System

Abstract

An object of a system according to an embodiment is to enable an individual to efficiently recall past important notes, information, and business know-how.SOLUTION: A system includes an accumulation unit, an analysis unit, and a provision unit. The storage unit stores information from a user. The analysis unit analyzes the information accumulated by the accumulation unit and provides a specific answer on the basis of an inquiry from a user. The providing unit provides the answer provided by the analysis unit to the user.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 technology has had the problem of making it difficult for individuals to efficiently recall important past notes, information, and business know-how.

[0005] The system according to the embodiment aims to enable individuals to efficiently recall important past notes, information, and business know-how. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, an analysis unit, and a provision unit. The storage unit stores information from a user. The analysis unit analyzes the information stored by the storage unit and provides a specific answer based on an inquiry from the user. The provision unit provides the answer provided by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can enable an individual to efficiently recall important past notes, information, and business know-how. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A private generative AI service according to an embodiment of the present invention is a system that allows users to quickly access important past notes, information, and business know-how when needed. The private generative AI service accumulates information from users and provides appropriate answers based on their inquiries. For example, when accumulating information, users can input important notes, information, business know-how, etc. into the generative AI. Then, when needed, the user queries the generative AI, which provides an appropriate answer from the accumulated information. This allows users to manage information without taking up space and is also effective for transferring business know-how. For example, when a user queries, "What was the progress of last year's project?", the generative AI analyzes the query and provides an appropriate answer from the accumulated information. This allows users to quickly access the necessary information. This allows the private generative AI service to quickly access important past notes, information, and business know-how when needed. For example, there is no need to store paper notes or files; these can be stored as digital data in the generative AI. This is also effective for transferring business know-how. For example, when a new employee takes over a task, they can refer to the information accumulated in the generative AI to smoothly start their work.

[0029] A private generative AI service according to an embodiment includes a storage unit, an analysis unit, and a provision unit. The storage unit stores information from a user. The information from the user includes, but is not limited to, text information, image information, and audio information. The storage unit automatically categorizes information entered by the user into categories, such as business, technical, and customer categories. The storage unit can also estimate the user's emotions using a generation AI and adjust the timing of information storage based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically stops storing information and resumes it when the user is relaxed. The analysis unit uses the generation AI to analyze the information stored by the storage unit and provide a specific answer based on the user's inquiry. The analysis is performed using, for example, natural language processing technology. For example, the generation AI analyzes the user's inquiry and searches for information from an appropriate category. The analysis unit can also estimate the user's emotions and adjust the analysis method for the inquiry based on the estimated user emotions. For example, if the user is nervous, the generation AI selects a simple analysis method and provides a quick answer. The providing unit provides the answer provided by the analysis unit to the user. The providing unit can provide answers not only in text format, but also in visual information such as graphs and tables. The providing unit can also use the generation AI to estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible way of expression. This allows the private generation AI service according to the embodiment to quickly access important past notes, information, and business know-how when needed.

[0030] The storage unit can automatically categorize information entered by a user. Examples of categories include, but are not limited to, business categories, technical categories, and customer categories. The storage unit, for example, automatically analyzes information entered by a user and categorizes the information into appropriate categories. For example, a generation AI analyzes the user's input and categorizes the information into business categories, technical categories, customer categories, and the like. The storage unit can also categorize information based on keywords set by the user. For example, if a user sets the keyword "project," the generation AI categorizes the information into a project category based on the keyword. This automatically organizes information and makes it easier to search. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can input information entered by a user into the generation AI and have the generation AI categorize the information.

[0031] The analysis unit can analyze a user's inquiry using natural language processing technology and search for information from an appropriate category. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can analyze a user's inquiry using morphological analysis and search for information from an appropriate category. For example, a generation AI can perform morphological analysis of the user's inquiry and search for information from an appropriate category. The analysis unit can also analyze a user's inquiry using grammatical analysis and search for information from an appropriate category. For example, a generation AI can perform grammatical analysis of the user's inquiry and search for information from an appropriate category. The analysis unit can also analyze a user's inquiry using semantic analysis and search for information from an appropriate category. For example, a generation AI can perform semantic analysis of the user's inquiry and search for information from an appropriate category. In this way, by using natural language processing technology, appropriate information can be quickly provided in response to a user's inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's inquiry into the generation AI and have the generation AI analyze the inquiry and search for information.

[0032] The providing unit can provide not only answers in text format but also visual information such as graphs and tables. Examples of visual information include, but are not limited to, graphs, tables, and charts. For example, the providing unit can provide not only answers in text format to a user's inquiry but also visual information such as graphs and tables. For example, the generation AI can provide answers in text format to a user's inquiry and further provide visual information using graphs and tables. The providing unit can also suggest an optimal display format based on the user's past selection history. For example, the generation AI can analyze the user's past selection history and suggest an optimal display format. Providing visual information thereby makes it easier for the user to understand the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to generate visual information.

[0033] The provision unit can provide the new employee with necessary information when handing over business know-how. Business know-how includes, but is not limited to, procedures, manuals, and best practices. For example, when a new employee takes over a job, the provision unit refers to information accumulated by the generation AI and provides the necessary information. For example, the generation AI searches for procedures and manuals and provides them to the new employee. The provision unit can also provide business know-how including best practices. For example, the generation AI searches for best practices and provides them to the new employee. This allows the new employee to start work smoothly. Some or all of the above-mentioned processing in the provision unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the provision unit can cause the generation AI to search for and provide business know-how.

[0034] The storage unit can analyze the user's past information input history and select the optimal storage method. Examples of optimal storage methods include, but are not limited to, selecting a database or a file format. For example, the storage unit analyzes the format (text, audio, image, etc.) of information frequently input by the user in the past and selects the optimal storage method. For example, the generation AI analyzes the user's past input history and selects the optimal database or file format. The storage unit can also propose an optimal storage schedule based on the amount and frequency of information input by the user in the past. For example, the generation AI analyzes the user's past input history and proposes the optimal storage schedule. The storage unit can also select a method for storing information for a specific time period based on the user's past input history. For example, the generation AI analyzes the user's past input history and selects a method for storing information for a specific time period. By selecting the optimal storage method based on the past input history, efficient information management becomes possible. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can have the generation AI analyze the user's past input history and select the optimal storage method.

[0035] When storing information, the storage unit can filter the information based on the user's current project or area of ​​interest. Examples of filtering include, but are not limited to, keyword filtering and category filtering. The storage unit, for example, prioritizes storing only information related to the user's current project. For example, the generation AI automatically filters and stores information related to the user's current project. The storage unit can also automatically filter and store highly relevant information based on the user's area of ​​interest. For example, the generation AI filters information based on the user's area of ​​interest and stores only important information. The storage unit can also filter information based on keywords set by the user and store only important information. For example, the generation AI filters information based on keywords set by the user and stores only important information. This allows for efficient storage of highly relevant information by filtering information based on the user's area of ​​interest. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can cause the generation AI to filter and store information.

