System

Abstract

An object of a system according to an embodiment is to improve the efficiency of contract management and to reduce labor.SOLUTION: A system includes a reading unit, an analysis unit, a classification unit, and an evaluation unit. The reading unit reads a document and converts the document into data. The analysis unit analyzes the data read by the reading unit. The classification unit performs pattern analysis and classification based on the data analyzed by the analysis unit. The evaluation unit generates a correlation diagram or a system diagram based on the data classified by the classification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that managing contracts is complicated and requires a great deal of effort, making it difficult to manage them efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of contract management and reduce the labor required. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading unit, an analysis unit, a classification unit, and an evaluation unit. The reading unit reads and digitizes documents. The analysis unit analyzes the data read by the reading unit. The classification unit performs pattern analysis and classification based on the data analyzed by the analysis unit. The evaluation unit generates a correlation diagram or a system diagram based on the data classified by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of contract management and reduce the amount of work required. [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) A fully automated contract management system according to an embodiment of the present invention uses a generation AI to streamline contract management. The fully automated contract management system scans contracts and converts them into digital data. The generation AI analyzes their contents, performs pattern analysis and classification, and generates correlation diagrams and system diagrams. For example, the fully automated contract management system scans contracts and converts them into PDF files, which are then analyzed by the generation AI. The generation AI then uses character recognition technology to extract the contents of the contract as text data. The fully automated contract management system then uses the generation AI to analyze the character arrangement and unique information in the contract to extract important information. For example, the system identifies the "contractor's name" and "contract date" in the contract and stores this information in a database. Furthermore, the fully automated contract management system uses the generation AI to perform pattern analysis based on the information in the contracts and classifies them by category. For example, the system classifies contracts by type, such as sales contracts, rental contracts, and service contracts. The fully automated contract management system then uses the generation AI to generate correlation diagrams and system diagrams based on the analysis results, visually displaying the relationships between contracts. For example, it generates a correlation diagram that shows how a specific contract is related to other contracts. This makes fully automated contract management systems much more efficient in contract management, reducing the workload for companies. Data security and confidentiality are also ensured, so companies can use them with peace of mind. This makes fully automated contract management systems much more efficient in contract management, reducing the workload for companies. Data security and confidentiality are also ensured, so companies can use them with peace of mind.

[0029] A fully automated contract management system according to an embodiment includes a reading unit, an analysis unit, a classification unit, and an evaluation unit. The reading unit scans a contract and reads it as digital data. For example, the reading unit scans a paper contract and converts it into a PDF file. The reading unit can also directly read contracts submitted in digital format. The reading unit can also read printed contracts using OCR technology. For example, the reading unit scans a handwritten contract with a high-resolution scanner and converts it into text information using OCR technology. Digital contracts submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit uses a generation AI to analyze the data read by the reading unit. For example, the analysis unit analyzes the character arrangement and unique information in the contract and extracts important information. The generation AI analyzes the content of the contract using a text generation AI (e.g., LLM). The analysis unit can also analyze the content of the contract using a multimodal generation AI. For example, the generation AI identifies the "contractor's name" and "contract date" in the contract and stores that information in a database. The classification unit uses the generation AI to perform pattern analysis and classification based on the data analyzed by the analysis unit. For example, the classification unit performs pattern analysis based on the information in the contract and classifies the contracts by category. The generation AI classifies the contracts by type. For example, the classification unit classifies the contracts by type, such as sales contracts, rental contracts, and service contracts. The evaluation unit uses the generation AI to generate a correlation diagram or a family tree based on the data classified by the classification unit. For example, the evaluation unit generates a correlation diagram or a family tree based on the analysis results to visually display the relationships between the contracts. The generation AI uses natural language processing technology to generate the correlation diagram or family tree. For example, the generation AI analyzes the degree of word correspondence and sentence structure similarity between contracts to generate the correlation diagram or family tree. As a result, the fully automated contract management system according to the embodiment significantly improves the efficiency of contract management and reduces the workload of companies. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.For example, the evaluation unit can generate correlation diagrams and system diagrams using an AI model that generates correlation diagrams and system diagrams of contracts based on the analysis results.

[0030] The reading unit can scan a contract and read it as digital data. For example, the reading unit scans the contract and converts it into a PDF file. The reading unit can also directly read a contract submitted in digital format. For example, the reading unit reads a digital contract in a specific file format. The reading unit can also read a printed contract using OCR technology. For example, the reading unit scans a handwritten contract with a high-resolution scanner and converts it into text information using OCR technology. This allows the contents of the contract to be efficiently read as digital data. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can input image data of the scanned contract into a generation AI and have the generation AI generate text data from the image data.

[0031] The analysis unit can analyze the character arrangement and unique information in a contract and extract important information. The analysis unit, for example, analyzes the character arrangement in a contract and extracts important information. For example, the analysis unit identifies the "contractor's name" and "contract date" in the contract and stores that information in a database. The analysis unit can also analyze unique information and extract important information. For example, the analysis unit identifies the "contract amount" and "contract period" in the contract and stores that information in a database. The analysis unit can also combine the character arrangement and unique information to comprehensively analyze important information. For example, the analysis unit extracts important information from a contract based on the character arrangement and unique information in the contract. This allows important information to be extracted efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the character arrangement and unique information in the contract into a generation AI and have the generation AI extract important information.

[0032] The classification unit can perform pattern analysis based on the information in the contract and classify the contract by category. The classification unit, for example, performs pattern analysis based on the information in the contract and classifies the contract by category. For example, the classification unit classifies the contract by type. For example, the classification unit classifies the contract by type, such as sales contract, rental contract, service contract, etc. The classification unit can also classify the contract by importance. For example, the classification unit prioritizes classification of contracts with high importance. Furthermore, the classification unit can also classify the contract based on attribute information of the contract submitter. For example, if the contract submitter is a large company, the classification unit classifies the contract into a specific category. This makes it easier to manage contracts by categorizing them. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without AI. For example, the classification unit can input the information in the contract into a generation AI and have the generation AI perform pattern analysis and classification.

[0033] The evaluation unit can generate a correlation diagram or a system diagram of the contracts based on the analysis results, and visually display the relationships between the contracts. The evaluation unit, for example, generates a correlation diagram or a system diagram of the contracts based on the analysis results, and visually displays the relationships between the contracts. For example, the evaluation unit generates a correlation diagram showing how a specific contract is related to other contracts. The evaluation unit can also generate a system diagram of the contracts, and visually display the hierarchical structure of the contracts. For example, the evaluation unit generates a system diagram showing the parent-child relationships of the contracts. Furthermore, the evaluation unit can make suggestions for improving the efficiency of contract management based on the correlation diagram or system diagram of the contracts. For example, the evaluation unit proposes a method for collectively managing related contracts based on the correlation diagram of the contracts. This visually displays the relationships between the contracts, further improving management efficiency. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can generate correlation diagrams and system diagrams using an AI model that generates correlation diagrams and system diagrams of contracts based on the analysis results.

