System

Abstract

An object of a system according to an embodiment is to enable quick access to necessary information by organizing and centrally managing information assets.SOLUTION: A system includes an information organization part, a fine tuning part, and an association presentation part. The information organization unit organizes and centrally manages information assets. The fine tuning unit optimizes the information organized by the information organizing unit. The countermeasure presentation unit presents a countermeasure procedure based on the information optimized by the fine tuning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the problem that information assets are dispersed, making it difficult to quickly access necessary information.

[0005] The system according to the embodiment aims to enable quick access to necessary information by organizing and centrally managing information assets. [Means for solving the problem]

[0006] The system according to the embodiment includes an information organization unit, a fine-tuning unit, and a response presentation unit. The information organization unit organizes and centrally manages information assets. The fine-tuning unit optimizes the information organized by the information organization unit. The response presentation unit presents a response procedure based on the information optimized by the fine-tuning unit. [Effects of the Invention]

[0007] The system according to the embodiment can organize and centrally manage information assets, thereby enabling quick access to required information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The project support system according to the embodiment of the present invention is a system that, in response to a project consultation from a department, presents step-by-step responses to similar project consultations that have occurred in the past. As a result, the project support system allows accounting staff to efficiently search for information and quickly access the information they need.

[0029] A case handling support system according to an embodiment includes an information organization unit, a fine-tuning unit, and a response presentation unit. The information organization unit organizes and centrally manages information assets. For example, the information organization unit has a function of collecting and organizing data related to past case consultations. The information organization unit registers, for example, the content, response methods, and results of past case consultations in a database and makes them searchable. The fine-tuning unit optimizes the information organized by the information organization unit. For example, the fine-tuning unit fine-tunes the information using a generation AI. The generation AI analyzes past case consultation data and proposes an optimal information organization method. The generation AI classifies the data according to the case type and response method, for example, and organizes it into a format that is easy to search. The response presentation unit presents response procedures based on the information optimized by the fine-tuning unit. For example, when a department receives a new case consultation, the response presentation unit uses the generation AI to present step-by-step responses to similar past cases. The generation AI provides specific procedures, for example, in the form of, "When a similar case occurred in the past, we first checked XX, and then performed XX." This enables the case response support system to organize and optimize information assets and present response procedures.

[0030] The information organizing unit can automatically tag case consultation data and organize the information hierarchically based on the tags. The information organizing unit, for example, uses generation AI to automatically tag past case consultation data. For example, tags are attached according to the type of case and the response method, and the information is organized hierarchically. Based on the tagged data, the information organizing unit has the generation AI organize the information hierarchically and format it in an easy-to-search format. For example, folders are created based on tags and related information is organized. When the generation AI performs tagging, the information organizing unit analyzes the content of past case consultation data and automatically selects appropriate tags. For example, tags are attached based on the content of the case and the response method. This makes it possible to organize information hierarchically.

[0031] The information organization unit can evaluate the importance and urgency of each case and organize information by prioritizing it. In the information organization unit, for example, the generation AI analyzes past case consultation data and evaluates the importance and urgency of each case. For example, the evaluation is based on the scope of the case's impact and the urgency of the response. In the information organization unit, the generation AI prioritizes and organizes the information based on the evaluation results. For example, cases with high importance and urgency are placed at the top and the information is organized in an easy-to-search format. In the information organization unit, the generation AI automatically sets evaluation criteria and quantitatively evaluates the importance and urgency of each case. For example, the scope of the case's impact and the urgency of the response are evaluated numerically. This makes it possible to prioritize information.

[0032] The fine-tuning unit can automatically select and apply the optimal information organization algorithm based on case consultation data. In the fine-tuning unit, for example, the generation AI analyzes past case consultation data and automatically selects the optimal information organization algorithm. For example, it selects an algorithm based on the type and amount of data. In the fine-tuning unit, the generation AI applies the selected algorithm to optimize the information. For example, it classifies and tags the data and formats it for easy searching. In the fine-tuning unit, the generation AI evaluates the effectiveness of the information organization algorithm and re-selects the algorithm as necessary. For example, it evaluates based on the accuracy and speed of search results. This allows the optimal information organization algorithm to be automatically selected and applied.

