System

Abstract

An object of the system according to the embodiment is to provide a personalized consultation by utilizing accumulated data and information from an experienced person in the same circumstances.SOLUTION: A system includes a database utilization part, an information collection part, an analysis part, and a consultation providing part. The database utilization unit utilizes accumulated data. The information collecting unit actively collects information from surrounding experienced persons in the same circumstances. The analysis part analyzes the collected word-of-mouth information and experience talk. The consultation providing unit provides a personalized consultation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology only utilizes accumulated data, which means that it has the problem of not being able to fully reflect the latest information or information from people with similar experience.

[0005] The system according to the embodiment aims to provide personalized consultation by utilizing accumulated data and information from people with similar experiences. [Means for solving the problem]

[0006] The system according to the embodiment includes a database utilization unit, an information collection unit, an analysis unit, and a consultation provision unit. The database utilization unit utilizes accumulated data. The information collection unit actively collects information from people in the vicinity who have similar experiences. The analysis unit analyzes the collected word-of-mouth information and personal experiences. The consultation provision unit provides personalized consultation. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized consultation by utilizing accumulated data and information from people who have experienced the same situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The personalized mental health, worries, and work support AI according to an embodiment of the present invention is a system that utilizes accumulated data, actively collects information from people around it who have similar experiences as new sources of information, and can provide consultation based on word-of-mouth information and personal experiences. As a result, the personalized mental health, worries, and work support AI can provide personalized mental care, consultations, and work support to users.

[0029] A personalized mental health, worry, and work support AI according to an embodiment includes a database utilization unit, an information collection unit, an analysis unit, and a consultation provision unit. The database utilization unit utilizes accumulated data. For example, the database utilization unit provides general solutions based on the user's behavioral data and past consultation history. The database utilization unit also generates answers to the user's consultation using a generation AI (e.g., a text generation AI or a multimodal generation AI). The information collection unit actively collects information from people with similar experiences. For example, the information collection unit collects related information from online communities and social media. The information collection unit can also collect information from specific sources based on a user request. The analysis unit analyzes the collected word-of-mouth information and personal experiences. For example, the analysis unit uses the generation AI to analyze the collected information and extract information useful to the user. The analysis unit can also incorporate an algorithm to evaluate the reliability of information and prioritize analysis of highly reliable information. The consultation provision unit provides personalized consultation. For example, the consultation provision unit takes into account the user's past consultation history and current situation to provide optimal advice. In addition, the consultation providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide emotionally positive advice. This allows the personalized mental care, worry consultation, and work support AI according to the embodiment to provide personalized mental care, worry consultation, and work support to the user.

[0030] The database utilization unit can analyze a user's past behavioral patterns and predict future behavior. For example, the database utilization unit analyzes a user's past consultation history and finds specific patterns. For example, if there is a tendency for stress to increase at a specific time, it can suggest preventative measures at that time. The database utilization unit also develops an algorithm to predict future behavior based on the user's past behavioral data. For example, it can predict what kind of worries the user will have next from past data and provide advice in advance. The database utilization unit also analyzes a user's past behavioral patterns and builds a system to predict future behavior. For example, it can predict what kind of support the user will need next from the content of past consultations and provide advice at the appropriate time. This makes it possible to predict a user's future behavior and provide appropriate advice in advance.

[0031] The database utilization unit can improve reliability by cross-referencing information in the database and extracting points of agreement from multiple information sources. The database utilization unit, for example, develops an algorithm that cross-references multiple information sources in the database and extracts points of agreement. For example, if advice from different experts matches, the reliability of that advice is determined to be high. The database utilization unit also uses cross-referencing to build a system that improves the reliability of information in the database. For example, if multiple users attempt the same solution and are successful, that solution is provided preferentially. The database utilization unit also improves reliability by cross-referencing information in the database and extracting points of agreement. For example, the same advice obtained from different information sources is provided preferentially to users. This can improve the reliability of the information in the database.

