System

Abstract

An object of a system according to an embodiment is to efficiently and effectively support a procurement process.SOLUTION: A system according to an embodiment includes a transaction record analysis unit, a contract digitization unit, and a bill processing unit. The transaction record analysis unit analyzes past transaction records and recommends an optimal supplier. The contract digitalization unit digitalizes a contract and analyzes the contents thereof. A bill processing part automatically reads the bill, accurately inputs data and optimizes payment conditions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not sufficiently save time and money in the procurement process, and there is a need for more efficient and effective support.

[0005] The system according to the embodiment aims to efficiently and effectively support the procurement process. [Means for solving the problem]

[0006] The system according to the embodiment includes a transaction record analysis unit, a contract digitization unit, and an invoice processing unit. The transaction record analysis unit analyzes past transaction records and recommends the most suitable supplier. The contract digitization unit digitizes contracts and analyzes their contents. The invoice processing unit automatically reads invoices, accurately inputs data, and optimizes payment terms. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and effectively support the procurement process. [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) An AI-assisted tool according to an embodiment of the present invention is a system specialized for the procurement process of a service provider. This system utilizes the power of AI at each stage of the procurement process to achieve time and cost savings and efficiently and effectively support the procurement operations of companies. As a result, the AI-assisted tool can efficiently and effectively manage the procurement process of a company, significantly saving time and costs.

[0029] An AI-assisted tool according to an embodiment includes a transaction record analysis unit, a contract digitization unit, and an invoice processing unit. The transaction record analysis unit analyzes past transaction records and recommends optimal suppliers. For example, the generation AI inputs a prompt with past transaction data as input, and analyzes the data to identify the most reliable and cost-effective supplier. The generation AI evaluates quality and cost performance based on the past transaction data and recommends the optimal supplier. The contract digitization unit digitizes contracts and analyzes their contents. For example, the generation AI inputs a prompt with scanned data of a contract as input, and analyzes the content to provide insights useful for negotiating terms when renewing a contract. The generation AI analyzes the content of the contract, identifies important clauses and risks, and optimizes the contract terms. The invoice processing unit automatically reads invoices, accurately enters data, and optimizes payment terms. For example, the generation AI automatically reads invoices in combination with OCR technology and accurately enters data. The generation AI inputs a prompt with scanned data of an invoice as input, and analyzes the data to optimize payment terms. As a result, the AI-assisted tool according to the embodiment can utilize the power of AI at each stage of the procurement process, saving time and costs and supporting corporate procurement operations efficiently and effectively.

[0030] The transaction record analysis unit can extract patterns related to specific seasons or events and recommend optimal suppliers for each season. The transaction record analysis unit, for example, uses a generation AI to analyze past transaction records and extract patterns related to specific seasons or events. For example, it analyzes transaction data for events such as Christmas and New Year's and identifies the optimal supplier for that time of year. The transaction record analysis unit also takes seasonal demand fluctuations into account, and the generation AI analyzes transaction records to recommend the optimal supplier for each season. For example, it identifies suppliers that meet summer and winter demand. The transaction record analysis unit also analyzes transaction data related to events or promotions using the generation AI and recommends the optimal supplier for that event. For example, it identifies suppliers that received high ratings during a specific promotion period. This allows the efficiency of procurement to be improved by recommending the optimal supplier for each season or event.

[0031] The transaction record analysis unit can recommend sustainable suppliers by adding a supplier's corporate social responsibility (CSR) activities and environmental considerations to the evaluation criteria. The transaction record analysis unit, for example, uses a generation AI to analyze transaction records and a supplier's CSR activity data to recommend sustainable suppliers. For example, it identifies suppliers who are engaged in environmental protection activities and social contribution activities. The transaction record analysis unit also adds a supplier's environmental considerations to the evaluation criteria, and the generation AI analyzes the transaction records to recommend environmentally friendly suppliers. For example, it identifies suppliers who provide eco-friendly products. The transaction record analysis unit also uses a generation AI to analyze a supplier's CSR activity data to recommend suppliers who fulfill social responsibility. For example, it identifies suppliers who improve working conditions and contribute to the local community. This allows companies to fulfill their social responsibility by recommending sustainable suppliers.

