system

Abstract

An object of a system according to an embodiment is to automate contract management for efficiently using an external API service.SOLUTION: A system according to an embodiment includes a contract management unit, a selection unit, and a processing management unit. The contract management unit manages a contract for using an external API service. The selection unit selects an available API service based on a contract managed by the contract management unit. The processing management unit performs necessary processing using the API service selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, contract management for efficiently using external API services is complicated, which can hinder automation.

[0005] The system according to the embodiment aims to automate contract management for efficient use of external API services. [Means for solving the problem]

[0006] A system according to an embodiment includes a contract management unit, a selection unit, and a processing management unit. The contract management unit manages contracts for using external API services. The selection unit selects an available API service based on the contract managed by the contract management unit. The processing management unit performs necessary processing using the API service selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate contract management for efficiently using external API services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention enables an AI to efficiently utilize external API services. This system completes a contract for using external API services in advance and presents a list of available API services to the generation AI. The generation AI can select and use the most appropriate API service from the presented API services. This enables the AI ​​to efficiently utilize external API services. This allows the system to efficiently utilize external API services and quickly perform necessary processing. For example, various processes, such as data analysis and image recognition, can be performed using external API services. This improves the AI's processing capabilities and enables more advanced processing. Furthermore, the process for using API services is simplified, thereby streamlining the operation of the AI.

[0029] An API utilization system according to an embodiment includes a contract management unit, a selection unit, and a process management unit. The contract management unit manages contracts for using external API services. For example, the contract management unit manages the content of contracts with API service providers and automates the necessary procedures. The contract management unit can also confirm API usage fees, terms of use, and the like and conclude contracts. For example, the contract management unit checks the details of the contract with the API service provider and performs the necessary procedures. The selection unit selects an available API service based on the contract managed by the contract management unit. For example, the selection unit selects the optimal API based on the functions required by the generation AI. For example, the selection unit can select the optimal API based on the functions required by the generation AI, such as an API suitable for data analysis or an API suitable for image recognition. The process management unit performs the necessary processing using the API service selected by the selection unit. The process management unit manages, for example, the data input and output formats and error handling methods. The process management unit can also analyze data using the selected API and output the results. For example, the process management unit analyzes data using the selected API and output the results. This allows the API utilization system according to the embodiment to efficiently utilize external API services.

[0030] The contract management unit manages the contract details with API service providers and can automate the necessary procedures. The contract management unit, for example, manages the contract details with API service providers and automates the necessary procedures. For example, the contract management unit checks API usage fees and terms of use and concludes a contract. The contract management unit can also automate contract renewal and cancellation procedures. For example, the contract management unit automatically notifies users when the contract renewal date is approaching and carries out the renewal procedure. This makes it possible to manage contract details and automate procedures. Some or all of the above-mentioned processing in the contract management unit may be performed using AI, for example, or may be performed without using AI. For example, the contract management unit can input contract details management into AI and have AI execute contract renewal and cancellation procedures.

[0031] The selection unit can select an appropriate API according to the functions required by the generation AI. For example, the selection unit selects an appropriate API according to the functions required by the generation AI. For example, the selection unit selects the optimal API according to the functions required by the generation AI, such as an API suitable for data analysis or an API suitable for image recognition. The selection unit can also select the optimal API based on the API's functions, performance, cost, etc. For example, the selection unit evaluates the API's functions and performance and selects the optimal API. This makes it possible to select the optimal API according to the functions required by the generation AI. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the API's functions and performance into AI and have the AI ​​select the optimal API.

[0032] The processing management unit can manage the data input and output formats and error handling methods. The processing management unit manages, for example, the data input and output formats and error handling methods. For example, the processing management unit supports JSON, XML, CSV, etc. as data input formats. The processing management unit can also manage error handling methods such as displaying error messages and retry processing. For example, the processing management unit displays an error message and performs retry processing when an error occurs. This makes it possible to manage the data input and output formats and error handling. Some or all of the above-mentioned processing in the processing management unit may be performed using, or without, AI, for example. For example, the processing management unit can input the data input format and error handling method into AI and have the AI ​​perform the management.

[0033] The processing management unit can analyze data using the selected API and output the results. The processing management unit, for example, analyzes data using the selected API and outputs the results. For example, the processing management unit performs statistical analysis or the application of a machine learning model as a method of data analysis. The processing management unit can also output the analysis results in text format or graph format. For example, the processing management unit analyzes data and outputs the results in text format. The processing management unit can also display the analysis results in graph format. This makes it possible to analyze data and output the results using the selected API. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input a data analysis method into AI and have the AI ​​perform the analysis and output the results.

[0034] The contract management unit can automatically propose appropriate contract terms by referencing past contract history. The contract management unit, for example, automatically proposes appropriate contract terms by referencing past contract history. For example, the contract management unit proposes similar terms for a new contract based on contract terms that were successful in the past. The contract management unit can also optimize contract terms with a specific API service provider based on past contract history. Furthermore, the contract management unit can analyze past contract history and automatically propose the most favorable contract terms. This makes it possible to automatically propose optimal contract terms based on past contract history. Some or all of the above-mentioned processing in the contract management unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract management unit can input past contract history into AI and have the AI ​​propose optimal contract terms.