[0036] When storing information, the storage unit can select the optimal storage means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs information by voice, the storage unit has the generation AI convert the voice data into text and store it. For example, the generation AI analyzes the voice data and stores it as text data. Also, when a user inputs information by image, the storage unit can analyze the image data and store it as text information. For example, the generation AI analyzes the image data and stores it as text data. Also, when a user inputs information by text, the storage unit can store the information as text data as is. For example, the generation AI stores the text data as is. This enables efficient information management by selecting the optimal storage means depending on the user's input method. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can cause the generation AI to analyze the user's input method and select the optimal storage means.

[0037] When storing information, the storage unit can prioritize storing highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, etc. For example, when the user is in a specific location, the storage unit prioritizes storing information related to that location. For example, the generation AI recognizes the user's current location and prioritizes storing information related to that location. Furthermore, when the user is moving, the storage unit can also accumulate information related to the current location in real time. For example, the generation AI tracks the user's movement and accumulates information related to the current location in real time. Furthermore, when the user frequently visits a specific area, the storage unit can prioritize storing information related to that area. For example, the generation AI analyzes the user's visit history and prioritizes storing information related to the specific area. This allows for efficient storage of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit may cause the generation AI to acquire geographical location information and accumulate related information.

[0038] When storing information, the storage unit can analyze the user's social media activity and store related information. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the content of comments. For example, the storage unit automatically stores information shared by the user on social media. For example, the generation AI monitors the user's social media account and automatically stores the shared information. The storage unit can also analyze the content posted by the user on social media and store related information. For example, the generation AI analyzes the content posted by the user and stores related information. The storage unit can also store related information based on the activities of the user's friends on social media. For example, the generation AI analyzes the activities of the user's friends and stores related information. This allows for efficient storage of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can cause the generation AI to analyze social media activity and store related information.

[0039] When storing information, the storage unit can customize the storage method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user reviews. The storage unit adjusts the storage method based on, for example, feedback provided by the user in the past. For example, the generation AI analyzes the user's feedback and adjusts the storage method. The storage unit can also change the storage frequency of specific information based on the user's feedback. For example, the generation AI changes the storage frequency of specific information based on the user's feedback. The storage unit can also customize the categories of information to be stored by reflecting the user's feedback. For example, the generation AI customizes the categories of information to be stored based on the user's feedback. This allows the storage method to be customized by reflecting the user's past feedback, enabling efficient information management. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can cause the generation AI to analyze the user's feedback and customize the storage method.

[0040] The storage unit can automatically extract and categorize the user's business know-how when storing information. Extraction of business know-how includes, but is not limited to, text mining and knowledge base construction. The storage unit, for example, automatically analyzes the business know-how input by the user and classifies it into appropriate categories. For example, a generation AI analyzes the user's input, extracts the business know-how, and classifies it into appropriate categories. The storage unit can also extract the user's business know-how and store it together with related information. For example, the generation AI extracts the business know-how and stores it together with related information. The storage unit can also periodically update the user's business know-how and store the latest information. For example, the generation AI periodically analyzes the business know-how and stores the latest information. This automatically extracts the user's business know-how and categorizes it, enabling efficient information management. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can cause the generation AI to extract and categorize the business know-how.

[0041] When analyzing a query, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. Evaluations of importance include, but are not limited to, frequency, urgency, and impact. For example, the analysis unit may have the generation AI perform a detailed analysis of highly important information to provide an accurate answer. For example, the generation AI may evaluate the importance of the information, perform a detailed analysis of the highly important information, and provide an accurate answer. The analysis unit may also have the generation AI perform a simplified analysis of low-importance information to provide a quick answer. For example, the generation AI may evaluate the importance of the information, perform a simplified analysis of the low-importance information, and provide a quick answer. The analysis unit may also have the generation AI determine analysis priorities based on the importance of the information to perform efficient analysis. For example, the generation AI may evaluate the importance of the information, determine analysis priorities, and perform efficient analysis. This allows for efficient information provision by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI evaluate the importance of information and adjust the level of detail of the analysis.

[0042] When analyzing an inquiry, the analysis unit can apply different analysis algorithms depending on the category of information. Examples of categories include, but are not limited to, business categories, technical categories, and customer categories. For example, the analysis unit analyzes text information by having the generation AI apply a natural language processing algorithm. For example, the generation AI analyzes text information using a natural language processing algorithm. The analysis unit can also analyze image information by having the generation AI apply an image analysis algorithm. For example, the generation AI analyzes image information using an image analysis algorithm. The analysis unit can also analyze audio information by having the generation AI apply a voice recognition algorithm. For example, the generation AI analyzes audio information using a voice recognition algorithm. This enables efficient information provision by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can cause the generation AI to apply an analysis algorithm depending on the category of information.

[0043] When analyzing a query, the analysis unit can improve the accuracy of the analysis by referring to the user's past query results. Past query results include, but are not limited to, query history and resolved cases. In the analysis unit, for example, the generation AI adjusts the analysis algorithm based on the user's past query results to improve accuracy. For example, the generation AI analyzes the user's past query results and adjusts the analysis algorithm to improve accuracy. The analysis unit can also analyze the user's past query patterns and the generation AI selects the optimal analysis method. For example, the generation AI analyzes the user's past query patterns and selects the optimal analysis method. The analysis unit can also use the user's past query results as feedback so that the generation AI can continuously improve the accuracy of the analysis. For example, the generation AI uses the user's past query results as feedback to continuously improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to the user's past query results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can cause the generation AI to analyze the user's past query results and adjust the analysis algorithm.

[0044] When analyzing a query, the analysis unit can determine the analysis priority based on the time of information submission. The submission time includes, but is not limited to, the submission date and time. For example, the analysis unit allows the generation AI to prioritize analysis of the most recent information and provide a prompt answer. For example, the generation AI evaluates the time of information submission and prioritizes analysis of the most recent information to provide a prompt answer. The analysis unit can also allow the generation AI to postpone analysis of older information. For example, the generation AI evaluates the time of information submission and postpones analysis of older information. The analysis unit can also allow the generation AI to dynamically adjust the analysis priority based on the time of information submission. For example, the generation AI evaluates the time of information submission and dynamically adjusts the analysis priority. This enables efficient information provision by determining the analysis priority based on the time of information submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the time of information submission and determine the analysis priority.

[0045] When analyzing a query, the analysis unit can adjust the order of analysis based on the relevance of the information. Evaluation of relevance includes, but is not limited to, keyword matching and category matching. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information and provide a quick answer. For example, the generation AI evaluates the relevance of the information and prioritizes analysis of highly relevant information to provide a quick answer. The analysis unit can also postpone analysis of less relevant information by the generation AI. For example, the generation AI evaluates the relevance of the information and then postpones analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. For example, the generation AI evaluates the relevance of the information and dynamically adjusts the order of analysis. This enables efficient information provision by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the relevance of the information and adjust the order of analysis.