[0034] The reading unit can apply an appropriate reading algorithm depending on the type of contract. For example, in the case of a sales contract, the generation AI can prioritize reading and digitizing specific keywords. In addition, in the case of a rental contract, the reading unit can focus on reading and digitizing property information and rental conditions. Furthermore, in the case of a service contract, the reading unit can also prioritize reading and digitizing information related to service content and fees. This enables optimal reading depending on the type of contract. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the type of contract and cause the generation AI to execute an appropriate reading algorithm.

[0035] The reading unit can select an appropriate reading means depending on the format of the contract. For example, in the case of a PDF-format contract, the generation AI uses OCR technology to extract text data. In addition, in the case of a Word-format contract, the reading unit can have the generation AI directly read and analyze the text data. Furthermore, in the case of an image-format contract, the reading unit can have the generation AI extract text data using image recognition technology. This enables optimal reading depending on the format of the contract. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the format of the contract and have the generation AI execute an appropriate reading means.

[0036] The reading unit can apply an appropriate language analysis algorithm depending on the language of the contract. For example, in the case of an English contract, the reading unit causes the generation AI to apply a language analysis algorithm specifically for English to digitize the contract. Furthermore, in the case of a Japanese contract, the reading unit can cause the generation AI to apply a language analysis algorithm specifically for Japanese to digitize the contract. Furthermore, in the case of a multilingual contract, the reading unit can cause the generation AI to sequentially apply analysis algorithms corresponding to each language to digitize the contract. This makes it possible to optimally digitize the contract depending on the language of the contract. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the language of the contract and cause the generation AI to execute an appropriate language analysis algorithm.

[0037] The reading unit can prioritize loading highly relevant contracts by taking into account the geographical place of issuance of the contract. For example, if the place of issuance of the contract is close to the user's location, the reading unit causes the generation AI to prioritize loading the contract. Furthermore, if the place of issuance of the contract is within the user's business area, the reading unit can also prioritize loading the contract if the place of issuance of the contract is the location of the user's business partner. In this way, highly relevant contracts can be prioritized by taking into account the geographical place of issuance of the contract. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the place of issuance of the contract and cause the generation AI to execute highly relevant contracts.

[0038] The reading unit can customize the reading means by taking into account the attribute information of the contract issuer. For example, if the issuer of the contract is a large company, the reading unit causes the generation AI to apply a highly accurate reading means. Furthermore, if the issuer of the contract is a small or medium-sized enterprise, the reading unit can cause the generation AI to apply a quick reading means. Furthermore, if the issuer of the contract is an individual, the reading unit can also cause the generation AI to apply a simple reading means. This makes it possible to apply the optimal reading means by taking into account the attribute information of the contract issuer. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the attribute information of the contract issuer and cause the generation AI to execute the appropriate reading means.

[0039] The reading unit can improve reading accuracy by referring to the contract's past change history. For example, the reading unit refers to the contract's past change history, and the generation AI focuses on reading the changed parts. The reading unit can also analyze the contract's past change history, and the generation AI can learn change patterns to improve reading accuracy. Furthermore, the reading unit can also adjust the reading method by taking into account the frequency and content of changes based on the contract's past change history. In this way, reading accuracy can be improved by referring to the contract's past change history. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can input the contract's past change history into the generation AI and cause the generation AI to improve reading accuracy.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the contract. For example, in the case of a contract of high importance, the analysis unit causes the generation AI to perform a detailed analysis and extract all information. In addition, in the case of a contract of low importance, the analysis unit can cause the generation AI to perform a simple analysis and extract only the main information. Furthermore, in the case of a contract of medium importance, the analysis unit can cause the generation AI to perform an analysis at an appropriate level of detail and extract the necessary information. This enables optimal analysis according to the importance of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can identify the importance of the contract and cause the generation AI to perform an appropriate level of analysis.

[0041] The analysis unit can apply an appropriate analysis algorithm depending on the category of the contract. For example, in the case of a sales contract, the analysis unit allows the generation AI to focus on analyzing information related to sales. In addition, in the case of a rental contract, the analysis unit allows the generation AI to focus on analyzing rental conditions and property information. Furthermore, in the case of a service contract, the analysis unit can also allow the generation AI to focus on analyzing information related to service content and fees. This enables optimal analysis depending on the category of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can identify the category of the contract and have the generation AI execute an appropriate analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can refer to the user's past analysis results and improve the accuracy when the generation AI analyzes a similar pattern. The analysis unit can also allow the generation AI to adjust the analysis priority based on the user's past analysis results. Furthermore, the analysis unit can learn the user's past analysis results and allow the generation AI to improve the efficiency of analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can determine the priority of analysis based on the time of submission of the contract. For example, in the case of a contract whose submission date is close, the analysis unit allows the generation AI to perform analysis first. In addition, in the case of a contract whose submission date is far away, the analysis unit can allow the generation AI to perform analysis later. Furthermore, in the case of a contract whose submission date is medium, the analysis unit can also allow the generation AI to perform analysis with appropriate priority. This enables optimal analysis according to the time of submission of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can identify the time of submission of the contract and have the generation AI perform analysis with appropriate priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the contract. For example, in the case of a highly relevant contract, the analysis unit allows the generation AI to perform analysis first. In addition, in the case of a low-relevance contract, the analysis unit can allow the generation AI to perform analysis later. Furthermore, in the case of a medium-relevance contract, the analysis unit can also allow the generation AI to perform analysis in an appropriate order. This enables optimal analysis according to the relevance of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can identify the relevance of the contract and have the generation AI perform the analysis in an appropriate order.

[0045] The analysis unit can adjust the use of technical terminology in the analysis depending on the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to use a lot of technical terminology to provide analysis results. Alternatively, if the user does not have technical expertise, the analysis unit can cause the generation AI to avoid technical terminology when providing analysis results. Furthermore, if the user has a medium level of expertise, the analysis unit can cause the generation AI to provide analysis results using appropriate technical terminology. This provides optimal analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can identify the user's level of expertise and cause the generation AI to use appropriate technical terminology.

[0046] The classification unit can improve the accuracy of classification by taking into account the interrelationships between contracts. For example, the classification unit analyzes the interrelationships between contracts, and the generation AI classifies related contracts into the same category. The classification unit can also enable the generation AI to adjust the priority of classification based on the interrelationships between contracts. Furthermore, the classification unit can learn the interrelationships between contracts, and the generation AI can improve the efficiency of classification. In this way, the accuracy of classification is improved by taking into account the interrelationships between contracts. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input the interrelationships between contracts into the generation AI and cause the generation AI to improve the accuracy of classification.