[0032] The contract digitization department can analyze not only the content of the contract but also the past contract fulfillment status, and extract contract conditions with a high fulfillment rate. For example, the contract digitization department uses generation AI to analyze the content of the contract and the past contract fulfillment status, and extract contract conditions with a high fulfillment rate. For example, it identifies successful contract conditions based on past contract data. The contract digitization department also builds a system in which generation AI analyzes the content of the contract and the fulfillment status, and extracts contract conditions with a high fulfillment rate. For example, it analyzes the success factors of contract fulfillment and proposes optimal contract conditions. The contract digitization department also uses generation AI to analyze data on the contract and the fulfillment status, and extracts contract conditions with a high fulfillment rate. For example, it identifies optimal conditions based on past contract fulfillment data. In this way, by extracting contract conditions with a high fulfillment rate, it is possible to improve the success rate of contracts.

[0033] When analyzing contracts, the contract digitization department can automatically detect legal risks and potential compliance violations and propose low-risk contract terms. For example, the contract digitization department uses generative AI to analyze the contents of contracts and automatically detect legal risks and potential compliance violations. For example, it analyzes contract clauses and identifies risky parts. In addition, when analyzing contracts, the contract digitization department builds a system in which generative AI detects legal risks and potential compliance violations and proposes low-risk contract terms. For example, it proposes optimal terms based on risk assessment. In addition, the contract digitization department uses generative AI to analyze the contents of contracts and detect legal risks and potential compliance violations. For example, it analyzes contract clauses and identifies risky parts. This reduces the possibility of legal risks and compliance violations, thereby realizing safe contracts.

[0034] The invoice processing unit analyzes not only invoice data but also past payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, the invoice processing unit uses a generation AI to analyze invoice data and past payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, it identifies risks based on past payment data. The invoice processing unit also builds a system in which a generation AI analyzes invoice data and payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, it identifies risks based on payment history. The invoice processing unit also builds a system in which a generation AI analyzes invoice data and payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, it identifies risks based on past payment data. This makes it possible to predict the risk of payment delays and propose optimal payment terms, thereby improving the efficiency and reliability of payments.

[0035] The invoice processing unit analyzes not only the invoice data but also the supplier's financial situation and credit information, and is able to propose low-risk payment terms. For example, the invoice processing unit uses a generation AI to analyze the invoice data and the supplier's financial situation and credit information, and propose low-risk payment terms. For example, it identifies optimal payment terms based on the supplier's credit score. The invoice processing unit also builds a system in which the generation AI analyzes the supplier's financial situation and credit information, and proposes low-risk payment terms. For example, it identifies payment terms based on credit information. The invoice processing unit also uses a generation AI to analyze the invoice data and the supplier's financial situation and credit information, and proposes low-risk payment terms. For example, it identifies optimal payment terms based on the supplier's financial data. In this way, it is possible to propose low-risk payment terms by analyzing the supplier's financial situation and credit information.

[0036] When analyzing invoices, the invoice processing unit can also refer to payment terms data from different industries and propose payment terms that incorporate best practices from those industries. For example, the invoice processing unit can use a generation AI to refer to payment terms data from different industries and propose payment terms that incorporate best practices. For example, it can identify optimal payment terms based on success stories from other industries. The invoice processing unit can also build a system in which a generation AI analyzes payment terms data from different industries and proposes payment terms that incorporate best practices from those industries. For example, it can refer to payment terms from other industries. The invoice processing unit can also use a generation AI to analyze payment terms data from different industries and propose payment terms that incorporate best practices. For example, it can identify optimal terms based on payment terms data from other industries. This makes it possible to optimize payment terms by incorporating best practices from other industries.

