System

Abstract

An object of the system according to the embodiment is to effectively utilize a chat log of a health consultation and to convert the chat log into content.SOLUTION: A system according to an embodiment includes a collection unit, a sorting unit, and a generation unit. The collection unit collects a chat log. The organizing unit organizes the chat logs collected by the collecting unit into question contents and answers. The generation unit generates an HTML file based on the question content and the answer arranged by the arrangement unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, chat logs of health consultations were simply accumulated as the know-how of the person providing the consultation, and this information was not effectively utilized, which was a problem.

[0005] The system according to the embodiment aims to effectively utilize chat logs of health consultations and turn them into content. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an organization unit, and a generation unit. The collection unit collects chat logs. The organization unit organizes the chat logs collected by the collection unit by dividing them into questions and answers. The generation unit generates an HTML file based on the questions and answers organized by the organization unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize chat logs of health consultations and turn them into content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes Healthcare Technologies' health consultation exchanges, organizes them into questions and answers using a generation AI, and generates the content as an HTML file for content creation. For example, the system collects chat logs, inputs them into a generation AI, and organizes them into questions and answers. The generation AI uses natural language processing technology to extract questions and answers from the chat logs and associate them. Next, the system generates an HTML file based on the organized questions and answers. The HTML file organizes the questions and answers in an easy-to-read format, allowing users to easily access them. For example, questions and answers can be categorized and linked to allow users to quickly access the information they need. This allows the system to effectively utilize Healthcare Technologies' health consultation know-how and provide useful content to users. For example, users can refer to past health consultation exchanges to help resolve their health-related questions. This also reduces the burden on the service provider, enabling more efficient health consultations.

[0029] An information processing system according to an embodiment includes a collection unit, an organization unit, and a generation unit. The collection unit collects chat logs. Chat logs include, but are not limited to, text messages, voice messages, and images. The collection unit can automatically collect chat logs for a specific period of time. The collection unit can also estimate a user's emotions and adjust the timing of chat log collection based on the estimated user emotions. For example, if a user is feeling stressed, the collection timing can be delayed to collect chat logs when the user is relaxed. The organization unit uses a generation AI to separate and organize the chat logs collected by the collection unit into questions and answers. The organization can be performed using, for example, topic modeling or clustering, but is not limited to, these examples. For example, the generation AI can classify the chat logs by topic using LDA (Latent Dirichlet Allocation). The organization unit can also estimate a user's emotions and adjust the organization method for the questions and answers based on the estimated user emotions. For example, if a user is feeling stressed, a simple and easy-to-understand organization method can be provided. The generating unit generates an HTML file based on the questions and answers organized by the organizing unit. For example, the HTML file may include questions and answers categorized and provided with links, but is not limited to this example. For example, the generating unit generates the HTML file to allow easy user access. The generating unit may also add a search function to the generated HTML file. For example, the generating unit may implement a keyword search or filtering function. Thus, the information processing system according to the embodiment efficiently collects and organizes chat logs and generates them as HTML files, thereby enabling effective utilization of health consultation know-how.

[0030] The collection unit can automatically collect chat logs for a specific period. The specific period includes, but is not limited to, a date range or a time period. For example, the collection unit has a mechanism for automatically collecting chat logs for a specific period. For example, the collection unit automatically collects chat logs within a specified date range. The collection unit can also automatically collect chat logs for a specific time period. This enables efficient data collection by automatically collecting chat logs for a specific period.

[0031] The organizing unit can organize the questions and answers using topic modeling or clustering. Topic modeling includes, but is not limited to, LDA (Latent Dirichlet Allocation). Clustering includes, but is not limited to, K-means and hierarchical clustering. The organizing unit classifies the chat logs by topic using, for example, LDA. The organizing unit can also divide the chat logs into clusters using K-means. Furthermore, the organizing unit can classify the chat logs hierarchically using hierarchical clustering. As a result, the use of topic modeling or clustering improves the accuracy of organizing the questions and answers.