[0032] The information collection unit can collect information not only from online communities and SNS, but also from expert blogs and forums. For example, the information collection unit builds a system that collects information not only from online communities and SNS, but also from expert blogs and forums. For example, it collects health-related information from blogs written by medical experts. The information collection unit also develops an algorithm that collects information from expert blogs and forums. For example, it collects the latest technology information from forums written by technical experts. The information collection unit also develops a system that collects information not only from online communities and SNS, but also from expert blogs and forums. For example, it collects learning-related information from blogs written by education experts. This makes it possible to collect information from a variety of information sources.

[0033] The information collecting unit can introduce an algorithm to evaluate the reliability of information when collecting information, and prioritize collection of highly reliable information. For example, the information collecting unit introduces an algorithm to evaluate the reliability of information when collecting information. For example, it calculates a reliability score of an information source and prioritizes collection of information with a high score. The information collecting unit also builds a system to prioritize collection of highly reliable information. For example, it prioritizes collection of information from information sources that have been certified by experts. The information collecting unit also develops an algorithm to evaluate the reliability of information, and prioritizes collection of highly reliable information. For example, it evaluates reliability based on the information source's past performance and evaluation. This allows highly reliable information to be collected preferentially.

[0034] The information gathering unit can expand the scope of information gathering to also collect information from different cultural spheres and regions. For example, the information gathering unit expands the scope of information gathering and builds a system that collects information from different cultural spheres and regions. For example, it collects information from overseas online communities. The information gathering unit also develops an algorithm that collects information from different cultural spheres and regions. For example, it collects information from sources in different languages, translates it, and provides it. The information gathering unit also expands the scope of information gathering and develops a system that collects information from different cultural spheres and regions. For example, it collects information on issues specific to the region. In this way, by collecting information from different cultural spheres and regions, it becomes possible to provide information from diverse perspectives.

[0035] The information collection unit can simultaneously collect audio and video multimedia information when collecting information. For example, the information collection unit builds a system that simultaneously collects multimedia information such as audio and video when collecting information. For example, it collects videos of experts' lectures. The information collection unit also develops an algorithm that simultaneously collects multimedia information. For example, it analyzes audio data and extracts important information. The information collection unit also develops a system that simultaneously collects multimedia information such as audio and video when collecting information. For example, it collects videos of user interviews. In this way, by collecting multimedia information such as audio and video, it becomes possible to provide information from a more multifaceted perspective.

[0036] The analysis unit can introduce an algorithm that evaluates the reliability of information when analyzing word-of-mouth information and personal experiences. For example, the analysis unit introduces an algorithm that evaluates the reliability of information when analyzing word-of-mouth information and personal experiences. For example, the analysis unit calculates a reliability score for the information source and prioritizes analysis of information with a high score. The analysis unit also develops an algorithm that evaluates the reliability of information and uses it in analyzing word-of-mouth information and personal experiences. For example, the reliability is evaluated based on the information source's past performance and evaluations. The analysis unit also builds a system that evaluates the reliability of word-of-mouth information and personal experiences and prioritizes analysis of highly reliable information. For example, the analysis unit prioritizes analysis of information from information sources that have been certified by experts. This makes it possible to evaluate the reliability of word-of-mouth information and personal experiences and provide highly reliable information.

[0037] The analysis unit can propose a specific action plan to the user based on the analysis results. The analysis unit, for example, builds a system that proposes a specific action plan to the user based on the analysis results. For example, the analysis unit proposes an action plan based on solutions tried by people with the same problem. The analysis unit also develops an algorithm that proposes a specific action plan to the user based on the analysis results of word-of-mouth information and personal experiences. For example, the analysis unit proposes an action plan based on success stories. The analysis unit also develops a system that proposes a specific action plan to the user based on the analysis results. For example, the analysis unit proposes an action plan based on solutions tried by people in the same situation. In this way, a specific action plan can be proposed based on the analysis results.

[0038] The analysis unit visualizes the analysis results in different formats, allowing the user to intuitively understand. The analysis unit, for example, builds a system that displays the analysis results in visual formats such as graphs and charts. For example, success rates and satisfaction levels are shown in graphs. The analysis unit also develops algorithms to visualize the analysis results, allowing the user to intuitively understand. For example, the effectiveness of solutions is shown in charts. The analysis unit also develops systems that visualize the analysis results in different formats, allowing the user to intuitively understand. For example, a comparison of solutions is shown in a graph. In this way, by visualizing the analysis results, the user can intuitively understand.