[0035] The contract management unit can evaluate the reliability of API service providers and prioritize contracts with reliable providers. For example, the contract management unit evaluates the reliability of API service providers and prioritizes contracts with reliable providers. For example, the contract management unit evaluates the past performance of API service providers and prioritizes reliable providers. The contract management unit can also select reliable providers based on reviews and ratings of API service providers. Furthermore, the contract management unit can analyze the contract history of API service providers and prioritize contracts with reliable providers. This makes it possible to prioritize contracts with reliable API service providers. Some or all of the above-mentioned processing in the contract management unit may be performed using, or without, AI. For example, the contract management unit can input reliability data of API service providers into AI and have the AI ​​evaluate reliability and determine the priority of contracts.

[0036] The contract management unit can automatically notify changes or updates to the contract content and quickly carry out the necessary procedures. The contract management unit, for example, can automatically notify changes or updates to the contract content and quickly carry out the necessary procedures. For example, the contract management unit can automatically notify when changes to the contract content occur and quickly carry out the necessary procedures. The contract management unit can also automatically notify when the contract renewal date is approaching and carry out the renewal procedures. Furthermore, the contract management unit can automate and quickly process procedures associated with changes or updates to the contract content. This makes it possible to automatically notify changes or updates to the contract content and quickly carry out the procedures. Some or all of the above-mentioned processing in the contract management unit may be performed using, for example, AI, or may be performed without AI. For example, the contract management unit can input data on changes or updates to the contract content into AI and have the AI ​​execute the notifications and procedures.

[0037] The contract management unit can select an appropriate contract taking into account the geographic distribution of API service providers. The contract management unit selects an appropriate contract taking into account, for example, the geographic distribution of API service providers. For example, the contract management unit selects the optimal contract taking into account the geographic distribution of API service providers. The contract management unit can also prioritize contracts with geographically close API service providers. Furthermore, the contract management unit can propose optimal contract terms based on the geographic distribution. This makes it possible to select the optimal contract taking into account the geographic distribution of API service providers. Some or all of the above-described processing in the contract management unit may be performed using, or without, AI. For example, the contract management unit can input geographic distribution data of API service providers into AI and have the AI ​​select the optimal contract.

[0038] The contract management department can perform risk assessment of the contract contents and prioritize contracts with low risk. For example, the contract management department can perform risk assessment of the contract contents and prioritize contracts with low risk. The contract management department can also analyze the risks of the contract contents and propose contract terms with low risk. Furthermore, the contract management department can select the optimal contract based on the risk assessment of the contract contents. This makes it possible to perform risk assessment of the contract contents and prioritize contracts with low risk. Some or all of the above-mentioned processing in the contract management department can be performed using, for example, AI, or can be performed without using AI. For example, the contract management department can input risk data of the contract contents into AI and have the AI ​​perform risk assessment and contract selection.

[0039] The contract management department can automatically check the legal requirements of the contract content and minimize legal risks. For example, the contract management department can automatically check the legal requirements of the contract content and minimize legal risks. The contract management department can also check the legal requirements of the contract content and automatically perform the necessary procedures. Furthermore, the contract management department can also propose optimal contract terms based on the legal requirements of the contract content. This makes it possible to automatically check the legal requirements of the contract content and minimize legal risks. Some or all of the above-mentioned processing in the contract management department may be performed using, or without, AI. For example, the contract management department can input legal requirement data of the contract content into AI and have the AI ​​check and minimize legal risks.

[0040] The selection unit can automatically suggest an appropriate API by referring to past API usage history. The selection unit, for example, automatically suggests an appropriate API by referring to past API usage history. For example, the selection unit may newly suggest a similar API based on past successful API usage history. The selection unit can also optimize APIs from a specific API service provider based on past API usage history. Furthermore, the selection unit can analyze past API usage history and automatically suggest the most advantageous API. This makes it possible to automatically suggest the optimal API based on past API usage history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past API usage history into AI and have the AI ​​suggest the optimal API.

[0041] The selection unit can evaluate the performance of APIs and prioritize APIs with better performance. For example, the selection unit can evaluate the performance of APIs and prioritize APIs with better performance. For example, the selection unit can evaluate the performance of APIs and prioritize APIs with higher performance. The selection unit can also analyze the performance of APIs and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the API performance evaluation. This makes it possible to evaluate the performance of APIs and prioritize APIs with higher performance. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API performance data into AI and cause the AI ​​to perform performance evaluation and API selection.

[0042] The selection unit can select an API with good cost performance taking into account the API usage fees. For example, the selection unit selects an API with good cost performance taking into account the API usage fees. The selection unit can also analyze API usage fees and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the API usage fees. This makes it possible to select an API with good cost performance taking into account the API usage fees. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API usage fee data into AI and have the AI ​​evaluate cost performance and select an API.

[0043] The selection unit can evaluate the support systems of API providers and prioritize APIs with good support. For example, the selection unit evaluates the support systems of API providers and prioritizes APIs with good support. For example, the selection unit evaluates the support systems of API providers and prioritizes APIs with solid support. The selection unit can also analyze the support systems of API providers and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the support systems of API providers. This makes it possible to evaluate the support systems of API providers and prioritize APIs with solid support. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input support system data of API providers into AI and cause the AI ​​to evaluate the support systems and select APIs.

