System

Abstract

An object of a system according to an embodiment is to efficiently perform identification of a risk of a contract, generation of a correction proposal, and progress management.SOLUTION: A system includes a risk specification part, a research part, a proposal part, and a progress management part. The risk specification unit specifies a risk of the contract. The research unit performs research on the risk identified by the risk identification unit. The suggester generates modification suggestions based on the research performed by the researcher. The progress management part manages the progress of the contract examination.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, identifying risks in contracts, generating revision proposals, and managing progress were often done manually, which was inefficient.

[0005] The system according to the embodiment aims to efficiently perform processes ranging from identifying risks in a contract to generating amendment proposals and managing progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a risk identification unit, a research unit, a proposal unit, and a progress management unit. The risk identification unit identifies risks in a contract. The research unit conducts research on the risks identified by the risk identification unit. The proposal unit generates a revised proposal based on the research conducted by the research unit. The progress management unit manages the progress of the contract review. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform processes ranging from identifying risks in a contract to generating revision proposals and managing progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​contract review platform according to an embodiment of the present invention is a system that comprehensively supports the identification of risks hidden in contracts, research, revision, and case management, thereby enabling legal departments to carry out their work quickly and efficiently.

[0029] An AI contract review platform according to an embodiment includes a risk identification unit, a research unit, a proposal unit, and a progress management unit. The risk identification unit identifies risks in a contract. For example, the generation AI scrutinizes the clauses and wording of a contract and identifies any ambiguous or unfavorable terms. The generation AI can also analyze the contents of a contract to identify potential risks. The research unit conducts research on the risks identified by the risk identification unit. For example, the generation AI proposes specific amendments to mitigate the risks based on past precedents and industry standards. The generation AI can also conduct research based on prompts containing information about the risks and instructions for amendments. The proposal unit generates amendment proposals based on the research conducted by the research unit. For example, the generation AI proposes amendments to contract clauses or additional clauses based on the research results. The generation AI can also generate specific amendment proposals to mitigate risks. The progress management unit manages the progress of the contract review. For example, the generation AI centrally manages information such as the current stage of the contract review, whether amendments have been completed, and whether risks have been resolved. The generation AI can also manage cases based on data on the progress of cases. As a result, the AI ​​contract review platform according to the embodiment improves the efficiency and quality of contract reviews. For example, it can ensure the safety of contracts by quickly identifying risks in contracts and proposing appropriate revisions. The case management function also enables centralized management of the progress of contract reviews, increasing the transparency of operations. Furthermore, automating contract reviews reduces the workload of legal departments, allowing them to focus on more important tasks.

[0030] The risk identification unit can improve accuracy by learning risk patterns based on a risk database of similar past contracts. For example, when the generation AI identifies risks in a contract, the risk identification unit refers to a risk database of similar past contracts to learn risk patterns and improve accuracy. For example, it can identify clauses or wording that have been problematic in the past and automatically detect contracts that contain similar risks. The generation AI can also learn risk patterns in specific industries or regions and identify risks based on the content of the contract. Furthermore, the generation AI can calculate a risk score for each clause in the contract based on the risk database and prioritize the identification of high-risk areas. This improves the accuracy of risk identification.

[0031] The risk identification unit can automatically obtain the latest information on relevant laws and regulations to understand the context of a contract and reflect this in the analysis. For example, when the generation AI identifies risks in a contract, the risk identification unit automatically obtains the latest information on relevant laws and regulations to understand the context of the contract and reflects this in the analysis. For example, it can identify risks by comparing the content of the contract with newly enacted laws and regulations. The generation AI can also identify risks in a contract by taking into account legal requirements in specific industries and regions. Furthermore, the generation AI can check whether the clauses in the contract comply with the latest laws and regulations and identify risks. This improves the accuracy of contract risk identification.

[0032] The risk identification unit also supports voice input or handwritten input, making it possible to analyze contract data in different formats. The risk identification unit also supports voice input or handwritten input, making it possible to analyze contract data in different formats, for example, when identifying risks in a contract. For example, using voice recognition technology, the contents of a contract read aloud can be converted into text data to identify risks. Also, using handwritten character recognition technology, the contents of a contract written by hand can be converted into text data to identify risks. Furthermore, by combining voice input and handwritten input, it is possible to analyze contract data in multiple formats at once and identify risks. This makes it possible to analyze contract data in different formats.

[0033] The risk identification unit can add a function to analyze contracts from different industries or countries and identify global risk patterns. The risk identification unit can add a function to analyze contracts from different industries or countries and identify global risk patterns, for example. For example, it can learn risk patterns in international transactions and identify risks according to the content of the contract. It can also learn risk patterns in a specific industry and identify risks according to the content of the contract. It can also identify risks in contracts by taking into account the laws and regulations and industry standards of different countries. This makes it possible to identify global risk patterns.

[0034] The research department can simulate the legal impact of each clause in a contract and assess the severity of the risk. For example, when the generative AI analyzes the contents of a contract, the research department can simulate the legal impact of each clause in the contract and assess the severity of the risk. For example, it can simulate the legal impact of a specific clause and assess the severity of the risk. It can also simulate the possibility of a clause being illegal or the risk of litigation and assess the severity of the risk. It can also simulate the impact of a clause on the entire contract and assess the severity of the risk. This makes it possible to assess the severity of the risk and take appropriate measures.