[0047] The classification unit can perform classification taking into account attribute information of the contract submitter. For example, if the contract submitter is a large company, the classification unit causes the generation AI to classify the contract into a specific category based on the attribute information. In addition, if the contract submitter is a small or medium-sized enterprise, the classification unit can cause the generation AI to classify the contract into another category based on the attribute information. Furthermore, if the contract submitter is an individual, the classification unit can cause the generation AI to classify the contract into yet another category based on the attribute information. This enables optimal classification by taking into account the attribute information of the contract submitter. Some or all of the above-described processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input attribute information of the contract submitter into the generation AI and have the generation AI perform classification.

[0048] The classification unit can weight the classification based on the frequency of contract submission. For example, in the case of a contract that is submitted frequently, the classification unit causes the generation AI to weight the contract based on that information and classify it into a specific category. In addition, in the case of a contract that is submitted infrequently, the classification unit can cause the generation AI to weight the contract based on that information and classify it into a different category. Furthermore, in the case of a contract that is submitted medium frequently, the classification unit can cause the generation AI to weight the contract appropriately and classify it into an appropriate category. This enables optimal classification according to the frequency of contract submission. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input the frequency of contract submission into the generation AI and have the generation AI perform classification weighting.

[0049] The classification unit can perform appropriate classification according to the geographical distribution of contracts. For example, if the places of issue of contracts are concentrated in a specific region, the classification unit causes the generation AI to classify the contracts into a specific category based on that information. In addition, if the places of issue of contracts are distributed across multiple regions, the classification unit can cause the generation AI to classify the contracts into another category based on that information. Furthermore, if the places of issue of contracts are distributed internationally, the classification unit can also cause the generation AI to classify the contracts into yet another category based on that information. This enables optimal classification according to the geographical distribution of contracts. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can identify the place of issue of a contract and cause the generation AI to perform an appropriate classification.

[0050] The classification unit can improve the accuracy of classification by referring to related literature of the contract. For example, the classification unit refers to related literature of the contract, and the generation AI improves the accuracy of classification based on that information. The classification unit can also cause the generation AI to adjust the priority of classification based on related literature of the contract. Furthermore, the classification unit can learn related literature of the contract, and the generation AI can improve the efficiency of classification. As a result, the accuracy of classification is improved by referring to related literature of the contract. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input related literature of the contract into the generation AI and cause the generation AI to improve the accuracy of classification.

[0051] The classification unit can perform appropriate classification according to the market value of the contract. For example, in the case of a contract with a high market value, the classification unit causes the generation AI to classify the contract into a specific category based on that information. In addition, in the case of a contract with a low market value, the classification unit can cause the generation AI to classify the contract into another category based on that information. Furthermore, in the case of a contract with a medium market value, the classification unit can cause the generation AI to assign appropriate weighting to the contract and classify it into an appropriate category. This enables optimal classification according to the market value of the contract. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input the market value of the contract into the generation AI and cause the generation AI to perform an appropriate classification.

[0052] The evaluation unit can predict current correlations by referring to past correlation data. For example, the evaluation unit refers to past correlation data, and the generation AI predicts current correlations and generates a correlation diagram. The evaluation unit can also improve the accuracy of the generation AI when generating a system diagram based on past correlation data. Furthermore, the evaluation unit can learn past correlation data, and the generation AI can improve the efficiency of generating correlation diagrams and system diagrams. This allows the current correlations to be accurately predicted by referring to past correlation data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input past correlation data to the generation AI and cause the generation AI to predict current correlations.

[0053] The evaluation unit can apply an appropriate correlation analysis method for each contract category. For example, in the case of a sales contract, the evaluation unit generates a correlation diagram by having the generation AI apply a correlation analysis method related to sales. In addition, in the case of a rental contract, the evaluation unit can generate a correlation diagram by having the generation AI apply a correlation analysis method related to rental conditions and property information. Furthermore, in the case of a service contract, the evaluation unit can generate a correlation diagram by having the generation AI apply a correlation analysis method related to service content and fees. This enables optimal correlation analysis according to the contract category. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can identify the contract category and have the generation AI execute an appropriate correlation analysis method.

[0054] The evaluation unit can analyze correlations based on attribute information of the contract submitter. For example, if the contract submitter is a large company, the evaluation unit causes the generation AI to analyze correlations based on the attribute information and generate a correlation diagram. Furthermore, if the contract submitter is a small or medium-sized enterprise, the evaluation unit can cause the generation AI to analyze another correlation based on the attribute information and generate a correlation diagram. Furthermore, if the contract submitter is an individual, the evaluation unit can cause the generation AI to analyze yet another correlation based on the attribute information and generate a correlation diagram. This enables optimal correlation analysis by taking into account the attribute information of the contract submitter. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input attribute information of the contract submitter into the generation AI and cause the generation AI to perform correlation analysis.

[0055] The evaluation unit can analyze changes in correlation depending on the time of contract submission. For example, in the case of a contract whose submission date is close, the evaluation unit allows the generation AI to analyze changes in correlation based on that information and generate a correlation diagram. In addition, in the case of a contract whose submission date is distant, the evaluation unit allows the generation AI to analyze different changes in correlation based on that information and generate a correlation diagram. Furthermore, in the case of a contract whose submission date is medium, the evaluation unit can also allow the generation AI to analyze moderate changes in correlation and generate a correlation diagram. This enables optimal correlation analysis depending on the time of contract submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can identify the time of contract submission and cause the generation AI to execute appropriate changes in correlation.

[0056] The evaluation unit can analyze correlations based on market data related to the contract. For example, the evaluation unit references the related market data, and the generation AI analyzes correlations based on that information to generate a correlation diagram. The evaluation unit can also cause the generation AI to adjust the priority of correlations based on the related market data. Furthermore, the evaluation unit can learn the related market data, and the generation AI can improve the efficiency of generating correlation diagrams and system diagrams. This improves the accuracy of correlations by referencing the market data related to the contract. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input related market data to the generation AI and cause the generation AI to perform correlation analysis.

[0057] The evaluation unit can analyze correlations based on the technical maturity of the contract. For example, in the case of a contract with high technical maturity, the evaluation unit causes the generation AI to analyze correlations based on that information and generate a correlation diagram. In addition, in the case of a contract with low technical maturity, the evaluation unit can cause the generation AI to analyze different correlations based on that information and generate a correlation diagram. Furthermore, in the case of a contract with medium technical maturity, the evaluation unit can cause the generation AI to analyze moderate correlations and generate a correlation diagram. This enables optimal correlation analysis according to the technical maturity of the contract. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can identify the technical maturity of the contract and cause the generation AI to perform an appropriate correlation analysis.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The fully automated contract management system may further include a notification unit. The notification unit may send notifications to the user when there are important changes to the contract or when a deadline is approaching. For example, the notification unit may send a reminder to the user when a contract renewal deadline is approaching. The notification unit may also send an alert to the user when an important change is made to the contract. Furthermore, the notification unit may notify the user of important events in the contract (for example, the contract's execution date or expiration date). This allows the user to respond in a timely manner without missing important information in the contract.