[0033] The fine-tuning unit can detect duplicate and redundant information and automatically delete unnecessary information. For example, the generation AI analyzes past case consultation data to detect duplicate and redundant information. For example, it identifies data with the same content or similar data. The fine-tuning unit then automatically deletes the detected duplicate and redundant information. For example, it combines data with the same content into one. The fine-tuning unit allows the generation AI to detect duplicate and redundant information in real time during the information organization process and deletes it immediately. For example, it deletes newly added data if it overlaps with existing data. This makes it possible to remove duplicate and redundant information.

[0034] The fine-tuning unit can integrate data of different formats and manage it centrally. For example, the fine-tuning unit builds a system in which the generation AI analyzes and integrates data of different formats. For example, it manages text data, image data, and audio data centrally. The fine-tuning unit allows the generation AI to organize the integrated data and put it in a format that is easy to search. For example, it tags and classifies data of different formats. The fine-tuning unit allows the generation AI to integrate data of different formats in real time and manage it immediately. For example, it automatically integrates newly added data. This allows data of different formats to be integrated and managed centrally.

[0035] The fine-tuning unit learns the user's search history and behavioral patterns, and can propose an individually optimized information organization method. In the fine-tuning unit, for example, the generation AI analyzes the user's search history and behavioral patterns, and proposes an individually optimized information organization method. For example, it presents the optimal organization method based on the user's past search history. The fine-tuning unit learns the user's behavioral patterns, and the generation AI dynamically adjusts the information organization method. For example, it changes the organization method based on the user's search frequency and search content. In the fine-tuning unit, the generation AI continuously optimizes the information organization method based on user feedback. For example, it improves the organization method based on user evaluations. This makes it possible to propose an information organization method that is optimized based on the user's search history and behavioral patterns.

[0036] The response presentation unit can automatically generate and present optimal response procedures based on case consultation data. In the response presentation unit, for example, the generation AI analyzes past case consultation data and automatically generates optimal response procedures. For example, procedures are generated based on the type of case and the response method. In the response presentation unit, the generation AI presents the generated response procedures so that the user can carry them out step by step. For example, the procedures are displayed in order and the user can proceed while checking them. In the response presentation unit, the generation AI evaluates the effectiveness of the response procedures and regenerates the procedures as necessary. For example, the procedures are improved based on past success stories and failure stories. In this way, optimal response procedures can be automatically generated and presented.

[0037] The response presentation unit monitors the execution results of each step in real time and can modify the procedure as necessary. For example, the response presentation unit builds a system in which the generation AI monitors the execution results of each step in real time and modifies the procedure as necessary. For example, it dynamically adjusts the procedure based on the execution results. The response presentation unit has the generation AI analyze the execution results and evaluate the effectiveness of the procedure. For example, it identifies areas for improvement in the procedure based on the success rate or execution time. The response presentation unit has the generation AI regenerate the procedure based on the execution results and present it to the user. For example, it suggests a new procedure if the execution results are insufficient. This makes it possible to monitor the execution results of each step in real time and modify the procedure.

[0038] The response presentation unit can automatically extract common procedures for different case consultations and create generic response procedures. In the response presentation unit, for example, the generation AI analyzes past case consultation data and automatically extracts common procedures. For example, it identifies response methods common to multiple cases. In the response presentation unit, the generation AI creates generic response procedures based on the extracted common procedures. For example, it consolidates the common procedures and presents them as a single procedure. In the response presentation unit, the generation AI evaluates the effectiveness of the generic response procedures and regenerates the procedures as necessary. For example, it improves the procedures based on past success stories and failure stories. This makes it possible to create generic response procedures.