[0035] The research department can analyze the interdependencies between clauses in a contract and identify whether risks will occur in a chain reaction. For example, when the generative AI analyzes the contents of a contract, the research department analyzes the interdependencies between clauses in the contract and identifies the possibility of risks occurring in a chain reaction. For example, if a clause depends on other clauses, the research department analyzes that dependency and identifies the risks. It can also analyze the impact on other clauses if a specific clause is changed and identify risks. Furthermore, it can visualize the dependencies between clauses and identify the possibility of risks occurring in a chain reaction. By identifying the possibility of risks occurring in a chain reaction, preventive measures can be taken.

[0036] The research department can also analyze visual data of contracts, including images and diagrams, to identify risks. For example, when analyzing the contents of a contract, the research department can also analyze visual data of contracts, including images and diagrams, to identify risks. For example, the research department can analyze the contents of diagrams and graphs included in a contract to identify risks. It can also analyze the contents of blueprints and sketches included in a contract to identify risks. It can also analyze the contents of photographs and illustrations included in a contract to identify risks. In this way, it is possible to identify risks by analyzing visual data.

[0037] The research department can automatically translate contracts in different languages ​​to perform risk analysis in multiple languages. For example, when analyzing the contents of a contract, the research department can automatically translate contracts in different languages ​​to perform risk analysis in multiple languages. For example, contracts in English, Chinese, etc. can be automatically translated to identify risks. The contents of a contract can also be translated into multiple languages ​​to identify risks in each language. Furthermore, the contents of a contract can also be automatically translated to identify risks based on the translation results. This makes risk analysis possible in multiple languages.

[0038] The amendment suggestion unit can make amendment suggestions to reduce risks based on a database of past precedents. For example, when the generation AI conducts research, the amendment suggestion unit refers to the database of past precedents and makes amendment suggestions to reduce risks. For example, it makes suggestions to amend specific clauses based on past precedents. The generation AI can also extract similar cases from the precedent database and suggest how to amend them. Furthermore, the generation AI can present specific amendment suggestions to reduce risks based on the precedent database. This makes it possible to make amendment suggestions to reduce risks based on past precedents.

[0039] The modification suggestion unit can automatically acquire the latest industry trends or best practices and reflect them in modification suggestions. For example, when the generative AI conducts research, the modification suggestion unit automatically acquires the latest industry trends and best practices and reflects them in modification suggestions. For example, it can propose modifications to contract clauses based on the latest industry standards. The generative AI can also take into account the latest industry trends and present modification suggestions to mitigate risks. Furthermore, the generative AI can propose modifications to the content of contracts based on the latest best practices. This makes it possible to propose modifications based on the latest industry trends and best practices.

[0040] The revision proposal unit can collect opinions from experts in different industries or fields and reflect them in revision proposals. For example, when conducting research and revision proposals, the revision proposal unit collects opinions from experts in different industries or fields and reflects them in revision proposals. For example, technical clauses of a contract can be revised based on the opinions of experts in the technical field. Legal clauses of a contract can also be revised based on the opinions of experts in the legal field. Furthermore, marketing-related clauses of a contract can be revised based on the opinions of experts in the marketing field. This makes it possible to propose revisions that reflect the opinions of experts in different industries and fields.

[0041] The revision suggestion unit can automatically generate a prototype of the revision suggestion and visually present it to the user. For example, when conducting research and revision suggestions, the revision suggestion unit automatically creates a prototype of the revision suggestion and visually presents it to the user. For example, the revision suggestion unit generates a visual prototype of the revision suggestion and presents it to the user. The generation AI can also generate a text prototype of the revision suggestion and present it to the user. Furthermore, the generation AI can generate an interactive prototype of the revision suggestion and present it to the user. This makes it easier for the user to understand by visually presenting the prototype of the revision suggestion.

[0042] When researching risks, the modification suggestion unit can quantitatively evaluate the probability of risk occurrence and the degree of impact and determine the priority of modification suggestions. For example, when the generation AI conducts research on risks, the modification suggestion unit quantitatively evaluates the probability of risk occurrence and the degree of impact and determines the priority of modification suggestions. For example, the modification suggestion unit scores the probability of risk occurrence and the degree of impact and determines the priority. The generation AI can also quantify the probability of risk occurrence and the degree of impact and determine the priority. Furthermore, the generation AI can graph the probability of risk occurrence and the degree of impact and determine the priority. This enables efficient risk management by evaluating the probability of risk occurrence and the degree of impact and determining the priority of modification suggestions.

[0043] The correction suggestion unit can identify the cause of a risk and propose a fundamental solution. For example, when the generation AI conducts research on a risk, the correction suggestion unit identifies the cause of the risk and proposes a correction to eliminate that cause. The generation AI can also identify the cause of a risk and propose specific corrections to eliminate that cause. Furthermore, the generation AI can identify the cause of a risk and propose long-term measures to resolve that cause. In this way, by identifying the cause of a risk and proposing a fundamental solution, the recurrence of the risk is prevented.