[0032] The generation unit can classify the questions and answers by category and specify a specific method for providing links. Examples of categories include, but are not limited to, types of health consultations and topics. Examples of links include, but are not limited to, hyperlink formats and link destination selection criteria. For example, the generation unit can classify the questions and answers by type of health consultation and provide links. The generation unit can also classify the questions and answers by topic and provide links. Furthermore, the generation unit can provide appropriate links based on link destination selection criteria. Thus, by classifying by category and providing links, users can quickly access the information they need.

[0033] The generation unit can generate an HTML file that allows easy user access. Methods for easy access include, but are not limited to, a navigation menu or a search function. For example, the generation unit can provide a navigation menu in the HTML file. The generation unit can also add a search function to the HTML file. Furthermore, the generation unit can devise a page layout for the HTML file to allow easy user access. This improves convenience by generating an HTML file that is easy for users to access.

[0034] The generation unit can specify a specific method for adding a search function to the generated HTML file. The search function includes, but is not limited to, a keyword search function and a filtering function, for example. The generation unit can add, for example, a keyword search function to the HTML file. The generation unit can also add a filtering function to the HTML file. Furthermore, the generation unit can also make efforts to display search results in an easy-to-read manner. By adding the search function, the user can quickly search for the information they need.

[0035] When collecting chat logs, the collection unit can analyze the user's past consultation history and select an appropriate collection method. For example, the collection unit preferentially collects related chat logs based on the content of consultations the user has frequently had in the past. The collection unit can also collect chat logs containing specific keywords from the user's past consultation history. Furthermore, the collection unit can analyze the user's past consultation history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past consultation history.

[0036] When collecting chat logs, the collection unit can filter them based on the user's current health condition or areas of interest. For example, the collection unit preferentially collects relevant chat logs based on the user's current health condition. The collection unit can also collect chat logs related to specific topics based on the user's areas of interest. Furthermore, the collection unit can select an optimal filtering method taking into account the user's health condition and areas of interest. As a result, highly relevant data can be collected by filtering based on the user's health condition and areas of interest.

[0037] When collecting chat logs, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user provides a consultation by voice, the collection unit prioritizes collecting voice data. Also, if the user provides a consultation by text, the collection unit can prioritize collecting text data. Furthermore, if the user provides a consultation using images, the collection unit can prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting chat logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes collecting chat logs related to that region. The collection unit can also collect chat logs related to region-specific health issues based on the user's geographical location information. Furthermore, the collection unit can select an optimal collection method by taking into account the user's geographical location information. In this way, data related to region-specific health issues can be prioritized by taking into account the user's geographical location information.

[0039] When collecting chat logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit collects related chat logs based on health information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related chat logs. Furthermore, the collection unit can collect related chat logs by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0040] When collecting chat logs, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit selects the optimal collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the collection means by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the optimal collection method can be selected by reflecting the user's past feedback.

[0041] When organizing the question content and the answer, the organizing unit can adjust the level of detail of the organization based on the importance of the consultation content. For example, the organizing unit performs detailed organization for important consultation content. The organizing unit can also perform concise organization for general consultation content. Furthermore, the organizing unit can also perform minimal organization for consultation content with low importance. In this way, by adjusting the level of detail of the organization based on the importance of the consultation content, important information can be organized in detail.

[0042] When organizing the question content and answers, the organizing unit can apply different organizing algorithms depending on the category of the consultation. For example, the organizing unit applies an organizing algorithm based on a specific health category to a health consultation. The organizing unit can also apply an organizing algorithm based on a mental health category to a mental health consultation. Furthermore, the organizing unit can also apply an organizing algorithm based on a nutrition category to a nutrition consultation. This allows for more appropriate organization by applying different organizing algorithms depending on the category of the consultation.

[0043] When organizing the questions and answers, the organizing unit can improve the accuracy of the organization by referring to the user's past consultation results. For example, the organizing unit organizes related questions and answers based on the user's past consultation results. The organizing unit can also analyze the user's past consultation results to improve the accuracy of the organization. Furthermore, the organizing unit can select the optimal organization method by referring to the user's past consultation results. In this way, the accuracy of the organization is improved by referring to the user's past consultation results.