[0046] When analyzing a query, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Evaluations of the level of expertise include, but are not limited to, the presence or absence of qualifications and years of work experience. For example, if the user has technical expertise, the analysis unit causes the generation AI to provide the analysis results using technical terminology. For example, if the generation AI evaluates the user's level of expertise and determines that the user has technical expertise, it provides the analysis results using technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can cause the generation AI to provide the analysis results in simple language. For example, if the generation AI evaluates the user's level of expertise and determines that the user does not have technical expertise, it provides the analysis results in simple language. Furthermore, the analysis unit can also adjust the way the generation AI presents the analysis results according to the user's level of expertise. For example, the generation AI evaluates the user's level of expertise and adjusts the way the analysis results are presented. This allows for efficient information provision by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI evaluate the user's level of expertise and adjust how the analysis results are presented.

[0047] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between pieces of information when analyzing a query. Evaluation of interrelationships includes, but is not limited to, the strength of association and co-occurrence. For example, the analysis unit analyzes the interrelationships between multiple pieces of information, and the generation AI provides a highly accurate answer. For example, the generation AI evaluates the interrelationships between pieces of information, analyzes the interrelationships between pieces of information, and provides a highly accurate answer. The analysis unit can also have the generation AI supplement and provide related information based on the interrelationships between pieces of information. For example, the generation AI evaluates the interrelationships between pieces of information and supplements and provides related information. The analysis unit can also have the generation AI continuously improve the accuracy of the analysis by taking into account the interrelationships between pieces of information. For example, the generation AI evaluates the interrelationships between pieces of information and continuously improves the accuracy of the analysis. As a result, the accuracy of the analysis is improved by taking the interrelationships between pieces of information into account. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the interrelationships between pieces of information and improve the accuracy of the analysis.

[0048] When providing an answer, the providing unit can adjust the level of detail of the answer based on the importance of the information. Evaluations of importance include, but are not limited to, frequency, urgency, and impact. For example, the providing unit may cause the generation AI to provide a detailed answer for highly important information. For example, the generation AI may evaluate the importance of the information and provide a detailed answer for highly important information. The providing unit may also cause the generation AI to provide a simplified answer for low-importance information. For example, the generation AI may evaluate the importance of the information and provide a simplified answer for low-importance information. The providing unit may also cause the generation AI to dynamically adjust the level of detail of the answer based on the importance of the information. For example, the generation AI may evaluate the importance of the information and dynamically adjust the level of detail of the answer. This enables efficient information provision by adjusting the level of detail of the answer based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may cause the generation AI to evaluate the importance of the information and adjust the level of detail of the answer.

[0049] When providing an answer, the providing unit can apply different display formats depending on the category of information. Examples of categories include, but are not limited to, business categories, technical categories, and customer categories. For example, in the providing unit, the generation AI provides an answer in text format for text information. For example, the generation AI provides an answer for text information in text format. In addition, the providing unit can also provide an answer for image information in image format. For example, the generation AI provides an answer for image information in image format. In addition, the providing unit can also provide an answer for visual information such as graphs and tables in an appropriate display format. For example, the generation AI provides an answer for visual information such as graphs and tables in an appropriate display format. This enables efficient information provision by applying different display formats depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to apply a display format depending on the category of information.

[0050] When providing an answer, the providing unit can improve the accuracy of the answer by referring to the user's past answer results. Past answer results include, but are not limited to, answer history and resolved cases. For example, the providing unit causes the generation AI to improve the accuracy of the answer based on the user's past answer results. For example, the generation AI analyzes the user's past answer results to improve the accuracy of the answer. The providing unit can also analyze the user's past answer patterns and have the generation AI select the optimal answer method. For example, the generation AI analyzes the user's past answer patterns and selects the optimal answer method. The providing unit can also use the user's past answer results as feedback so that the generation AI can continuously improve the accuracy of the answer. For example, the generation AI uses the user's past answer results as feedback to continuously improve the accuracy of the answer. As a result, the accuracy of the answer is improved by referring to the user's past answer results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can cause the generation AI to analyze the user's past answer results and improve the accuracy of the answer.

[0051] When providing an answer, the providing unit can determine the priority of the answer based on the time of submission of the information. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the providing unit causes the generation AI to prioritize providing an answer for the most recent information. For example, the generation AI evaluates the time of submission of the information and prioritizes providing an answer for the most recent information. The providing unit can also cause the generation AI to postpone providing an answer for older information. For example, the generation AI evaluates the time of submission of the information and postpones providing an answer for older information. The providing unit can also cause the generation AI to dynamically adjust the priority of the answers depending on the time of submission of the information. For example, the generation AI evaluates the time of submission of the information and dynamically adjusts the priority of the answers. This enables efficient information provision by determining the priority of answers based on the time of submission of the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to evaluate the time of submission of the information and determine the priority of the answers.

[0052] When providing answers, the providing unit can adjust the order of answers based on the relevance of the information. Evaluation of relevance includes, but is not limited to, keyword matching and category matching. For example, the providing unit allows the generation AI to prioritize providing answers to highly relevant information. For example, the generation AI evaluates the relevance of information and prioritizes providing answers to highly relevant information. The providing unit can also allow the generation AI to postpone providing answers to less relevant information. For example, the generation AI evaluates the relevance of information and postpones providing answers to less relevant information. The providing unit can also allow the generation AI to dynamically adjust the order of answers based on the relevance of the information. For example, the generation AI evaluates the relevance of information and dynamically adjusts the order of answers. This enables efficient information provision by adjusting the order of answers based on the relevance of information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to evaluate the relevance of information and adjust the order of answers.

[0053] When providing an answer, the providing unit can adjust the use of technical terminology in the answer depending on the user's level of expertise. Evaluations of the level of expertise include, but are not limited to, the presence or absence of qualifications and years of work experience. For example, if the user has technical expertise, the providing unit causes the generation AI to provide an answer using technical terminology. For example, if the generation AI evaluates the user's level of expertise and determines that the user has technical expertise, it provides the answer using technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also cause the generation AI to provide an answer in simple language. For example, if the generation AI evaluates the user's level of expertise and determines that the user does not have technical expertise, it provides the answer in simple language. Furthermore, the providing unit can also cause the generation AI to adjust the way the answer is expressed depending on the user's level of expertise. For example, the generation AI evaluates the user's level of expertise and adjusts the way the answer is expressed. This enables efficient information provision by adjusting the use of technical terminology in the answer depending on the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to evaluate the user's level of expertise and adjust the way the answer is expressed.