[0044] The revision proposal unit can refer to risk management methods from different industries or fields and reflect them in the revision proposal. For example, when making a revision proposal for a risk, the revision proposal unit refers to risk management methods from different industries or fields and reflects them in the revision proposal. For example, the revision proposal unit can refer to risk management methods from the financial industry and propose a revision proposal to reduce contract risks. It can also refer to risk management methods from the medical industry and propose a revision proposal to reduce contract risks. It can also refer to risk management methods from the construction industry and propose a revision proposal to reduce contract risks. This makes it possible to propose revisions that reflect risk management methods from different industries and fields.

[0045] The modification suggestion unit can automatically simulate the modification proposal and evaluate the risk after the modification. For example, when the generation AI proposes a modification to a risk, the modification suggestion unit automatically simulates the modification proposal and evaluates the risk after the modification. For example, the modification proposal is simulated and the risk after the modification is quantified and evaluated. The generation AI can also simulate the modification proposal and graph the risk after the modification for evaluation. Furthermore, the generation AI can simulate the modification proposal and visualize and evaluate the risk after the modification. In this way, by simulating the modification proposal and evaluating the risk after the modification, the effectiveness of the modification proposal can be confirmed in advance.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The risk identification department identifies risks in contracts. For example, the generation AI scrutinizes contract clauses and wording to identify ambiguous or unfavorable terms. The generation AI can also analyze the contents of contracts to identify potential risks. The research department conducts research on the risks identified by the risk identification department. For example, the generation AI proposes specific revisions to mitigate risks based on past precedents and industry standards. The generation AI can also conduct research based on prompts containing information about the risks and instructions for revisions. The proposal department generates revision proposals based on the research conducted by the research department. For example, the generation AI proposes revisions or additional clauses to contract clauses based on the research results. The generation AI can also generate specific revision proposals to mitigate risks. The progress management department manages the progress of contract reviews. For example, the generation AI centrally manages information such as the current stage of contract review, whether revisions have been completed, and whether risks have been resolved. The generation AI can also manage projects based on data on the project's progress. As a result, the AI ​​contract review platform according to the embodiment realizes efficiency and quality improvement in contract reviews. For example, it ensures the safety of contracts by quickly identifying risks in contracts and proposing appropriate revisions. In addition, the case management function enables centralized management of the progress of contract reviews, increasing the transparency of operations. Furthermore, automating contract reviews reduces the workload of legal departments, allowing them to focus on more important tasks.

[0048] The risk identification unit can improve accuracy by learning risk patterns based on a risk database of similar past contracts. For example, when the generation AI identifies risks in a contract, it refers to a risk database of similar past contracts to learn risk patterns and improve accuracy. For example, it can identify clauses and wording that have been problematic in the past and automatically detect contracts that contain similar risks. The generation AI can also learn risk patterns in specific industries and regions and identify risks based on the content of the contract. Furthermore, the generation AI can calculate a risk score for each clause in the contract based on the risk database and prioritize the identification of high-risk areas. This improves the accuracy of risk identification.

[0049] The risk identification unit can automatically obtain the latest information on relevant laws and regulations to understand the context of a contract and reflect this in the analysis. For example, when the generation AI identifies risks in a contract, it automatically obtains the latest information on relevant laws and regulations to understand the context of the contract and reflects this in the analysis. For example, it can identify risks by comparing the content of the contract with newly enacted laws and regulations. The generation AI can also identify risks in a contract by taking into account legal requirements in specific industries and regions. Furthermore, the generation AI can check whether the clauses in the contract comply with the latest laws and regulations and identify risks. This improves the accuracy of contract risk identification.

[0050] The risk identification unit can also handle voice input or handwritten input, making it possible to analyze contract data in different formats. For example, when identifying risks in a contract, it can also handle voice input or handwritten input, making it possible to analyze contract data in different formats. For example, using voice recognition technology, the contents of a contract read aloud can be converted into text data to identify risks. Also, using handwritten character recognition technology, the contents of a contract written by hand can be converted into text data to identify risks. Furthermore, by combining voice input and handwritten input, it is possible to analyze contract data in multiple formats at once and identify risks. This makes it possible to analyze contract data in different formats.

[0051] The risk identification unit can add a function to analyze contracts from different industries or countries and identify global risk patterns. For example, a function can be added to analyze contracts from different industries or countries and identify global risk patterns. For example, the unit can learn risk patterns in international transactions and identify risks according to the content of the contract. It can also learn risk patterns in a specific industry and identify risks according to the content of the contract. Furthermore, it can identify risks in contracts by taking into account the legal regulations and industry standards of different countries. This makes it possible to identify global risk patterns.

[0052] The research department can simulate the legal impact of each clause in a contract to assess the severity of the risk. For example, when the generative AI analyzes the contents of a contract, it simulates the legal impact of each clause in the contract and assesses the severity of the risk. For example, it can simulate the legal impact of a specific clause and assess the severity of the risk. It can also simulate the possibility of a clause being illegal or the risk of litigation and assess the severity of the risk. It can also simulate the impact of a clause on the entire contract and assess the severity of the risk. This allows for an assessment of the severity of the risk, enabling appropriate responses to be taken.