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

[0038] The transaction record analysis unit can also analyze the geographic location information of suppliers to recommend suppliers that optimize logistics costs and delivery times. For example, generative AI can be used to analyze the location of suppliers and the locations of a company's warehouses and factories to identify the most efficient logistics route. The transaction record analysis unit can also recommend suppliers that shorten delivery times and reduce logistics costs based on the suppliers' geographic location information. For example, it can prioritize the recommendation of nearby suppliers. The transaction record analysis unit can also analyze the geographic location information of suppliers to recommend suppliers located in areas with low disaster risk. This can optimize logistics costs and delivery times, further streamlining a company's procurement operations.

[0039] The transaction record analysis department can also recommend technologically superior suppliers by adding suppliers' technological innovation and R&D activities to the evaluation criteria. For example, generative AI can be used to analyze suppliers' patent application data and R&D investment data to identify technologically superior suppliers. The transaction record analysis department can also recommend suppliers that offer the latest technology based on suppliers' technological innovation data. For example, it can identify suppliers that offer innovative products and services. The transaction record analysis department can also evaluate suppliers' R&D activities to recommend technologically superior suppliers. This allows companies to improve their competitiveness by selecting technologically superior suppliers.

[0040] The transaction record analysis unit can also analyze the customer satisfaction data of suppliers and recommend suppliers with high customer satisfaction. For example, generative AI can be used to analyze customer reviews and feedback data of suppliers to identify suppliers with high ratings. The transaction record analysis unit can also recommend reliable suppliers based on the supplier's customer satisfaction data. For example, suppliers can be identified based on customer satisfaction scores. The transaction record analysis unit can also analyze the customer satisfaction data of suppliers and recommend suppliers with high customer ratings. This makes it possible to improve the reliability of a company's procurement operations by selecting suppliers with high customer satisfaction.

[0041] When analyzing the contents of a contract, the Contract Digitization Department can also refer to industry standards and best practices to propose optimal contract terms. For example, it can use generative AI to analyze industry-standard contract terms data and identify the optimal terms. The Contract Digitization Department can also propose low-risk contract terms based on industry standards and best practices. For example, it can refer to success stories from other companies. The Contract Digitization Department can also build a system in which generative AI analyzes industry-standard data and proposes optimal contract terms. This makes it possible to optimize contract terms by incorporating industry standards and best practices.

[0042] The invoice processing unit can analyze not only the invoice data but also the supplier's financial situation and credit information to propose low-risk payment terms. For example, a generation AI can be used to analyze invoice data and a supplier's financial situation and credit information to propose low-risk payment terms. For example, optimal payment terms can be identified based on the supplier's credit score. The invoice processing unit can also build a system in which a generation AI analyzes a supplier's financial situation and credit information to propose low-risk payment terms. For example, payment terms can be identified based on credit information. The invoice processing unit can also build a system in which a generation AI analyzes invoice data and a supplier's financial situation and credit information to propose low-risk payment terms. This makes it possible to propose low-risk payment terms by analyzing a supplier's financial situation and credit information.

[0043] When analyzing invoices, the invoice processing unit can also refer to payment terms data from other industries and propose payment terms that incorporate best practices from those industries. For example, a generation AI can be used to refer to payment terms data from other industries and propose payment terms that incorporate best practices. For example, optimal payment terms can be identified based on success stories from other industries. The invoice processing unit can also build a system in which a generation AI analyzes payment terms data from other industries and proposes payment terms that incorporate best practices from those industries. For example, it can refer to payment terms from other industries. The invoice processing unit can also use a generation AI to analyze payment terms data from other industries and propose payment terms that incorporate best practices. This makes it possible to optimize payment terms by incorporating best practices from other industries.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The transaction record analysis unit analyzes past transaction records and recommends the most suitable supplier. For example, the generation AI inputs a prompt with past transaction data as input, and the generation AI analyzes the data to identify the most reliable and cost-effective supplier. Based on the past transaction data, the generation AI evaluates quality and cost performance and recommends the most suitable supplier. Step 2: The contract digitization department digitizes the contract and analyzes its content. For example, the generation AI inputs a prompt using scanned data of the contract as input, and the generation AI analyzes the content to provide insights useful for negotiating terms when renewing the contract. The generation AI analyzes the content of the contract, identifies important clauses and risks, and optimizes the contract terms. Step 3: The invoice processing unit automatically reads the invoice, accurately inputs the data, and optimizes the payment terms. For example, the generation AI combines OCR technology to automatically read the invoice and accurately input the data. The generation AI inputs the scanned data of the invoice as input information and enters the prompt, and the generation AI analyzes the data to optimize the payment terms.

