System

Abstract

An object of a system according to an embodiment is to safely use company information and reduce a Al Harthi risk.SOLUTION: A system includes a response generation unit and a data verification unit. The response generation unit generates a response based on the decision tree. The data verification unit verifies the accuracy of the response generated by the response generation unit by collating the response with the company data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies pose the risk of hallucination and misuse of corporate data in conversational AI responses.

[0005] The system according to the embodiment aims to ensure the safe use of corporate data and reduce the risk of hallucination. [Means for solving the problem]

[0006] The system according to the embodiment includes a response generation unit and a data verification unit. The response generation unit generates a response based on a decision tree. The data verification unit verifies the accuracy of the response generated by the response generation unit by checking it against company data. [Effects of the Invention]

[0007] The system according to the embodiment enables safe use of corporate data and reduces the risk of hallucination. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 ​​response system according to an embodiment of the present invention provides decision tree-based response generation and data verification functions while ensuring the safe use of corporate data, thereby reducing the risk of hallucination and misuse of corporate data in conversational AI technology.

[0029] An AI response system according to an embodiment includes a response generation unit and a data verification unit. The response generation unit generates a response based on a decision tree. For example, if a user asks, "What is the price of the new product?", the response generation unit generates an appropriate response including price information using the decision tree. Furthermore, if a user asks, "What were last year's sales figures?", the response generation unit obtains sales data from a corporate database, verifies the accuracy of the data, and then generates a response. The data verification unit verifies the accuracy of the response generated by the response generation unit by comparing it with corporate data. For example, the data verification unit verifies the accuracy of the response based on data obtained from the corporate database. The data verification unit can also cross-check multiple data sources to improve data accuracy. This enables the AI ​​response system to safely use corporate data and reduce the risk of hallucination.

[0030] The response generation unit can generate more personalized responses by reflecting the user's past dialogue history. For example, the response generation unit analyzes the user's past dialogue history and extracts frequently used keywords and phrases. This allows the user's preferences and interests to be reflected in the branching conditions of the decision tree, generating more personalized responses. This makes it possible to generate more personalized responses by reflecting the user's past dialogue history.

[0031] The response generation unit can dynamically change the structure of the decision tree to generate the most appropriate response in real time. The response generation unit develops an algorithm that changes the structure of the decision tree in real time, for example, in response to user input. For example, when a user asks a new question, a new node corresponding to that question is dynamically added. This allows the structure of the decision tree to be dynamically changed, enabling the most appropriate response in real time.

[0032] The response generation unit can construct the multilingual decision tree to accommodate different languages ​​or cultural areas. The response generation unit, for example, constructs a multilingual decision tree to enable response generation in different languages. For example, a decision tree is created that supports multiple languages, such as English, Japanese, and French. This enables responses that are compatible with different languages ​​and cultural areas.

[0033] The response generation unit can provide region-specific information by reflecting the user's geographical location information. For example, the response generation unit acquires the user's geographical location information and sets branching conditions for the decision tree based on that information. For example, if the user is in a specific city, information related to that city is provided. This allows the user's geographical location information to be reflected, making it possible to provide region-specific information.

[0034] The generation AI can automatically make completion suggestions in response to the user's input prompt and suggest specific questions. For example, the generation AI analyzes the prompt entered by the user and automatically completes related keywords and phrases. For example, if the user enters "of a new product," it will suggest a specific question such as "What is the price of the new product?" This allows the AI ​​to automatically make completion suggestions in response to the user's input prompt, encouraging the user to ask more specific questions.

[0035] The generation AI can analyze the user's input prompt and generate a response based on past dialogue history. For example, the generation AI analyzes the user's input prompt and extracts relevant information from the past dialogue history. For example, if the user inputs "What were the sales figures last year?", the generation AI will refer to information about sales from the past dialogue history. This allows the generation AI to analyze the user's input prompt and refer to the past dialogue history to generate a more appropriate response.