[0044] The selection unit can evaluate the security of APIs and select APIs with good security. For example, the selection unit evaluates the security of APIs and selects APIs with good security. For example, the selection unit evaluates the security of APIs and selects APIs with high security. The selection unit can also analyze the security of APIs and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the security evaluation of APIs. This makes it possible to evaluate the security of APIs and select APIs with high security. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input security data of APIs into AI and have the AI ​​perform the security evaluation and select APIs.

[0045] The selection unit can evaluate API compatibility and select an API that is highly compatible with an existing system. The selection unit, for example, evaluates API compatibility and selects an API that is highly compatible with an existing system. For example, the selection unit evaluates API compatibility and selects an API that is highly compatible with an existing system. The selection unit can also analyze API compatibility and propose an optimal API. Furthermore, the selection unit can select an optimal API based on the API compatibility evaluation. This makes it possible to evaluate API compatibility and select an API that is highly compatible with an existing system. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API compatibility data into AI and cause the AI ​​to perform compatibility evaluation and API selection.

[0046] The processing management unit can automatically suggest an optimal processing method by referring to past processing history. The processing management unit, for example, automatically suggests an optimal processing method by referring to past processing history. For example, the processing management unit suggests a similar method for a new process based on a processing method that was successful in the past. The processing management unit can also optimize a specific processing method from past processing history. Furthermore, the processing management unit can analyze past processing history and automatically suggest the most advantageous processing method. This makes it possible to automatically suggest an optimal processing method based on past processing history. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input past processing history into AI and have the AI ​​execute the suggestion of an optimal processing method.

[0047] The processing management unit can evaluate the success rate of the processing and prioritize processing methods with high success rates. For example, the processing management unit evaluates the success rate of the processing and prioritizes processing methods with high success rates. The processing management unit can also analyze the success rate of the processing and propose an optimal processing method. Furthermore, the processing management unit can select an optimal processing method based on the evaluation of the success rate of the processing. This makes it possible to evaluate the success rate of the processing and prioritize processing methods with high success rates. Some or all of the above-mentioned processing in the processing management unit may be performed using, or without, AI. For example, the processing management unit can input processing success rate data into AI and have the AI ​​evaluate the success rate and select a processing method.

[0048] The processing management unit can select an efficient processing method taking into account the execution time of the processing. The processing management unit, for example, selects an efficient processing method taking into account the execution time of the processing. For example, the processing management unit selects an efficient processing method taking into account the execution time of the processing. The processing management unit can also analyze the execution time of the processing and propose an optimal processing method. Furthermore, the processing management unit can select an optimal processing method based on the execution time of the processing. This makes it possible to select an efficient processing method taking into account the execution time of the processing. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input execution time data of the processing into AI and have the AI ​​evaluate the execution time and select a processing method.

[0049] The processing management unit can evaluate the error handling methods of the processing and select a processing method with fewer errors. For example, the processing management unit evaluates the error handling methods of the processing and selects a processing method with fewer errors. For example, the processing management unit evaluates the error handling methods of the processing and selects a processing method with fewer errors. The processing management unit can also analyze the error handling methods of the processing and propose an optimal processing method. Furthermore, the processing management unit can select an optimal processing method based on the error handling methods of the processing. This makes it possible to evaluate the error handling methods of the processing and select a processing method with fewer errors. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input error handling data of the processing into AI and have the AI ​​evaluate the error handling methods and select a processing method.

[0050] The processing management unit can evaluate the data format of the processing and select an appropriate data format. The processing management unit, for example, evaluates the data format of the processing and selects an appropriate data format. For example, the processing management unit evaluates the data format of the processing and selects an optimal data format. The processing management unit can also analyze the data format of the processing and propose an optimal data format. Furthermore, the processing management unit can select an optimal processing method based on the data format of the processing. This makes it possible to evaluate the data format of the processing and select an optimal data format. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input data of the processing data into AI and have the AI ​​evaluate the data format and select a processing method.

[0051] The processing management unit can automatically notify the user of the processing results and improve the processing method by reflecting user feedback. For example, the processing management unit can automatically notify the user of the processing results and improve the processing method by reflecting user feedback. For example, the processing management unit can automatically notify the user of the processing results and improve the processing method based on user feedback. The processing management unit can also notify the user of the processing results in real time and reflect user feedback. Furthermore, the processing management unit can notify the user of the processing results and propose an optimal processing method based on user feedback. This makes it possible to automatically notify the user of the processing results and improve the processing method by reflecting user feedback. Some or all of the above-described processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input processing result data into AI and have the AI ​​perform notification and reflect the feedback.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The selection unit can evaluate the performance of APIs and prioritize APIs with good performance. For example, it can evaluate the response time and throughput of APIs and select the most efficient API. It can also prioritize APIs that provide stable services by taking into account the reliability and availability of the APIs. It can also evaluate the security of APIs and select APIs with high security. This enables the selection unit to select the optimal API in terms of performance, reliability, and security. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input API performance data into AI and have the AI ​​perform performance evaluation and API selection.

[0054] The contract management unit can automatically propose appropriate contract terms by referencing past contract history. For example, it can propose similar terms for a new contract based on contract terms that have been successful in the past. It can also optimize contract terms with a specific API service provider based on past contract history. It can also analyze past contract history and automatically propose the most favorable contract terms. This makes it possible to automatically propose optimal contract terms based on past contract history. Some or all of the above-mentioned processing in the contract management unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract management unit can input past contract history into AI and have the AI ​​propose optimal contract terms.