[0060] The fully automated contract management system can further include a translation unit. The translation unit can translate the contents of the contract into multiple languages. For example, the translation unit translates an English contract into Japanese. The translation unit can also translate a Japanese contract into English. The translation unit can also translate the contents of the contract into other languages ​​(for example, French or German). This allows the fully automated contract management system to be used effectively in international business environments.

[0061] The fully automated contract management system may further include a history tracking unit. The history tracking unit records the change history of a contract in detail, allowing the user to review past changes. For example, the history tracking unit may save each version of the contract and highlight changes. The history tracking unit may also record the user who made the change and the date and time of the change. Furthermore, the history tracking unit may visualize the progress of the contract based on the change history. This allows the user to easily track the change history of the contract and revert to a previous version if necessary.

[0062] The fully automated contract management system may further include a template generation unit. The template generation unit generates standard contract templates and allows users to use them when creating new contracts. For example, the template generation unit generates a standard template for a sales contract. The template generation unit can also generate a standard template for a rental contract. The template generation unit can also generate a standard template for a service contract. This allows users to efficiently create contracts by using standardized templates.

[0063] The fully automated contract management system may further include a risk assessment unit. The risk assessment unit may analyze the contents of a contract and assess potential risks. For example, the risk assessment unit may identify ambiguous clauses or unfavorable terms in the contract and issue a warning to the user. The risk assessment unit may also calculate a risk score based on the contents of the contract and indicate the degree of risk to the user. Furthermore, the risk assessment unit may learn from risk data of past contracts and improve the accuracy of risk assessment. This allows the user to understand the risks of a contract in advance and take appropriate measures.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reading unit scans the contract and converts it into digital data. For example, the reading unit scans a paper contract and converts it into a PDF file. The reading unit can also directly read contracts submitted in digital format. The reading unit can also read printed contracts using OCR technology. For example, the reading unit scans a handwritten contract with a high-resolution scanner and converts it into text information using OCR technology. Digital contracts submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The analysis unit uses the generation AI to analyze the data read by the reading unit. For example, the analysis unit analyzes the character arrangement and unique information in the contract and extracts important information. The generation AI analyzes the contents of the contract using a text generation AI (e.g., LLM). The analysis unit can also analyze the contents of the contract using a multimodal generation AI. For example, the generation AI identifies the "contractor's name" and "contract date" in the contract and stores that information in a database. Step 3: The classification unit uses the generation AI to perform pattern analysis and classification based on the data analyzed by the analysis unit. For example, the classification unit performs pattern analysis based on the information in the contract and classifies the contract into categories. The generation AI classifies the contract according to its type. For example, it classifies the contract according to its type, such as sales contract, rental contract, service contract, etc. Step 4: The evaluation unit uses the generation AI to generate correlation diagrams and family trees based on the data classified by the classification unit. For example, the evaluation unit generates correlation diagrams and family trees of contracts based on the analysis results, visually displaying the relationships between contracts. The generation AI uses natural language processing technology to generate correlation diagrams and family trees of contracts. For example, the generation AI analyzes the degree of agreement between words and the similarity of sentence structure between contracts to generate correlation diagrams and family trees.

[0066] (Example 2) A fully automated contract management system according to an embodiment of the present invention uses a generation AI to streamline contract management. The fully automated contract management system scans contracts and converts them into digital data. The generation AI analyzes their contents, performs pattern analysis and classification, and generates correlation diagrams and system diagrams. For example, the fully automated contract management system scans contracts and converts them into PDF files, which are then analyzed by the generation AI. The generation AI then uses character recognition technology to extract the contents of the contract as text data. The fully automated contract management system then uses the generation AI to analyze the character arrangement and unique information in the contract to extract important information. For example, the system identifies the "contractor's name" and "contract date" in the contract and stores this information in a database. Furthermore, the fully automated contract management system uses the generation AI to perform pattern analysis based on the information in the contracts and classifies them by category. For example, the system classifies contracts by type, such as sales contracts, rental contracts, and service contracts. The fully automated contract management system then uses the generation AI to generate correlation diagrams and system diagrams based on the analysis results, visually displaying the relationships between contracts. For example, it generates a correlation diagram that shows how a specific contract is related to other contracts. This makes fully automated contract management systems much more efficient in contract management, reducing the workload for companies. Data security and confidentiality are also ensured, so companies can use them with peace of mind. This makes fully automated contract management systems much more efficient in contract management, reducing the workload for companies. Data security and confidentiality are also ensured, so companies can use them with peace of mind.

[0067] A fully automated contract management system according to an embodiment includes a reading unit, an analysis unit, a classification unit, and an evaluation unit. The reading unit scans a contract and reads it as digital data. For example, the reading unit scans a paper contract and converts it into a PDF file. The reading unit can also directly read contracts submitted in digital format. The reading unit can also read printed contracts using OCR technology. For example, the reading unit scans a handwritten contract with a high-resolution scanner and converts it into text information using OCR technology. Digital contracts submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit uses a generation AI to analyze the data read by the reading unit. For example, the analysis unit analyzes the character arrangement and unique information in the contract and extracts important information. The generation AI analyzes the content of the contract using a text generation AI (e.g., LLM). The analysis unit can also analyze the content of the contract using a multimodal generation AI. For example, the generation AI identifies the "contractor's name" and "contract date" in the contract and stores that information in a database. The classification unit uses the generation AI to perform pattern analysis and classification based on the data analyzed by the analysis unit. For example, the classification unit performs pattern analysis based on the information in the contract and classifies the contracts by category. The generation AI classifies the contracts by type. For example, the classification unit classifies the contracts by type, such as sales contracts, rental contracts, and service contracts. The evaluation unit uses the generation AI to generate a correlation diagram or a family tree based on the data classified by the classification unit. For example, the evaluation unit generates a correlation diagram or a family tree based on the analysis results to visually display the relationships between the contracts. The generation AI uses natural language processing technology to generate the correlation diagram or family tree. For example, the generation AI analyzes the degree of word correspondence and sentence structure similarity between contracts to generate the correlation diagram or family tree. As a result, the fully automated contract management system according to the embodiment significantly improves the efficiency of contract management and reduces the workload of companies. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.For example, the evaluation unit can generate correlation diagrams and system diagrams using an AI model that generates correlation diagrams and system diagrams of contracts based on the analysis results.

[0068] The reading unit can scan a contract and read it as digital data. For example, the reading unit scans the contract and converts it into a PDF file. The reading unit can also directly read a contract submitted in digital format. For example, the reading unit reads a digital contract in a specific file format. The reading unit can also read a printed contract using OCR technology. For example, the reading unit scans a handwritten contract with a high-resolution scanner and converts it into text information using OCR technology. This allows the contents of the contract to be efficiently read as digital data. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can input image data of the scanned contract into a generation AI and have the generation AI generate text data from the image data.