[0036] The generation AI can automatically translate a user's input prompt into different languages ​​and generate responses that support multiple languages. The generation AI, for example, builds a system that automatically translates a user's input prompt and generates responses in different languages. For example, a prompt entered in English can be translated into Japanese and a response can be generated in Japanese. This makes it possible to automatically translate into different languages ​​and generate responses that support multiple languages.

[0037] Generative AI can suggest related images or videos in response to a user's input prompt, providing visual information. For example, generative AI can analyze a user's input prompt and build a system that automatically suggests related images and videos. For example, if a user inputs "What is the design of the new product?", it can suggest product design images. This makes it possible to suggest related images and videos in response to a user's input prompt, providing visual information.

[0038] Before generating a response, generative AI can cross-check multiple data sources to increase the accuracy of the data. For example, before generating a response, generative AI can cross-check multiple data sources to confirm the consistency of the data. For example, it can refer to corporate databases, public databases, news articles, etc. to verify the accuracy of the data. This cross-checking multiple data sources can increase the accuracy of the data.

[0039] Generative AI can perform the data verification process in real time, minimizing delays in response generation. For example, generative AI can build a system that performs the data verification process in real time, minimizing delays in response generation. For example, it can optimize database queries to achieve rapid data verification. This makes it possible to minimize delays in response generation by performing the data verification process in real time.

[0040] Generative AI can link its data verification function with databases from different industries and fields, enabling verification from a wide range of data sources. For example, generative AI can link with databases from different industries and fields to build a system that verifies data from a wide range of data sources. For example, it can integrate medical databases, financial databases, and technology databases. This makes it possible to verify from a wide range of data sources by linking with databases from different industries and fields.

[0041] Generative AI can refer to relevant external data sources when verifying data. For example, generative AI can build a system that refers to relevant external data sources when verifying data. For example, it can automatically collect public databases and news articles and check the accuracy of the data. This makes it possible to improve the accuracy of data verification by referring to relevant external data sources.

[0042] When generating a response, the generative AI can generate multiple candidate responses and select the most reliable response from among them. For example, the generative AI can develop an algorithm that generates multiple candidate responses and selects the most reliable response from among them. For example, it can calculate a reliability score for each candidate response and select the response with the highest score. This allows the risk of hallucination to be reduced by generating multiple candidate responses and selecting the most reliable response from among them.

[0043] Generative AI can dynamically update predefined rule sets when generating responses. For example, generative AI develops algorithms that dynamically update predefined rule sets when generating responses. For example, if new data is added, the rule set is updated based on that data. This makes it possible to reduce the risk of hallucination by dynamically updating predefined rule sets.

[0044] When generating a response, generative AI can combine different algorithms to generate a response. For example, generative AI can build a system that generates a response by combining different algorithms. For example, it can combine a decision tree algorithm with a neural network algorithm. This allows the risk of hallucination to be reduced by combining different algorithms.

[0045] The generative AI can reflect user feedback in real time when generating a response. For example, the generative AI can build a system that reflects user feedback in real time when generating a response. For example, the user can evaluate the response, and the response can be adjusted based on that evaluation. In this way, the risk of hallucination can be reduced by reflecting user feedback in real time.

[0046] It is possible to dynamically manage access rights to corporate data and change the access rights according to the user's position or job content. A system that dynamically manages access rights to corporate data can be built, and access rights can be changed according to the user's position or job content. For example, broad access rights can be granted to managers, and limited access rights can be set for general employees. In this way, by dynamically managing access rights to corporate data, it is possible to grant appropriate access rights according to the user's position and job content.

[0047] It is possible to introduce algorithms that monitor corporate data usage history in real time and detect unauthorized use. It is possible to develop algorithms that monitor corporate data usage history in real time and detect unauthorized use. For example, it can detect abnormal access patterns and issue an alert. This makes it possible to monitor data usage history in real time and detect unauthorized use, thereby ensuring the safe use of corporate data.