[0044] When organizing the questions and answers, the organizing unit can determine the order of priority based on the time of submission of the inquiry. For example, the organizing unit prioritizes organizing the most recently submitted inquiry. The organizing unit can also postpone organizing the previously submitted inquiry. Furthermore, the organizing unit can select the optimal organizing method based on the time of submission. In this way, by determining the order of priority based on the time of submission of the inquiry, the latest information can be prioritized.

[0045] When organizing the questions and answers, the organizing unit can adjust the order of organizing based on the relevance of the consultation. For example, the organizing unit prioritizes organizing highly relevant questions and answers. The organizing unit can also postpone organizing less relevant questions and answers. Furthermore, the organizing unit can select the optimal organizing method based on the relevance of the consultation. In this way, by adjusting the order of organizing based on the relevance of the consultation, highly relevant information can be organized with priority.

[0046] When organizing question contents and answers, the organizing unit can adjust the use of technical terms in the organization according to the user's level of expertise. For example, if the user has technical knowledge, the organizing unit provides an organization method that makes extensive use of technical terms. In addition, if the user does not have technical knowledge, the organizing unit can organize the questions and answers using simple language. Furthermore, the organizing unit can select the optimal organization method according to the user's level of expertise. This allows for organization that is easy to understand by adjusting the use of technical terms according to the user's level of expertise.

[0047] When generating an HTML file, the generator can adjust the level of detail based on the importance of the question content and answers. For example, the generator generates a detailed HTML file for important questions and answers. The generator can also generate a concise HTML file for general questions and answers. Furthermore, the generator can generate an HTML file containing minimal information for questions and answers with low importance. In this way, by adjusting the level of detail based on the importance of the question content and answers, important information can be displayed in detail.

[0048] When generating an HTML file, the generation unit can apply different generation algorithms depending on the category of the question and answer. For example, the generation unit applies a generation algorithm based on a specific health category to a health consultation. The generation unit can also apply a generation algorithm based on a mental health category to a mental health consultation. Furthermore, the generation unit can apply a generation algorithm based on a nutrition category to a nutrition consultation. In this way, by applying different generation algorithms depending on the category of the question and answer, a more appropriate HTML file can be generated.

[0049] When generating an HTML file, the generation unit can improve the accuracy of generation by referring to the user's past access history. For example, the generation unit generates an HTML file including related questions and answers based on the user's past access history. The generation unit can also analyze the user's past access history to improve the accuracy of generation. Furthermore, the generation unit can also select the optimal generation method by referring to the user's past access history. In this way, the accuracy of generation is improved by referring to the user's past access history.

[0050] When generating an HTML file, the generation unit can determine the generation priority based on the question content and the time of submission of the answer. For example, the generation unit can include recently submitted questions and answers in the HTML file with priority. The generation unit can also include previously submitted questions and answers later in the HTML file. Furthermore, the generation unit can select the optimal generation method based on the time of submission. In this way, by determining the generation priority based on the question content and the time of submission of the answer, the latest information can be displayed with priority.

[0051] When generating an HTML file, the generation unit can adjust the order of generation based on the relevance between the question content and the answer. For example, the generation unit can include highly relevant questions and answers in the HTML file with priority. The generation unit can also include less relevant questions and answers later in the HTML file. Furthermore, the generation unit can select the optimal generation method based on the relevance between the question and the answer. In this way, by adjusting the order of generation based on the relevance between the question content and the answer, highly relevant information can be displayed with priority.

[0052] When generating an HTML file, the generation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the generation unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the generation unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the generation unit can provide a display method that includes detailed information. In this way, the optimal display method can be provided by taking into account the user's device information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When collecting chat logs, the collection unit can analyze the user's past consultation history and select an appropriate collection method. For example, the collection unit preferentially collects related chat logs based on the content of consultations the user has frequently had in the past. The collection unit can also collect chat logs containing specific keywords from the user's past consultation history. Furthermore, the collection unit can analyze the user's past consultation history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past consultation history.

[0055] When collecting chat logs, the collection unit can filter them based on the user's current health condition or areas of interest. For example, the collection unit preferentially collects relevant chat logs based on the user's current health condition. The collection unit can also collect chat logs related to specific topics based on the user's areas of interest. Furthermore, the collection unit can select an optimal filtering method taking into account the user's health condition and areas of interest. As a result, highly relevant data can be collected by filtering based on the user's health condition and areas of interest.