[0039] The analysis department can integrate the analysis results with other datasets to gain new insights. For example, the analysis department will build a system that integrates the analysis results with patent data to gain new insights. For example, it will analyze technological trends and the competitive situation. The analysis department will also integrate the analysis results with market data to discover new business opportunities. For example, it will analyze solutions that address specific market needs. The analysis department will also develop a system that integrates the analysis results with other datasets to gain new insights. For example, it will integrate with a database of academic papers to analyze the latest research trends. In this way, new insights can be gained by integrating the analysis results with other datasets.

[0040] The consultation providing unit can predict future consultation content based on the user's past consultation history and prepare for it in advance. The consultation providing unit, for example, analyzes the user's past consultation history and builds a system that predicts future consultation content. For example, it prepares advice in advance for problems that are expected at a specific time. The consultation providing unit also develops an algorithm that predicts future consultation content based on the past consultation history. For example, it predicts what kind of problem the user will have next based on past data and provides advice in advance. The consultation providing unit also analyzes the user's past consultation history and develops a system that predicts future consultation content. For example, it predicts what kind of support the user will need next based on past consultation content and provides advice at an appropriate time. In this way, by predicting future consultation content and preparing in advance, it is possible to provide quick and appropriate advice.

[0041] The consultation providing unit can introduce an algorithm that dynamically changes the priority of advice depending on the individual situation of the user. The consultation providing unit, for example, develops an algorithm that dynamically changes the priority of advice depending on the individual situation of the user. For example, advice is provided depending on urgency or importance. The consultation providing unit also builds a system that dynamically changes the priority of advice depending on the individual situation. For example, advice is provided based on the user's current situation and past history. The consultation providing unit also develops an algorithm that dynamically changes the priority of advice depending on the individual situation of the user and incorporates it into the system. For example, optimal advice is provided depending on the user's situation. This makes it possible to dynamically change the priority of advice depending on the individual situation of the user.

[0042] The consultation providing unit can provide personalized consultation content in different formats. For example, the consultation providing unit builds a system that provides personalized consultation content in different formats, such as text, audio, or video. For example, it allows the user to select a format according to their preferences. The consultation providing unit also develops an algorithm that provides consultation content in different formats. For example, it adds a function that converts text to audio. The consultation providing unit also develops a system that provides personalized consultation content in different formats. For example, it provides advice in video format. As a result, by providing personalized consultation content in different formats, it becomes possible to provide information according to the user's preferences.

[0043] The consultation providing unit can link the user's consultation content with other AI systems and provide multifaceted advice by integrating knowledge from different fields. For example, the consultation providing unit builds a system that links the user's consultation content with other AI systems and provides multifaceted advice by integrating knowledge from different fields. For example, it links with medical AI to provide health advice. The consultation providing unit also links with other AI systems to develop algorithms that integrate knowledge from different fields. For example, it links with financial AI to provide economic advice. The consultation providing unit also links the user's consultation content with other AI systems and develops a system that integrates knowledge from different fields to provide multifaceted advice. For example, it links with education AI to provide advice on learning. This makes it possible to provide multifaceted advice by integrating knowledge from different fields.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The database utilization unit can predict future behavior based on a user's past behavioral data. For example, it can analyze a user's past consultation history and find specific patterns. If stress tends to increase at certain times, it can suggest preventative measures at that time. The database utilization unit can also develop an algorithm to predict future behavior based on a user's past behavioral data. It can predict what kind of worries the user will have next from past data and provide advice in advance. Furthermore, the database utilization unit can analyze a user's past behavioral patterns and build a system to predict future behavior. It can predict what kind of support the user will need next from the content of past consultations and provide advice at the appropriate time. This makes it possible to predict a user's future behavior and provide appropriate advice in advance.

[0046] The database utilization unit can improve reliability by cross-referencing information in the database and extracting points of agreement from multiple information sources. For example, an algorithm can be developed that cross-references multiple information sources in the database and extracts points of agreement. If advice from different experts matches, the advice can be determined to be highly reliable. The database utilization unit can also use cross-referencing to build a system that improves the reliability of information in the database. If multiple users attempt the same solution and are successful, that solution can be provided preferentially. Furthermore, the database utilization unit can improve reliability by cross-referencing information in the database and extracting points of agreement. The same advice obtained from different information sources can be provided preferentially to users. This improves the reliability of the information in the database.