[0055] The processing management unit can evaluate the success rate of processing and prioritize processing methods with high success rates. For example, it can select a processing method with a high success rate based on past processing history. It can also monitor the success rate of processing in real time and propose the optimal processing method. Furthermore, it can identify areas for improvement in the processing method based on the success rate of processing and continuously optimize the processing method. This makes it possible to evaluate the success rate of processing and prioritize processing methods with high success rates. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input processing success rate data into AI and have the AI ​​evaluate the success rate and select the processing method.

[0056] The contract management unit can evaluate the reliability of API service providers and prioritize contracts with reliable providers. For example, it can evaluate the past performance of API service providers and prioritize reliable providers. It can also select reliable providers based on their reviews and ratings. It can also analyze the contract history of API service providers and prioritize contracts with reliable providers. This makes it possible to prioritize contracts with reliable API service providers. Some or all of the above-mentioned processing in the contract management unit can be performed using, for example, AI, or without AI. For example, the contract management unit can input reliability data of API service providers into AI and have the AI ​​evaluate reliability and determine the priority of contracts.

[0057] The selection unit can select an API with good cost performance taking into account the API usage fees. For example, the selection unit selects an API with good cost performance taking into account the API usage fees. The selection unit can also analyze the API usage fees and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the API usage fees. This makes it possible to select an API with good cost performance taking into account the API usage fees. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API usage fee data into AI and have the AI ​​evaluate cost performance and select an API.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The Contract Management Department manages contracts for using external API services. The Contract Management Department manages the details of contracts with API service providers and automates the necessary procedures. The Contract Management Department can also check API usage fees and terms of use and conclude contracts. Step 2: The selection unit selects available API services based on the contracts managed by the contract management unit. The selection unit selects the optimal API depending on the functions required by the generation AI. For example, it can select the optimal API depending on the functions required by the generation AI, such as an API suitable for data analysis or an API suitable for image recognition. Step 3: The processing manager performs the necessary processing using the API service selected by the selector. The processing manager manages the data input and output formats and error handling methods. It can also analyze data using the selected API and output the results.

[0060] (Example 2) A system according to an embodiment of the present invention enables an AI to efficiently utilize external API services. This system completes a contract for using external API services in advance and presents a list of available API services to the generation AI. The generation AI can select and use the most appropriate API service from the presented API services. This enables the AI ​​to efficiently utilize external API services. This allows the system to efficiently utilize external API services and quickly perform necessary processing. For example, various processes, such as data analysis and image recognition, can be performed using external API services. This improves the AI's processing capabilities and enables more advanced processing. Furthermore, the process for using API services is simplified, thereby streamlining the operation of the AI.

[0061] An API utilization system according to an embodiment includes a contract management unit, a selection unit, and a process management unit. The contract management unit manages contracts for using external API services. For example, the contract management unit manages the content of contracts with API service providers and automates the necessary procedures. The contract management unit can also confirm API usage fees, terms of use, and the like and conclude contracts. For example, the contract management unit checks the details of the contract with the API service provider and performs the necessary procedures. The selection unit selects an available API service based on the contract managed by the contract management unit. For example, the selection unit selects the optimal API based on the functions required by the generation AI. For example, the selection unit can select the optimal API based on the functions required by the generation AI, such as an API suitable for data analysis or an API suitable for image recognition. The process management unit performs the necessary processing using the API service selected by the selection unit. The process management unit manages, for example, the data input and output formats and error handling methods. The process management unit can also analyze data using the selected API and output the results. For example, the process management unit analyzes data using the selected API and output the results. This allows the API utilization system according to the embodiment to efficiently utilize external API services.

[0062] The contract management unit manages the contract details with API service providers and can automate the necessary procedures. The contract management unit, for example, manages the contract details with API service providers and automates the necessary procedures. For example, the contract management unit checks API usage fees and terms of use and concludes a contract. The contract management unit can also automate contract renewal and cancellation procedures. For example, the contract management unit automatically notifies users when the contract renewal date is approaching and carries out the renewal procedure. This makes it possible to manage contract details and automate procedures. Some or all of the above-mentioned processing in the contract management unit may be performed using AI, for example, or may be performed without using AI. For example, the contract management unit can input contract details management into AI and have AI execute contract renewal and cancellation procedures.

[0063] The selection unit can select an appropriate API according to the functions required by the generation AI. For example, the selection unit selects an appropriate API according to the functions required by the generation AI. For example, the selection unit selects the optimal API according to the functions required by the generation AI, such as an API suitable for data analysis or an API suitable for image recognition. The selection unit can also select the optimal API based on the API's functions, performance, cost, etc. For example, the selection unit evaluates the API's functions and performance and selects the optimal API. This makes it possible to select the optimal API according to the functions required by the generation AI. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the API's functions and performance into AI and have the AI ​​select the optimal API.

[0064] The processing management unit can manage the data input and output formats and error handling methods. The processing management unit manages, for example, the data input and output formats and error handling methods. For example, the processing management unit supports JSON, XML, CSV, etc. as data input formats. The processing management unit can also manage error handling methods such as displaying error messages and retry processing. For example, the processing management unit displays an error message and performs retry processing when an error occurs. This makes it possible to manage the data input and output formats and error handling. Some or all of the above-mentioned processing in the processing management unit may be performed using, or without, AI, for example. For example, the processing management unit can input the data input format and error handling method into AI and have the AI ​​perform the management.