[0056] When collecting chat logs, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user provides a consultation by voice, the collection unit prioritizes collecting voice data. Also, if the user provides a consultation by text, the collection unit can prioritize collecting text data. Furthermore, if the user provides a consultation using images, the collection unit can prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0057] When collecting chat logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes collecting chat logs related to that region. The collection unit can also collect chat logs related to region-specific health issues based on the user's geographical location information. Furthermore, the collection unit can select an optimal collection method by taking into account the user's geographical location information. In this way, data related to region-specific health issues can be prioritized by taking into account the user's geographical location information.

[0058] When collecting chat logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit collects related chat logs based on health information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related chat logs. Furthermore, the collection unit can collect related chat logs by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0059] When collecting chat logs, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit selects the optimal collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the collection means by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the optimal collection method can be selected by reflecting the user's past feedback.

[0060] When organizing the question content and the answer, the organizing unit can adjust the level of detail of the organization based on the importance of the consultation content. For example, the organizing unit performs detailed organization for important consultation content. The organizing unit can also perform concise organization for general consultation content. Furthermore, the organizing unit can also perform minimal organization for consultation content with low importance. In this way, by adjusting the level of detail of the organization based on the importance of the consultation content, important information can be organized in detail.

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

[0062] Step 1: The collection unit collects chat logs. The chat logs include text messages, voice messages, images, etc. The collection unit can automatically collect chat logs for a specific period of time, and can also estimate the user's emotions and adjust the timing of collecting chat logs based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect chat logs when the user is in a relaxed state. Step 2: The organizer uses the generation AI to organize the chat logs collected by the collection unit into questions and answers. Organizing is done using topic modeling and clustering. For example, the generation AI uses LDA (Latent Dirichlet Allocation) to classify the chat logs by topic. The organizer can also estimate the user's emotions and adjust the way questions and answers are organized based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple and easy-to-understand organization method. Step 3: The generator generates an HTML file based on the questions and answers organized by the organizer. The HTML file classifies the questions and answers by category and provides links. The generator generates the HTML file to make it easy for users to access, and can also add a search function to the generated HTML file. For example, the generator implements keyword search and filtering functions.

[0063] (Example 2) A system according to an embodiment of the present invention utilizes Healthcare Technologies' health consultation exchanges, organizes them into questions and answers using a generation AI, and generates the content as an HTML file for content creation. For example, the system collects chat logs, inputs them into a generation AI, and organizes them into questions and answers. The generation AI uses natural language processing technology to extract questions and answers from the chat logs and associate them. Next, the system generates an HTML file based on the organized questions and answers. The HTML file organizes the questions and answers in an easy-to-read format, allowing users to easily access them. For example, questions and answers can be categorized and linked to allow users to quickly access the information they need. This allows the system to effectively utilize Healthcare Technologies' health consultation know-how and provide useful content to users. For example, users can refer to past health consultation exchanges to help resolve their health-related questions. This also reduces the burden on the service provider, enabling more efficient health consultations.

[0064] An information processing system according to an embodiment includes a collection unit, an organization unit, and a generation unit. The collection unit collects chat logs. Chat logs include, but are not limited to, text messages, voice messages, and images. The collection unit can automatically collect chat logs for a specific period of time. The collection unit can also estimate a user's emotions and adjust the timing of chat log collection based on the estimated user emotions. For example, if a user is feeling stressed, the collection timing can be delayed to collect chat logs when the user is relaxed. The organization unit uses a generation AI to separate and organize the chat logs collected by the collection unit into questions and answers. The organization can be performed using, for example, topic modeling or clustering, but is not limited to, these examples. For example, the generation AI can classify the chat logs by topic using LDA (Latent Dirichlet Allocation). The organization unit can also estimate a user's emotions and adjust the organization method for the questions and answers based on the estimated user emotions. For example, if a user is feeling stressed, a simple and easy-to-understand organization method can be provided. The generating unit generates an HTML file based on the questions and answers organized by the organizing unit. For example, the HTML file may include questions and answers categorized and provided with links, but is not limited to this example. For example, the generating unit generates the HTML file to allow easy user access. The generating unit may also add a search function to the generated HTML file. For example, the generating unit may implement a keyword search or filtering function. Thus, the information processing system according to the embodiment efficiently collects and organizes chat logs and generates them as HTML files, thereby enabling effective utilization of health consultation know-how.

