System

Abstract

An object of the system according to the embodiment is to propose appropriate solutions from countless in-house systems and support sites and to efficiently process similar inquiries.SOLUTION: A system includes a problem input unit, an analysis unit, a proposal unit, and a learning unit. The problem input unit inputs a problem or an issue that the user wants to solve. The analysis unit analyzes the issue or the problem input by the issue input unit. The proposal unit proposes an appropriate system or support destination on the basis of the result analyzed by the analysis unit. The learning unit learns a similar inquiry.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find the appropriate solution among the numerous internal systems and support sites, resulting in the issue of repeated similar inquiries.

[0005] The system according to the embodiment aims to propose appropriate solutions from numerous in-house systems and support sites and to efficiently handle similar inquiries. [Means for solving the problem]

[0006] The system according to the embodiment includes a problem input unit, an analysis unit, a proposal unit, and a learning unit. The problem input unit inputs a problem or issue that a user wants to solve. The analysis unit analyzes the problem or issue input by the problem input unit. The proposal unit proposes an appropriate system or support source based on the results of the analysis by the analysis unit. The learning unit learns from similar inquiries. [Effects of the Invention]

[0007] The system according to the embodiment can propose appropriate solutions from numerous in-house systems and support sites and efficiently handle similar inquiries. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​help desk system according to an embodiment of the present invention is a system that integrates information about numerous internal systems and support sites to efficiently support problem-solving. As a result, the AI ​​help desk system can suggest appropriate systems and support contacts by having users input the issues or problems they want to solve, enabling a prompt response.

[0029] The AI ​​help desk system according to the embodiment includes a problem input unit, an analysis unit, a proposal unit, and a learning unit. The problem input unit inputs a problem or issue that a user wants to solve. For example, the user may input "I'm looking for a new project management tool." The problem input unit may also input "I need a data analysis tool." The problem input unit may also input "I'm thinking about a new marketing strategy." The analysis unit analyzes the problem or issue input by the problem input unit. For example, the analysis unit may analyze the user's input using text analysis technology. The analysis unit may also analyze the user's input using data mining technology. The analysis unit may also analyze the user's input using a machine learning algorithm. The proposal unit proposes an appropriate system or support provider based on the results of the analysis by the analysis unit. For example, the proposal unit may propose an optimal system based on past data. The proposal unit may also propose an appropriate support provider using algorithmic optimization. The proposal unit may also propose an optimal system or support provider based on the user's input. The learning unit learns from similar inquiries. For example, the learning unit learns similar inquiries using supervised learning. Alternatively, the learning unit can also learn similar inquiries using unsupervised learning. Alternatively, the learning unit can also learn similar inquiries using reinforcement learning. This allows the AI ​​help desk system according to the embodiment to integrate information on numerous in-house systems and support sites and efficiently support problem resolution.

[0030] The suggestion unit can refer to past solution cases for a problem and suggest the solution with the highest success rate. For example, when a user inputs "I'm looking for a new project management tool," the generation AI will refer to past cases where similar problems have been solved and suggest the project management tool with the highest success rate. Also, when a user inputs "I need a data analysis tool," the suggestion unit can refer to past use cases of data analysis tools and suggest the most effective tool. Also, when a user inputs "I'm thinking of a new marketing strategy," the suggestion unit can refer to past successful marketing strategies and suggest the strategy with the highest success rate. This allows the suggestion unit to suggest the optimal solution based on past success cases.

[0031] The analysis unit can analyze the user's past inquiry history and provide individually optimized support. For example, when a user inputs, "I don't know how to use the project management tool," the analysis unit causes the generation AI to analyze the past inquiry history and provide the user with the most suitable support information. In addition, when a user inputs, "Please tell me how to set up the data analysis tool," the analysis unit can cause the generation AI to suggest the most suitable setting method for the user based on the past inquiry history. In addition, when a user inputs, "I want to know how to plan a marketing strategy," the analysis unit can cause the generation AI to analyze the past inquiry history and provide the user with the most suitable planning method. This makes it possible to provide the most suitable support based on the user's past inquiry history.

[0032] The problem input unit has a function that allows the user to input problems by voice, and the generation AI can make appropriate suggestions using voice recognition. For example, when the user inputs by voice, "I'm looking for a new project management tool," the generation AI can use voice recognition to suggest an appropriate project management tool. Also, when the user inputs by voice, "I need a data analysis tool," the generation AI can use voice recognition to suggest an appropriate data analysis tool. Also, when the user inputs by voice, "I'm thinking of a new marketing strategy," the generation AI can use voice recognition to suggest an appropriate marketing strategy. This allows the user to input problems by voice and receive appropriate suggestions.