[0054] The providing unit can provide an answer including a visual display of the information. Examples of visual displays include, but are not limited to, graphs, tables, charts, etc. For example, if a user requests visual information, the providing unit causes the generation AI to provide an answer including a graph or table. For example, the generation AI provides an answer including a graph or table in response to a user's inquiry. Furthermore, if a user requests text information, the providing unit can also cause the generation AI to provide an answer in text format. For example, the generation AI provides an answer in text format in response to a user's inquiry. Furthermore, the providing unit can also suggest an optimal display format based on the user's past selection history. For example, the generation AI analyzes the user's past selection history and suggests an optimal display format. In this way, including a visual display of the information makes it easier for the user to understand the information. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can cause the generation AI to generate and provide a visual display.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The analysis unit can predict the intent of a query based on the user's past inquiry history and provide an appropriate answer. For example, if a user has frequently inquired about the "progress of a project" in the past, the generation AI can analyze that history and automatically provide information about the progress the next time the user inquires. Also, if a user frequently uses a specific keyword, it can prioritize searches for information related to that keyword. Furthermore, it is possible to analyze the user's inquiry history and refer to data from other users with similar inquiry patterns to provide more accurate answers. This enables efficient information provision that takes into account the user's past behavior.

[0057] The storage unit can prioritize the storage of region-specific information based on the user's geographical location information. For example, if the user is in a specific region, news and event information related to that region can be automatically stored. Also, if the user is traveling, tourist information and traffic information for the destination can be prioritized. Furthermore, if the user frequently visits a specific region, it is also possible to store business information and market trends related to that region. This allows for the storage of highly relevant information based on the user's geographical location.

[0058] The providing unit can improve the accuracy of answers based on the user's past feedback. For example, the generating AI analyzes feedback provided by the user in the past and adjusts the content of the answer by reflecting that feedback. Also, if a user gives a high rating to a specific answer, the AI ​​can refer to that answering method when providing future answers. Furthermore, it is possible to customize the format and expression of answers based on the user's feedback. This makes it possible to provide highly accurate information by utilizing user feedback.

[0059] The providing unit can adjust the level of detail of the answer depending on the user's level of expertise. For example, if the user has specialized knowledge, the generating AI can provide detailed technical information, and if the user does not have specialized knowledge, the generating AI can provide an explanation in simple terms. Also, if the user has an intermediate level of knowledge, the generating AI can provide an answer with a medium level of detail. Furthermore, it is possible to customize the format and expression of the answer depending on the user's level of expertise. This allows for flexible information provision according to the user's level of expertise.

[0060] The storage unit can analyze a user's social media activity and store related information. For example, it can automatically store information shared by the user on social media and store related information by analyzing the user's posts. It can also store related information by referring to the activities of the user's friends. It can also store highly relevant information preferentially based on the content of the user's likes and comments on social media. This allows for efficient information storage that utilizes the user's social media activity.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The storage unit stores information from the user. Information from the user includes text information, image information, audio information, etc. The storage unit automatically classifies the information entered by the user into categories. For example, it can classify the information into business categories, technical categories, customer categories, etc. The storage unit also uses a generation AI to estimate the user's emotions and adjusts the timing of information storage based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically stops storing information temporarily and resumes it when the user is relaxed. Step 2: The analysis unit uses the generation AI to analyze the information accumulated by the accumulation unit and provide a specific answer based on the user's inquiry. The analysis is performed using natural language processing technology. For example, the generation AI analyzes the user's inquiry and searches for information from the appropriate category. The analysis unit also estimates the user's emotions and adjusts the method of analyzing the inquiry based on the estimated user emotions. For example, if the user is nervous, the generation AI selects a simple analysis method and provides a quick answer. Step 3: The providing unit provides the answer provided by the analysis unit to the user. The providing unit can provide answers not only in text format but also visual information such as graphs and tables. The providing unit also uses the generation AI to estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, if the user is nervous, the generation AI will provide a simple, highly visible way of expressing the answer.

[0063] (Example 2) A private generative AI service according to an embodiment of the present invention is a system that allows users to quickly access important past notes, information, and business know-how when needed. The private generative AI service accumulates information from users and provides appropriate answers based on their inquiries. For example, when accumulating information, users can input important notes, information, business know-how, etc. into the generative AI. Then, when needed, the user queries the generative AI, which provides an appropriate answer from the accumulated information. This allows users to manage information without taking up space and is also effective for transferring business know-how. For example, when a user queries, "What was the progress of last year's project?", the generative AI analyzes the query and provides an appropriate answer from the accumulated information. This allows users to quickly access the necessary information. This allows the private generative AI service to quickly access important past notes, information, and business know-how when needed. For example, there is no need to store paper notes or files; these can be stored as digital data in the generative AI. This is also effective for transferring business know-how. For example, when a new employee takes over a task, they can refer to the information accumulated in the generative AI to smoothly start their work.

[0064] A private generative AI service according to an embodiment includes a storage unit, an analysis unit, and a provision unit. The storage unit stores information from a user. The information from the user includes, but is not limited to, text information, image information, and audio information. The storage unit automatically categorizes information entered by the user into categories, such as business, technical, and customer categories. The storage unit can also estimate the user's emotions using a generation AI and adjust the timing of information storage based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically stops storing information and resumes it when the user is relaxed. The analysis unit uses the generation AI to analyze the information stored by the storage unit and provide a specific answer based on the user's inquiry. The analysis is performed using, for example, natural language processing technology. For example, the generation AI analyzes the user's inquiry and searches for information from an appropriate category. The analysis unit can also estimate the user's emotions and adjust the analysis method for the inquiry based on the estimated user emotions. For example, if the user is nervous, the generation AI selects a simple analysis method and provides a quick answer. The providing unit provides the answer provided by the analysis unit to the user. The providing unit can provide answers not only in text format, but also in visual information such as graphs and tables. The providing unit can also use the generation AI to estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible way of expression. This allows the private generation AI service according to the embodiment to quickly access important past notes, information, and business know-how when needed.

[0065] The storage unit can automatically categorize information entered by a user. Examples of categories include, but are not limited to, business categories, technical categories, and customer categories. The storage unit, for example, automatically analyzes information entered by a user and categorizes the information into appropriate categories. For example, a generation AI analyzes the user's input and categorizes the information into business categories, technical categories, customer categories, and the like. The storage unit can also categorize information based on keywords set by the user. For example, if a user sets the keyword "project," the generation AI categorizes the information into a project category based on the keyword. This automatically organizes information and makes it easier to search. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can input information entered by a user into the generation AI and have the generation AI categorize the information.

[0066] The analysis unit can analyze a user's inquiry using natural language processing technology and search for information from an appropriate category. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can analyze a user's inquiry using morphological analysis and search for information from an appropriate category. For example, a generation AI can perform morphological analysis of the user's inquiry and search for information from an appropriate category. The analysis unit can also analyze a user's inquiry using grammatical analysis and search for information from an appropriate category. For example, a generation AI can perform grammatical analysis of the user's inquiry and search for information from an appropriate category. The analysis unit can also analyze a user's inquiry using semantic analysis and search for information from an appropriate category. For example, a generation AI can perform semantic analysis of the user's inquiry and search for information from an appropriate category. In this way, by using natural language processing technology, appropriate information can be quickly provided in response to a user's inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's inquiry into the generation AI and have the generation AI analyze the inquiry and search for information.