[0046] (Example 2) An AI-assisted tool according to an embodiment of the present invention is a system specialized for the procurement process of a service provider. This system utilizes the power of AI at each stage of the procurement process to achieve time and cost savings and efficiently and effectively support the procurement operations of companies. As a result, the AI-assisted tool can efficiently and effectively manage the procurement process of a company, significantly saving time and costs.

[0047] An AI-assisted tool according to an embodiment includes a transaction record analysis unit, a contract digitization unit, and an invoice processing unit. The transaction record analysis unit analyzes past transaction records and recommends optimal suppliers. For example, the generation AI inputs a prompt with past transaction data as input, and analyzes the data to identify the most reliable and cost-effective supplier. The generation AI evaluates quality and cost performance based on the past transaction data and recommends the optimal supplier. The contract digitization unit digitizes contracts and analyzes their contents. For example, the generation AI inputs a prompt with scanned data of a contract as input, and analyzes the content to provide insights useful for negotiating terms when renewing a contract. The generation AI analyzes the content of the contract, identifies important clauses and risks, and optimizes the contract terms. The invoice processing unit automatically reads invoices, accurately enters data, and optimizes payment terms. For example, the generation AI automatically reads invoices in combination with OCR technology and accurately enters data. The generation AI inputs a prompt with scanned data of an invoice as input, and analyzes the data to optimize payment terms. As a result, the AI-assisted tool according to the embodiment can utilize the power of AI at each stage of the procurement process, saving time and costs and supporting corporate procurement operations efficiently and effectively.

[0048] The transaction record analysis unit can extract patterns related to specific seasons or events and recommend optimal suppliers for each season. The transaction record analysis unit, for example, uses a generation AI to analyze past transaction records and extract patterns related to specific seasons or events. For example, it analyzes transaction data for events such as Christmas and New Year's and identifies the optimal supplier for that time of year. The transaction record analysis unit also takes seasonal demand fluctuations into account, and the generation AI analyzes transaction records to recommend the optimal supplier for each season. For example, it identifies suppliers that meet summer and winter demand. The transaction record analysis unit also analyzes transaction data related to events or promotions using the generation AI and recommends the optimal supplier for that event. For example, it identifies suppliers that received high ratings during a specific promotion period. This allows the efficiency of procurement to be improved by recommending the optimal supplier for each season or event.

[0049] The transaction record analysis unit can recommend sustainable suppliers by adding a supplier's corporate social responsibility (CSR) activities and environmental considerations to the evaluation criteria. The transaction record analysis unit, for example, uses a generation AI to analyze transaction records and a supplier's CSR activity data to recommend sustainable suppliers. For example, it identifies suppliers who are engaged in environmental protection activities and social contribution activities. The transaction record analysis unit also adds a supplier's environmental considerations to the evaluation criteria, and the generation AI analyzes the transaction records to recommend environmentally friendly suppliers. For example, it identifies suppliers who provide eco-friendly products. The transaction record analysis unit also uses a generation AI to analyze a supplier's CSR activity data to recommend suppliers who fulfill social responsibility. For example, it identifies suppliers who improve working conditions and contribute to the local community. This allows companies to fulfill their social responsibility by recommending sustainable suppliers.