[0065] The processing management unit can analyze data using the selected API and output the results. The processing management unit, for example, analyzes data using the selected API and outputs the results. For example, the processing management unit performs statistical analysis or the application of a machine learning model as a method of data analysis. The processing management unit can also output the analysis results in text format or graph format. For example, the processing management unit analyzes data and outputs the results in text format. The processing management unit can also display the analysis results in graph format. This makes it possible to analyze data and output the results using the selected API. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input a data analysis method into AI and have the AI ​​perform the analysis and output the results.

[0066] The contract management unit can estimate the user's emotions and prioritize contracts based on the estimated user emotions. The contract management unit, for example, estimates the user's emotions and prioritizes contracts based on the estimated user emotions. For example, if the user is feeling stressed, the contract management unit prioritizes important contracts and completes them quickly. The contract management unit can also flexibly adjust contract priorities to match the user's pace when the user is relaxed. Furthermore, if the user is in a hurry, the contract management unit can immediately process the most important contracts and postpone other contracts. This makes it possible to prioritize contracts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the contract management unit may be performed using AI, or without AI. For example, the contract management unit can input user emotion data into AI and have the AI ​​determine the priority of contracts.

[0067] The contract management unit can automatically propose appropriate contract terms by referencing past contract history. The contract management unit, for example, automatically proposes appropriate contract terms by referencing past contract history. For example, the contract management unit proposes similar terms for a new contract based on contract terms that were successful in the past. The contract management unit can also optimize contract terms with a specific API service provider based on past contract history. Furthermore, the contract management unit can analyze past contract history and automatically propose the most favorable contract terms. This makes it possible to automatically propose optimal contract terms based on past contract history. Some or all of the above-mentioned processing in the contract management unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract management unit can input past contract history into AI and have the AI ​​propose optimal contract terms.

[0068] The contract management unit can evaluate the reliability of API service providers and prioritize contracts with reliable providers. For example, the contract management unit evaluates the reliability of API service providers and prioritizes contracts with reliable providers. For example, the contract management unit evaluates the past performance of API service providers and prioritizes reliable providers. The contract management unit can also select reliable providers based on reviews and ratings of API service providers. Furthermore, the contract management unit can analyze the contract history of API service providers and prioritize contracts with reliable providers. This makes it possible to prioritize contracts with reliable API service providers. Some or all of the above-mentioned processing in the contract management unit may be performed using, or without, AI. For example, the contract management unit can input reliability data of API service providers into AI and have the AI ​​evaluate reliability and determine the priority of contracts.

[0069] The contract management unit can automatically notify changes or updates to the contract content and quickly carry out the necessary procedures. The contract management unit, for example, can automatically notify changes or updates to the contract content and quickly carry out the necessary procedures. For example, the contract management unit can automatically notify when changes to the contract content occur and quickly carry out the necessary procedures. The contract management unit can also automatically notify when the contract renewal date is approaching and carry out the renewal procedures. Furthermore, the contract management unit can automate and quickly process procedures associated with changes or updates to the contract content. This makes it possible to automatically notify changes or updates to the contract content and quickly carry out the procedures. Some or all of the above-mentioned processing in the contract management unit may be performed using, for example, AI, or may be performed without AI. For example, the contract management unit can input data on changes or updates to the contract content into AI and have the AI ​​execute the notifications and procedures.

[0070] The contract management unit can estimate the user's emotions and adjust the display method of the contract content based on the estimated user emotions. For example, the contract management unit can estimate the user's emotions and adjust the display method of the contract content based on the estimated user emotions. For example, if the user is nervous, the contract management unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the contract management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the contract management unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the contract content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the contract management unit can be performed using, for example, an AI, or without an AI. For example, the contract management unit can input the user's emotion data into an AI and have the AI ​​adjust the display method of the contract content.

[0071] The contract management unit can select an appropriate contract taking into account the geographic distribution of API service providers. The contract management unit selects an appropriate contract taking into account, for example, the geographic distribution of API service providers. For example, the contract management unit selects the optimal contract taking into account the geographic distribution of API service providers. The contract management unit can also prioritize contracts with geographically close API service providers. Furthermore, the contract management unit can propose optimal contract terms based on the geographic distribution. This makes it possible to select the optimal contract taking into account the geographic distribution of API service providers. Some or all of the above-described processing in the contract management unit may be performed using, or without, AI. For example, the contract management unit can input geographic distribution data of API service providers into AI and have the AI ​​select the optimal contract.

[0072] The contract management department can perform risk assessment of the contract contents and prioritize contracts with low risk. For example, the contract management department can perform risk assessment of the contract contents and prioritize contracts with low risk. The contract management department can also analyze the risks of the contract contents and propose contract terms with low risk. Furthermore, the contract management department can select the optimal contract based on the risk assessment of the contract contents. This makes it possible to perform risk assessment of the contract contents and prioritize contracts with low risk. Some or all of the above-mentioned processing in the contract management department can be performed using, for example, AI, or can be performed without using AI. For example, the contract management department can input risk data of the contract contents into AI and have the AI ​​perform risk assessment and contract selection.