[0065] The collection unit can automatically collect chat logs for a specific period. The specific period includes, but is not limited to, a date range or a time period. For example, the collection unit has a mechanism for automatically collecting chat logs for a specific period. For example, the collection unit automatically collects chat logs within a specified date range. The collection unit can also automatically collect chat logs for a specific time period. This enables efficient data collection by automatically collecting chat logs for a specific period.

[0066] The organizing unit can organize the questions and answers using topic modeling or clustering. Topic modeling includes, but is not limited to, LDA (Latent Dirichlet Allocation). Clustering includes, but is not limited to, K-means and hierarchical clustering. The organizing unit classifies the chat logs by topic using, for example, LDA. The organizing unit can also divide the chat logs into clusters using K-means. Furthermore, the organizing unit can classify the chat logs hierarchically using hierarchical clustering. As a result, the use of topic modeling or clustering improves the accuracy of organizing the questions and answers.

[0067] The generation unit can classify the questions and answers by category and specify a specific method for providing links. Examples of categories include, but are not limited to, types of health consultations and topics. Examples of links include, but are not limited to, hyperlink formats and link destination selection criteria. For example, the generation unit can classify the questions and answers by type of health consultation and provide links. The generation unit can also classify the questions and answers by topic and provide links. Furthermore, the generation unit can provide appropriate links based on link destination selection criteria. Thus, by classifying by category and providing links, users can quickly access the information they need.

[0068] The generation unit can generate an HTML file that allows easy user access. Methods for easy access include, but are not limited to, a navigation menu or a search function. For example, the generation unit can provide a navigation menu in the HTML file. The generation unit can also add a search function to the HTML file. Furthermore, the generation unit can devise a page layout for the HTML file to allow easy user access. This improves convenience by generating an HTML file that is easy for users to access.

[0069] The generation unit can specify a specific method for adding a search function to the generated HTML file. The search function includes, but is not limited to, a keyword search function and a filtering function, for example. The generation unit can add, for example, a keyword search function to the HTML file. The generation unit can also add a filtering function to the HTML file. Furthermore, the generation unit can also make efforts to display search results in an easy-to-read manner. By adding the search function, the user can quickly search for the information they need.

[0070] The collection unit can estimate the user's emotions and adjust the timing of chat log collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to collect chat logs when the user is relaxed. Also, if the user is relaxed, the collection unit can immediately collect chat logs and analyze them in real time. Furthermore, if the user is in a hurry, the collection unit can advance the collection timing to quickly collect chat logs. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] When collecting chat logs, the collection unit can analyze the user's past consultation history and select an appropriate collection method. For example, the collection unit preferentially collects related chat logs based on the content of consultations the user has frequently had in the past. The collection unit can also collect chat logs containing specific keywords from the user's past consultation history. Furthermore, the collection unit can analyze the user's past consultation history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past consultation history.

[0072] When collecting chat logs, the collection unit can filter them based on the user's current health condition or areas of interest. For example, the collection unit preferentially collects relevant chat logs based on the user's current health condition. The collection unit can also collect chat logs related to specific topics based on the user's areas of interest. Furthermore, the collection unit can select an optimal filtering method taking into account the user's health condition and areas of interest. As a result, highly relevant data can be collected by filtering based on the user's health condition and areas of interest.

[0073] When collecting chat logs, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user provides a consultation by voice, the collection unit prioritizes collecting voice data. Also, if the user provides a consultation by text, the collection unit can prioritize collecting text data. Furthermore, if the user provides a consultation using images, the collection unit can prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0074] The collection unit can estimate the user's emotions and determine the priority of chat logs to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting chat logs related to stress. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting chat logs related to relaxation. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting chat logs related to urgent consultations. In this way, by determining the priority of chat logs to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] When collecting chat logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes collecting chat logs related to that region. The collection unit can also collect chat logs related to region-specific health issues based on the user's geographical location information. Furthermore, the collection unit can select an optimal collection method by taking into account the user's geographical location information. In this way, data related to region-specific health issues can be prioritized by taking into account the user's geographical location information.