[0047] The information collection unit can collect information not only from online communities and social networking sites, but also from expert blogs and forums. For example, a system can be built that collects information not only from online communities and social networking sites, but also from expert blogs and forums. Health-related information can be collected from blogs written by medical experts. The information collection unit can also develop an algorithm that collects information from expert blogs and forums. The latest technology information can be collected from forums written by technical experts. Still further, the information collection unit can develop a system that collects information not only from online communities and social networking sites, but also from expert blogs and forums. Learning-related information can be collected from blogs written by education experts. This makes it possible to collect information from a variety of information sources.

[0048] The information collection unit can introduce an algorithm that evaluates the reliability of information when collecting information, and prioritize the collection of highly reliable information. For example, an algorithm that evaluates the reliability of information can be introduced when collecting information. A reliability score of an information source can be calculated, and information with a high score can be preferentially collected. The information collection unit can also build a system that preferentially collects highly reliable information. Information from information sources that have been certified by experts can be preferentially collected. Furthermore, the information collection unit can develop an algorithm that evaluates the reliability of information, and prioritize the collection of highly reliable information. Reliability can be evaluated based on the information source's past performance and evaluation. This allows highly reliable information to be preferentially collected.

[0049] The information gathering unit can expand the scope of information gathering to include information from different cultural spheres and regions. For example, it can expand the scope of information gathering and build a system that collects information from different cultural spheres and regions. It can collect information from overseas online communities. The information gathering unit can also develop algorithms that collect information from different cultural spheres and regions. It can collect information from sources in different languages, translate it, and provide it. Furthermore, the information gathering unit can expand the scope of information gathering and develop a system that collects information from different cultural spheres and regions. It can collect information on issues specific to the region. By collecting information from different cultural spheres and regions, it becomes possible to provide information from diverse perspectives.

[0050] The information collection unit can simultaneously collect audio and video multimedia information when collecting information. For example, a system can be built that simultaneously collects multimedia information such as audio and video when collecting information. Videos of expert lectures can be collected. The information collection unit can also develop an algorithm that simultaneously collects multimedia information. Audio data can be analyzed and important information can be extracted. Furthermore, the information collection unit can develop a system that simultaneously collects multimedia information such as audio and video when collecting information. Videos of user interviews can be collected. In this way, by collecting multimedia information such as audio and video, it becomes possible to provide information from a more multifaceted perspective.

[0051] The analysis unit can introduce an algorithm that evaluates the reliability of information when analyzing word-of-mouth information and personal experiences. For example, an algorithm that evaluates the reliability of information can be introduced when analyzing word-of-mouth information and personal experiences. A reliability score for the information source can be calculated, and information with a high score can be preferentially analyzed. The analysis unit can also develop an algorithm that evaluates the reliability of information and use it in analyzing word-of-mouth information and personal experiences. Reliability can be evaluated based on the information source's past performance and evaluations. Furthermore, the analysis unit can build a system that evaluates the reliability of word-of-mouth information and personal experiences, and prioritize analysis of highly reliable information. Information from information sources that have been certified by experts can be prioritized in analysis. This makes it possible to evaluate the reliability of word-of-mouth information and personal experiences, and provide highly reliable information.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The database utilization unit utilizes the accumulated data. For example, the database utilization unit provides general solutions based on the user's behavioral data and past consultation history. The database utilization unit also uses generation AI (for example, text generation AI or multimodal generation AI) to generate answers to the user's consultation. Step 2: The information gathering unit actively gathers information from people around you who have similar experiences. For example, the information gathering unit gathers relevant information from online communities and social networking sites. The information gathering unit can also gather information from specific sources based on the user's request. Step 3: The analysis unit analyzes the collected word-of-mouth information and personal experiences. For example, the analysis unit uses generative AI to analyze the collected information and extract information that is useful to users. The analysis unit can also introduce an algorithm to evaluate the reliability of the information and prioritize analysis of highly reliable information. Step 4: The advice providing unit provides personalized advice. For example, the advice providing unit considers the user's past consultation history and current situation to provide optimal advice. The advice providing unit can also use an emotion estimation function to monitor the user's emotional state in real time and provide emotionally positive advice.