[0048] To ensure the safe use of corporate data, security measures can be strengthened by referring to security protocols from different industries and fields. To ensure the safe use of corporate data, security measures can be strengthened by referring to security protocols from different industries and fields. For example, security standards from the financial industry can be adopted. By referring to security protocols from different industries and fields, security measures can be strengthened by referring to security protocols from different industries and fields.

[0049] Generative AI can refer to relevant external data sources to ensure the safe use of corporate data. For example, generative AI can build a system that references relevant external data sources to ensure the safe use of corporate data. For example, it can automatically collect public databases and news articles and verify the accuracy of the data. This makes it possible to ensure the safe use of corporate data by referencing relevant external data sources.

[0050] Generative AI can refer to relevant external data sources to ensure the safe use of corporate data. For example, generative AI can build a system that references relevant external data sources to ensure the safe use of corporate data. For example, it can automatically collect public databases and news articles and verify the accuracy of the data. This makes it possible to ensure the safe use of corporate data by referencing relevant external data sources.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The response generation unit can provide relevant news articles and the latest industry trends based on the user's input. For example, if a user asks, "What are the market trends for our new product?", the response generation unit will refer to the latest news articles and market reports and provide relevant information. Also, if a user asks, "What are our competitors doing?", the response generation unit can provide the latest news and analytical reports about competitors. This allows users to make decisions based on the latest information.

[0053] The response generation unit can provide related academic papers and research results based on the user's input. For example, if a user asks, "What are the research results for new technologies?", the response generation unit will refer to related academic papers and research results and provide the latest information. Also, if a user asks, "What are the research trends for a specific technology?", the response generation unit can provide related research papers and review articles. This allows users to obtain information based on the latest research results.

[0054] The response generation unit can provide relevant laws, regulations, and guidelines based on the user's input. For example, if a user asks, "What are the laws and regulations for a new product?", the response generation unit will refer to the relevant laws, regulations, and guidelines and provide appropriate information. Also, if a user asks, "What are the regulatory trends in a specific industry?", the unit can provide the latest laws, regulations, and guidelines. This allows the user to take appropriate action based on laws and regulations.

[0055] The response generation unit can provide related patent information and technical literature based on the user's input. For example, if a user asks, "What is the patent information for new technology?", the response generation unit will refer to related patent information and provide the latest information. Also, if a user asks, "What are the patent trends for a specific technology?", it can provide related patent documents and technical reports. This allows users to create technology strategies based on patent information.

[0056] The response generation unit can provide relevant market research reports and consumer trends based on the user's input. For example, if a user asks, "What is the market research report for new products?", the response generation unit will refer to the relevant market research report and provide the latest information. Also, if a user asks, "What are consumer purchasing trends?", the response generation unit can provide the relevant consumer trend report. This allows the user to create a strategy based on market research.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The response generator generates a response based on a decision tree. For example, if a user asks, "What is the price of the new product?", the response generator uses the decision tree to generate an appropriate response that includes price information. Similarly, if a user asks, "What were last year's sales figures?", the response generator retrieves sales data from the company database and verifies that the data is accurate before generating a response. Step 2: The data verification unit verifies the accuracy of the responses generated by the response generation unit by checking them against company data. For example, the data verification unit may verify the accuracy of the responses based on data obtained from a company database. The data verification unit may also cross-check multiple data sources to enhance data accuracy.

[0059] (Example 2) The AI ​​response system according to an embodiment of the present invention provides decision tree-based response generation and data verification functions while ensuring the safe use of corporate data, thereby reducing the risk of hallucination and misuse of corporate data in conversational AI technology.