[0073] The contract management department can automatically check the legal requirements of the contract content and minimize legal risks. For example, the contract management department can automatically check the legal requirements of the contract content and minimize legal risks. The contract management department can also check the legal requirements of the contract content and automatically perform the necessary procedures. Furthermore, the contract management department can also propose optimal contract terms based on the legal requirements of the contract content. This makes it possible to automatically check the legal requirements of the contract content and minimize legal risks. Some or all of the above-mentioned processing in the contract management department may be performed using, or without, AI. For example, the contract management department can input legal requirement data of the contract content into AI and have the AI ​​check and minimize legal risks.

[0074] The selection unit can estimate a user's emotions and adjust API selection criteria based on the estimated user emotions. For example, the selection unit estimates a user's emotions and adjusts API selection criteria based on the estimated user emotions. For example, if the user is stressed, the selection unit provides simple selection criteria to minimize the selection procedure. Furthermore, if the user is relaxed, the selection unit can provide detailed selection options and suggest customizable selection criteria. Furthermore, if the user is in a hurry, the selection unit can quickly select the optimal API. This makes it possible to adjust API selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input user emotion data into an AI and have the AI ​​adjust the API selection criteria.

[0075] The selection unit can automatically suggest an appropriate API by referring to past API usage history. The selection unit, for example, automatically suggests an appropriate API by referring to past API usage history. For example, the selection unit may newly suggest a similar API based on past successful API usage history. The selection unit can also optimize APIs from a specific API service provider based on past API usage history. Furthermore, the selection unit can analyze past API usage history and automatically suggest the most advantageous API. This makes it possible to automatically suggest the optimal API based on past API usage history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past API usage history into AI and have the AI ​​suggest the optimal API.

[0076] The selection unit can evaluate the performance of APIs and prioritize APIs with better performance. For example, the selection unit can evaluate the performance of APIs and prioritize APIs with better performance. For example, the selection unit can evaluate the performance of APIs and prioritize APIs with higher performance. The selection unit can also analyze the performance of APIs and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the API performance evaluation. This makes it possible to evaluate the performance of APIs and prioritize APIs with higher performance. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API performance data into AI and cause the AI ​​to perform performance evaluation and API selection.

[0077] The selection unit can select an API with good cost performance taking into account the API usage fees. For example, the selection unit selects an API with good cost performance taking into account the API usage fees. The selection unit can also analyze API usage fees and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the API usage fees. This makes it possible to select an API with good cost performance taking into account the API usage fees. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API usage fee data into AI and have the AI ​​evaluate cost performance and select an API.

[0078] The selection unit can estimate the user's emotions and adjust the selection order of APIs based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and adjusts the selection order of APIs based on the estimated user emotions. For example, when the user is feeling stressed, the selection unit prioritizes selecting important APIs. The selection unit can also flexibly adjust the selection order of APIs when the user is relaxed. Furthermore, when the user is in a hurry, the selection unit can quickly select the most important APIs. This makes it possible to adjust the selection order of APIs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input user emotion data into an AI and have the AI ​​adjust the selection order of APIs.

[0079] The selection unit can evaluate the support systems of API providers and prioritize APIs with good support. For example, the selection unit evaluates the support systems of API providers and prioritizes APIs with good support. For example, the selection unit evaluates the support systems of API providers and prioritizes APIs with solid support. The selection unit can also analyze the support systems of API providers and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the support systems of API providers. This makes it possible to evaluate the support systems of API providers and prioritize APIs with solid support. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input support system data of API providers into AI and cause the AI ​​to evaluate the support systems and select APIs.

[0080] The selection unit can evaluate the security of APIs and select APIs with good security. For example, the selection unit evaluates the security of APIs and selects APIs with good security. For example, the selection unit evaluates the security of APIs and selects APIs with high security. The selection unit can also analyze the security of APIs and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the security evaluation of APIs. This makes it possible to evaluate the security of APIs and select APIs with high security. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input security data of APIs into AI and have the AI ​​perform the security evaluation and select APIs.

[0081] The selection unit can evaluate API compatibility and select an API that is highly compatible with an existing system. The selection unit, for example, evaluates API compatibility and selects an API that is highly compatible with an existing system. For example, the selection unit evaluates API compatibility and selects an API that is highly compatible with an existing system. The selection unit can also analyze API compatibility and propose an optimal API. Furthermore, the selection unit can select an optimal API based on the API compatibility evaluation. This makes it possible to evaluate API compatibility and select an API that is highly compatible with an existing system. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API compatibility data into AI and cause the AI ​​to perform compatibility evaluation and API selection.

[0082] The process management unit can estimate the user's emotions and determine the priority of processes based on the estimated user emotions. For example, the process management unit estimates the user's emotions and determines the priority of processes based on the estimated user emotions. For example, when the user is feeling stressed, the process management unit prioritizes important processes. The process management unit can also flexibly adjust the priority of processes when the user is relaxed. Furthermore, when the user is in a hurry, the process management unit can immediately perform the most important process. This makes it possible to determine the priority of processes based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the process management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the process management unit can input the user's emotion data into an AI and have the AI ​​determine the priority of processes.

[0083] The processing management unit can automatically suggest an optimal processing method by referring to past processing history. The processing management unit, for example, automatically suggests an optimal processing method by referring to past processing history. For example, the processing management unit suggests a similar method for a new process based on a processing method that was successful in the past. The processing management unit can also optimize a specific processing method from past processing history. Furthermore, the processing management unit can analyze past processing history and automatically suggest the most advantageous processing method. This makes it possible to automatically suggest an optimal processing method based on past processing history. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input past processing history into AI and have the AI ​​execute the suggestion of an optimal processing method.