[0039] The response presentation unit can present customized response procedures according to the user's skill level and experience. In the response presentation unit, for example, the generation AI analyzes the user's skill level and experience and presents customized response procedures. For example, it provides procedures for beginners and procedures for advanced users. In the response presentation unit, the generation AI dynamically adjusts the procedures according to the user's skill level and experience. For example, it improves the procedures based on user feedback. In the response presentation unit, the generation AI learns the user's skill level and experience and continuously provides optimal procedures. For example, it updates the procedures according to the user's growth. This makes it possible to customize the procedures according to the user's skill level and experience.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The case handling support system can further include a skill evaluation unit that evaluates the skill level of the user. The skill evaluation unit analyzes the user's past case handling history and performance to evaluate the skill level. For example, it calculates the skill level based on the success rate and response time of past case handling. Based on the evaluation results, the skill evaluation unit can present a response procedure that is suitable for the user. For example, it can provide procedures for beginners and procedures for advanced users. This makes it possible to provide the optimal response procedure according to the user's skill level.

[0042] The case response support system can further include a feedback collection unit that collects user feedback. After a user performs a response procedure, the feedback collection unit collects feedback about the effectiveness of the procedure and areas for improvement. For example, it provides an interface for the user to evaluate each step of the procedure. The feedback collection unit can analyze the collected feedback and use it to improve the response procedure. For example, it can identify steps that users have rated poorly and suggest improvements. This makes it possible to continuously improve the response procedure based on user feedback.

[0043] The case response support system can further include a behavior learning unit that learns the user's behavioral patterns. The behavior learning unit analyzes the user's past operation history and search history to learn the user's behavioral patterns. For example, it identifies keywords that the user frequently searches for and information that the user frequently accesses. The behavior learning unit can provide the user with the most suitable information based on the learned behavioral patterns. For example, it can display information that the user frequently searches for with priority. This makes it possible to provide the most suitable information based on the user's behavioral patterns.

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

[0045] Step 1: The Information Organization Department organizes and centrally manages information assets. For example, the Information Organization Department has the function of collecting and organizing data related to past consultations. The Information Organization Department registers the content, response methods, and results of past consultations in a database and makes them searchable. Step 2: The fine-tuning unit optimizes the information organized by the information organization unit. For example, the fine-tuning unit uses generation AI to fine-tune the information. The generation AI analyzes past case consultation data and proposes the optimal method for organizing information. The generation AI classifies the data according to the type of case and how it is handled, and organizes it into a format that is easy to search. Step 3: The response presentation unit presents response procedures based on the information optimized by the fine-tuning unit. For example, when a department consults on a new case, the response presentation unit uses the generation AI to present step-by-step responses to similar cases in the past. The generation AI presents specific procedures in the form of, "When a similar case occurred in the past, we first checked XX, and then performed XX."

[0046] (Example 2) The project support system according to the embodiment of the present invention is a system that, in response to a project consultation from a department, presents step-by-step responses to similar project consultations that have occurred in the past. As a result, the project support system allows accounting staff to efficiently search for information and quickly access the information they need.

[0047] A case handling support system according to an embodiment includes an information organization unit, a fine-tuning unit, and a response presentation unit. The information organization unit organizes and centrally manages information assets. For example, the information organization unit has a function of collecting and organizing data related to past case consultations. The information organization unit registers, for example, the content, response methods, and results of past case consultations in a database and makes them searchable. The fine-tuning unit optimizes the information organized by the information organization unit. For example, the fine-tuning unit fine-tunes the information using a generation AI. The generation AI analyzes past case consultation data and proposes an optimal information organization method. The generation AI classifies the data according to the case type and response method, for example, and organizes it into a format that is easy to search. The response presentation unit presents response procedures based on the information optimized by the fine-tuning unit. For example, when a department receives a new case consultation, the response presentation unit uses the generation AI to present step-by-step responses to similar past cases. The generation AI provides specific procedures, for example, in the form of, "When a similar case occurred in the past, we first checked XX, and then performed XX." This enables the case response support system to organize and optimize information assets and present response procedures.