[0060] An AI response system according to an embodiment includes a response generation unit and a data verification unit. The response generation unit generates a response based on a decision tree. For example, if a user asks, "What is the price of the new product?", the response generation unit generates an appropriate response including price information using the decision tree. Furthermore, if a user asks, "What were last year's sales figures?", the response generation unit obtains sales data from a corporate database, verifies the accuracy of the data, and then generates a response. The data verification unit verifies the accuracy of the response generated by the response generation unit by comparing it with corporate data. For example, the data verification unit verifies the accuracy of the response based on data obtained from the corporate database. The data verification unit can also cross-check multiple data sources to improve data accuracy. This enables the AI ​​response system to safely use corporate data and reduce the risk of hallucination.

[0061] The response generation unit can generate more personalized responses by reflecting the user's past dialogue history. For example, the response generation unit analyzes the user's past dialogue history and extracts frequently used keywords and phrases. This allows the user's preferences and interests to be reflected in the branching conditions of the decision tree, generating more personalized responses. This makes it possible to generate more personalized responses by reflecting the user's past dialogue history.

[0062] The response generation unit can select a response that corresponds to the user's emotional state, based on the user's emotional state estimated by the generation AI. For example, the response generation unit analyzes the user's input text and performs emotional analysis. For example, if the user inputs "I'm tired today," the emotional analysis will detect "fatigue" and generate a response that corresponds to that. This makes it possible to generate a response that takes the user's emotional state into consideration.

[0063] The response generation unit can dynamically change the structure of the decision tree to generate the most appropriate response in real time. The response generation unit develops an algorithm that changes the structure of the decision tree in real time, for example, in response to user input. For example, when a user asks a new question, a new node corresponding to that question is dynamically added. This allows the structure of the decision tree to be dynamically changed, enabling the most appropriate response in real time.

[0064] The response generation unit can construct the multilingual decision tree to accommodate different languages ​​or cultural areas. The response generation unit, for example, constructs a multilingual decision tree to enable response generation in different languages. For example, a decision tree is created that supports multiple languages, such as English, Japanese, and French. This enables responses that are compatible with different languages ​​and cultural areas.

[0065] The response generation unit can provide region-specific information by reflecting the user's geographical location information. For example, the response generation unit acquires the user's geographical location information and sets branching conditions for the decision tree based on that information. For example, if the user is in a specific city, information related to that city is provided. This allows the user's geographical location information to be reflected, making it possible to provide region-specific information.

[0066] The generation AI can automatically make completion suggestions in response to the user's input prompt and suggest specific questions. For example, the generation AI analyzes the prompt entered by the user and automatically completes related keywords and phrases. For example, if the user enters "of a new product," it will suggest a specific question such as "What is the price of the new product?" This allows the AI ​​to automatically make completion suggestions in response to the user's input prompt, encouraging the user to ask more specific questions.

[0067] The generation AI can analyze the user's input prompt and generate a response based on past dialogue history. For example, the generation AI analyzes the user's input prompt and extracts relevant information from the past dialogue history. For example, if the user inputs "What were the sales figures last year?", the generation AI will refer to information about sales from the past dialogue history. This allows the generation AI to analyze the user's input prompt and refer to the past dialogue history to generate a more appropriate response.

[0068] The generation AI can generate a response to a user's input prompt based on the estimated user's emotional state. For example, the generation AI analyzes the user's input prompt and performs emotion analysis. For example, if the user inputs "I'm tired today," the generation AI can detect "fatigue" through emotion analysis and generate a response accordingly. This allows for a more appropriate response to be generated by reflecting the user's emotional state.

[0069] The generation AI can automatically translate a user's input prompt into different languages ​​and generate responses that support multiple languages. The generation AI, for example, builds a system that automatically translates a user's input prompt and generates responses in different languages. For example, a prompt entered in English can be translated into Japanese and a response can be generated in Japanese. This makes it possible to automatically translate into different languages ​​and generate responses that support multiple languages.

[0070] Generative AI can suggest related images or videos in response to a user's input prompt, providing visual information. For example, generative AI can analyze a user's input prompt and build a system that automatically suggests related images and videos. For example, if a user inputs "What is the design of the new product?", it can suggest product design images. This makes it possible to suggest related images and videos in response to a user's input prompt, providing visual information.