[0076] When collecting chat logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit collects related chat logs based on health information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related chat logs. Furthermore, the collection unit can collect related chat logs by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0077] When collecting chat logs, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit selects the optimal collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the collection means by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the optimal collection method can be selected by reflecting the user's past feedback.

[0078] The organizing unit can estimate the user's emotions and adjust the way questions and answers are organized based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit can provide a simple and easy-to-understand organizing method. If the user is relaxed, the organizing unit can also provide an organizing method that includes detailed information. If the user is in a hurry, the organizing unit can also provide an organizing method that focuses on the main points. This allows for more appropriate organization by adjusting the organizing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] When organizing the question content and the answer, the organizing unit can adjust the level of detail of the organization based on the importance of the consultation content. For example, the organizing unit performs detailed organization for important consultation content. The organizing unit can also perform concise organization for general consultation content. Furthermore, the organizing unit can also perform minimal organization for consultation content with low importance. In this way, by adjusting the level of detail of the organization based on the importance of the consultation content, important information can be organized in detail.

[0080] When organizing the question content and answers, the organizing unit can apply different organizing algorithms depending on the category of the consultation. For example, the organizing unit applies an organizing algorithm based on a specific health category to a health consultation. The organizing unit can also apply an organizing algorithm based on a mental health category to a mental health consultation. Furthermore, the organizing unit can also apply an organizing algorithm based on a nutrition category to a nutrition consultation. This allows for more appropriate organization by applying different organizing algorithms depending on the category of the consultation.

[0081] When organizing the questions and answers, the organizing unit can improve the accuracy of the organization by referring to the user's past consultation results. For example, the organizing unit organizes related questions and answers based on the user's past consultation results. The organizing unit can also analyze the user's past consultation results to improve the accuracy of the organization. Furthermore, the organizing unit can select the optimal organization method by referring to the user's past consultation results. In this way, the accuracy of the organization is improved by referring to the user's past consultation results.

[0082] The organizing unit can estimate the user's emotions and determine the priorities of organizing based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit can prioritize organizing questions and answers related to stress. Furthermore, if the user is relaxed, the organizing unit can prioritize organizing questions and answers related to relaxation. Furthermore, if the user is in a hurry, the organizing unit can prioritize organizing questions and answers related to urgent consultations. In this way, by determining the priorities of organizing based on the user's emotions, important information can be prioritized and organized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] When organizing the questions and answers, the organizing unit can determine the order of priority based on the time of submission of the inquiry. For example, the organizing unit prioritizes organizing the most recently submitted inquiry. The organizing unit can also postpone organizing the previously submitted inquiry. Furthermore, the organizing unit can select the optimal organizing method based on the time of submission. In this way, by determining the order of priority based on the time of submission of the inquiry, the latest information can be prioritized.

[0084] When organizing the questions and answers, the organizing unit can adjust the order of organizing based on the relevance of the consultation. For example, the organizing unit prioritizes organizing highly relevant questions and answers. The organizing unit can also postpone organizing less relevant questions and answers. Furthermore, the organizing unit can select the optimal organizing method based on the relevance of the consultation. In this way, by adjusting the order of organizing based on the relevance of the consultation, highly relevant information can be organized with priority.

[0085] When organizing question contents and answers, the organizing unit can adjust the use of technical terms in the organization according to the user's level of expertise. For example, if the user has technical knowledge, the organizing unit provides an organization method that makes extensive use of technical terms. In addition, if the user does not have technical knowledge, the organizing unit can organize the questions and answers using simple language. Furthermore, the organizing unit can select the optimal organization method according to the user's level of expertise. This allows for organization that is easy to understand by adjusting the use of technical terms according to the user's level of expertise.