[0050] The transaction record analysis unit can use the emotion estimation function to analyze the emotional data of personnel in past transactions and preferentially recommend suppliers who expressed a lot of positive emotions. For example, the transaction record analysis unit can use the emotion estimation function to analyze the emotional data of personnel in past transactions and identify suppliers who expressed a lot of positive emotions. For example, it can recommend suppliers based on post-transaction satisfaction and evaluations. Furthermore, the transaction record analysis unit uses a generation AI to analyze transaction records and emotional data and preferentially recommend suppliers who expressed a lot of positive emotions. For example, it can identify suppliers based on emotional data regarding communication during transactions and service quality. Furthermore, the transaction record analysis unit can analyze emotional data from past transactions and build a system that recommends suppliers who expressed a lot of positive emotions. For example, it can identify suppliers based on post-transaction feedback and reviews. This can improve transaction satisfaction by preferentially recommending suppliers who expressed a lot of positive emotions.

[0051] The contract digitization department can analyze not only the content of the contract but also the past contract fulfillment status, and extract contract conditions with a high fulfillment rate. For example, the contract digitization department uses generation AI to analyze the content of the contract and the past contract fulfillment status, and extract contract conditions with a high fulfillment rate. For example, it identifies successful contract conditions based on past contract data. The contract digitization department also builds a system in which generation AI analyzes the content of the contract and the fulfillment status, and extracts contract conditions with a high fulfillment rate. For example, it analyzes the success factors of contract fulfillment and proposes optimal contract conditions. The contract digitization department also uses generation AI to analyze data on the contract and the fulfillment status, and extracts contract conditions with a high fulfillment rate. For example, it identifies optimal conditions based on past contract fulfillment data. In this way, by extracting contract conditions with a high fulfillment rate, it is possible to improve the success rate of contracts.

[0052] When analyzing contracts, the contract digitization department can automatically detect legal risks and potential compliance violations and propose low-risk contract terms. For example, the contract digitization department uses generative AI to analyze the contents of contracts and automatically detect legal risks and potential compliance violations. For example, it analyzes contract clauses and identifies risky parts. In addition, when analyzing contracts, the contract digitization department builds a system in which generative AI detects legal risks and potential compliance violations and proposes low-risk contract terms. For example, it proposes optimal terms based on risk assessment. In addition, the contract digitization department uses generative AI to analyze the contents of contracts and detect legal risks and potential compliance violations. For example, it analyzes contract clauses and identifies risky parts. This reduces the possibility of legal risks and compliance violations, thereby realizing safe contracts.

[0053] The contract digitization unit can use the emotion estimation function to analyze the emotional data of the person in charge during contract negotiations and prioritize proposing contract terms that were evoked with a high level of positive emotion. For example, the contract digitization unit can use the emotion estimation function to analyze the emotional data of the person in charge during contract negotiations and identify contract terms that were evoked with a high level of positive emotion. For example, it can propose optimal terms based on the emotional data during negotiations. The contract digitization unit also builds a system in which the generative AI analyzes the emotional data during contract negotiations and prioritizes proposing contract terms that were evoked with a high level of positive emotion. For example, it can identify terms based on the emotional score during negotiations. The contract digitization unit also analyzes the emotional data during contract negotiations and proposes contract terms that were evoked with a high level of positive emotion. For example, it can identify optimal terms based on the emotional data during negotiations. This can improve the success rate of contract negotiations by prioritizing proposing contract terms that were evoked with a high level of positive emotion.

[0054] The invoice processing unit analyzes not only invoice data but also past payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, the invoice processing unit uses a generation AI to analyze invoice data and past payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, it identifies risks based on past payment data. The invoice processing unit also builds a system in which a generation AI analyzes invoice data and payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, it identifies risks based on payment history. The invoice processing unit also builds a system in which a generation AI analyzes invoice data and payment history, predicts the risk of payment delays, and proposes optimal payment terms. For example, it identifies risks based on past payment data. This makes it possible to predict the risk of payment delays and propose optimal payment terms, thereby improving the efficiency and reliability of payments.