[0053] The research department can analyze the interdependencies between clauses in a contract to identify whether risks will occur in a chain reaction. For example, when the generative AI analyzes the contents of a contract, it analyzes the interdependencies between clauses and identifies the possibility of risks occurring in a chain reaction. For example, if a clause depends on other clauses, it analyzes that dependency and identifies the risk. It can also analyze the impact on other clauses if a specific clause is changed and identify risks. Furthermore, it can visualize the dependencies between clauses and identify the possibility of risks occurring in a chain reaction. By identifying the possibility of risks occurring in a chain reaction, preventative measures can be taken.

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

[0055] Step 1: The risk identification unit identifies risks in the contract. For example, the generation AI scrutinizes the clauses and wording of the contract and points out any ambiguous wording or unfavorable conditions. The generation AI can also analyze the contents of the contract to identify potential risks. Step 2: The Research Department conducts research on the risks identified by the Risk Identification Department. For example, Generative AI can suggest specific modifications to mitigate risks based on past legal precedents or industry standards. Generative AI can also conduct research based on prompts that contain information about the risks and instructions for modifications. Step 3: The Proposal Department generates proposed modifications based on the research conducted by the Research Department. For example, the Generative AI may propose modifications or additional clauses to contract clauses based on the research results. The Generative AI may also generate specific modifications to mitigate risks. Step 4: The progress management department manages the progress of contract review. For example, the generation AI centrally manages information such as the stage of contract review, whether revisions have been completed, and whether risks have been resolved. The generation AI can also manage projects based on data on the progress of projects.

[0056] (Example 2) The AI ​​contract review platform according to an embodiment of the present invention is a system that comprehensively supports the identification of risks hidden in contracts, research, revision, and case management, thereby enabling legal departments to carry out their work quickly and efficiently.

[0057] An AI contract review platform according to an embodiment includes a risk identification unit, a research unit, a proposal unit, and a progress management unit. The risk identification unit identifies risks in a contract. For example, the generation AI scrutinizes the clauses and wording of a contract and identifies any ambiguous or unfavorable terms. The generation AI can also analyze the contents of a contract to identify potential risks. The research unit conducts research on the risks identified by the risk identification unit. For example, the generation AI proposes specific amendments to mitigate the risks based on past precedents and industry standards. The generation AI can also conduct research based on prompts containing information about the risks and instructions for amendments. The proposal unit generates amendment proposals based on the research conducted by the research unit. For example, the generation AI proposes amendments to contract clauses or additional clauses based on the research results. The generation AI can also generate specific amendment proposals to mitigate risks. The progress management unit manages the progress of the contract review. For example, the generation AI centrally manages information such as the current stage of the contract review, whether amendments have been completed, and whether risks have been resolved. The generation AI can also manage cases based on data on the progress of cases. As a result, the AI ​​contract review platform according to the embodiment improves the efficiency and quality of contract reviews. For example, it can ensure the safety of contracts by quickly identifying risks in contracts and proposing appropriate revisions. The case management function also enables centralized management of the progress of contract reviews, increasing the transparency of operations. Furthermore, automating contract reviews reduces the workload of legal departments, allowing them to focus on more important tasks.

[0058] The risk identification unit can improve accuracy by learning risk patterns based on a risk database of similar past contracts. For example, when the generation AI identifies risks in a contract, the risk identification unit refers to a risk database of similar past contracts to learn risk patterns and improve accuracy. For example, it can identify clauses or wording that have been problematic in the past and automatically detect contracts that contain similar risks. The generation AI can also learn risk patterns in specific industries or regions and identify risks based on the content of the contract. Furthermore, the generation AI can calculate a risk score for each clause in the contract based on the risk database and prioritize the identification of high-risk areas. This improves the accuracy of risk identification.

[0059] The risk identification unit can automatically obtain the latest information on relevant laws and regulations to understand the context of a contract and reflect this in the analysis. For example, when the generation AI identifies risks in a contract, the risk identification unit automatically obtains the latest information on relevant laws and regulations to understand the context of the contract and reflects this in the analysis. For example, it can identify risks by comparing the content of the contract with newly enacted laws and regulations. The generation AI can also identify risks in a contract by taking into account legal requirements in specific industries and regions. Furthermore, the generation AI can check whether the clauses in the contract comply with the latest laws and regulations and identify risks. This improves the accuracy of contract risk identification.

[0060] The risk identification unit can use the emotion estimation function to analyze the emotions of the contract creator or the parties involved, and identify emotionally unstable parts and identify them as risks. The risk identification unit can, for example, use the emotion estimation function to analyze the emotions of the contract creator or the parties involved, and identify emotionally unstable parts and identify them as risks. For example, parts that caused strong emotional reactions when the contract was created can be identified as risks. The emotion estimation function can also be used to identify parts that caused emotional conflicts during contract negotiations as risks. Furthermore, the emotion estimation function can also be used to identify parts that caused strong emotional reactions when the contract was reviewed as risks. In this way, emotionally unstable parts can be identified and risks can be identified.