[0033] The analysis unit can refer to the user's schedule and suggest the optimal support time. For example, when a user inputs, "I don't know how to use the project management tool," the analysis unit allows the generation AI to refer to the user's schedule and suggest the optimal support time. Also, when a user inputs, "Please tell me how to set up the data analysis tool," the analysis unit allows the generation AI to refer to the user's schedule and suggest the optimal support time. Also, when a user inputs, "I want to know how to develop a marketing strategy," the analysis unit allows the generation AI to refer to the user's schedule and suggest the optimal support time. This makes it possible to suggest the optimal support time based on the user's schedule.

[0034] The proposal department can monitor the usage of other headquarters' systems in real time and provide the latest information. For example, when a user inputs "I need a data analysis tool," the generation AI in the proposal department can monitor the usage of other headquarters' systems in real time and provide information on the latest data analysis tools. Also, when a user inputs "I'm looking for a project management tool," the generation AI in the proposal department can monitor the usage of other headquarters' systems in real time and provide information on the latest project management tools. Also, when a user inputs "I want to know about marketing strategy tools," the generation AI can monitor the usage of other headquarters' systems in real time and provide information on the latest marketing tools. This makes it possible to provide the latest information on other headquarters' systems in real time.

[0035] The proposal department can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated system to the user. For example, when a user inputs "I need a data analysis tool," the generation AI in the proposal department can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated data analysis tool. Also, when a user inputs "I'm looking for a project management tool," the generation AI in the proposal department can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated project management tool. Also, when a user inputs "I'd like to know about marketing strategy tools," the generation AI can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated marketing tool. This allows the proposal department to suggest the optimal system based on the evaluations and feedback of systems from other headquarters.

[0036] The proposal department can provide a visual demo when introducing the system of another headquarters, allowing the user to intuitively understand how to use the system. For example, when a user inputs "I need a data analysis tool," the proposal department can have the generation AI provide a visual demo of the system of the other headquarters, allowing the user to intuitively understand how to use it. Also, when a user inputs "I'm looking for a project management tool," the proposal department can have the generation AI provide a visual demo of the system of the other headquarters, allowing the user to intuitively understand how to use it. Also, when a user inputs "I want to know about marketing strategy tools," the proposal department can have the generation AI provide a visual demo of the system of the other headquarters, allowing the user to intuitively understand how to use it. This allows the user to intuitively understand how to use the system of the other headquarters.

[0037] The proposal department can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. For example, when a user inputs "I need a data analysis tool," the proposal department can have the generation AI add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, when a user inputs "I'm looking for a project management tool," the proposal department can have the generation AI add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, when a user inputs "I want to know about marketing strategy tools," the proposal department can have the generation AI add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. This allows users to ask questions directly about the systems of the other headquarters.

[0038] The proposal unit can analyze the company's internal organizational chart and identify the most appropriate department and person in charge. For example, when a user inputs, "We are considering a new marketing strategy," the generation AI in the proposal unit can analyze the company's internal organizational chart and identify the most appropriate marketing department and person in charge. Alternatively, when a user inputs, "We are considering introducing a project management tool," the generation AI in the proposal unit can analyze the company's internal organizational chart and identify the most appropriate project management department and person in charge. Alternatively, when a user inputs, "We are considering introducing a data analysis tool," the generation AI in the proposal unit can analyze the company's internal organizational chart and identify the most appropriate data analysis department and person in charge. This allows the optimal department and person in charge to be identified based on the company's internal organizational chart.

[0039] The proposal unit can suggest the optimal department for a user's problem based on past success stories. For example, if a user inputs "I'm thinking about a new marketing strategy," the generation AI can suggest the optimal marketing department based on past success stories. Also, if a user inputs "I'm considering introducing a project management tool," the generation AI can suggest the optimal project management department based on past success stories. Also, if a user inputs "I'm considering introducing a data analysis tool," the generation AI can suggest the optimal data analysis department based on past success stories. This makes it possible to suggest the optimal department based on past success stories.