[0048] The information organizing unit can automatically tag case consultation data and organize the information hierarchically based on the tags. The information organizing unit, for example, uses generation AI to automatically tag past case consultation data. For example, tags are attached according to the type of case and the response method, and the information is organized hierarchically. Based on the tagged data, the information organizing unit has the generation AI organize the information hierarchically and format it in an easy-to-search format. For example, folders are created based on tags and related information is organized. When the generation AI performs tagging, the information organizing unit analyzes the content of past case consultation data and automatically selects appropriate tags. For example, tags are attached based on the content of the case and the response method. This makes it possible to organize information hierarchically.

[0049] The information organization unit can evaluate the importance and urgency of each case and organize information by prioritizing it. In the information organization unit, for example, the generation AI analyzes past case consultation data and evaluates the importance and urgency of each case. For example, the evaluation is based on the scope of the case's impact and the urgency of the response. In the information organization unit, the generation AI prioritizes and organizes the information based on the evaluation results. For example, cases with high importance and urgency are placed at the top and the information is organized in an easy-to-search format. In the information organization unit, the generation AI automatically sets evaluation criteria and quantitatively evaluates the importance and urgency of each case. For example, the scope of the case's impact and the urgency of the response are evaluated numerically. This makes it possible to prioritize information.

[0050] The information organizing unit can use the emotion estimation function to analyze the emotions of the person in charge during case consultations and highlight emotionally important information. The information organizing unit, for example, uses the emotion estimation function to analyze the emotions of the person in charge during past case consultations. For example, it calculates an emotion score for the consultation content and the response method. The information organizing unit highlights emotionally important information based on the emotion score. For example, it displays information with strong positive emotions in a conspicuous manner. The information organizing unit uses the emotion estimation function to analyze past case consultation data and automatically extract emotionally important information. For example, it displays information with a high emotion score preferentially. This makes it possible to highlight emotionally important information.

[0051] The fine-tuning unit can automatically select and apply the optimal information organization algorithm based on case consultation data. In the fine-tuning unit, for example, the generation AI analyzes past case consultation data and automatically selects the optimal information organization algorithm. For example, it selects an algorithm based on the type and amount of data. In the fine-tuning unit, the generation AI applies the selected algorithm to optimize the information. For example, it classifies and tags the data and formats it for easy searching. In the fine-tuning unit, the generation AI evaluates the effectiveness of the information organization algorithm and re-selects the algorithm as necessary. For example, it evaluates based on the accuracy and speed of search results. This allows the optimal information organization algorithm to be automatically selected and applied.

[0052] The fine-tuning unit can detect duplicate and redundant information and automatically delete unnecessary information. For example, the generation AI analyzes past case consultation data to detect duplicate and redundant information. For example, it identifies data with the same content or similar data. The fine-tuning unit then automatically deletes the detected duplicate and redundant information. For example, it combines data with the same content into one. The fine-tuning unit allows the generation AI to detect duplicate and redundant information in real time during the information organization process and deletes it immediately. For example, it deletes newly added data if it overlaps with existing data. This makes it possible to remove duplicate and redundant information.

[0053] The fine-tuning unit uses the emotion estimation function to analyze the user's emotions during the information organization process and can optimize the information so that the user does not feel stressed. The fine-tuning unit, for example, uses the emotion estimation function to analyze the user's emotions in real time during the information organization process. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The fine-tuning unit optimizes the information so that the user does not feel stressed based on the emotion score. For example, it adjusts the way the information is displayed if the emotion score is low. The fine-tuning unit uses the emotion estimation function to provide feedback according to the user's emotions during the information organization process. For example, it presents information that will help the user relax if the user feels stressed. This allows the information to be optimized so that the user does not feel stressed.