[0071] Before generating a response, generative AI can cross-check multiple data sources to increase the accuracy of the data. For example, before generating a response, generative AI can cross-check multiple data sources to confirm the consistency of the data. For example, it can refer to corporate databases, public databases, news articles, etc. to verify the accuracy of the data. This cross-checking multiple data sources can increase the accuracy of the data.

[0072] When verifying data, the generation AI can select a method of presenting data according to the user's estimated emotional state. For example, when verifying data, the generation AI analyzes the user's emotional state and selects a method of presenting data according to the emotion. For example, if the user is feeling "anxious," it will prioritize presenting data that gives a sense of security. This makes it possible to present data that takes the user's emotional state into consideration.

[0073] Generative AI can perform the data verification process in real time, minimizing delays in response generation. For example, generative AI can build a system that performs the data verification process in real time, minimizing delays in response generation. For example, it can optimize database queries to achieve rapid data verification. This makes it possible to minimize delays in response generation by performing the data verification process in real time.

[0074] Generative AI can link its data verification function with databases from different industries and fields, enabling verification from a wide range of data sources. For example, generative AI can link with databases from different industries and fields to build a system that verifies data from a wide range of data sources. For example, it can integrate medical databases, financial databases, and technology databases. This makes it possible to verify from a wide range of data sources by linking with databases from different industries and fields.

[0075] Generative AI can refer to relevant external data sources when verifying data. For example, generative AI can build a system that refers to relevant external data sources when verifying data. For example, it can automatically collect public databases and news articles and check the accuracy of the data. This makes it possible to improve the accuracy of data verification by referring to relevant external data sources.

[0076] When verifying data, the generation AI can select a method of presenting data according to the user's estimated emotional state. For example, when verifying data, the generation AI analyzes the user's emotional state and selects a method of presenting data according to the emotion. For example, if the user is feeling "anxious," it will prioritize presenting data that gives a sense of security. This makes it possible to present data that takes the user's emotional state into consideration.

[0077] When generating a response, the generative AI can generate multiple candidate responses and select the most reliable response from among them. For example, the generative AI can develop an algorithm that generates multiple candidate responses and selects the most reliable response from among them. For example, it can calculate a reliability score for each candidate response and select the response with the highest score. This allows the risk of hallucination to be reduced by generating multiple candidate responses and selecting the most reliable response from among them.

[0078] When generating a response, the generative AI can select a response that corresponds to the user's estimated emotional state. For example, the generative AI can analyze the user's emotional state and develop an algorithm that selects a response that corresponds to that emotion. For example, if the user is feeling "anxious," it can select a response that gives a sense of security. This makes it possible to generate a response that takes the user's emotional state into consideration.

[0079] Generative AI can dynamically update predefined rule sets when generating responses. For example, generative AI develops algorithms that dynamically update predefined rule sets when generating responses. For example, if new data is added, the rule set is updated based on that data. This makes it possible to reduce the risk of hallucination by dynamically updating predefined rule sets.

[0080] When generating a response, generative AI can combine different algorithms to generate a response. For example, generative AI can build a system that generates a response by combining different algorithms. For example, it can combine a decision tree algorithm with a neural network algorithm. This allows the risk of hallucination to be reduced by combining different algorithms.

[0081] The generative AI can reflect user feedback in real time when generating a response. For example, the generative AI can build a system that reflects user feedback in real time when generating a response. For example, the user can evaluate the response, and the response can be adjusted based on that evaluation. In this way, the risk of hallucination can be reduced by reflecting user feedback in real time.

[0082] It is possible to dynamically manage access rights to corporate data and change the access rights according to the user's position or job content. A system that dynamically manages access rights to corporate data can be built, and access rights can be changed according to the user's position or job content. For example, broad access rights can be granted to managers, and limited access rights can be set for general employees. In this way, by dynamically managing access rights to corporate data, it is possible to grant appropriate access rights according to the user's position and job content.