[0054] (Example 2) The personalized mental health, worries, and work support AI according to an embodiment of the present invention is a system that utilizes accumulated data, actively collects information from people around it who have similar experiences as new sources of information, and can provide consultation based on word-of-mouth information and personal experiences. As a result, the personalized mental health, worries, and work support AI can provide personalized mental care, consultations, and work support to users.

[0055] A personalized mental health, worry, and work support AI according to an embodiment includes a database utilization unit, an information collection unit, an analysis unit, and a consultation provision unit. The database utilization unit utilizes accumulated data. For example, the database utilization unit provides general solutions based on the user's behavioral data and past consultation history. The database utilization unit also generates answers to the user's consultation using a generation AI (e.g., a text generation AI or a multimodal generation AI). The information collection unit actively collects information from people with similar experiences. For example, the information collection unit collects related information from online communities and social media. The information collection unit can also collect information from specific sources based on a user request. The analysis unit analyzes the collected word-of-mouth information and personal experiences. For example, the analysis unit uses the generation AI to analyze the collected information and extract information useful to the user. The analysis unit can also incorporate an algorithm to evaluate the reliability of information and prioritize analysis of highly reliable information. The consultation provision unit provides personalized consultation. For example, the consultation provision unit takes into account the user's past consultation history and current situation to provide optimal advice. In addition, the consultation providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide emotionally positive advice. This allows the personalized mental care, worry consultation, and work support AI according to the embodiment to provide personalized mental care, worry consultation, and work support to the user.

[0056] The database utilization unit can analyze a user's past behavioral patterns and predict future behavior. For example, the database utilization unit analyzes a user's past consultation history and finds specific patterns. For example, if there is a tendency for stress to increase at a specific time, it can suggest preventative measures at that time. The database utilization unit also develops an algorithm to predict future behavior based on the user's past behavioral data. For example, it can predict what kind of worries the user will have next from past data and provide advice in advance. The database utilization unit also analyzes a user's past behavioral patterns and builds a system to predict future behavior. For example, it can predict what kind of support the user will need next from the content of past consultations and provide advice at the appropriate time. This makes it possible to predict a user's future behavior and provide appropriate advice in advance.

[0057] The database utilization unit can improve reliability by cross-referencing information in the database and extracting points of agreement from multiple information sources. The database utilization unit, for example, develops an algorithm that cross-references multiple information sources in the database and extracts points of agreement. For example, if advice from different experts matches, the reliability of that advice is determined to be high. The database utilization unit also uses cross-referencing to build a system that improves the reliability of information in the database. For example, if multiple users attempt the same solution and are successful, that solution is provided preferentially. The database utilization unit also improves reliability by cross-referencing information in the database and extracting points of agreement. For example, the same advice obtained from different information sources is provided preferentially to users. This can improve the reliability of the information in the database.

[0058] The database utilization unit can use the emotion estimation function to analyze the user's emotional response to past consultation content and provide emotionally positive answers preferentially. The database utilization unit, for example, analyzes the user's emotional response to past consultation content and provides answers that elicit positive emotions preferentially. For example, it re-provides advice to which the user previously responded positively. The database utilization unit also uses the emotion estimation function to analyze the user's emotional response to past consultation content in real time and provides positive answers preferentially. For example, it re-provides advice that the user previously found enjoyable. The database utilization unit also builds a system that analyzes the user's emotional response to past consultation content and provides emotionally positive answers preferentially. For example, it re-provides advice that the user previously found satisfying. This makes it possible to provide positive answers that correspond to the user's emotions.

[0059] The information collection unit can collect information not only from online communities and SNS, but also from expert blogs and forums. For example, the information collection unit builds a system that collects information not only from online communities and SNS, but also from expert blogs and forums. For example, it collects health-related information from blogs written by medical experts. The information collection unit also develops an algorithm that collects information from expert blogs and forums. For example, it collects the latest technology information from forums written by technical experts. The information collection unit also develops a system that collects information not only from online communities and SNS, but also from expert blogs and forums. For example, it collects learning-related information from blogs written by education experts. This makes it possible to collect information from a variety of information sources.