[0054] The fine-tuning unit can integrate data of different formats and manage it centrally. For example, the fine-tuning unit builds a system in which the generation AI analyzes and integrates data of different formats. For example, it manages text data, image data, and audio data centrally. The fine-tuning unit allows the generation AI to organize the integrated data and put it in a format that is easy to search. For example, it tags and classifies data of different formats. The fine-tuning unit allows the generation AI to integrate data of different formats in real time and manage it immediately. For example, it automatically integrates newly added data. This allows data of different formats to be integrated and managed centrally.

[0055] The fine-tuning unit learns the user's search history and behavioral patterns, and can propose an individually optimized information organization method. In the fine-tuning unit, for example, the generation AI analyzes the user's search history and behavioral patterns, and proposes an individually optimized information organization method. For example, it presents the optimal organization method based on the user's past search history. The fine-tuning unit learns the user's behavioral patterns, and the generation AI dynamically adjusts the information organization method. For example, it changes the organization method based on the user's search frequency and search content. In the fine-tuning unit, the generation AI continuously optimizes the information organization method based on user feedback. For example, it improves the organization method based on user evaluations. This makes it possible to propose an information organization method that is optimized based on the user's search history and behavioral patterns.

[0056] The fine-tuning unit can use the emotion estimation function to monitor the user's emotions in real time during the information optimization process and apply an information organization method that elicits positive emotions. The fine-tuning unit, for example, uses the emotion estimation function to monitor the user's emotions in real time during the information optimization process. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The fine-tuning unit applies an information organization method that elicits positive emotions based on the emotion score. For example, it preferentially displays information with a high emotion score. The fine-tuning unit uses the emotion estimation function to dynamically adjust the information organization method according to the user's emotions. For example, if negative emotions are detected, it preferentially displays positive information. This makes it possible to optimize the information organization method according to the user's emotions.

[0057] The response presentation unit can automatically generate and present optimal response procedures based on case consultation data. In the response presentation unit, for example, the generation AI analyzes past case consultation data and automatically generates optimal response procedures. For example, procedures are generated based on the type of case and the response method. In the response presentation unit, the generation AI presents the generated response procedures so that the user can carry them out step by step. For example, the procedures are displayed in order and the user can proceed while checking them. In the response presentation unit, the generation AI evaluates the effectiveness of the response procedures and regenerates the procedures as necessary. For example, the procedures are improved based on past success stories and failure stories. In this way, optimal response procedures can be automatically generated and presented.

[0058] The response presentation unit monitors the execution results of each step in real time and can modify the procedure as necessary. For example, the response presentation unit builds a system in which the generation AI monitors the execution results of each step in real time and modifies the procedure as necessary. For example, it dynamically adjusts the procedure based on the execution results. The response presentation unit has the generation AI analyze the execution results and evaluate the effectiveness of the procedure. For example, it identifies areas for improvement in the procedure based on the success rate or execution time. The response presentation unit has the generation AI regenerate the procedure based on the execution results and present it to the user. For example, it suggests a new procedure if the execution results are insufficient. This makes it possible to monitor the execution results of each step in real time and modify the procedure.

[0059] The correspondence presentation unit can use the emotion estimation function to analyze the emotion the user feels when performing each step and present a procedure that elicits positive emotions. The correspondence presentation unit, for example, uses the emotion estimation function to analyze the emotion the user feels when performing each step in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The correspondence presentation unit presents a procedure that elicits positive emotions based on the emotion score. For example, it preferentially displays procedures with high emotion scores. The correspondence presentation unit uses the emotion estimation function to dynamically adjust procedures according to the user's emotions. For example, if negative emotions are detected, it preferentially displays positive procedures. This makes it possible to optimize procedures according to the user's emotions.

[0060] The response presentation unit can automatically extract common procedures for different case consultations and create generic response procedures. In the response presentation unit, for example, the generation AI analyzes past case consultation data and automatically extracts common procedures. For example, it identifies response methods common to multiple cases. In the response presentation unit, the generation AI creates generic response procedures based on the extracted common procedures. For example, it consolidates the common procedures and presents them as a single procedure. In the response presentation unit, the generation AI evaluates the effectiveness of the generic response procedures and regenerates the procedures as necessary. For example, it improves the procedures based on past success stories and failure stories. This makes it possible to create generic response procedures.