[0083] To ensure the safe use of corporate data, generative AI can select how to present data based on the estimated user's emotional state. For example, generative AI can build a system that analyzes the user's emotional state and selects how to present data based on that emotion. For example, if the user is feeling "anxious," it will prioritize presenting data that gives a sense of security. This makes it possible to present data that takes the user's emotional state into consideration.

[0084] It is possible to introduce algorithms that monitor corporate data usage history in real time and detect unauthorized use. It is possible to develop algorithms that monitor corporate data usage history in real time and detect unauthorized use. For example, it can detect abnormal access patterns and issue an alert. This makes it possible to monitor data usage history in real time and detect unauthorized use, thereby ensuring the safe use of corporate data.

[0085] To ensure the safe use of corporate data, security measures can be strengthened by referring to security protocols from different industries and fields. To ensure the safe use of corporate data, security measures can be strengthened by referring to security protocols from different industries and fields. For example, security standards from the financial industry can be adopted. By referring to security protocols from different industries and fields, security measures can be strengthened by referring to security protocols from different industries and fields.

[0086] Generative AI can refer to relevant external data sources to ensure the safe use of corporate data. For example, generative AI can build a system that references relevant external data sources to ensure the safe use of corporate data. For example, it can automatically collect public databases and news articles and verify the accuracy of the data. This makes it possible to ensure the safe use of corporate data by referencing relevant external data sources.

[0087] To ensure the safe use of corporate data, generative AI can select how to present data based on the estimated user's emotional state. For example, generative AI can build a system that analyzes the user's emotional state and selects how to present data based on that emotion. For example, if the user is feeling "anxious," it will prioritize presenting data that gives a sense of security. This makes it possible to present data that takes the user's emotional state into consideration.

[0088] Generative AI can refer to relevant external data sources to ensure the safe use of corporate data. For example, generative AI can build a system that references relevant external data sources to ensure the safe use of corporate data. For example, it can automatically collect public databases and news articles and verify the accuracy of the data. This makes it possible to ensure the safe use of corporate data by referencing relevant external data sources.

[0089] To ensure the safe use of corporate data, generative AI can select how to present data based on the estimated user's emotional state. For example, generative AI can build a system that analyzes the user's emotional state and selects how to present data based on that emotion. For example, if the user is feeling "anxious," it will prioritize presenting data that gives a sense of security. This makes it possible to present data that takes the user's emotional state into consideration.

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

[0091] The response generation unit can provide relevant news articles and the latest industry trends based on the user's input. For example, if a user asks, "What are the market trends for our new product?", the response generation unit will refer to the latest news articles and market reports and provide relevant information. Also, if a user asks, "What are our competitors doing?", the response generation unit can provide the latest news and analytical reports about competitors. This allows users to make decisions based on the latest information.

[0092] The response generation unit can provide related academic papers and research results based on the user's input. For example, if a user asks, "What are the research results for new technologies?", the response generation unit will refer to related academic papers and research results and provide the latest information. Also, if a user asks, "What are the research trends for a specific technology?", the response generation unit can provide related research papers and review articles. This allows users to obtain information based on the latest research results.

[0093] The response generation unit can provide relevant laws, regulations, and guidelines based on the user's input. For example, if a user asks, "What are the laws and regulations for a new product?", the response generation unit will refer to the relevant laws, regulations, and guidelines and provide appropriate information. Also, if a user asks, "What are the regulatory trends in a specific industry?", the unit can provide the latest laws, regulations, and guidelines. This allows the user to take appropriate action based on laws and regulations.

[0094] The response generation unit can provide related patent information and technical literature based on the user's input. For example, if a user asks, "What is the patent information for new technology?", the response generation unit will refer to related patent information and provide the latest information. Also, if a user asks, "What are the patent trends for a specific technology?", it can provide related patent documents and technical reports. This allows users to create technology strategies based on patent information.