[0055] The invoice processing unit can use the emotion estimation function to analyze the emotion data of staff involved in invoice processing and prioritize proposing payment terms that evoke a lot of positive emotion. For example, the invoice processing unit can use the emotion estimation function to analyze the emotion data of staff involved in invoice processing and identify payment terms that evoke a lot of positive emotion. For example, the invoice processing unit can propose optimal terms based on the emotion score for the payment terms. The invoice processing unit also builds a system in which a generative AI analyzes emotion data during invoice processing and prioritizes proposing payment terms that evoke a lot of positive emotion. For example, the invoice processing unit can identify terms based on the emotion score. The invoice processing unit also analyzes emotion data related to invoice processing and proposes payment terms that evoke a lot of positive emotion. For example, the invoice processing unit can identify optimal terms based on the emotion data for the payment terms. This can improve satisfaction with payment processing by prioritizing proposing payment terms that evoke a lot of positive emotion.

[0056] The invoice processing unit analyzes not only the invoice data but also the supplier's financial situation and credit information, and is able to propose low-risk payment terms. For example, the invoice processing unit uses a generation AI to analyze the invoice data and the supplier's financial situation and credit information, and propose low-risk payment terms. For example, it identifies optimal payment terms based on the supplier's credit score. The invoice processing unit also builds a system in which the generation AI analyzes the supplier's financial situation and credit information, and proposes low-risk payment terms. For example, it identifies payment terms based on credit information. The invoice processing unit also uses a generation AI to analyze the invoice data and the supplier's financial situation and credit information, and proposes low-risk payment terms. For example, it identifies optimal payment terms based on the supplier's financial data. In this way, it is possible to propose low-risk payment terms by analyzing the supplier's financial situation and credit information.

[0057] When analyzing invoices, the invoice processing unit can also refer to payment terms data from different industries and propose payment terms that incorporate best practices from those industries. For example, the invoice processing unit can use a generation AI to refer to payment terms data from different industries and propose payment terms that incorporate best practices. For example, it can identify optimal payment terms based on success stories from other industries. The invoice processing unit can also build a system in which a generation AI analyzes payment terms data from different industries and proposes payment terms that incorporate best practices from those industries. For example, it can refer to payment terms from other industries. The invoice processing unit can also use a generation AI to analyze payment terms data from different industries and propose payment terms that incorporate best practices. For example, it can identify optimal terms based on payment terms data from other industries. This makes it possible to optimize payment terms by incorporating best practices from other industries.

[0058] The invoice processing unit can use the emotion estimation function to collect the user's emotional reactions to invoice processing and propose payment terms that elicit positive emotions. The invoice processing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to invoice processing and proposes payment terms that elicit positive emotions based on that data. For example, it identifies optimal terms based on the user's emotion score. The invoice processing unit also builds a system in which a generative AI analyzes emotional reaction data during invoice processing and proposes payment terms that elicit positive emotions. For example, it identifies terms based on the emotion score. The invoice processing unit also uses the emotion estimation function to collect the user's emotional reactions to invoice processing and proposes payment terms that elicit positive emotions. For example, it identifies optimal terms based on the emotion data. This makes it possible to improve user satisfaction by proposing payment terms that elicit positive emotions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The transaction record analysis unit can also analyze the geographic location information of suppliers to recommend suppliers that optimize logistics costs and delivery times. For example, generative AI can be used to analyze the location of suppliers and the locations of a company's warehouses and factories to identify the most efficient logistics route. The transaction record analysis unit can also recommend suppliers that shorten delivery times and reduce logistics costs based on the suppliers' geographic location information. For example, it can prioritize the recommendation of nearby suppliers. The transaction record analysis unit can also analyze the geographic location information of suppliers to recommend suppliers located in areas with low disaster risk. This can optimize logistics costs and delivery times, further streamlining a company's procurement operations.

