreting the document based on that intent 2. The system of claim 1.

3. The analysis unit Analyze contracts from different legal systems and cultural spheres and make comparisons from an international perspective.

2. The system of claim 1.

4. The notification unit When identifying deviations or unfair terms, refer to past cases and legal precedents to present specific risks and impacts.

2. The system of claim 1.

5. The analysis unit Analyzing users' emotional reactions to each clause in a document and identifying areas where users feel uneasy The system of claim 1 .

6. The analysis unit Monitors changes in emotions in real time as users read legal documents and restructures the documents to make them easier to understand.

2. The system of claim 1.

7. The notification unit Analyze the emotions users feel about unfair conditions and provide appropriate advice based on those emotions 2. The system of claim 1.

8. The notification unit Track changes in users' emotions after they understand the unfair terms and follow up to reassure them.

2. The system of claim 1.

Images