[0040] The suggestion unit can refer to the user's schedule and suggest the optimal timing to contact the department. For example, when the user inputs "I'm thinking about a new marketing strategy," the suggestion unit's generation AI can refer to the user's schedule and suggest the optimal timing to contact the marketing department. Also, when the user inputs "I'm considering introducing a project management tool," the suggestion unit's generation AI can refer to the user's schedule and suggest the optimal timing to contact the project management department. Also, when the user inputs "I'm considering introducing a data analysis tool," the suggestion unit's generation AI can refer to the user's schedule and suggest the optimal timing to contact the data analysis department. This makes it possible to suggest the optimal timing to contact based on the user's schedule.

[0041] The proposal unit can provide a link that allows the user to directly contact the appropriate department with the proposal. For example, when the user inputs "We are considering a new marketing strategy," the generation AI of the proposal unit can provide a direct contact link to the marketing department. Also, when the user inputs "We are considering introducing a project management tool," the generation AI of the proposal unit can provide a direct contact link to the project management department. Also, when the user inputs "We are considering introducing a data analysis tool," the generation AI can provide a direct contact link to the data analysis department. This allows the user to directly contact the appropriate department.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can analyze the user's past inquiry history and provide individually optimized support. For example, if a user inputs, "I don't know how to use the project management tool," the generation AI will analyze the past inquiry history and provide the user with the most suitable support information. In addition, if a user inputs, "Please tell me how to set up the data analysis tool," the analysis unit can also suggest the most suitable setting method for the user based on the past inquiry history. In addition, if a user inputs, "I want to know how to plan a marketing strategy," the analysis unit can also analyze the past inquiry history and provide the user with the most suitable planning method. This makes it possible to provide the most suitable support based on the user's past inquiry history.

[0044] The proposal department can monitor the usage of other headquarters' systems in real time and provide the latest information. For example, if a user inputs, "I need a data analysis tool," the generation AI can monitor the usage of other headquarters' systems in real time and provide information on the latest data analysis tools. Also, if a user inputs, "I'm looking for a project management tool," the proposal department can use the generation AI to monitor the usage of other headquarters' systems in real time and provide information on the latest project management tools. Also, if a user inputs, "I want to know about marketing strategy tools," the generation AI can monitor the usage of other headquarters' systems in real time and provide information on the latest marketing tools. This makes it possible to provide the latest information on other headquarters' systems in real time.

[0045] The proposal department can collect system evaluations and feedback from other headquarters and suggest the most highly rated system to the user. For example, if a user inputs "I need a data analysis tool," the generation AI can collect system evaluations and feedback from other headquarters and suggest the most highly rated data analysis tool. Also, if a user inputs "I'm looking for a project management tool," the proposal department can have the generation AI collect system evaluations and feedback from other headquarters and suggest the most highly rated project management tool. Also, if a user inputs "I'd like to know about marketing strategy tools," the proposal department can have the generation AI collect system evaluations and feedback from other headquarters and suggest the most highly rated marketing tool. This allows the proposal department to suggest the optimal system based on the system evaluations and feedback from other headquarters.

[0046] The proposal department can provide a visual demo when introducing the other headquarters' system, allowing the user to intuitively understand how to use the system. For example, if a user inputs, "I need a data analysis tool," the generation AI can provide a visual demo of the other headquarters' system, allowing the user to intuitively understand how to use it. Also, if a user inputs, "I'm looking for a project management tool," the proposal department can provide a visual demo of the other headquarters' system, allowing the user to intuitively understand how to use it. Also, if a user inputs, "I want to know about marketing strategy tools," the generation AI can provide a visual demo of the other headquarters' system, allowing the user to intuitively understand how to use it. This allows the user to intuitively understand how to use the other headquarters' system.

[0047] The proposal department can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. For example, if a user inputs, "I need a data analysis tool," the generation AI can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, if a user inputs, "I'm looking for a project management tool," the proposal department can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, if a user inputs, "I'd like to know about marketing strategy tools," the generation AI can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. This allows users to ask questions directly about the systems of the other headquarters.

[0048] The proposal department can analyze the company's organizational chart and identify the most appropriate department and person in charge. For example, if a user inputs, "We are considering a new marketing strategy," the generation AI can analyze the company's organizational chart and identify the most appropriate marketing department and person in charge. Alternatively, if a user inputs, "We are considering introducing a project management tool," the generation AI can analyze the company's organizational chart and identify the most appropriate project management department and person in charge. Alternatively, if a user inputs, "We are considering introducing a data analysis tool," the generation AI can analyze the company's organizational chart and identify the most appropriate data analysis department and person in charge. This allows the optimal department and person in charge to be identified based on the company's organizational chart.