[0061] The risk identification unit also supports voice input or handwritten input, making it possible to analyze contract data in different formats. The risk identification unit also supports voice input or handwritten input, making it possible to analyze contract data in different formats, for example, when identifying risks in a contract. For example, using voice recognition technology, the contents of a contract read aloud can be converted into text data to identify risks. Also, using handwritten character recognition technology, the contents of a contract written by hand can be converted into text data to identify risks. Furthermore, by combining voice input and handwritten input, it is possible to analyze contract data in multiple formats at once and identify risks. This makes it possible to analyze contract data in different formats.

[0062] The risk identification unit can add a function to analyze contracts from different industries or countries and identify global risk patterns. The risk identification unit can add a function to analyze contracts from different industries or countries and identify global risk patterns, for example. For example, it can learn risk patterns in international transactions and identify risks according to the content of the contract. It can also learn risk patterns in a specific industry and identify risks according to the content of the contract. It can also identify risks in contracts by taking into account the laws and regulations and industry standards of different countries. This makes it possible to identify global risk patterns.

[0063] The risk identification unit can use the emotion estimation function to monitor the user's emotions in real time when identifying risks in a contract, and take the user's emotions into consideration when pointing out risks. The risk identification unit can use the emotion estimation function to monitor the user's emotions in real time when identifying risks in a contract, and take the user's emotions into consideration when pointing out risks. For example, if the user is in an emotionally unstable state, the risk can be pointed out in a gentler manner. Also, if the user is in an emotionally calm state, the risk can be pointed out in more detail. Furthermore, the method of pointing out risks can be adjusted depending on the user's emotional state. This makes it possible to point out risks while taking the user's emotions into consideration.

[0064] The research department can simulate the legal impact of each clause in a contract and assess the severity of the risk. For example, when the generative AI analyzes the contents of a contract, the research department can simulate the legal impact of each clause in the contract and assess the severity of the risk. For example, it can simulate the legal impact of a specific clause and assess the severity of the risk. It can also simulate the possibility of a clause being illegal or the risk of litigation and assess the severity of the risk. It can also simulate the impact of a clause on the entire contract and assess the severity of the risk. This makes it possible to assess the severity of the risk and take appropriate measures.

[0065] The research department can analyze the interdependencies between clauses in a contract and identify whether risks will occur in a chain reaction. For example, when the generative AI analyzes the contents of a contract, the research department analyzes the interdependencies between clauses in the contract and identifies the possibility of risks occurring in a chain reaction. For example, if a clause depends on other clauses, the research department analyzes that dependency and identifies the risks. It can also analyze the impact on other clauses if a specific clause is changed and identify risks. Furthermore, it can visualize the dependencies between clauses and identify the possibility of risks occurring in a chain reaction. By identifying the possibility of risks occurring in a chain reaction, preventive measures can be taken.

[0066] The research department can use the emotion estimation function to predict the emotional reactions of the parties involved when analyzing the contents of a contract and identify emotionally sensitive sections as risks. For example, the research department can use the emotion estimation function to predict the emotional reactions of the parties involved when analyzing the contents of a contract and identify emotionally sensitive sections as risks. For example, the research department can predict the possibility that a specific clause will cause a strong emotional reaction from the parties involved and identify it as a risk. It can also identify sections that are likely to cause emotional conflicts during contract negotiations as risks. Furthermore, it can identify sections that cause strong emotional reactions when reviewing a contract as risks. This makes it possible to identify emotionally sensitive sections and identify risks.

[0067] The research department can also analyze visual data of contracts, including images and diagrams, to identify risks. For example, when analyzing the contents of a contract, the research department can also analyze visual data of contracts, including images and diagrams, to identify risks. For example, the research department can analyze the contents of diagrams and graphs included in a contract to identify risks. It can also analyze the contents of blueprints and sketches included in a contract to identify risks. It can also analyze the contents of photographs and illustrations included in a contract to identify risks. In this way, it is possible to identify risks by analyzing visual data.

[0068] The research department can automatically translate contracts in different languages ​​to perform risk analysis in multiple languages. For example, when analyzing the contents of a contract, the research department can automatically translate contracts in different languages ​​to perform risk analysis in multiple languages. For example, contracts in English, Chinese, etc. can be automatically translated to identify risks. The contents of a contract can also be translated into multiple languages ​​to identify risks in each language. Furthermore, the contents of a contract can also be automatically translated to identify risks based on the translation results. This makes risk analysis possible in multiple languages.

[0069] The research unit can use the emotion estimation function to analyze the contents of a contract and take the user's emotions into consideration when pointing out risks. For example, the research unit can use the emotion estimation function to consider the user's emotions when analyzing the contents of a contract and take the user's emotions into consideration when pointing out risks. For example, if the user is in an emotionally unstable state, the research unit can point out risks in a gentler manner. Also, if the user is in an emotionally calm state, the research unit can point out risks in more detail. Furthermore, the method of pointing out risks can be adjusted depending on the user's emotional state. This makes it possible to point out risks while taking the user's emotions into consideration.