[0061] The response presentation unit can present customized response procedures according to the user's skill level and experience. In the response presentation unit, for example, the generation AI analyzes the user's skill level and experience and presents customized response procedures. For example, it provides procedures for beginners and procedures for advanced users. In the response presentation unit, the generation AI dynamically adjusts the procedures according to the user's skill level and experience. For example, it improves the procedures based on user feedback. In the response presentation unit, the generation AI learns the user's skill level and experience and continuously provides optimal procedures. For example, it updates the procedures according to the user's growth. This makes it possible to customize the procedures according to the user's skill level and experience.

[0062] The correspondence presentation unit uses the emotion estimation function to monitor the emotion of the user when performing each step in real time, and can continuously optimize procedures that elicit positive emotions. The correspondence presentation unit, for example, uses the emotion estimation function to monitor the emotion of the user when performing each step in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The correspondence presentation unit continuously optimizes procedures that elicit positive emotions based on the emotion score. For example, it preferentially displays procedures with high emotion scores. The correspondence presentation unit uses the emotion estimation function to dynamically adjust procedures according to the user's emotions. For example, if negative emotions are detected, it preferentially displays positive procedures. This makes it possible to continuously optimize procedures according to the user's emotions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The case handling support system can further include a skill evaluation unit that evaluates the skill level of the user. The skill evaluation unit analyzes the user's past case handling history and performance to evaluate the skill level. For example, it calculates the skill level based on the success rate and response time of past case handling. Based on the evaluation results, the skill evaluation unit can present a response procedure that is suitable for the user. For example, it can provide procedures for beginners and procedures for advanced users. This makes it possible to provide the optimal response procedure according to the user's skill level.

[0065] The case response support system can further include a feedback collection unit that collects user feedback. After a user performs a response procedure, the feedback collection unit collects feedback about the effectiveness of the procedure and areas for improvement. For example, it provides an interface for the user to evaluate each step of the procedure. The feedback collection unit can analyze the collected feedback and use it to improve the response procedure. For example, it can identify steps that users have rated poorly and suggest improvements. This makes it possible to continuously improve the response procedure based on user feedback.

[0066] The case response support system can further include a behavior learning unit that learns the user's behavioral patterns. The behavior learning unit analyzes the user's past operation history and search history to learn the user's behavioral patterns. For example, it identifies keywords that the user frequently searches for and information that the user frequently accesses. The behavior learning unit can provide the user with the most suitable information based on the learned behavioral patterns. For example, it can display information that the user frequently searches for with priority. This makes it possible to provide the most suitable information based on the user's behavioral patterns.

[0067] The case response support system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the response procedures based on the estimated emotions. The emotion adjustment unit analyzes the user's facial expressions and voice and calculates an emotion score. For example, if the user is feeling stressed, the emotion adjustment unit can simplify the procedures. Based on the emotion score, the emotion adjustment unit can provide information that helps the user relax. For example, procedures that elicit positive emotions can be displayed preferentially. This allows the response procedures to be adjusted according to the user's emotions.

[0068] The case response support system can further include an emotion feedback unit that estimates the user's emotion and provides feedback based on the estimated emotion. The emotion feedback unit analyzes the user's facial expression and voice and calculates an emotion score. For example, if the user is showing positive emotion, a praising message is displayed. The emotion feedback unit can provide feedback that helps the user maintain their motivation based on the emotion score. For example, if the user is showing negative emotion, an encouraging message is displayed. This makes it possible to provide feedback that corresponds to the user's emotion.

[0069] The case response support system can further include an emotion display adjustment unit that estimates the user's emotion and adjusts the information display method based on the estimated emotion. The emotion display adjustment unit analyzes the user's facial expressions and voice and calculates an emotion score. For example, if the user is feeling stressed, the emotion display adjustment unit displays information in a concise manner. Based on the emotion score, the emotion display adjustment unit can provide a display method that allows the user to relax. For example, it can use colors and fonts that evoke positive emotions. This makes it possible to adjust the information display method according to the user's emotion.