[0049] The proposal unit can suggest the optimal department for a user's problem based on past success stories. For example, if a user inputs, "I'm thinking about a new marketing strategy," the generation AI can suggest the optimal marketing department based on past success stories. Also, if a user inputs, "I'm considering introducing a project management tool," the proposal unit can suggest the optimal project management department based on past success stories. Also, if a user inputs, "I'm considering introducing a data analysis tool," the generation AI can suggest the optimal data analysis department based on past success stories. This makes it possible to suggest the optimal department based on past success stories.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: In the problem input section, the user inputs the problem or issue they want to solve. For example, the user might input, "I'm looking for a new project management tool." The user could also input, "I need a data analysis tool." The user could also input, "I'm thinking about a new marketing strategy." Step 2: The analysis unit analyzes the tasks and problems entered by the task input unit. For example, it analyzes the user's input using text analysis technology, data mining technology, and machine learning algorithms. Step 3: The proposal unit proposes an appropriate system or support provider based on the results of the analysis by the analysis unit. For example, it can propose an optimal system based on past data or propose an appropriate support provider using algorithmic optimization. Step 4: The learning unit learns similar queries. For example, the learning unit can use supervised learning, unsupervised learning, or reinforcement learning to learn similar queries.

[0052] (Example 2) The AI ​​help desk system according to an embodiment of the present invention is a system that integrates information about numerous internal systems and support sites to efficiently support problem-solving. As a result, the AI ​​help desk system can suggest appropriate systems and support contacts by having users input the issues or problems they want to solve, enabling a prompt response.

[0053] The AI ​​help desk system according to the embodiment includes a problem input unit, an analysis unit, a proposal unit, and a learning unit. The problem input unit inputs a problem or issue that a user wants to solve. For example, the user may input "I'm looking for a new project management tool." The problem input unit may also input "I need a data analysis tool." The problem input unit may also input "I'm thinking about a new marketing strategy." The analysis unit analyzes the problem or issue input by the problem input unit. For example, the analysis unit may analyze the user's input using text analysis technology. The analysis unit may also analyze the user's input using data mining technology. The analysis unit may also analyze the user's input using a machine learning algorithm. The proposal unit proposes an appropriate system or support provider based on the results of the analysis by the analysis unit. For example, the proposal unit may propose an optimal system based on past data. The proposal unit may also propose an appropriate support provider using algorithmic optimization. The proposal unit may also propose an optimal system or support provider based on the user's input. The learning unit learns from similar inquiries. For example, the learning unit learns similar inquiries using supervised learning. Alternatively, the learning unit can also learn similar inquiries using unsupervised learning. Alternatively, the learning unit can also learn similar inquiries using reinforcement learning. This allows the AI ​​help desk system according to the embodiment to integrate information on numerous in-house systems and support sites and efficiently support problem resolution.

[0054] The suggestion unit can refer to past solution cases for a problem and suggest the solution with the highest success rate. For example, when a user inputs "I'm looking for a new project management tool," the generation AI will refer to past cases where similar problems have been solved and suggest the project management tool with the highest success rate. Also, when a user inputs "I need a data analysis tool," the suggestion unit can refer to past use cases of data analysis tools and suggest the most effective tool. Also, when a user inputs "I'm thinking of a new marketing strategy," the suggestion unit can refer to past successful marketing strategies and suggest the strategy with the highest success rate. This allows the suggestion unit to suggest the optimal solution based on past success cases.

[0055] The analysis unit can analyze the user's past inquiry history and provide individually optimized support. For example, when a user inputs, "I don't know how to use the project management tool," the analysis unit causes the generation AI to analyze the past inquiry history and provide the user with the most suitable support information. In addition, when a user inputs, "Please tell me how to set up the data analysis tool," the analysis unit can cause the generation AI to suggest the most suitable setting method for the user based on the past inquiry history. In addition, when a user inputs, "I want to know how to plan a marketing strategy," the analysis unit can cause the generation AI to analyze the past inquiry history and provide the user with the most suitable planning method. This makes it possible to provide the most suitable support based on the user's past inquiry history.