[0069] The analysis unit can analyze the character arrangement and unique information in a contract and extract important information. The analysis unit, for example, analyzes the character arrangement in a contract and extracts important information. For example, the analysis unit identifies the "contractor's name" and "contract date" in the contract and stores that information in a database. The analysis unit can also analyze unique information and extract important information. For example, the analysis unit identifies the "contract amount" and "contract period" in the contract and stores that information in a database. The analysis unit can also combine the character arrangement and unique information to comprehensively analyze important information. For example, the analysis unit extracts important information from a contract based on the character arrangement and unique information in the contract. This allows important information to be extracted efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the character arrangement and unique information in the contract into a generation AI and have the generation AI extract important information.

[0070] The classification unit can perform pattern analysis based on the information in the contract and classify the contract by category. The classification unit, for example, performs pattern analysis based on the information in the contract and classifies the contract by category. For example, the classification unit classifies the contract by type. For example, the classification unit classifies the contract by type, such as sales contract, rental contract, service contract, etc. The classification unit can also classify the contract by importance. For example, the classification unit prioritizes classification of contracts with high importance. Furthermore, the classification unit can also classify the contract based on attribute information of the contract submitter. For example, if the contract submitter is a large company, the classification unit classifies the contract into a specific category. This makes it easier to manage contracts by categorizing them. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without AI. For example, the classification unit can input the information in the contract into a generation AI and have the generation AI perform pattern analysis and classification.

[0071] The evaluation unit can generate a correlation diagram or a system diagram of the contracts based on the analysis results, and visually display the relationships between the contracts. The evaluation unit, for example, generates a correlation diagram or a system diagram of the contracts based on the analysis results, and visually displays the relationships between the contracts. For example, the evaluation unit generates a correlation diagram showing how a specific contract is related to other contracts. The evaluation unit can also generate a system diagram of the contracts, and visually display the hierarchical structure of the contracts. For example, the evaluation unit generates a system diagram showing the parent-child relationships of the contracts. Furthermore, the evaluation unit can make suggestions for improving the efficiency of contract management based on the correlation diagram or system diagram of the contracts. For example, the evaluation unit proposes a method for collectively managing related contracts based on the correlation diagram of the contracts. This visually displays the relationships between the contracts, further improving management efficiency. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can generate correlation diagrams and system diagrams using an AI model that generates correlation diagrams and system diagrams of contracts based on the analysis results.

[0072] The reading unit can estimate the user's emotions and adjust the timing of reading the contract based on the estimated user emotions. For example, if the user is feeling stressed, the reading unit can cause the generation AI to temporarily delay reading the contract and wait until the user relaxes. Furthermore, if the user is concentrating, the reading unit can cause the generation AI to immediately start reading the contract and efficiently digitize it. Furthermore, if the user is in a hurry, the reading unit can also cause the generation AI to quickly read the contract and immediately complete digitization. This enables efficient digitization by adjusting the timing of reading the contract according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The reading unit can apply an appropriate reading algorithm depending on the type of contract. For example, in the case of a sales contract, the generation AI can prioritize reading and digitizing specific keywords. In addition, in the case of a rental contract, the reading unit can focus on reading and digitizing property information and rental conditions. Furthermore, in the case of a service contract, the reading unit can also prioritize reading and digitizing information related to service content and fees. This enables optimal reading depending on the type of contract. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the type of contract and cause the generation AI to execute an appropriate reading algorithm.

[0074] The reading unit can select an appropriate reading means depending on the format of the contract. For example, in the case of a PDF-format contract, the generation AI uses OCR technology to extract text data. In addition, in the case of a Word-format contract, the reading unit can have the generation AI directly read and analyze the text data. Furthermore, in the case of an image-format contract, the reading unit can have the generation AI extract text data using image recognition technology. This enables optimal reading depending on the format of the contract. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the format of the contract and have the generation AI execute an appropriate reading means.

[0075] The reading unit can apply an appropriate language analysis algorithm depending on the language of the contract. For example, in the case of an English contract, the reading unit causes the generation AI to apply a language analysis algorithm specifically for English to digitize the contract. Furthermore, in the case of a Japanese contract, the reading unit can cause the generation AI to apply a language analysis algorithm specifically for Japanese to digitize the contract. Furthermore, in the case of a multilingual contract, the reading unit can cause the generation AI to sequentially apply analysis algorithms corresponding to each language to digitize the contract. This makes it possible to optimally digitize the contract depending on the language of the contract. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the language of the contract and cause the generation AI to execute an appropriate language analysis algorithm.

[0076] The reading unit can estimate the user's emotions and determine the priority of the contracts to be read based on the estimated user emotions. For example, if the user is stressed, the reading unit can cause the generation AI to prioritize reading contracts of lower importance. Furthermore, if the user is relaxed, the reading unit can cause the generation AI to prioritize reading contracts of higher importance. Furthermore, if the user is in a hurry, the reading unit can cause the generation AI to prioritize reading contracts with an approaching deadline. This enables efficient data conversion by determining the priority of contracts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reading unit can be performed using AI, for example, or without AI. For example, the reading unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0077] The reading unit can prioritize loading highly relevant contracts by taking into account the geographical place of issuance of the contract. For example, if the place of issuance of the contract is close to the user's location, the reading unit causes the generation AI to prioritize loading the contract. Furthermore, if the place of issuance of the contract is within the user's business area, the reading unit can also prioritize loading the contract if the place of issuance of the contract is the location of the user's business partner. In this way, highly relevant contracts can be prioritized by taking into account the geographical place of issuance of the contract. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the place of issuance of the contract and cause the generation AI to execute highly relevant contracts.

[0078] The reading unit can customize the reading means by taking into account the attribute information of the contract issuer. For example, if the issuer of the contract is a large company, the reading unit causes the generation AI to apply a highly accurate reading means. Furthermore, if the issuer of the contract is a small or medium-sized enterprise, the reading unit can cause the generation AI to apply a quick reading means. Furthermore, if the issuer of the contract is an individual, the reading unit can also cause the generation AI to apply a simple reading means. This makes it possible to apply the optimal reading means by taking into account the attribute information of the contract issuer. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can identify the attribute information of the contract issuer and cause the generation AI to execute the appropriate reading means.

[0079] The reading unit can improve reading accuracy by referring to the contract's past change history. For example, the reading unit refers to the contract's past change history, and the generation AI focuses on reading the changed parts. The reading unit can also analyze the contract's past change history, and the generation AI can learn change patterns to improve reading accuracy. Furthermore, the reading unit can also adjust the reading method by taking into account the frequency and content of changes based on the contract's past change history. In this way, reading accuracy can be improved by referring to the contract's past change history. Some or all of the above-mentioned processing in the reading unit may be performed using AI, for example, or may be performed without using AI. For example, the reading unit can input the contract's past change history into the generation AI and cause the generation AI to improve reading accuracy.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the generation AI can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is stressed, the generation AI can provide visually easy-to-understand analysis results. This enables efficient analysis by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the contract. For example, in the case of a contract of high importance, the analysis unit causes the generation AI to perform a detailed analysis and extract all information. In addition, in the case of a contract of low importance, the analysis unit can cause the generation AI to perform a simple analysis and extract only the main information. Furthermore, in the case of a contract of medium importance, the analysis unit can cause the generation AI to perform an analysis at an appropriate level of detail and extract the necessary information. This enables optimal analysis according to the importance of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can identify the importance of the contract and cause the generation AI to perform an appropriate level of analysis.