[0086] The generation unit can estimate the user's emotions and adjust the method for generating the HTML file based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a simple, highly visible HTML file. If the user is relaxed, the generation unit can also generate an HTML file containing detailed information. Furthermore, if the user is in a hurry, the generation unit can also generate an HTML file that focuses on the main points. This allows for the generation of a more appropriate HTML file by adjusting the method for generating the HTML file based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] When generating an HTML file, the generator can adjust the level of detail based on the importance of the question content and answers. For example, the generator generates a detailed HTML file for important questions and answers. The generator can also generate a concise HTML file for general questions and answers. Furthermore, the generator can generate an HTML file containing minimal information for questions and answers with low importance. In this way, by adjusting the level of detail based on the importance of the question content and answers, important information can be displayed in detail.

[0088] When generating an HTML file, the generation unit can apply different generation algorithms depending on the category of the question and answer. For example, the generation unit applies a generation algorithm based on a specific health category to a health consultation. The generation unit can also apply a generation algorithm based on a mental health category to a mental health consultation. Furthermore, the generation unit can apply a generation algorithm based on a nutrition category to a nutrition consultation. In this way, by applying different generation algorithms depending on the category of the question and answer, a more appropriate HTML file can be generated.

[0089] When generating an HTML file, the generation unit can improve the accuracy of generation by referring to the user's past access history. For example, the generation unit generates an HTML file including related questions and answers based on the user's past access history. The generation unit can also analyze the user's past access history to improve the accuracy of generation. Furthermore, the generation unit can also select the optimal generation method by referring to the user's past access history. In this way, the accuracy of generation is improved by referring to the user's past access history.

[0090] The generation unit can estimate the user's emotions and adjust the display method of the HTML file based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can provide a simple, highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the HTML file based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When generating an HTML file, the generation unit can determine the generation priority based on the question content and the time of submission of the answer. For example, the generation unit can include recently submitted questions and answers in the HTML file with priority. The generation unit can also include previously submitted questions and answers later in the HTML file. Furthermore, the generation unit can select the optimal generation method based on the time of submission. In this way, by determining the generation priority based on the question content and the time of submission of the answer, the latest information can be displayed with priority.

[0092] When generating an HTML file, the generation unit can adjust the order of generation based on the relevance between the question content and the answer. For example, the generation unit can include highly relevant questions and answers in the HTML file with priority. The generation unit can also include less relevant questions and answers later in the HTML file. Furthermore, the generation unit can select the optimal generation method based on the relevance between the question and the answer. In this way, by adjusting the order of generation based on the relevance between the question content and the answer, highly relevant information can be displayed with priority.

[0093] When generating an HTML file, the generation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the generation unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the generation unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the generation unit can provide a display method that includes detailed information. In this way, the optimal display method can be provided by taking into account the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, organization unit, and generation unit described above may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects chat logs using the camera 42 and microphone 38B of the smart device 14, and adjusts the collection timing using the control unit 46A. The organization unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes questions and answers using a generation AI. The generation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an HTML file based on the organized questions and answers. Each of the collection unit, organization unit, and generation unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, organization unit, and generation unit described above may be realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects chat logs using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the collection timing using the control unit 46A. The organization unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes questions and answers using a generation AI. The generation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an HTML file based on the organized questions and answers. Each of the collection unit, organization unit, and generation unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, organization unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects chat logs using the camera 42 and microphone 238 of the headset-type terminal 314, and adjusts the collection timing using the control unit 46A. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes questions and answers using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an HTML file based on the organized questions and answers. Each of the collection unit, organization unit, and generation unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, organization unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects chat logs using the camera 42 and microphone 238 of the robot 414, and adjusts the collection timing using the control unit 46A. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes questions and answers using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an HTML file based on the organized questions and answers. Each of the collection unit, organization unit, and generation unit may be realized, for example, by the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The collection unit can estimate the user's emotions and adjust the timing of chat log collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect the chat logs when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect chat logs and analyze them in real time. Furthermore, if the user is in a hurry, the collection unit can also advance the collection timing to quickly collect chat logs. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When collecting chat logs, the collection unit can analyze the user's past consultation history and select an appropriate collection method. For example, the collection unit preferentially collects related chat logs based on the content of consultations the user has frequently had in the past. The collection unit can also collect chat logs containing specific keywords from the user's past consultation history. Furthermore, the collection unit can analyze the user's past consultation history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past consultation history.