[0056] The analysis unit can use the emotion estimation function to analyze the user's emotions when inputting information and suggest support methods to reduce stress. For example, when a user inputs "I don't know how to use the project management tool," the emotion estimation function can detect the user's stress and suggest support methods to help the user relax. Furthermore, when a user inputs "Please tell me how to set up the data analysis tool," the emotion estimation function can detect the user's anxiety and suggest support methods to give the user a sense of security. Furthermore, when a user inputs "I want to know how to develop a marketing strategy," the emotion estimation function can detect the user's impatience and suggest support methods to help the user respond calmly. In this way, the analysis unit can analyze the user's emotions and suggest support methods to reduce stress.

[0057] The problem input unit has a function that allows the user to input problems by voice, and the generation AI can make appropriate suggestions using voice recognition. For example, when the user inputs by voice, "I'm looking for a new project management tool," the generation AI can use voice recognition to suggest an appropriate project management tool. Also, when the user inputs by voice, "I need a data analysis tool," the generation AI can use voice recognition to suggest an appropriate data analysis tool. Also, when the user inputs by voice, "I'm thinking of a new marketing strategy," the generation AI can use voice recognition to suggest an appropriate marketing strategy. This allows the user to input problems by voice and receive appropriate suggestions.

[0058] The analysis unit can refer to the user's schedule and suggest the optimal support time. For example, when a user inputs, "I don't know how to use the project management tool," the analysis unit allows the generation AI to refer to the user's schedule and suggest the optimal support time. Also, when a user inputs, "Please tell me how to set up the data analysis tool," the analysis unit allows the generation AI to refer to the user's schedule and suggest the optimal support time. Also, when a user inputs, "I want to know how to develop a marketing strategy," the analysis unit allows the generation AI to refer to the user's schedule and suggest the optimal support time. This makes it possible to suggest the optimal support time based on the user's schedule.

[0059] The analysis unit can use the emotion estimation function to analyze the emotion of the user when inputting information in real time and provide positive feedback. For example, when the user inputs "I don't know how to use the project management tool," the emotion estimation function can analyze the user's emotion in real time and provide positive feedback. Furthermore, when the user inputs "Please tell me how to set up the data analysis tool," the emotion estimation function can analyze the user's emotion in real time and provide positive feedback. Furthermore, when the user inputs "I want to know how to develop a marketing strategy," the emotion estimation function can analyze the user's emotion in real time and provide positive feedback. In this way, the user's emotion can be analyzed in real time and positive feedback can be provided.

[0060] The proposal department can monitor the usage of other headquarters' systems in real time and provide the latest information. For example, when a user inputs "I need a data analysis tool," the generation AI in the proposal department can monitor the usage of other headquarters' systems in real time and provide information on the latest data analysis tools. Also, when a user inputs "I'm looking for a project management tool," the generation AI in the proposal department can monitor the usage of other headquarters' systems in real time and provide information on the latest project management tools. Also, when a user inputs "I want to know about marketing strategy tools," the generation AI can monitor the usage of other headquarters' systems in real time and provide information on the latest marketing tools. This makes it possible to provide the latest information on other headquarters' systems in real time.

[0061] The proposal department can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated system to the user. For example, when a user inputs "I need a data analysis tool," the generation AI in the proposal department can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated data analysis tool. Also, when a user inputs "I'm looking for a project management tool," the generation AI in the proposal department can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated project management tool. Also, when a user inputs "I'd like to know about marketing strategy tools," the generation AI can collect evaluations and feedback of systems from other headquarters and suggest the most highly rated marketing tool. This allows the proposal department to suggest the optimal system based on the evaluations and feedback of systems from other headquarters.

[0062] The proposal unit can use the emotion estimation function to analyze the user's emotional response to the systems of other headquarters and introduce the system with the highest satisfaction rate. For example, when a user inputs, "I need a data analysis tool," the emotion estimation function analyzes the user's emotional response to the systems of other headquarters and introduces the data analysis tool with the highest satisfaction rate. Also, when a user inputs, "I'm looking for a project management tool," the emotion estimation function can analyze the user's emotional response to the systems of other headquarters and introduce the project management tool with the highest satisfaction rate. Also, when a user inputs, "I want to know about marketing strategy tools," the emotion estimation function can analyze the user's emotional response to the systems of other headquarters and introduce the marketing tool with the highest satisfaction rate. In this way, the proposal unit can introduce the optimal system based on the user's emotional response to the systems of other headquarters.