[0067] The providing unit can provide not only answers in text format but also visual information such as graphs and tables. Examples of visual information include, but are not limited to, graphs, tables, and charts. For example, the providing unit can provide not only answers in text format to a user's inquiry but also visual information such as graphs and tables. For example, the generation AI can provide answers in text format to a user's inquiry and further provide visual information using graphs and tables. The providing unit can also suggest an optimal display format based on the user's past selection history. For example, the generation AI can analyze the user's past selection history and suggest an optimal display format. Providing visual information thereby makes it easier for the user to understand the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to generate visual information.

[0068] The provision unit can provide the new employee with necessary information when handing over business know-how. Business know-how includes, but is not limited to, procedures, manuals, and best practices. For example, when a new employee takes over a job, the provision unit refers to information accumulated by the generation AI and provides the necessary information. For example, the generation AI searches for procedures and manuals and provides them to the new employee. The provision unit can also provide business know-how including best practices. For example, the generation AI searches for best practices and provides them to the new employee. This allows the new employee to start work smoothly. Some or all of the above-mentioned processing in the provision unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the provision unit can cause the generation AI to search for and provide business know-how.

[0069] The storage unit can estimate the user's emotions and adjust the timing of information storage based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the generation AI automatically suspends information storage and resumes it when the user relaxes. For example, if the generation AI recognizes the user's facial expression and determines that the user is feeling stressed, it temporarily suspends information storage. The storage unit can also actively store information when the user is concentrating, efficiently collecting data. For example, if the generation AI analyzes the user's voice and determines that the user is concentrating, it actively stores information. If the user is tired, the generation AI can delay information storage and resume it after the user has rested. For example, if the generation AI analyzes the user's text and determines that the user is tired, it delays information storage. This enables efficient information management by adjusting the timing of information storage according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit may cause the generation AI to estimate the user's emotions and adjust the timing of information storage.

[0070] The storage unit can analyze the user's past information input history and select the optimal storage method. Examples of optimal storage methods include, but are not limited to, selecting a database or a file format. For example, the storage unit analyzes the format (text, audio, image, etc.) of information frequently input by the user in the past and selects the optimal storage method. For example, the generation AI analyzes the user's past input history and selects the optimal database or file format. The storage unit can also propose an optimal storage schedule based on the amount and frequency of information input by the user in the past. For example, the generation AI analyzes the user's past input history and proposes the optimal storage schedule. The storage unit can also select a method for storing information for a specific time period based on the user's past input history. For example, the generation AI analyzes the user's past input history and selects a method for storing information for a specific time period. By selecting the optimal storage method based on the past input history, efficient information management becomes possible. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can have the generation AI analyze the user's past input history and select the optimal storage method.

[0071] When storing information, the storage unit can filter the information based on the user's current project or area of ​​interest. Examples of filtering include, but are not limited to, keyword filtering and category filtering. The storage unit, for example, prioritizes storing only information related to the user's current project. For example, the generation AI automatically filters and stores information related to the user's current project. The storage unit can also automatically filter and store highly relevant information based on the user's area of ​​interest. For example, the generation AI filters information based on the user's area of ​​interest and stores only important information. The storage unit can also filter information based on keywords set by the user and store only important information. For example, the generation AI filters information based on keywords set by the user and stores only important information. This allows for efficient storage of highly relevant information by filtering information based on the user's area of ​​interest. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can cause the generation AI to filter and store information.

[0072] When storing information, the storage unit can select the optimal storage means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs information by voice, the storage unit has the generation AI convert the voice data into text and store it. For example, the generation AI analyzes the voice data and stores it as text data. Also, when a user inputs information by image, the storage unit can analyze the image data and store it as text information. For example, the generation AI analyzes the image data and stores it as text data. Also, when a user inputs information by text, the storage unit can store the information as text data as is. For example, the generation AI stores the text data as is. This enables efficient information management by selecting the optimal storage means depending on the user's input method. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can cause the generation AI to analyze the user's input method and select the optimal storage means.

[0073] The storage unit can estimate the user's emotions, evaluate the importance of information based on the estimated user's emotions, and determine the priority of storage. Evaluations of importance include, but are not limited to, frequency, urgency, and impact. For example, if the user is excited, the storage unit can evaluate the information as high importance and prioritize storage. For example, if the generation AI recognizes the user's facial expression and determines that the user is excited, the storage unit can evaluate the information as high importance and prioritize storage. Furthermore, if the user is relaxed, the storage unit can evaluate the information as normal importance and store it with standard priority. For example, if the generation AI analyzes the user's voice and determines that the user is relaxed, the storage unit can evaluate the information as normal importance and store it with standard priority. Furthermore, if the user is tired, the storage unit can evaluate the information as low importance and store it later. For example, if the generation AI analyzes the user's text and determines that the user is tired, the storage unit can evaluate the information as low importance and store it later. This enables efficient information management by evaluating the importance of information based on the user's emotions and determining the priority of storage. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the storage unit may cause the generation AI to estimate the user's emotion, evaluate the importance of information, and determine the priority of storage.

[0074] When storing information, the storage unit can prioritize storing highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, etc. For example, when the user is in a specific location, the storage unit prioritizes storing information related to that location. For example, the generation AI recognizes the user's current location and prioritizes storing information related to that location. Furthermore, when the user is moving, the storage unit can also accumulate information related to the current location in real time. For example, the generation AI tracks the user's movement and accumulates information related to the current location in real time. Furthermore, when the user frequently visits a specific area, the storage unit can prioritize storing information related to that area. For example, the generation AI analyzes the user's visit history and prioritizes storing information related to the specific area. This allows for efficient storage of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit may cause the generation AI to acquire geographical location information and accumulate related information.

[0075] When storing information, the storage unit can analyze the user's social media activity and store related information. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the content of comments. For example, the storage unit automatically stores information shared by the user on social media. For example, the generation AI monitors the user's social media account and automatically stores the shared information. The storage unit can also analyze the content posted by the user on social media and store related information. For example, the generation AI analyzes the content posted by the user and stores related information. The storage unit can also store related information based on the activities of the user's friends on social media. For example, the generation AI analyzes the activities of the user's friends and stores related information. This allows for efficient storage of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can cause the generation AI to analyze social media activity and store related information.

[0076] When storing information, the storage unit can customize the storage method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user reviews. The storage unit adjusts the storage method based on, for example, feedback provided by the user in the past. For example, the generation AI analyzes the user's feedback and adjusts the storage method. The storage unit can also change the storage frequency of specific information based on the user's feedback. For example, the generation AI changes the storage frequency of specific information based on the user's feedback. The storage unit can also customize the categories of information to be stored by reflecting the user's feedback. For example, the generation AI customizes the categories of information to be stored based on the user's feedback. This allows the storage method to be customized by reflecting the user's past feedback, enabling efficient information management. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can cause the generation AI to analyze the user's feedback and customize the storage method.