[0097] When collecting chat logs, the collection unit can filter them based on the user's current health condition or areas of interest. For example, the collection unit preferentially collects relevant chat logs based on the user's current health condition. The collection unit can also collect chat logs related to specific topics based on the user's areas of interest. Furthermore, the collection unit can select an optimal filtering method taking into account the user's health condition and areas of interest. As a result, highly relevant data can be collected by filtering based on the user's health condition and areas of interest.

[0098] When collecting chat logs, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user provides a consultation by voice, the collection unit prioritizes collecting voice data. Also, if the user provides a consultation by text, the collection unit can prioritize collecting text data. Furthermore, if the user provides a consultation using images, the collection unit can prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0099] The collection unit can estimate the user's emotions and determine the priority of chat logs to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting chat logs related to stress. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting chat logs related to relaxation. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting chat logs related to urgent consultations. In this way, by determining the priority of chat logs to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] When collecting chat logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes collecting chat logs related to that region. The collection unit can also collect chat logs related to region-specific health issues based on the user's geographical location information. Furthermore, the collection unit can select an optimal collection method by taking into account the user's geographical location information. In this way, data related to region-specific health issues can be prioritized by taking into account the user's geographical location information.

[0101] When collecting chat logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit collects related chat logs based on health information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related chat logs. Furthermore, the collection unit can collect related chat logs by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0102] When collecting chat logs, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit selects the optimal collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the collection means by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and improve the collection method. In this way, the optimal collection method can be selected by reflecting the user's past feedback.

[0103] The organizing unit can estimate the user's emotions and adjust the way questions and answers are organized based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit can provide a simple and easy-to-understand organizing method. If the user is relaxed, the organizing unit can also provide an organizing method that includes detailed information. If the user is in a hurry, the organizing unit can also provide an organizing method that focuses on the main points. This allows for more appropriate organization by adjusting the organizing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] When organizing the question content and the answer, the organizing unit can adjust the level of detail of the organization based on the importance of the consultation content. For example, the organizing unit performs detailed organization for important consultation content. The organizing unit can also perform concise organization for general consultation content. Furthermore, the organizing unit can also perform minimal organization for consultation content with low importance. In this way, by adjusting the level of detail of the organization based on the importance of the consultation content, important information can be organized in detail.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection unit collects chat logs. The chat logs include text messages, voice messages, images, etc. The collection unit can automatically collect chat logs for a specific period of time, and can also estimate the user's emotions and adjust the timing of collecting chat logs based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect chat logs when the user is in a relaxed state. Step 2: The organizer uses the generation AI to organize the chat logs collected by the collection unit into questions and answers. Organizing is done using topic modeling and clustering. For example, the generation AI uses LDA (Latent Dirichlet Allocation) to classify the chat logs by topic. The organizer can also estimate the user's emotions and adjust the way questions and answers are organized based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple and easy-to-understand organization method. Step 3: The generator generates an HTML file based on the questions and answers organized by the organizer. The HTML file classifies the questions and answers by category and provides links. The generator generates the HTML file to make it easy for users to access, and can also add a search function to the generated HTML file. For example, the generator implements keyword search and filtering functions.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 collection unit that collects chat logs; an organizing unit that organizes the chat logs collected by the collecting unit by dividing them into questions and answers; a generating unit that generates an HTML file based on the questions and answers organized by the organizing unit; Equipped with A system characterized by:

2. The collecting unit Automatically collect chat logs for a specific period 2. The system of claim 1.

3. The organizing unit Organize questions and answers using topic modeling or clustering 2. The system of claim 1.

4. The generation unit Categorize questions and answers by category and clearly indicate how to create links.

2. The system of claim 1.

5. The generation unit Generate HTML files for easy user access 2. The system of claim 1.

6. The generation unit Demonstrate how to add search functionality to generated HTML files 2. The system of claim 1.

7. The collecting unit We clarify a specific method for estimating user emotions and adjust the timing of chat log collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting chat logs, analyze the user's past consultation history and select the appropriate collection method.

2. The system of claim 1.

Images