[0082] The analysis unit can apply an appropriate analysis algorithm depending on the category of the contract. For example, in the case of a sales contract, the analysis unit allows the generation AI to focus on analyzing information related to sales. In addition, in the case of a rental contract, the analysis unit allows the generation AI to focus on analyzing rental conditions and property information. Furthermore, in the case of a service contract, the analysis unit can also allow the generation AI to focus on analyzing information related to service content and fees. This enables optimal analysis depending on the category of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can identify the category of the contract and have the generation AI execute an appropriate analysis algorithm.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can refer to the user's past analysis results and improve the accuracy when the generation AI analyzes a similar pattern. The analysis unit can also allow the generation AI to adjust the analysis priority based on the user's past analysis results. Furthermore, the analysis unit can learn the user's past analysis results and allow the generation AI to improve the efficiency of analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can cause the generation AI to provide a short, concise analysis result. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can cause the generation AI to provide a visually easy-to-understand analysis result. This enables efficient analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The analysis unit can determine the priority of analysis based on the time of submission of the contract. For example, in the case of a contract whose submission date is close, the analysis unit allows the generation AI to perform analysis first. In addition, in the case of a contract whose submission date is far away, the analysis unit can allow the generation AI to perform analysis later. Furthermore, in the case of a contract whose submission date is medium, the analysis unit can also allow the generation AI to perform analysis with appropriate priority. This enables optimal analysis according to the time of submission of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can identify the time of submission of the contract and have the generation AI perform analysis with appropriate priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the contract. For example, in the case of a highly relevant contract, the analysis unit allows the generation AI to perform analysis first. In addition, in the case of a low-relevance contract, the analysis unit can allow the generation AI to perform analysis later. Furthermore, in the case of a medium-relevance contract, the analysis unit can also allow the generation AI to perform analysis in an appropriate order. This enables optimal analysis according to the relevance of the contract. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can identify the relevance of the contract and have the generation AI perform the analysis in an appropriate order.

[0087] The analysis unit can adjust the use of technical terminology in the analysis depending on the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to use a lot of technical terminology to provide analysis results. Alternatively, if the user does not have technical expertise, the analysis unit can cause the generation AI to avoid technical terminology when providing analysis results. Furthermore, if the user has a medium level of expertise, the analysis unit can cause the generation AI to provide analysis results using appropriate technical terminology. This provides optimal analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can identify the user's level of expertise and cause the generation AI to use appropriate technical terminology.

[0088] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated user emotions. For example, if the user is relaxed, the generation AI can apply detailed classification criteria. Furthermore, if the user is in a hurry, the classification unit can also apply simple classification criteria. Furthermore, if the user is stressed, the generation AI can apply visually easy-to-understand classification criteria. This allows the optimal classification criteria to be applied according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the classification unit can be performed using, for example, an AI, or without an AI. For example, the classification unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The classification unit can improve the accuracy of classification by taking into account the interrelationships between contracts. For example, the classification unit analyzes the interrelationships between contracts, and the generation AI classifies related contracts into the same category. The classification unit can also enable the generation AI to adjust the priority of classification based on the interrelationships between contracts. Furthermore, the classification unit can learn the interrelationships between contracts, and the generation AI can improve the efficiency of classification. In this way, the accuracy of classification is improved by taking into account the interrelationships between contracts. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input the interrelationships between contracts into the generation AI and cause the generation AI to improve the accuracy of classification.

[0090] The classification unit can perform classification taking into account attribute information of the contract submitter. For example, if the contract submitter is a large company, the classification unit causes the generation AI to classify the contract into a specific category based on the attribute information. In addition, if the contract submitter is a small or medium-sized enterprise, the classification unit can cause the generation AI to classify the contract into another category based on the attribute information. Furthermore, if the contract submitter is an individual, the classification unit can cause the generation AI to classify the contract into yet another category based on the attribute information. This enables optimal classification by taking into account the attribute information of the contract submitter. Some or all of the above-described processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input attribute information of the contract submitter into the generation AI and have the generation AI perform classification.

[0091] The classification unit can weight the classification based on the frequency of contract submission. For example, in the case of a contract that is submitted frequently, the classification unit causes the generation AI to weight the contract based on that information and classify it into a specific category. In addition, in the case of a contract that is submitted infrequently, the classification unit can cause the generation AI to weight the contract based on that information and classify it into a different category. Furthermore, in the case of a contract that is submitted medium frequently, the classification unit can cause the generation AI to weight the contract appropriately and classify it into an appropriate category. This enables optimal classification according to the frequency of contract submission. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input the frequency of contract submission into the generation AI and have the generation AI perform classification weighting.

[0092] The classification unit can estimate the user's emotions and adjust the order in which the classification results are displayed based on the estimated user emotions. For example, when the user is relaxed, the classification unit allows the generation AI to display detailed classification results in an orderly manner. Furthermore, when the user is in a hurry, the classification unit allows the generation AI to prioritize displaying classification results that highlight the main points. Furthermore, when the user is feeling stressed, the classification unit can also display classification results that are visually easy to understand. This allows the classification results to be displayed in an optimal order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The classification unit can perform appropriate classification according to the geographical distribution of contracts. For example, if the places of issue of contracts are concentrated in a specific region, the classification unit causes the generation AI to classify the contracts into a specific category based on that information. In addition, if the places of issue of contracts are distributed across multiple regions, the classification unit can cause the generation AI to classify the contracts into another category based on that information. Furthermore, if the places of issue of contracts are distributed internationally, the classification unit can also cause the generation AI to classify the contracts into yet another category based on that information. This enables optimal classification according to the geographical distribution of contracts. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can identify the place of issue of a contract and cause the generation AI to perform an appropriate classification.

[0094] The classification unit can improve the accuracy of classification by referring to related literature of the contract. For example, the classification unit refers to related literature of the contract, and the generation AI improves the accuracy of classification based on that information. The classification unit can also cause the generation AI to adjust the priority of classification based on related literature of the contract. Furthermore, the classification unit can learn related literature of the contract, and the generation AI can improve the efficiency of classification. As a result, the accuracy of classification is improved by referring to related literature of the contract. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input related literature of the contract into the generation AI and cause the generation AI to improve the accuracy of classification.