[0084] The processing management unit can evaluate the success rate of the processing and prioritize processing methods with high success rates. For example, the processing management unit evaluates the success rate of the processing and prioritizes processing methods with high success rates. The processing management unit can also analyze the success rate of the processing and propose an optimal processing method. Furthermore, the processing management unit can select an optimal processing method based on the evaluation of the success rate of the processing. This makes it possible to evaluate the success rate of the processing and prioritize processing methods with high success rates. Some or all of the above-mentioned processing in the processing management unit may be performed using, or without, AI. For example, the processing management unit can input processing success rate data into AI and have the AI ​​evaluate the success rate and select a processing method.

[0085] The processing management unit can select an efficient processing method taking into account the execution time of the processing. The processing management unit, for example, selects an efficient processing method taking into account the execution time of the processing. For example, the processing management unit selects an efficient processing method taking into account the execution time of the processing. The processing management unit can also analyze the execution time of the processing and propose an optimal processing method. Furthermore, the processing management unit can select an optimal processing method based on the execution time of the processing. This makes it possible to select an efficient processing method taking into account the execution time of the processing. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input execution time data of the processing into AI and have the AI ​​evaluate the execution time and select a processing method.

[0086] The process management unit can estimate the user's emotions and adjust the display method of the process based on the estimated user emotions. For example, the process management unit estimates the user's emotions and adjusts the display method of the process based on the estimated user emotions. For example, if the user is nervous, the process management unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the process management unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the process management unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the process management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the process management unit can input user emotion data into an AI and have the AI ​​adjust the display method of the process.

[0087] The processing management unit can evaluate the error handling methods of the processing and select a processing method with fewer errors. For example, the processing management unit evaluates the error handling methods of the processing and selects a processing method with fewer errors. For example, the processing management unit evaluates the error handling methods of the processing and selects a processing method with fewer errors. The processing management unit can also analyze the error handling methods of the processing and propose an optimal processing method. Furthermore, the processing management unit can select an optimal processing method based on the error handling methods of the processing. This makes it possible to evaluate the error handling methods of the processing and select a processing method with fewer errors. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input error handling data of the processing into AI and have the AI ​​evaluate the error handling methods and select a processing method.

[0088] The processing management unit can evaluate the data format of the processing and select an appropriate data format. The processing management unit, for example, evaluates the data format of the processing and selects an appropriate data format. For example, the processing management unit evaluates the data format of the processing and selects an optimal data format. The processing management unit can also analyze the data format of the processing and propose an optimal data format. Furthermore, the processing management unit can select an optimal processing method based on the data format of the processing. This makes it possible to evaluate the data format of the processing and select an optimal data format. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input data of the processing data into AI and have the AI ​​evaluate the data format and select a processing method.

[0089] The processing management unit can automatically notify the user of the processing results and improve the processing method by reflecting user feedback. For example, the processing management unit can automatically notify the user of the processing results and improve the processing method by reflecting user feedback. For example, the processing management unit can automatically notify the user of the processing results and improve the processing method based on user feedback. The processing management unit can also notify the user of the processing results in real time and reflect user feedback. Furthermore, the processing management unit can notify the user of the processing results and propose an optimal processing method based on user feedback. This makes it possible to automatically notify the user of the processing results and improve the processing method by reflecting user feedback. Some or all of the above-described processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input processing result data into AI and have the AI ​​perform notification and reflect the feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the contract management unit, selection unit, and processing management unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the contract management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the contract details with the API service provider and automates the necessary procedures. The selection unit is realized, for example, by the control unit 46A of the smart device 14, and selects the optimal API according to the functions required by the generation AI. The processing management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using the selected API and outputs the results. === Hard Collateral 1-2 === Each of the multiple elements, including the contract management unit, selection unit, and processing management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the contract management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the contract details with the API service provider and automates the necessary procedures. The selection unit is realized, for example, by the control unit 46A of the smart glasses 214, and selects the optimal API according to the functions required by the generation AI. The processing management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using the selected API and outputs the results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned contract management unit, selection unit, and processing management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the contract management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the contents of the contract with the API service provider and automates the necessary procedures. The selection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and selects the optimal API depending on the functions required by the generation AI. The processing management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using the selected API and outputs the results. === Hard Collateral 1-4 === Each of the multiple elements including the contract management unit, selection unit, and processing management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the contract management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the contents of the contract with the API service provider and automates the necessary procedures. The selection unit is realized, for example, by the control unit 46A of the robot 414, and selects the optimal API according to the functions required by the generation AI. The processing management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using the selected API and outputs the results.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The contract management unit can estimate the user's emotions and prioritize contracts based on the estimated user emotions. For example, if the user is stressed, important contracts can be prioritized and completed quickly. Furthermore, if the user is relaxed, the contract priorities can be flexibly adjusted to match the user's pace. Furthermore, if the user is in a hurry, the most important contracts can be processed immediately and other contracts can be postponed. This makes it possible to prioritize contracts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the contract management unit can be performed using, for example, AI, or without AI. For example, the contract management unit can input the user's emotion data into AI and have the AI ​​determine the priority of contracts.