[0070] The case response support system can further include an emotion priority adjustment unit that estimates the user's emotion and adjusts the priority of information based on the estimated emotion. The emotion priority adjustment unit analyzes the user's facial expression and voice and calculates an emotion score. For example, if the user is showing positive emotion, important information is displayed with priority. The emotion priority adjustment unit can provide information that helps the user relax based on the emotion score. For example, if the user is showing negative emotion, relaxing information is displayed with priority. This makes it possible to adjust the priority of information according to the user's emotion.

[0071] The case response support system can further include an emotion display timing adjustment unit that estimates the user's emotion and adjusts the timing of displaying information based on the estimated emotion. The emotion display timing adjustment unit analyzes the user's facial expressions and voice and calculates an emotion score. For example, if the user is feeling stressed, the emotion display timing adjustment unit delays the display of information. Based on the emotion score, the emotion display timing adjustment unit can provide information at a timing that allows the user to relax. For example, the information can be displayed at a timing that elicits positive emotions. This makes it possible to adjust the timing of displaying information according to the user's emotion.

[0072] The case response support system can further include an emotion display format adjustment unit that estimates the user's emotion and adjusts the display format of information based on the estimated emotion. The emotion display format adjustment unit analyzes the user's facial expressions and voice and calculates an emotion score. For example, if the user is feeling stressed, the emotion display format adjustment unit displays information in a concise manner. Based on the emotion score, the emotion display format adjustment unit can provide a display format that allows the user to relax. For example, it can use colors and fonts that evoke positive emotions. This allows the display format of information to be adjusted according to the user's emotion.

[0073] The case response support system can further include an emotion display order adjustment unit that estimates the user's emotion and adjusts the display order of information based on the estimated emotion. The emotion display order adjustment unit analyzes the user's facial expression and voice and calculates an emotion score. For example, if the user is showing positive emotion, important information is displayed with priority. The emotion display order adjustment unit can provide information that helps the user relax based on the emotion score. For example, if the user is showing negative emotion, relaxing information is displayed with priority. This makes it possible to adjust the display order of information according to the user's emotion.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The Information Organization Department organizes and centrally manages information assets. For example, the Information Organization Department has the function of collecting and organizing data related to past consultations. The Information Organization Department registers the content, response methods, and results of past consultations in a database and makes them searchable. Step 2: The fine-tuning unit optimizes the information organized by the information organization unit. For example, the fine-tuning unit uses generation AI to fine-tune the information. The generation AI analyzes past case consultation data and proposes the optimal method for organizing information. The generation AI classifies the data according to the type of case and how it is handled, and organizes it into a format that is easy to search. Step 3: The response presentation unit presents response procedures based on the information optimized by the fine-tuning unit. For example, when a department consults on a new case, the response presentation unit uses the generation AI to present step-by-step responses to similar cases in the past. The generation AI presents specific procedures in the form of, "When a similar case occurred in the past, we first checked XX, and then performed XX."

[0076] 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.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] 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.

[0103] 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.

[0104] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0143] 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. The Information Organizing Department organizes and centrally manages information assets, a fine tuning unit that optimizes the information organized by the information organizing unit; a response presentation unit that presents a response procedure based on the information optimized by the fine tuning unit. A system characterized by:

2. The information organizing unit Automatically tag case consultation data and organize information hierarchically based on the tags 2. The system of claim 1.

3. The information organizing unit Evaluate the importance and urgency of each issue, prioritize, and organize information 2. The system of claim 1.

4. The information organizing unit Analyze the emotions of staff during case consultations and highlight emotionally important information 2. The system of claim 1.

5. The fine tuning unit Automatically selects and applies the optimal information organization algorithm based on case consultation data 2. The system of claim 1.

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