[0095] The classification unit can perform appropriate classification according to the market value of the contract. For example, in the case of a contract with a high market value, the classification unit causes the generation AI to classify the contract into a specific category based on that information. In addition, in the case of a contract with a low market value, the classification unit can cause the generation AI to classify the contract into another category based on that information. Furthermore, in the case of a contract with a medium market value, the classification unit can cause the generation AI to assign appropriate weighting to the contract and classify it into an appropriate category. This enables optimal classification according to the market value of the contract. Some or all of the above-mentioned processing in the classification unit may be performed using AI, for example, or may be performed without using AI. For example, the classification unit can input the market value of the contract into the generation AI and cause the generation AI to perform an appropriate classification.

[0096] The evaluation unit can estimate the user's emotions and adjust the display method of the correlation diagram or system diagram based on the estimated user emotions. For example, when the user is relaxed, the evaluation unit causes the generation AI to display a detailed correlation diagram or system diagram. Furthermore, when the user is in a hurry, the evaluation unit can cause the generation AI to display a concise correlation diagram or system diagram that focuses on the main points. Furthermore, when the user is stressed, the evaluation unit can cause the generation AI to display a visually easy-to-understand correlation diagram or system diagram. This allows the optimal display method to be applied according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit may be performed using AI, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0097] The evaluation unit can predict current correlations by referring to past correlation data. For example, the evaluation unit refers to past correlation data, and the generation AI predicts current correlations and generates a correlation diagram. The evaluation unit can also improve the accuracy of the generation AI when generating a system diagram based on past correlation data. Furthermore, the evaluation unit can learn past correlation data, and the generation AI can improve the efficiency of generating correlation diagrams and system diagrams. This allows the current correlations to be accurately predicted by referring to past correlation data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input past correlation data to the generation AI and cause the generation AI to predict current correlations.

[0098] The evaluation unit can apply an appropriate correlation analysis method for each contract category. For example, in the case of a sales contract, the evaluation unit generates a correlation diagram by having the generation AI apply a correlation analysis method related to sales. In addition, in the case of a rental contract, the evaluation unit can generate a correlation diagram by having the generation AI apply a correlation analysis method related to rental conditions and property information. Furthermore, in the case of a service contract, the evaluation unit can generate a correlation diagram by having the generation AI apply a correlation analysis method related to service content and fees. This enables optimal correlation analysis according to the contract category. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can identify the contract category and have the generation AI execute an appropriate correlation analysis method.

[0099] The evaluation unit can analyze correlations based on attribute information of the contract submitter. For example, if the contract submitter is a large company, the evaluation unit causes the generation AI to analyze correlations based on the attribute information and generate a correlation diagram. Furthermore, if the contract submitter is a small or medium-sized enterprise, the evaluation unit can cause the generation AI to analyze another correlation based on the attribute information and generate a correlation diagram. Furthermore, if the contract submitter is an individual, the evaluation unit can cause the generation AI to analyze yet another correlation based on the attribute information and generate a correlation diagram. This enables optimal correlation analysis by taking into account the attribute information of the contract submitter. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input attribute information of the contract submitter into the generation AI and cause the generation AI to perform correlation analysis.

[0100] The evaluation unit can estimate the user's emotions and adjust the importance of the correlation diagram or tree system diagram based on the estimated user emotions. For example, when the user is relaxed, the evaluation unit causes the generation AI to display a detailed correlation diagram or tree system diagram. Furthermore, when the user is in a hurry, the evaluation unit can cause the generation AI to display a concise correlation diagram or tree system diagram that focuses on the main points. Furthermore, when the user is stressed, the evaluation unit can cause the generation AI to display a visually easy-to-understand correlation diagram or tree system diagram. This allows the correlation diagram or tree system diagram to be displayed with optimal importance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit may be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0101] The evaluation unit can analyze changes in correlation depending on the time of contract submission. For example, in the case of a contract whose submission date is close, the evaluation unit allows the generation AI to analyze changes in correlation based on that information and generate a correlation diagram. In addition, in the case of a contract whose submission date is distant, the evaluation unit allows the generation AI to analyze different changes in correlation based on that information and generate a correlation diagram. Furthermore, in the case of a contract whose submission date is medium, the evaluation unit can also allow the generation AI to analyze moderate changes in correlation and generate a correlation diagram. This enables optimal correlation analysis depending on the time of contract submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can identify the time of contract submission and cause the generation AI to execute appropriate changes in correlation.

[0102] The evaluation unit can analyze correlations based on market data related to the contract. For example, the evaluation unit references the related market data, and the generation AI analyzes correlations based on that information to generate a correlation diagram. The evaluation unit can also cause the generation AI to adjust the priority of correlations based on the related market data. Furthermore, the evaluation unit can learn the related market data, and the generation AI can improve the efficiency of generating correlation diagrams and system diagrams. This improves the accuracy of correlations by referencing the market data related to the contract. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input related market data to the generation AI and cause the generation AI to perform correlation analysis.

[0103] The evaluation unit can analyze correlations based on the technical maturity of the contract. For example, in the case of a contract with high technical maturity, the evaluation unit causes the generation AI to analyze correlations based on that information and generate a correlation diagram. In addition, in the case of a contract with low technical maturity, the evaluation unit can cause the generation AI to analyze different correlations based on that information and generate a correlation diagram. Furthermore, in the case of a contract with medium technical maturity, the evaluation unit can cause the generation AI to analyze moderate correlations and generate a correlation diagram. This enables optimal correlation analysis according to the technical maturity of the contract. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can identify the technical maturity of the contract and cause the generation AI to perform an appropriate correlation analysis. === Hard Collateral 1-1 === Each of the multiple elements including the reading unit, analysis unit, classification unit, and evaluation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit can scan a contract using the scanner or camera 42 of the smart device 14 and read it as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the contract using a generation AI. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and classifies the contract according to its type. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram or a system diagram of the contract. === Hard Collateral 1-2 === Each of the multiple elements, including the reading unit, analysis unit, classification unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit can scan a contract using the camera 42 of the smart glasses 214 and read it as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the contract using a generation AI. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and classifies the contract according to its type. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram or a system diagram of the contract. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reading unit, analysis unit, classification unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reading unit can scan a contract using the camera 42 of the headset-type terminal 314 and read it as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the contract using a generation AI. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and classifies the contract according to its type. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram or a system diagram of the contract. === Hard Collateral 1-4 === Each of the multiple elements including the reading unit, analysis unit, classification unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit can scan a contract using the camera 42 of the robot 414 and read it as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the contract using a generation AI. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and classifies the contract according to its type. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram or a system diagram of the contract.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The fully automated contract management system may further include a notification unit. The notification unit may send notifications to the user when there are important changes to the contract or when a deadline is approaching. For example, the notification unit may send a reminder to the user when a contract renewal deadline is approaching. The notification unit may also send an alert to the user when an important change is made to the contract. Furthermore, the notification unit may notify the user of important events in the contract (for example, the contract's execution date or expiration date). This allows the user to respond in a timely manner without missing important information in the contract.