[0061] The transaction record analysis department can also recommend technologically superior suppliers by adding suppliers' technological innovation and R&D activities to the evaluation criteria. For example, generative AI can be used to analyze suppliers' patent application data and R&D investment data to identify technologically superior suppliers. The transaction record analysis department can also recommend suppliers that offer the latest technology based on suppliers' technological innovation data. For example, it can identify suppliers that offer innovative products and services. The transaction record analysis department can also evaluate suppliers' R&D activities to recommend technologically superior suppliers. This allows companies to improve their competitiveness by selecting technologically superior suppliers.

[0062] The transaction record analysis unit can also analyze the customer satisfaction data of suppliers and recommend suppliers with high customer satisfaction. For example, generative AI can be used to analyze customer reviews and feedback data of suppliers to identify suppliers with high ratings. The transaction record analysis unit can also recommend reliable suppliers based on the supplier's customer satisfaction data. For example, suppliers can be identified based on customer satisfaction scores. The transaction record analysis unit can also analyze the customer satisfaction data of suppliers and recommend suppliers with high customer ratings. This makes it possible to improve the reliability of a company's procurement operations by selecting suppliers with high customer satisfaction.

[0063] The transaction record analysis unit can also use the emotion estimation function to analyze communication history with suppliers and prioritize recommend suppliers with a high level of positive emotion. For example, it can analyze email and chat history to identify suppliers with a high level of positive emotion. The transaction record analysis unit can also use the generative AI to analyze communication history and emotion data and prioritize recommend suppliers with a high level of positive emotion. For example, it can identify suppliers based on emotion data from interactions during transactions. The transaction record analysis unit can also analyze past communication history and build a system that recommends suppliers with a high level of positive emotion. This can facilitate transactions by preferentially recommending suppliers with a high level of positive emotion.

[0064] The contract digitization department can analyze not only the content of the contract but also the emotional data of the person in charge during contract negotiations, and prioritize proposing contract terms that evoke a lot of positive emotions. For example, it can propose optimal terms based on emotional data during negotiations. The contract digitization department can also build a system in which generative AI analyzes emotional data during contract negotiations and prioritizes proposing contract terms that evoke a lot of positive emotions. For example, it can identify conditions based on the emotional score during negotiations. The contract digitization department can also analyze emotional data during contract negotiations and propose contract terms that evoke a lot of positive emotions. This can improve the success rate of contract negotiations by prioritizing proposing contract terms that evoke a lot of positive emotions.

[0065] When analyzing the contents of a contract, the Contract Digitization Department can also refer to industry standards and best practices to propose optimal contract terms. For example, it can use generative AI to analyze industry-standard contract terms data and identify the optimal terms. The Contract Digitization Department can also propose low-risk contract terms based on industry standards and best practices. For example, it can refer to success stories from other companies. The Contract Digitization Department can also build a system in which generative AI analyzes industry-standard data and proposes optimal contract terms. This makes it possible to optimize contract terms by incorporating industry standards and best practices.

[0066] The contract digitization department can also use the emotion estimation function to collect the user's emotional response to the contract content and propose contract terms that elicit positive emotions. For example, it can identify optimal terms based on the emotion score for the contract content. The contract digitization department can also build a system in which a generative AI analyzes emotional response data for the contract content and proposes contract terms that elicit positive emotions. For example, it can identify terms based on the emotion score. The contract digitization department can also use the emotion estimation function to collect the user's emotional response to the contract content and propose contract terms that elicit positive emotions. In this way, by proposing contract terms that elicit positive emotions, it is possible to improve user satisfaction.