[0070] The amendment suggestion unit can make amendment suggestions to reduce risks based on a database of past precedents. For example, when the generation AI conducts research, the amendment suggestion unit refers to the database of past precedents and makes amendment suggestions to reduce risks. For example, it makes suggestions to amend specific clauses based on past precedents. The generation AI can also extract similar cases from the precedent database and suggest how to amend them. Furthermore, the generation AI can present specific amendment suggestions to reduce risks based on the precedent database. This makes it possible to make amendment suggestions to reduce risks based on past precedents.

[0071] The modification suggestion unit can automatically acquire the latest industry trends or best practices and reflect them in modification suggestions. For example, when the generative AI conducts research, the modification suggestion unit automatically acquires the latest industry trends and best practices and reflects them in modification suggestions. For example, it can propose modifications to contract clauses based on the latest industry standards. The generative AI can also take into account the latest industry trends and present modification suggestions to mitigate risks. Furthermore, the generative AI can propose modifications to the content of contracts based on the latest best practices. This makes it possible to propose modifications based on the latest industry trends and best practices.

[0072] The revision suggestion unit can use the emotion estimation function to predict the emotional reactions of stakeholders when proposing revisions based on research results, and propose revisions that are emotionally acceptable. For example, the revision suggestion unit can use the emotion estimation function to predict the emotional reactions of stakeholders when proposing revisions based on research results, and propose revisions that are emotionally acceptable. For example, it can revise parts that provoke strong emotional reactions using softer expressions. The emotion estimation function can also be used to adjust parts that are likely to cause emotional conflict. Furthermore, the emotion estimation function can be used to predict emotional reactions and adjust revision proposals. This makes it possible to propose revisions while taking into consideration the emotions of stakeholders.

[0073] The revision proposal unit can collect opinions from experts in different industries or fields and reflect them in revision proposals. For example, when conducting research and revision proposals, the revision proposal unit collects opinions from experts in different industries or fields and reflects them in revision proposals. For example, technical clauses of a contract can be revised based on the opinions of experts in the technical field. Legal clauses of a contract can also be revised based on the opinions of experts in the legal field. Furthermore, marketing-related clauses of a contract can be revised based on the opinions of experts in the marketing field. This makes it possible to propose revisions that reflect the opinions of experts in different industries and fields.

[0074] The revision suggestion unit can automatically generate a prototype of the revision suggestion and visually present it to the user. For example, when conducting research and revision suggestions, the revision suggestion unit automatically creates a prototype of the revision suggestion and visually presents it to the user. For example, the revision suggestion unit generates a visual prototype of the revision suggestion and presents it to the user. The generation AI can also generate a text prototype of the revision suggestion and present it to the user. Furthermore, the generation AI can generate an interactive prototype of the revision suggestion and present it to the user. This makes it easier for the user to understand by visually presenting the prototype of the revision suggestion.

[0075] The revision suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when conducting research and revision suggestions, and propose revision suggestions that are emotionally easy to accept. For example, the revision suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when conducting research and revision suggestions, and propose revision suggestions that are emotionally easy to accept. For example, if the user is in an emotionally unstable state, the revision suggestion unit can present the revision suggestion in a gentler manner. Also, if the user is in an emotionally calm state, the revision suggestion can be presented in detail. Furthermore, the method of presenting the revision suggestion can be adjusted depending on the user's emotional state. This makes it possible to propose revisions while taking the user's emotions into consideration.

[0076] When researching risks, the modification suggestion unit can quantitatively evaluate the probability of risk occurrence and the degree of impact and determine the priority of modification suggestions. For example, when the generation AI conducts research on risks, the modification suggestion unit quantitatively evaluates the probability of risk occurrence and the degree of impact and determines the priority of modification suggestions. For example, the modification suggestion unit scores the probability of risk occurrence and the degree of impact and determines the priority. The generation AI can also quantify the probability of risk occurrence and the degree of impact and determine the priority. Furthermore, the generation AI can graph the probability of risk occurrence and the degree of impact and determine the priority. This enables efficient risk management by evaluating the probability of risk occurrence and the degree of impact and determining the priority of modification suggestions.

[0077] The correction suggestion unit can identify the cause of a risk and propose a fundamental solution. For example, when the generation AI conducts research on a risk, the correction suggestion unit identifies the cause of the risk and proposes a correction to eliminate that cause. The generation AI can also identify the cause of a risk and propose specific corrections to eliminate that cause. Furthermore, the generation AI can identify the cause of a risk and propose long-term measures to resolve that cause. In this way, by identifying the cause of a risk and proposing a fundamental solution, the recurrence of the risk is prevented.

[0078] The revision suggestion unit can use the emotion estimation function to predict the emotional reactions of stakeholders when proposing revisions to risks, and propose revisions that are emotionally easy to accept. For example, the revision suggestion unit uses the emotion estimation function to predict the emotional reactions of stakeholders when proposing revisions to risks, and proposes revisions that are emotionally easy to accept. For example, it modifies parts that are likely to provoke strong emotional reactions using softer language. The emotion estimation function can also be used to adjust parts that are likely to cause emotional conflict. Furthermore, the emotion estimation function can also be used to predict emotional reactions and adjust revision proposals. This makes it possible to propose revisions while taking into consideration the emotions of stakeholders.