[0106] The fully automated contract management system can further include a translation unit. The translation unit can translate the contents of the contract into multiple languages. For example, the translation unit translates an English contract into Japanese. The translation unit can also translate a Japanese contract into English. The translation unit can also translate the contents of the contract into other languages ​​(for example, French or German). This allows the fully automated contract management system to be used effectively in international business environments.

[0107] The fully automated contract management system may further include a history tracking unit. The history tracking unit records the change history of a contract in detail, allowing the user to review past changes. For example, the history tracking unit may save each version of the contract and highlight changes. The history tracking unit may also record the user who made the change and the date and time of the change. Furthermore, the history tracking unit may visualize the progress of the contract based on the change history. This allows the user to easily track the change history of the contract and revert to a previous version if necessary.

[0108] The fully automated contract management system may further include a template generation unit. The template generation unit generates standard contract templates and allows users to use them when creating new contracts. For example, the template generation unit generates a standard template for a sales contract. The template generation unit can also generate a standard template for a rental contract. The template generation unit can also generate a standard template for a service contract. This allows users to efficiently create contracts by using standardized templates.

[0109] The fully automated contract management system may further include a risk assessment unit. The risk assessment unit may analyze the contents of a contract and assess potential risks. For example, the risk assessment unit may identify ambiguous clauses or unfavorable terms in the contract and issue a warning to the user. The risk assessment unit may also calculate a risk score based on the contents of the contract and indicate the degree of risk to the user. Furthermore, the risk assessment unit may learn from risk data of past contracts and improve the accuracy of risk assessment. This allows the user to understand the risks of a contract in advance and take appropriate measures.

[0110] The fully automated contract management system may further include an emotion analysis unit. The emotion analysis unit can analyze the contents of the contract and the user's reactions to infer emotions. For example, the emotion analysis unit may infer whether the user is feeling anxious based on the contents of the contract. The emotion analysis unit may also analyze the user's facial expressions and voice data to infer the user's emotions. Furthermore, the emotion analysis unit may adjust the content of the contract based on the inferred emotions. This makes it possible to manage contracts in a way that takes the user's emotions into consideration.

[0111] The fully automated contract management system may further include a user feedback unit. The user feedback unit may collect user feedback regarding contract management and use the collected feedback to improve the system. For example, the user feedback unit may collect feedback regarding the speed at which contracts are loaded and the accuracy of analysis. The user feedback unit may also collect feedback regarding how contracts are classified and displayed. Furthermore, the user feedback unit may identify areas for improvement in the system based on the collected feedback and make updates. This makes it possible to improve the system in accordance with user needs.

[0112] The fully automated contract management system may further include a stress reduction unit. The stress reduction unit may estimate the user's stress level and take appropriate measures. For example, if the user is feeling high stress, the stress reduction unit may simplify the operation of the system. The stress reduction unit may also change the interface design to help the user relax. Furthermore, the stress reduction unit may provide the user with advice and reminders to relax. This may reduce the user's stress and provide a comfortable operating environment.

[0113] The fully automated contract management system may further include an emotion feedback unit. The emotion feedback unit may provide feedback to the system based on the user's emotion. For example, if the user feels anxious, the emotion feedback unit may cause the system to display a reassuring message. If the user feels relaxed, the emotion feedback unit may also cause the system to provide detailed information. Furthermore, if the user feels stressed, the emotion feedback unit may also suggest that the system simplify operations. In this way, appropriate feedback is provided according to the user's emotion.

[0114] The fully automated contract management system may further include an emotion monitoring unit. The emotion monitoring unit may monitor the user's emotions in real time and adjust the system's operation. For example, if the user is feeling stressed, the emotion monitoring unit may adjust the system's operating speed. Also, if the user is relaxed, the emotion monitoring unit may enable the system to provide detailed information. Furthermore, the emotion monitoring unit may change the system interface according to the user's emotions. This allows the system to operate optimally according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reading unit scans the contract and converts it into digital data. For example, the reading unit scans a paper contract and converts it into a PDF file. The reading unit can also directly read contracts submitted in digital format. The reading unit can also read printed contracts using OCR technology. For example, the reading unit scans a handwritten contract with a high-resolution scanner and converts it into text information using OCR technology. Digital contracts submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The analysis unit uses the generation AI to analyze the data read by the reading unit. For example, the analysis unit analyzes the character arrangement and unique information in the contract and extracts important information. The generation AI analyzes the contents of the contract using a text generation AI (e.g., LLM). The analysis unit can also analyze the contents of the contract using a multimodal generation AI. For example, the generation AI identifies the "contractor's name" and "contract date" in the contract and stores that information in a database. Step 3: The classification unit uses the generation AI to perform pattern analysis and classification based on the data analyzed by the analysis unit. For example, the classification unit performs pattern analysis based on the information in the contract and classifies the contract into categories. The generation AI classifies the contract according to its type. For example, it classifies the contract according to its type, such as sales contract, rental contract, service contract, etc. Step 4: The evaluation unit uses the generation AI to generate correlation diagrams and family trees based on the data classified by the classification unit. For example, the evaluation unit generates correlation diagrams and family trees of contracts based on the analysis results, visually displaying the relationships between contracts. The generation AI uses natural language processing technology to generate correlation diagrams and family trees of contracts. For example, the generation AI analyzes the degree of agreement between words and the similarity of sentence structure between contracts to generate correlation diagrams and family trees.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 reading unit that reads and digitizes documents; an analysis unit that analyzes the data read by the reading unit; a classification unit that performs pattern analysis and classification based on the data analyzed by the analysis unit; an evaluation unit that generates a correlation diagram or a system diagram based on the data classified by the classification unit. A system characterized by:

2. The reading unit Scan the contract and import it as digital data 2. The system of claim 1.

3. The analysis unit Analyze the character arrangement and unique information in the contract to extract important information 2. The system of claim 1.

4. The classification unit Conduct pattern analysis based on the information in the contract and categorize the contract 2. The system of claim 1.

5. The evaluation unit Generate correlation diagrams and system diagrams of contracts based on the analysis results, visually displaying the relationships between contracts 2. The system of claim 1.

6. The reading unit Infer user sentiment and adjust the timing of contract loading based on the estimated user sentiment 2. The system of claim 1. It is necessary to clarify the methods for estimating specific emotions and the criteria for adjustment.

7. The reading unit Applying the appropriate loading algorithm depending on the type of agreement 2. The system of claim 1.

8. The reading unit Select the appropriate import method depending on the contract format 2. The system of claim 1.

9. The reading unit Applying the appropriate language analysis algorithm depending on the language of the contract 2. The system of claim 1.

10. The reading unit Inferring user sentiment and prioritizing which contracts to load based on the estimated user sentiment 2. The system of claim 1. It is necessary to clarify how to estimate specific emotions and the criteria for determining priorities.

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