[0092] The selection unit can evaluate the performance of APIs and prioritize APIs with good performance. For example, it can evaluate the response time and throughput of APIs and select the most efficient API. It can also prioritize APIs that provide stable services by taking into account the reliability and availability of the APIs. It can also evaluate the security of APIs and select APIs with high security. This enables the selection unit to select the optimal API in terms of performance, reliability, and security. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input API performance data into AI and have the AI ​​perform performance evaluation and API selection.

[0093] The process management unit can estimate the user's emotions and determine the priority of processes based on the estimated user emotions. For example, if the user is feeling stressed, important processes can be prioritized. Furthermore, if the user is relaxed, the process priority can be flexibly adjusted. Furthermore, if the user is in a hurry, the most important processes can be performed immediately. This makes it possible to determine the priority of processes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the process management unit can be performed using, for example, an AI, or without an AI. For example, the process management unit can input user emotion data into an AI and have the AI ​​determine the priority of processes.

[0094] The contract management unit can automatically propose appropriate contract terms by referencing past contract history. For example, it can propose similar terms for a new contract based on contract terms that have been successful in the past. It can also optimize contract terms with a specific API service provider based on past contract history. It can also analyze past contract history and automatically propose the most favorable contract terms. This makes it possible to automatically propose optimal contract terms based on past contract history. Some or all of the above-mentioned processing in the contract management unit may be performed using, for example, AI, or may be performed without using AI. For example, the contract management unit can input past contract history into AI and have the AI ​​propose optimal contract terms.

[0095] The selection unit can estimate the user's emotions and adjust the API selection criteria based on the estimated user emotions. For example, if the user is feeling stressed, simple selection criteria can be provided to minimize the selection procedure. Alternatively, if the user is relaxed, detailed selection options can be provided and customizable selection criteria can be suggested. Furthermore, if the user is in a hurry, the system can quickly select the optimal API. This makes it possible to adjust the API selection criteria based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into an AI and have the AI ​​adjust the API selection criteria.

[0096] The processing management unit can evaluate the success rate of processing and prioritize processing methods with high success rates. For example, it can select a processing method with a high success rate based on past processing history. It can also monitor the success rate of processing in real time and propose the optimal processing method. Furthermore, it can identify areas for improvement in the processing method based on the success rate of processing and continuously optimize the processing method. This makes it possible to evaluate the success rate of processing and prioritize processing methods with high success rates. Some or all of the above-mentioned processing in the processing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing management unit can input processing success rate data into AI and have the AI ​​evaluate the success rate and select the processing method.

[0097] The contract management unit can evaluate the reliability of API service providers and prioritize contracts with reliable providers. For example, it can evaluate the past performance of API service providers and prioritize reliable providers. It can also select reliable providers based on their reviews and ratings. It can also analyze the contract history of API service providers and prioritize contracts with reliable providers. This makes it possible to prioritize contracts with reliable API service providers. Some or all of the above-mentioned processing in the contract management unit can be performed using, for example, AI, or without AI. For example, the contract management unit can input reliability data of API service providers into AI and have the AI ​​evaluate reliability and determine the priority of contracts.

[0098] The process management unit can estimate the user's emotions and adjust the display method of the process based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the process management unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the process management unit can input user emotion data into an AI and have the AI ​​adjust the display method of the process.

[0099] The selection unit can select an API with good cost performance taking into account the API usage fees. For example, the selection unit selects an API with good cost performance taking into account the API usage fees. The selection unit can also analyze the API usage fees and propose the optimal API. Furthermore, the selection unit can select the optimal API based on the API usage fees. This makes it possible to select an API with good cost performance taking into account the API usage fees. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input API usage fee data into AI and have the AI ​​evaluate cost performance and select an API.

[0100] The contract management unit can estimate the user's emotions and adjust the display method of the contract content based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the contract content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract management unit can be performed using, for example, an AI, or without an AI. For example, the contract management unit can input the user's emotion data into an AI and have the AI ​​adjust the display method of the contract content.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The Contract Management Department manages contracts for using external API services. The Contract Management Department manages the details of contracts with API service providers and automates the necessary procedures. The Contract Management Department can also check API usage fees and terms of use and conclude contracts. Step 2: The selection unit selects available API services based on the contracts managed by the contract management unit. The selection unit selects the optimal API depending on the functions required by the generation AI. For example, it can select the optimal API depending on the functions required by the generation AI, such as an API suitable for data analysis or an API suitable for image recognition. Step 3: The processing manager performs the necessary processing using the API service selected by the selector. The processing manager manages the data input and output formats and error handling methods. It can also analyze data using the selected API and output the results.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 contract management department that manages contracts for using external API services; a selection unit that selects available API services based on the contracts managed by the contract management unit; a processing management unit that performs necessary processing using the API service selected by the selection unit; Equipped with A system characterized by:

2. The contract management unit Manage contract details with API service providers and automate necessary procedures 2. The system of claim 1.

3. The selection unit Select the appropriate API depending on the functionality required by the generative AI 2. The system of claim 1.

4. The processing management unit Manage data input and output formats and error handling methods 2. The system of claim 1.

5. The processing management unit Analyze data using the selected API and output the results 2. The system of claim 1.

6. The contract management unit Estimate user sentiment and prioritize contracts based on the estimated user sentiment 2. The system of claim 1.

7. The contract management unit Automatically suggest appropriate contract terms by referencing past contract history 2. The system of claim 1.

8. The contract management unit Evaluate the trustworthiness of API service providers and prioritize contracts with trusted providers 2. The system of claim 1.

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