[0079] The revision proposal unit can refer to risk management methods from different industries or fields and reflect them in the revision proposal. For example, when making a revision proposal for a risk, the revision proposal unit refers to risk management methods from different industries or fields and reflects them in the revision proposal. For example, the revision proposal unit can refer to risk management methods from the financial industry and propose a revision proposal to reduce contract risks. It can also refer to risk management methods from the medical industry and propose a revision proposal to reduce contract risks. It can also refer to risk management methods from the construction industry and propose a revision proposal to reduce contract risks. This makes it possible to propose revisions that reflect risk management methods from different industries and fields.

[0080] The modification suggestion unit can automatically simulate the modification proposal and evaluate the risk after the modification. For example, when the generation AI proposes a modification to a risk, the modification suggestion unit automatically simulates the modification proposal and evaluates the risk after the modification. For example, the modification proposal is simulated and the risk after the modification is quantified and evaluated. The generation AI can also simulate the modification proposal and graph the risk after the modification for evaluation. Furthermore, the generation AI can simulate the modification proposal and visualize and evaluate the risk after the modification. In this way, by simulating the modification proposal and evaluating the risk after the modification, the effectiveness of the modification proposal can be confirmed in advance.

[0081] The revision suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when implementing a revision suggestion for a risk, and propose a revision suggestion that is emotionally easy to accept. For example, the revision suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when implementing a revision suggestion for a risk, and propose a revision suggestion that is emotionally easy to accept. For example, if the user is in an emotionally unstable state, the revision suggestion can be presented in a softer manner. Also, if the user is in an emotionally calm state, the revision suggestion can be presented in detail. Furthermore, the method of presenting the revision suggestion can be adjusted depending on the user's emotional state. This makes it possible to propose revisions while taking the user's emotions into consideration.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The risk identification department identifies risks in contracts. For example, the generation AI scrutinizes contract clauses and wording to identify ambiguous or unfavorable terms. The generation AI can also analyze the contents of contracts to identify potential risks. The research department conducts research on the risks identified by the risk identification department. For example, the generation AI proposes specific revisions to mitigate risks based on past precedents and industry standards. The generation AI can also conduct research based on prompts containing information about the risks and instructions for revisions. The proposal department generates revision proposals based on the research conducted by the research department. For example, the generation AI proposes revisions or additional clauses to contract clauses based on the research results. The generation AI can also generate specific revision proposals to mitigate risks. The progress management department manages the progress of contract reviews. For example, the generation AI centrally manages information such as the current stage of contract review, whether revisions have been completed, and whether risks have been resolved. The generation AI can also manage projects based on data on the project's progress. As a result, the AI ​​contract review platform according to the embodiment realizes efficiency and quality improvement in contract reviews. For example, it ensures the safety of contracts by quickly identifying risks in contracts and proposing appropriate revisions. In addition, the case management function enables centralized management of the progress of contract reviews, increasing the transparency of operations. Furthermore, automating contract reviews reduces the workload of legal departments, allowing them to focus on more important tasks.

[0084] The risk identification unit can improve accuracy by learning risk patterns based on a risk database of similar past contracts. For example, when the generation AI identifies risks in a contract, it refers to a risk database of similar past contracts to learn risk patterns and improve accuracy. For example, it can identify clauses and wording that have been problematic in the past and automatically detect contracts that contain similar risks. The generation AI can also learn risk patterns in specific industries and regions and identify risks based on the content of the contract. Furthermore, the generation AI can calculate a risk score for each clause in the contract based on the risk database and prioritize the identification of high-risk areas. This improves the accuracy of risk identification.

[0085] The risk identification unit can automatically obtain the latest information on relevant laws and regulations to understand the context of a contract and reflect this in the analysis. For example, when the generation AI identifies risks in a contract, it automatically obtains the latest information on relevant laws and regulations to understand the context of the contract and reflects this in the analysis. For example, it can identify risks by comparing the content of the contract with newly enacted laws and regulations. The generation AI can also identify risks in a contract by taking into account legal requirements in specific industries and regions. Furthermore, the generation AI can check whether the clauses in the contract comply with the latest laws and regulations and identify risks. This improves the accuracy of contract risk identification.

[0086] The risk identification unit can use the emotion estimation function to analyze the emotions of the contract creator or the parties involved, and identify emotionally unstable parts and identify them as risks. For example, the emotion estimation function can be used to analyze the emotions of the contract creator or the parties involved, and identify emotionally unstable parts and identify them as risks. For example, parts that caused strong emotional reactions when the contract was created can be identified as risks. The emotion estimation function can also be used to identify parts that caused emotional conflicts during contract negotiations as risks. Furthermore, the emotion estimation function can also be used to identify parts that caused strong emotional reactions when the contract was reviewed as risks. In this way, emotionally unstable parts can be identified and risks can be identified.