[0077] The storage unit can automatically extract and categorize the user's business know-how when storing information. Extraction of business know-how includes, but is not limited to, text mining and knowledge base construction. The storage unit, for example, automatically analyzes the business know-how input by the user and classifies it into appropriate categories. For example, a generation AI analyzes the user's input, extracts the business know-how, and classifies it into appropriate categories. The storage unit can also extract the user's business know-how and store it together with related information. For example, the generation AI extracts the business know-how and stores it together with related information. The storage unit can also periodically update the user's business know-how and store the latest information. For example, the generation AI periodically analyzes the business know-how and stores the latest information. This automatically extracts the user's business know-how and categorizes it, enabling efficient information management. Some or all of the above-described processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can cause the generation AI to extract and categorize the business know-how.

[0078] The analysis unit can estimate the user's emotions and adjust the query analysis method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the analysis unit causes the generation AI to select a simple analysis method and provide a quick answer. For example, if the generation AI recognizes the user's facial expression and determines that the user is nervous, it selects a simple analysis method and provides a quick answer. The analysis unit can also cause the generation AI to select a detailed analysis method and provide more information if the user is relaxed. For example, if the generation AI analyzes the user's voice and determines that the user is relaxed, it selects a detailed analysis method and provides more information. The analysis unit can also cause the generation AI to select a quick analysis method and provide a quick answer if the user is in a hurry. For example, if the generation AI analyzes the user's text and determines that the user is in a hurry, it selects a quick analysis method and provides a quick answer. This allows for efficient information provision by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may cause the generation AI to estimate the user's emotions and adjust the analysis method.

[0079] When analyzing a query, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. Evaluations of importance include, but are not limited to, frequency, urgency, and impact. For example, the analysis unit may have the generation AI perform a detailed analysis of highly important information to provide an accurate answer. For example, the generation AI may evaluate the importance of the information, perform a detailed analysis of the highly important information, and provide an accurate answer. The analysis unit may also have the generation AI perform a simplified analysis of low-importance information to provide a quick answer. For example, the generation AI may evaluate the importance of the information, perform a simplified analysis of the low-importance information, and provide a quick answer. The analysis unit may also have the generation AI determine analysis priorities based on the importance of the information to perform efficient analysis. For example, the generation AI may evaluate the importance of the information, determine analysis priorities, and perform efficient analysis. This allows for efficient information provision by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI evaluate the importance of information and adjust the level of detail of the analysis.

[0080] When analyzing an inquiry, the analysis unit can apply different analysis algorithms depending on the category of information. Examples of categories include, but are not limited to, business categories, technical categories, and customer categories. For example, the analysis unit analyzes text information by having the generation AI apply a natural language processing algorithm. For example, the generation AI analyzes text information using a natural language processing algorithm. The analysis unit can also analyze image information by having the generation AI apply an image analysis algorithm. For example, the generation AI analyzes image information using an image analysis algorithm. The analysis unit can also analyze audio information by having the generation AI apply a voice recognition algorithm. For example, the generation AI analyzes audio information using a voice recognition algorithm. This enables efficient information provision by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can cause the generation AI to apply an analysis algorithm depending on the category of information.

[0081] When analyzing a query, the analysis unit can improve the accuracy of the analysis by referring to the user's past query results. Past query results include, but are not limited to, query history and resolved cases. In the analysis unit, for example, the generation AI adjusts the analysis algorithm based on the user's past query results to improve accuracy. For example, the generation AI analyzes the user's past query results and adjusts the analysis algorithm to improve accuracy. The analysis unit can also analyze the user's past query patterns and the generation AI selects the optimal analysis method. For example, the generation AI analyzes the user's past query patterns and selects the optimal analysis method. The analysis unit can also use the user's past query results as feedback so that the generation AI can continuously improve the accuracy of the analysis. For example, the generation AI uses the user's past query results as feedback to continuously improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to the user's past query results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can cause the generation AI to analyze the user's past query results and adjust the analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the analysis unit determines that the user is nervous, the generation AI provides a simple, highly visible display method. For example, if the generation AI recognizes the user's facial expression and determines that the user is nervous, it provides a simple, highly visible display method. Furthermore, if the analysis unit determines that the user is relaxed, the generation AI can provide a display method that includes detailed information. For example, if the generation AI analyzes the user's voice and determines that the user is relaxed, it provides a display method that includes detailed information. Furthermore, if the analysis unit determines that the user is in a hurry, the generation AI can provide a display method that focuses on the main points. For example, if the generation AI analyzes the user's text and determines that the user is in a hurry, it provides a display method that focuses on the main points. This enables efficient information provision by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may cause the generation AI to estimate the user's emotions and adjust the display method of the analysis results.

[0083] When analyzing a query, the analysis unit can determine the analysis priority based on the time of information submission. The submission time includes, but is not limited to, the submission date and time. For example, the analysis unit allows the generation AI to prioritize analysis of the most recent information and provide a prompt answer. For example, the generation AI evaluates the time of information submission and prioritizes analysis of the most recent information to provide a prompt answer. The analysis unit can also allow the generation AI to postpone analysis of older information. For example, the generation AI evaluates the time of information submission and postpones analysis of older information. The analysis unit can also allow the generation AI to dynamically adjust the analysis priority based on the time of information submission. For example, the generation AI evaluates the time of information submission and dynamically adjusts the analysis priority. This enables efficient information provision by determining the analysis priority based on the time of information submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the time of information submission and determine the analysis priority.

[0084] When analyzing a query, the analysis unit can adjust the order of analysis based on the relevance of the information. Evaluation of relevance includes, but is not limited to, keyword matching and category matching. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information and provide a quick answer. For example, the generation AI evaluates the relevance of the information and prioritizes analysis of highly relevant information to provide a quick answer. The analysis unit can also postpone analysis of less relevant information by the generation AI. For example, the generation AI evaluates the relevance of the information and then postpones analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. For example, the generation AI evaluates the relevance of the information and dynamically adjusts the order of analysis. This enables efficient information provision by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the relevance of the information and adjust the order of analysis.

[0085] When analyzing a query, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Evaluations of the level of expertise include, but are not limited to, the presence or absence of qualifications and years of work experience. For example, if the user has technical expertise, the analysis unit causes the generation AI to provide the analysis results using technical terminology. For example, if the generation AI evaluates the user's level of expertise and determines that the user has technical expertise, it provides the analysis results using technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can cause the generation AI to provide the analysis results in simple language. For example, if the generation AI evaluates the user's level of expertise and determines that the user does not have technical expertise, it provides the analysis results in simple language. Furthermore, the analysis unit can also adjust the way the generation AI presents the analysis results according to the user's level of expertise. For example, the generation AI evaluates the user's level of expertise and adjusts the way the analysis results are presented. This allows for efficient information provision by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI evaluate the user's level of expertise and adjust how the analysis results are presented.