[0063] The proposal department can provide a visual demo when introducing the system of another headquarters, allowing the user to intuitively understand how to use the system. For example, when a user inputs "I need a data analysis tool," the proposal department can have the generation AI provide a visual demo of the system of the other headquarters, allowing the user to intuitively understand how to use it. Also, when a user inputs "I'm looking for a project management tool," the proposal department can have the generation AI provide a visual demo of the system of the other headquarters, allowing the user to intuitively understand how to use it. Also, when a user inputs "I want to know about marketing strategy tools," the proposal department can have the generation AI provide a visual demo of the system of the other headquarters, allowing the user to intuitively understand how to use it. This allows the user to intuitively understand how to use the system of the other headquarters.

[0064] The proposal department can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. For example, when a user inputs "I need a data analysis tool," the proposal department can have the generation AI add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, when a user inputs "I'm looking for a project management tool," the proposal department can have the generation AI add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, when a user inputs "I want to know about marketing strategy tools," the proposal department can have the generation AI add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. This allows users to ask questions directly about the systems of the other headquarters.

[0065] The suggestion unit can use the emotion estimation function to analyze the user's emotions when receiving an introduction to the system and provide additional information at the optimal timing. For example, when the user inputs "I need a data analysis tool," the emotion estimation function analyzes the user's emotions and provides additional information at the optimal timing. Also, when the user inputs "I'm looking for a project management tool," the emotion estimation function can analyze the user's emotions and provide additional information at the optimal timing. Also, when the user inputs "I want to know about marketing strategy tools," the emotion estimation function can analyze the user's emotions and provide additional information at the optimal timing. In this way, the user's emotions can be analyzed and additional information can be provided at the optimal timing.

[0066] The proposal unit can analyze the company's internal organizational chart and identify the most appropriate department and person in charge. For example, when a user inputs, "We are considering a new marketing strategy," the generation AI in the proposal unit can analyze the company's internal organizational chart and identify the most appropriate marketing department and person in charge. Alternatively, when a user inputs, "We are considering introducing a project management tool," the generation AI in the proposal unit can analyze the company's internal organizational chart and identify the most appropriate project management department and person in charge. Alternatively, when a user inputs, "We are considering introducing a data analysis tool," the generation AI in the proposal unit can analyze the company's internal organizational chart and identify the most appropriate data analysis department and person in charge. This allows the optimal department and person in charge to be identified based on the company's internal organizational chart.

[0067] The proposal unit can suggest the optimal department for a user's problem based on past success stories. For example, if a user inputs "I'm thinking about a new marketing strategy," the generation AI can suggest the optimal marketing department based on past success stories. Also, if a user inputs "I'm considering introducing a project management tool," the generation AI can suggest the optimal project management department based on past success stories. Also, if a user inputs "I'm considering introducing a data analysis tool," the generation AI can suggest the optimal data analysis department based on past success stories. This makes it possible to suggest the optimal department based on past success stories.

[0068] The suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest the least stressful approach. For example, when the user inputs "I'm thinking about a new marketing strategy," the emotion estimation function can detect the user's stress and suggest the least stressful approach. Also, when the user inputs "I'm considering introducing a project management tool," the emotion estimation function can detect the user's anxiety and suggest the least stressful approach. Also, when the user inputs "I'm considering introducing a data analysis tool," the emotion estimation function can detect the user's impatience and suggest the least stressful approach. In this way, the suggestion unit can analyze the user's emotions and suggest the least stressful approach.

[0069] The suggestion unit can refer to the user's schedule and suggest the optimal timing to contact the department. For example, when the user inputs "I'm thinking about a new marketing strategy," the suggestion unit's generation AI can refer to the user's schedule and suggest the optimal timing to contact the marketing department. Also, when the user inputs "I'm considering introducing a project management tool," the suggestion unit's generation AI can refer to the user's schedule and suggest the optimal timing to contact the project management department. Also, when the user inputs "I'm considering introducing a data analysis tool," the suggestion unit's generation AI can refer to the user's schedule and suggest the optimal timing to contact the data analysis department. This makes it possible to suggest the optimal timing to contact based on the user's schedule.