[0067] The invoice processing unit can analyze not only the invoice data but also the supplier's financial situation and credit information to propose low-risk payment terms. For example, a generation AI can be used to analyze invoice data and a supplier's financial situation and credit information to propose low-risk payment terms. For example, optimal payment terms can be identified based on the supplier's credit score. The invoice processing unit can also build a system in which a generation AI analyzes a supplier's financial situation and credit information to propose low-risk payment terms. For example, payment terms can be identified based on credit information. The invoice processing unit can also build a system in which a generation AI analyzes invoice data and a supplier's financial situation and credit information to propose low-risk payment terms. This makes it possible to propose low-risk payment terms by analyzing a supplier's financial situation and credit information.

[0068] The invoice processing unit can also use the emotion estimation function to collect the user's emotional response to invoice processing and propose payment terms that elicit positive emotions. For example, it can identify optimal terms based on the user's emotion score. The invoice processing unit can also build a system in which a generative AI analyzes emotional response data during invoice processing and proposes payment terms that elicit positive emotions. For example, it can identify terms based on the emotion score. The invoice processing unit can also use the emotion estimation function to collect the user's emotional response to invoice processing and propose payment terms that elicit positive emotions. This makes it possible to improve user satisfaction by proposing payment terms that elicit positive emotions.

[0069] When analyzing invoices, the invoice processing unit can also refer to payment terms data from other industries and propose payment terms that incorporate best practices from those industries. For example, a generation AI can be used to refer to payment terms data from other industries and propose payment terms that incorporate best practices. For example, optimal payment terms can be identified based on success stories from other industries. The invoice processing unit can also build a system in which a generation AI analyzes payment terms data from other industries and proposes payment terms that incorporate best practices from those industries. For example, it can refer to payment terms from other industries. The invoice processing unit can also use a generation AI to analyze payment terms data from other industries and propose payment terms that incorporate best practices. This makes it possible to optimize payment terms by incorporating best practices from other industries.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The transaction record analysis unit analyzes past transaction records and recommends the most suitable supplier. For example, the generation AI inputs a prompt with past transaction data as input, and the generation AI analyzes the data to identify the most reliable and cost-effective supplier. Based on the past transaction data, the generation AI evaluates quality and cost performance and recommends the most suitable supplier. Step 2: The contract digitization department digitizes the contract and analyzes its content. For example, the generation AI inputs a prompt using scanned data of the contract as input, and the generation AI analyzes the content to provide insights useful for negotiating terms when renewing the contract. The generation AI analyzes the content of the contract, identifies important clauses and risks, and optimizes the contract terms. Step 3: The invoice processing unit automatically reads the invoice, accurately inputs the data, and optimizes the payment terms. For example, the generation AI combines OCR technology to automatically read the invoice and accurately input the data. The generation AI inputs the scanned data of the invoice as input information and enters the prompt, and the generation AI analyzes the data to optimize the payment terms.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 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 transaction record analysis unit that analyzes past transaction records and recommends optimal suppliers; A contract digitization department that digitizes contracts and analyzes their contents; an invoice processing unit that automatically reads invoices, accurately enters data, and optimizes payment terms; A system characterized by:

2. The transaction record analysis unit Extracting patterns related to specific seasons or events and recommending the best suppliers for each season 2. The system of claim 1.

3. The transaction record analysis unit Recommend sustainable suppliers by adding their corporate social responsibility (CSR) activities and environmental friendliness to the evaluation criteria.

2. The system of claim 1.

4. The transaction record analysis unit Analyzes the emotional data of personnel in past transactions and prioritizes recommendations of suppliers with a high level of positive emotion.

2. The system of claim 1.

5. The contract digitization unit Analyze not only the content of the contract but also past contract fulfillment status to extract contract conditions with high fulfillment rates 2. The system of claim 1.

6. The contract digitization unit When analyzing contracts, it automatically detects potential legal risks and compliance violations and suggests contract terms with lower risks.

2. The system of claim 1.

7. The contract digitization unit Analyzes the emotional data of contract negotiators and prioritizes proposals for contract terms that evoke positive emotions 2. The system of claim 1.

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