[0095] The response generation unit can provide relevant market research reports and consumer trends based on the user's input. For example, if a user asks, "What is the market research report for new products?", the response generation unit will refer to the relevant market research report and provide the latest information. Also, if a user asks, "What are consumer purchasing trends?", the response generation unit can provide the relevant consumer trend report. This allows the user to create a strategy based on market research.

[0096] The response generation unit can generate a response that corresponds to the user's emotional state. For example, if the user inputs "I'm very happy today," the response generation unit generates a response such as "That's great! Did something special happen?". Also, if the user inputs "I'm very sad today," the response generation unit can generate a response such as "Is there anything you'd like to talk about?". This makes it possible to generate an appropriate response according to the user's emotional state.

[0097] The response generation unit can provide resources and support information according to the user's emotional state. For example, if the user inputs "I'm feeling stressed," the response generation unit can generate a response such as "Here are resources for stress management." Alternatively, if the user inputs "I'm feeling anxious," the response generation unit can generate a response such as "Here is support information for reducing anxiety." This makes it possible to provide appropriate resources and support information according to the user's emotional state.

[0098] The response generation unit can provide entertainment content that corresponds to the user's emotional state based on the user's emotional state. For example, if the user inputs "I'm feeling happy today," the response generation unit can generate a response such as "Here are some recommendations for fun movies and music." Alternatively, if the user inputs "I'm bored today," the response generation unit can generate a response such as "Here are some recommendations for interesting articles and videos." This makes it possible to provide entertainment content that corresponds to the user's emotional state.

[0099] The response generation unit can provide health management information according to the user's emotional state. For example, if the user inputs "I'm tired," the response generation unit can generate a response such as "Here's how to relax." Alternatively, if the user inputs "I'm not feeling well," the response generation unit can generate a response such as "Here's some advice for managing your health." This makes it possible to provide health management information according to the user's emotional state.

[0100] The response generation unit can provide learning resources that correspond to the user's emotional state. For example, if the user inputs "I'm not motivated," the response generation unit can generate a response such as "Here are some learning resources to help you increase your motivation." Also, if the user inputs "I want to deepen my knowledge," the response generation unit can generate a response such as "Here are some recommended learning resources." This makes it possible to provide learning resources that correspond to the user's emotional state.

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

[0102] Step 1: The response generator generates a response based on a decision tree. For example, if a user asks, "What is the price of the new product?", the response generator uses the decision tree to generate an appropriate response that includes price information. Similarly, if a user asks, "What were last year's sales figures?", the response generator retrieves sales data from the company database and verifies that the data is accurate before generating a response. Step 2: The data verification unit verifies the accuracy of the responses generated by the response generation unit by checking them against company data. For example, the data verification unit may verify the accuracy of the responses based on data obtained from a company database. The data verification unit may also cross-check multiple data sources to enhance data accuracy.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 response generation unit that generates a response based on the decision tree; a data verification unit that verifies the accuracy of the response generated by the response generation unit by comparing it with company data. A system characterized by:

2. The response generation unit Generate more personalized responses based on the user's past interactions 2. The system of claim 1.

3. The response generation unit Build multilingual decision trees to accommodate different languages ​​or cultures 2. The system of claim 1.

4. The generated AI is Automatically completes and suggests specific questions when users type 2. The system of claim 1.

5. The generated AI is Cross-checking multiple data sources to enhance the accuracy of the data before generating the response 2. The system of claim 1.

6. The generated AI is When generating the response, a plurality of response candidates are generated, and the most reliable response is selected from among them.

2. The system of claim 1.

7. Dynamically manage access rights to the corporate data and change the access rights depending on the user's job title or job duties.

2. The system of claim 1.

8. The response generation unit Based on the user's emotional state estimated by the generation AI, a response appropriate to the emotion is selected.

2. The system of claim 1.

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