[0086] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between pieces of information when analyzing a query. Evaluation of interrelationships includes, but is not limited to, the strength of association and co-occurrence. For example, the analysis unit analyzes the interrelationships between multiple pieces of information, and the generation AI provides a highly accurate answer. For example, the generation AI evaluates the interrelationships between pieces of information, analyzes the interrelationships between pieces of information, and provides a highly accurate answer. The analysis unit can also have the generation AI supplement and provide related information based on the interrelationships between pieces of information. For example, the generation AI evaluates the interrelationships between pieces of information and supplements and provides related information. The analysis unit can also have the generation AI continuously improve the accuracy of the analysis by taking into account the interrelationships between pieces of information. For example, the generation AI evaluates the interrelationships between pieces of information and continuously improves the accuracy of the analysis. As a result, the accuracy of the analysis is improved by taking the interrelationships between pieces of information into account. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the interrelationships between pieces of information and improve the accuracy of the analysis.

[0087] The providing unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the providing unit causes the generation AI to provide a simple, highly visible expression. For example, if the generation AI recognizes the user's facial expression and determines that the user is nervous, it provides a simple, highly visible expression. The providing unit can also cause the generation AI to provide a more detailed expression if the user is relaxed. For example, if the generation AI analyzes the user's voice and determines that the user is relaxed, it provides a more detailed expression. For example, if the generation AI analyzes the user's text and determines that the user is in a hurry, it provides a more concise expression that hits the key points. This allows for efficient information provision by adjusting the way the answer is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit may cause the generation AI to estimate the user's emotions and adjust the way the answer is expressed.

[0088] When providing an answer, the providing unit can adjust the level of detail of the answer based on the importance of the information. Evaluations of importance include, but are not limited to, frequency, urgency, and impact. For example, the providing unit may cause the generation AI to provide a detailed answer for highly important information. For example, the generation AI may evaluate the importance of the information and provide a detailed answer for highly important information. The providing unit may also cause the generation AI to provide a simplified answer for low-importance information. For example, the generation AI may evaluate the importance of the information and provide a simplified answer for low-importance information. The providing unit may also cause the generation AI to dynamically adjust the level of detail of the answer based on the importance of the information. For example, the generation AI may evaluate the importance of the information and dynamically adjust the level of detail of the answer. This enables efficient information provision by adjusting the level of detail of the answer based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may cause the generation AI to evaluate the importance of the information and adjust the level of detail of the answer.

[0089] When providing an answer, the providing unit can apply different display formats depending on the category of information. Examples of categories include, but are not limited to, business categories, technical categories, and customer categories. For example, in the providing unit, the generation AI provides an answer in text format for text information. For example, the generation AI provides an answer for text information in text format. In addition, the providing unit can also provide an answer for image information in image format. For example, the generation AI provides an answer for image information in image format. In addition, the providing unit can also provide an answer for visual information such as graphs and tables in an appropriate display format. For example, the generation AI provides an answer for visual information such as graphs and tables in an appropriate display format. This enables efficient information provision by applying different display formats depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to apply a display format depending on the category of information.

[0090] When providing an answer, the providing unit can improve the accuracy of the answer by referring to the user's past answer results. Past answer results include, but are not limited to, answer history and resolved cases. For example, the providing unit causes the generation AI to improve the accuracy of the answer based on the user's past answer results. For example, the generation AI analyzes the user's past answer results to improve the accuracy of the answer. The providing unit can also analyze the user's past answer patterns and have the generation AI select the optimal answer method. For example, the generation AI analyzes the user's past answer patterns and selects the optimal answer method. The providing unit can also use the user's past answer results as feedback so that the generation AI can continuously improve the accuracy of the answer. For example, the generation AI uses the user's past answer results as feedback to continuously improve the accuracy of the answer. As a result, the accuracy of the answer is improved by referring to the user's past answer results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can cause the generation AI to analyze the user's past answer results and improve the accuracy of the answer.

[0091] The providing unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the providing unit can provide the generation AI with a short, to-the-point answer. For example, if the generation AI recognizes the user's facial expression and determines that the user is in a hurry, it can provide a short, to-the-point answer. Furthermore, if the user is relaxed, the providing unit can provide the generation AI with a longer answer including detailed explanations. For example, if the generation AI analyzes the user's voice and determines that the user is relaxed, it can provide a longer answer including detailed explanations. Furthermore, if the user is excited, the providing unit can provide the generation AI with an answer that adds visually stimulating effects. For example, if the generation AI analyzes the user's text and determines that the user is excited, it can provide an answer that adds visually stimulating effects. This enables efficient information provision by adjusting the length of the answer according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit may cause the generation AI to estimate the user's emotions and adjust the length of the response.

[0092] When providing an answer, the providing unit can determine the priority of the answer based on the time of submission of the information. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the providing unit causes the generation AI to prioritize providing an answer for the most recent information. For example, the generation AI evaluates the time of submission of the information and prioritizes providing an answer for the most recent information. The providing unit can also cause the generation AI to postpone providing an answer for older information. For example, the generation AI evaluates the time of submission of the information and postpones providing an answer for older information. The providing unit can also cause the generation AI to dynamically adjust the priority of the answers depending on the time of submission of the information. For example, the generation AI evaluates the time of submission of the information and dynamically adjusts the priority of the answers. This enables efficient information provision by determining the priority of answers based on the time of submission of the information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to evaluate the time of submission of the information and determine the priority of the answers.

[0093] When providing answers, the providing unit can adjust the order of answers based on the relevance of the information. Evaluation of relevance includes, but is not limited to, keyword matching and category matching. For example, the providing unit allows the generation AI to prioritize providing answers to highly relevant information. For example, the generation AI evaluates the relevance of information and prioritizes providing answers to highly relevant information. The providing unit can also allow the generation AI to postpone providing answers to less relevant information. For example, the generation AI evaluates the relevance of information and postpones providing answers to less relevant information. The providing unit can also allow the generation AI to dynamically adjust the order of answers based on the relevance of the information. For example, the generation AI evaluates the relevance of information and dynamically adjusts the order of answers. This enables efficient information provision by adjusting the order of answers based on the relevance of information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to evaluate the relevance of information and adjust the order of answers.

[0094] When providing an answer, the providing unit can adjust the use of technical terminology in the answer depending on the user's level of expertise. Evaluations of the level of expertise include, but are not limited to, the presence or absence of qualifications and years of work experience. For example, if the user has technical expertise, the providing unit causes the generation AI to provide an answer using technical terminology. For example, if the generation AI evaluates the user's level of expertise and determines that the user has technical expertise, it provides the answer using technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also cause the generation AI to provide an answer in simple language. For example, if the generation AI evaluates the user's level of expertise and determines that the user does not have technical expertise, it provides the answer in simple language. Furthermore, the providing unit can also cause the generation AI to adjust the way the answer is expressed depending on the user's level of expertise. For example, the generation AI evaluates the user's level of expertise and adjusts the way the answer is expressed. This enables efficient information provision by adjusting the use of technical terminology in the answer depending on the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to evaluate the user's level of expertise and adjust the way the answer is expressed.