[0070] The proposal unit can provide a link that allows the user to directly contact the appropriate department with the proposal. For example, when the user inputs "We are considering a new marketing strategy," the generation AI of the proposal unit can provide a direct contact link to the marketing department. Also, when the user inputs "We are considering introducing a project management tool," the generation AI of the proposal unit can provide a direct contact link to the project management department. Also, when the user inputs "We are considering introducing a data analysis tool," the generation AI can provide a direct contact link to the data analysis department. This allows the user to directly contact the appropriate department.

[0071] The suggestion unit can use the emotion estimation function to analyze the emotion of the user when receiving a suggestion and provide optimal feedback. For example, when the user inputs "I'm thinking about a new marketing strategy," the emotion estimation function analyzes the user's emotion and provides optimal feedback. Also, when the user inputs "I'm considering introducing a project management tool," the emotion estimation function can analyze the user's emotion and provide optimal feedback. Also, when the user inputs "I'm considering introducing a data analysis tool," the emotion estimation function can analyze the user's emotion and provide optimal feedback. In this way, the user's emotion can be analyzed and optimal feedback can be provided.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The analysis unit can analyze the user's past inquiry history and provide individually optimized support. For example, if a user inputs, "I don't know how to use the project management tool," the generation AI will analyze the past inquiry history and provide the user with the most suitable support information. In addition, if a user inputs, "Please tell me how to set up the data analysis tool," the analysis unit can also suggest the most suitable setting method for the user based on the past inquiry history. In addition, if a user inputs, "I want to know how to plan a marketing strategy," the analysis unit can also analyze the past inquiry history and provide the user with the most suitable planning method. This makes it possible to provide the most suitable support based on the user's past inquiry history.

[0074] The proposal department can monitor the usage of other headquarters' systems in real time and provide the latest information. For example, if a user inputs, "I need a data analysis tool," the generation AI can monitor the usage of other headquarters' systems in real time and provide information on the latest data analysis tools. Also, if a user inputs, "I'm looking for a project management tool," the proposal department can use the generation AI to monitor the usage of other headquarters' systems in real time and provide information on the latest project management tools. Also, if a user inputs, "I want to know about marketing strategy tools," the generation AI can monitor the usage of other headquarters' systems in real time and provide information on the latest marketing tools. This makes it possible to provide the latest information on other headquarters' systems in real time.

[0075] The proposal department can collect system evaluations and feedback from other headquarters and suggest the most highly rated system to the user. For example, if a user inputs "I need a data analysis tool," the generation AI can collect system evaluations and feedback from other headquarters and suggest the most highly rated data analysis tool. Also, if a user inputs "I'm looking for a project management tool," the proposal department can have the generation AI collect system evaluations and feedback from other headquarters and suggest the most highly rated project management tool. Also, if a user inputs "I'd like to know about marketing strategy tools," the proposal department can have the generation AI collect system evaluations and feedback from other headquarters and suggest the most highly rated marketing tool. This allows the proposal department to suggest the optimal system based on the system evaluations and feedback from other headquarters.

[0076] The proposal unit can use the emotion estimation function to analyze the user's emotional response to systems from other headquarters and introduce the system with the highest satisfaction rate. For example, when a user inputs, "I need a data analysis tool," the emotion estimation function analyzes the user's emotional response to systems from other headquarters and introduces the data analysis tool with the highest satisfaction rate. Also, when a user inputs, "I'm looking for a project management tool," the emotion estimation function can analyze the user's emotional response to systems from other headquarters and introduce the project management tool with the highest satisfaction rate. Also, when a user inputs, "I want to know about marketing strategy tools," the emotion estimation function can analyze the user's emotional response to systems from other headquarters and introduce the marketing tool with the highest satisfaction rate. In this way, the proposal unit can introduce the optimal system based on the user's emotional response to systems from other headquarters.

[0077] The proposal department can provide a visual demo when introducing the other headquarters' system, allowing the user to intuitively understand how to use the system. For example, if a user inputs, "I need a data analysis tool," the generation AI can provide a visual demo of the other headquarters' system, allowing the user to intuitively understand how to use it. Also, if a user inputs, "I'm looking for a project management tool," the proposal department can provide a visual demo of the other headquarters' system, allowing the user to intuitively understand how to use it. Also, if a user inputs, "I want to know about marketing strategy tools," the generation AI can provide a visual demo of the other headquarters' system, allowing the user to intuitively understand how to use it. This allows the user to intuitively understand how to use the other headquarters' system.