[0087] The risk identification unit can also handle voice input or handwritten input, making it possible to analyze contract data in different formats. For example, when identifying risks in a contract, it can also handle voice input or handwritten input, making it possible to analyze contract data in different formats. For example, using voice recognition technology, the contents of a contract read aloud can be converted into text data to identify risks. Also, using handwritten character recognition technology, the contents of a contract written by hand can be converted into text data to identify risks. Furthermore, by combining voice input and handwritten input, it is possible to analyze contract data in multiple formats at once and identify risks. This makes it possible to analyze contract data in different formats.

[0088] The risk identification unit can add a function to analyze contracts from different industries or countries and identify global risk patterns. For example, a function can be added to analyze contracts from different industries or countries and identify global risk patterns. For example, the unit can learn risk patterns in international transactions and identify risks according to the content of the contract. It can also learn risk patterns in a specific industry and identify risks according to the content of the contract. Furthermore, it can identify risks in contracts by taking into account the legal regulations and industry standards of different countries. This makes it possible to identify global risk patterns.

[0089] The risk identification unit can use the emotion estimation function to monitor the user's emotions in real time when identifying risks in a contract, and take the user's emotions into consideration when pointing out risks. For example, the emotion estimation function can be used to monitor the user's emotions in real time when identifying risks in a contract, and take the user's emotions into consideration when pointing out risks. For example, if the user is in an emotionally unstable state, the risk can be pointed out in a gentler manner. Also, if the user is in an emotionally calm state, the risk can be pointed out in more detail. Furthermore, the method of pointing out risks can be adjusted depending on the user's emotional state. This makes it possible to point out risks while taking the user's emotions into consideration.

[0090] The research department can simulate the legal impact of each clause in a contract to assess the severity of the risk. For example, when the generative AI analyzes the contents of a contract, it simulates the legal impact of each clause in the contract and assesses the severity of the risk. For example, it can simulate the legal impact of a specific clause and assess the severity of the risk. It can also simulate the possibility of a clause being illegal or the risk of litigation and assess the severity of the risk. It can also simulate the impact of a clause on the entire contract and assess the severity of the risk. This allows for an assessment of the severity of the risk, enabling appropriate responses to be taken.

[0091] The research department can analyze the interdependencies between clauses in a contract to identify whether risks will occur in a chain reaction. For example, when the generative AI analyzes the contents of a contract, it analyzes the interdependencies between clauses and identifies the possibility of risks occurring in a chain reaction. For example, if a clause depends on other clauses, it analyzes that dependency and identifies the risk. It can also analyze the impact on other clauses if a specific clause is changed and identify risks. Furthermore, it can visualize the dependencies between clauses and identify the possibility of risks occurring in a chain reaction. By identifying the possibility of risks occurring in a chain reaction, preventative measures can be taken.

[0092] The research department can use the emotion estimation function to predict the emotional reactions of the parties involved when analyzing the contents of a contract and identify emotionally sensitive sections as risks. For example, when analyzing the contents of a contract, the emotion estimation function can predict the emotional reactions of the parties involved and identify emotionally sensitive sections as risks. For example, it can predict the possibility that a specific clause will cause a strong emotional reaction from the parties involved and identify it as a risk. It can also identify sections that are likely to cause emotional conflict during contract negotiations as risks. Furthermore, it can identify sections that cause strong emotional reactions as risks when reviewing a contract. This makes it possible to identify emotionally sensitive sections and identify risks.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The risk identification unit identifies risks in the contract. For example, the generation AI scrutinizes the clauses and wording of the contract and points out any ambiguous wording or unfavorable conditions. The generation AI can also analyze the contents of the contract to identify potential risks. Step 2: The Research Department conducts research on the risks identified by the Risk Identification Department. For example, Generative AI can suggest specific modifications to mitigate risks based on past legal precedents or industry standards. Generative AI can also conduct research based on prompts that contain information about the risks and instructions for modifications. Step 3: The Proposal Department generates proposed modifications based on the research conducted by the Research Department. For example, the Generative AI may propose modifications or additional clauses to contract clauses based on the research results. The Generative AI may also generate specific modifications to mitigate risks. Step 4: The progress management department manages the progress of contract review. For example, the generation AI centrally manages information such as the stage of contract review, whether revisions have been completed, and whether risks have been resolved. The generation AI can also manage projects based on data on the progress of projects.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 risk identification department that identifies risks in the contract; a research unit that conducts research on the risks identified by the risk identification unit; a suggestion unit that generates revision suggestions based on the research performed by the research unit; A progress management unit that manages the progress of contract screening. A system characterized by:

2. The risk identification unit Improve accuracy by learning risk patterns based on a risk database of similar past contracts 2. The system of claim 1.

3. The risk identification unit Automatically retrieve and incorporate relevant legal or regulatory updates into your analysis to understand the context of your contracts 2. The system of claim 1.

4. The risk identification unit Analyze the emotions of the drafters or parties of the contract and identify any emotionally unstable areas and identify them as risks.

2. The system of claim 1.

5. The risk identification unit Supports voice input or handwritten input, enabling analysis of contract data in different formats 2. The system of claim 1.

6. The risk identification unit Add the ability to analyze contracts across different industries or countries to identify global risk patterns 2. The system of claim 1.

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