[0095] The providing unit can provide an answer including a visual display of the information. Examples of visual displays include, but are not limited to, graphs, tables, charts, etc. For example, if a user requests visual information, the providing unit causes the generation AI to provide an answer including a graph or table. For example, the generation AI provides an answer including a graph or table in response to a user's inquiry. Furthermore, if a user requests text information, the providing unit can also cause the generation AI to provide an answer in text format. For example, the generation AI provides an answer in text format in response to a user's inquiry. Furthermore, the providing unit can also suggest an optimal display format based on the user's past selection history. For example, the generation AI analyzes the user's past selection history and suggests an optimal display format. In this way, including a visual display of the information makes it easier for the user to understand the information. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can cause the generation AI to generate and provide a visual display. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned storage unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned storage unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned storage unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the storage unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned storage unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can predict the intent of a query based on the user's past inquiry history and provide an appropriate answer. For example, if a user has frequently inquired about the "progress of a project" in the past, the generation AI can analyze that history and automatically provide information about the progress the next time the user inquires. Also, if a user frequently uses a specific keyword, it can prioritize searches for information related to that keyword. Furthermore, it is possible to analyze the user's inquiry history and refer to data from other users with similar inquiry patterns to provide more accurate answers. This enables efficient information provision that takes into account the user's past behavior.

[0098] The providing unit can estimate the user's emotions and adjust the tone of the answer based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can provide an answer in a gentle tone, and if the user is relaxed, it can provide an answer in a more detailed and professional tone. Also, if the user is excited, the generation AI can provide an answer in an energetic tone. Furthermore, it is possible to adjust the length and level of detail of the answer depending on the user's emotions. This enables flexible information provision that takes the user's emotions into consideration.

[0099] The storage unit can prioritize the storage of region-specific information based on the user's geographical location information. For example, if the user is in a specific region, news and event information related to that region can be automatically stored. Also, if the user is traveling, tourist information and traffic information for the destination can be prioritized. Furthermore, if the user frequently visits a specific region, it is also possible to store business information and market trends related to that region. This allows for the storage of highly relevant information based on the user's geographical location.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is making an urgent inquiry, the generation AI can detect that emotion and prioritize analysis. If the user is relaxed, analysis can be performed with normal priority. Furthermore, if the user is feeling stressed, the generation AI can quickly analyze and provide a response as soon as possible. This allows for flexible analysis according to the user's emotions.

[0101] The providing unit can improve the accuracy of answers based on the user's past feedback. For example, the generating AI analyzes feedback provided by the user in the past and adjusts the content of the answer by reflecting that feedback. Also, if a user gives a high rating to a specific answer, the AI ​​can refer to that answering method when providing future answers. Furthermore, it is possible to customize the format and expression of answers based on the user's feedback. This makes it possible to provide highly accurate information by utilizing user feedback.

[0102] The storage unit can estimate the user's emotions and filter information based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can detect that emotion and prioritize storing relaxation information and entertainment information to reduce stress. Also, if the user is concentrating, the generation AI can detect that emotion and prioritize storing information related to work. Furthermore, if the user is tired, the generation AI can detect that emotion and prioritize storing information about rest. This allows for flexible information filtering according to the user's emotions.

[0103] The providing unit can adjust the level of detail of the answer depending on the user's level of expertise. For example, if the user has specialized knowledge, the generating AI can provide detailed technical information, and if the user does not have specialized knowledge, the generating AI can provide an explanation in simple terms. Also, if the user has an intermediate level of knowledge, the generating AI can provide an answer with a medium level of detail. Furthermore, it is possible to customize the format and expression of the answer depending on the user's level of expertise. This allows for flexible information provision according to the user's level of expertise.

[0104] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the generation AI will provide a simple, highly visible display method, and if the user is relaxed, it will provide a display method with detailed information. Also, if the user is in a hurry, the generation AI can provide a concise display method that focuses on the main points. Furthermore, it is possible to adjust the color and font size of the display method depending on the user's emotions. This allows for flexible information display that takes the user's emotions into consideration.

[0105] The storage unit can analyze a user's social media activity and store related information. For example, it can automatically store information shared by the user on social media and store related information by analyzing the user's posts. It can also store related information by referring to the activities of the user's friends. It can also store highly relevant information preferentially based on the content of the user's likes and comments on social media. This allows for efficient information storage that utilizes the user's social media activity.

[0106] The providing unit can estimate the user's emotions and adjust the length of the answer based on the estimated emotions. For example, if the user is in a hurry, the generation AI can provide a short, to-the-point answer, while if the user is relaxed, it can provide a longer answer with detailed explanations. Also, if the user is excited, the generation AI can provide an answer with a visually stimulating effect. Furthermore, it is possible to customize the format and expression of the answer depending on the user's emotions. This allows for flexible information provision according to the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The storage unit stores information from the user. Information from the user includes text information, image information, audio information, etc. The storage unit automatically classifies the information entered by the user into categories. For example, it can classify the information into business categories, technical categories, customer categories, etc. The storage unit also uses a generation AI to estimate the user's emotions and adjusts the timing of information storage based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically stops storing information temporarily and resumes it when the user is relaxed. Step 2: The analysis unit uses the generation AI to analyze the information accumulated by the accumulation unit and provide a specific answer based on the user's inquiry. The analysis is performed using natural language processing technology. For example, the generation AI analyzes the user's inquiry and searches for information from the appropriate category. The analysis unit also estimates the user's emotions and adjusts the method of analyzing the inquiry based on the estimated user emotions. For example, if the user is nervous, the generation AI selects a simple analysis method and provides a quick answer. Step 3: The providing unit provides the answer provided by the analysis unit to the user. The providing unit can provide answers not only in text format but also visual information such as graphs and tables. The providing unit also uses the generation AI to estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, if the user is nervous, the generation AI will provide a simple, highly visible way of expressing the answer.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0137] 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.

[0138] 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.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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, in order to avoid confusion and to 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 storage unit for storing information from a user; an analysis unit that analyzes the information stored by the storage unit and provides a specific answer based on an inquiry from a user; a providing unit that provides the answer provided by the analysis unit to the user. A system characterized by:

2. The storage unit is Automatically categorize information entered by the user 2. The system of claim 1.

3. The analysis unit Analyze user inquiries using natural language processing technology and search for information from the appropriate category 2. The system of claim 1.

4. The providing unit Provide answers in text format as well as visual information such as graphs and tables 2. The system of claim 1.

5. The providing unit Providing necessary information to new employees when transferring business know-how 2. The system of claim 1.

6. The storage unit is Estimate the user's emotions and adjust the timing of information accumulation based on the results 2. The system of claim 1.

7. The storage unit is Analyze the user's past information input history and select the optimal storage method 2. The system of claim 1.

8. The storage unit is As information accumulates, it is filtered based on the user's current projects and areas of interest.

2. The system of claim 1.

9. The storage unit is When storing information, select the optimal storage method according to the user's input method.

2. The system of claim 1.

10. The storage unit is Estimates user emotions, evaluates the importance of information based on the results, and determines the priority of storage.

2. The system of claim 1.

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