[0078] The proposal department can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. For example, if a user inputs, "I need a data analysis tool," the generation AI can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, if a user inputs, "I'm looking for a project management tool," the proposal department can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. Also, if a user inputs, "I'd like to know about marketing strategy tools," the generation AI can add a chat function to the system introduction of the other headquarters, allowing users to ask questions directly. This allows users to ask questions directly about the systems of the other headquarters.

[0079] The suggestion unit can use the emotion estimation function to analyze the user's emotions when receiving an introduction to the system and provide additional information at the optimal timing. For example, when a user inputs "I need a data analysis tool," the emotion estimation function analyzes the user's emotions and provides additional information at the optimal timing. Also, when a user inputs "I'm looking for a project management tool," the suggestion unit can analyze the user's emotions and provide additional information at the optimal timing. Also, when a user inputs "I want to know about marketing strategy tools," the emotion estimation function can analyze the user's emotions and provide additional information at the optimal timing. In this way, the user's emotions can be analyzed and additional information can be provided at the optimal timing.

[0080] The proposal department can analyze the company's organizational chart and identify the most appropriate department and person in charge. For example, if a user inputs, "We are considering a new marketing strategy," the generation AI can analyze the company's organizational chart and identify the most appropriate marketing department and person in charge. Alternatively, if a user inputs, "We are considering introducing a project management tool," the generation AI can analyze the company's organizational chart and identify the most appropriate project management department and person in charge. Alternatively, if a user inputs, "We are considering introducing a data analysis tool," the generation AI can analyze the company's organizational chart and identify the most appropriate data analysis department and person in charge. This allows the optimal department and person in charge to be identified based on the company's organizational chart.

[0081] The proposal unit can suggest the optimal department for a user's problem based on past success stories. For example, if a user inputs, "I'm thinking about a new marketing strategy," the generation AI can suggest the optimal marketing department based on past success stories. Also, if a user inputs, "I'm considering introducing a project management tool," the proposal unit can suggest the optimal project management department based on past success stories. Also, if a user inputs, "I'm considering introducing a data analysis tool," the generation AI can suggest the optimal data analysis department based on past success stories. This makes it possible to suggest the optimal department based on past success stories.

[0082] The suggestion unit can use the emotion estimation function to analyze the user's emotions and suggest the least stressful approach. For example, when a user inputs "I'm thinking about a new marketing strategy," the emotion estimation function can detect the user's stress and suggest the least stressful approach. Furthermore, when a user inputs "I'm considering introducing a project management tool," the suggestion unit can detect the user's anxiety and suggest the least stressful approach. Furthermore, when a user inputs "I'm considering introducing a data analysis tool," the emotion estimation function can detect the user's impatience and suggest the least stressful approach. In this way, the suggestion unit can analyze the user's emotions and suggest the least stressful approach.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: In the problem input section, the user inputs the problem or issue they want to solve. For example, the user might input, "I'm looking for a new project management tool." The user could also input, "I need a data analysis tool." The user could also input, "I'm thinking about a new marketing strategy." Step 2: The analysis unit analyzes the tasks and problems entered by the task input unit. For example, it analyzes the user's input using text analysis technology, data mining technology, and machine learning algorithms. Step 3: The proposal unit proposes an appropriate system or support provider based on the results of the analysis by the analysis unit. For example, it can propose an optimal system based on past data or propose an appropriate support provider using algorithmic optimization. Step 4: The learning unit learns similar queries. For example, the learning unit can use supervised learning, unsupervised learning, or reinforcement learning to learn similar queries.

[0085] 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.

[0086] 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.

[0087] 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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 task input section where a user inputs a task or problem they want to solve; an analysis unit that analyzes the tasks and problems input by the task input unit; a proposal unit that proposes an appropriate system or support provider based on the results of the analysis by the analysis unit; a learning unit that learns similar queries; A system characterized by:

2. The proposal unit For the above issues, we will refer to past solutions and propose the most successful solution.

2. The system of claim 1.

3. The analysis unit Analyze users' past inquiry history to provide individually optimized support 2. The system of claim 1.

4. The analysis unit Analyzing the user's emotions when typing and proposing support methods to reduce stress 2. The system of claim 1.

5. The task input unit It has a function that allows users to input tasks by voice, Generative AI uses voice recognition to make appropriate suggestions 2. The system of claim 1.

6. The analysis unit Refer to the user's schedule and suggest the best support time 2. The system of claim 1.

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