[0060] The information collecting unit can introduce an algorithm to evaluate the reliability of information when collecting information, and prioritize collection of highly reliable information. For example, the information collecting unit introduces an algorithm to evaluate the reliability of information when collecting information. For example, it calculates a reliability score of an information source and prioritizes collection of information with a high score. The information collecting unit also builds a system to prioritize collection of highly reliable information. For example, it prioritizes collection of information from information sources that have been certified by experts. The information collecting unit also develops an algorithm to evaluate the reliability of information, and prioritizes collection of highly reliable information. For example, it evaluates reliability based on the information source's past performance and evaluation. This allows highly reliable information to be collected preferentially.

[0061] The information collection unit can use the emotion estimation function to analyze the emotional tone of the collected information and provide positive information preferentially. The information collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional tone of the collected information. For example, information with a positive tone is provided preferentially. The information collection unit also analyzes the emotional tone of the collected information and develops an algorithm that provides positive information preferentially. For example, information with a high emotion score is provided preferentially. The information collection unit also uses the emotion estimation function to analyze the emotional tone of the collected information and develops a system that provides positive information preferentially. For example, information to which the user has a positive reaction is provided preferentially. This enables information to be provided that takes the user's emotions into consideration by providing positive information preferentially.

[0062] The information gathering unit can expand the scope of information gathering to also collect information from different cultural spheres and regions. For example, the information gathering unit expands the scope of information gathering and builds a system that collects information from different cultural spheres and regions. For example, it collects information from overseas online communities. The information gathering unit also develops an algorithm that collects information from different cultural spheres and regions. For example, it collects information from sources in different languages, translates it, and provides it. The information gathering unit also expands the scope of information gathering and develops a system that collects information from different cultural spheres and regions. For example, it collects information on issues specific to the region. In this way, by collecting information from different cultural spheres and regions, it becomes possible to provide information from diverse perspectives.

[0063] The information collection unit can simultaneously collect audio and video multimedia information when collecting information. For example, the information collection unit builds a system that simultaneously collects multimedia information such as audio and video when collecting information. For example, it collects videos of experts' lectures. The information collection unit also develops an algorithm that simultaneously collects multimedia information. For example, it analyzes audio data and extracts important information. The information collection unit also develops a system that simultaneously collects multimedia information such as audio and video when collecting information. For example, it collects videos of user interviews. In this way, by collecting multimedia information such as audio and video, it becomes possible to provide information from a more multifaceted perspective.

[0064] The information collection unit uses the emotion estimation function to monitor the user's emotional response to the collected information in real time and provide optimal information. The information collection unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the collected information in real time. For example, information to which the user has a positive response is preferentially provided. The information collection unit also monitors the user's emotional response to the collected information in real time and develops an algorithm that provides optimal information. For example, information to which the user has a negative response is eliminated. The information collection unit also uses the emotion estimation function to monitor the user's emotional response to the collected information in real time and develops a system that provides optimal information. For example, information that the user is satisfied with is provided again. In this way, the user's emotional response can be monitored in real time and optimal information can be provided.

[0065] The analysis unit can introduce an algorithm that evaluates the reliability of information when analyzing word-of-mouth information and personal experiences. For example, the analysis unit introduces an algorithm that evaluates the reliability of information when analyzing word-of-mouth information and personal experiences. For example, the analysis unit calculates a reliability score for the information source and prioritizes analysis of information with a high score. The analysis unit also develops an algorithm that evaluates the reliability of information and uses it in analyzing word-of-mouth information and personal experiences. For example, the reliability is evaluated based on the information source's past performance and evaluations. The analysis unit also builds a system that evaluates the reliability of word-of-mouth information and personal experiences and prioritizes analysis of highly reliable information. For example, the analysis unit prioritizes analysis of information from information sources that have been certified by experts. This makes it possible to evaluate the reliability of word-of-mouth information and personal experiences and provide highly reliable information.

[0066] The analysis unit can propose a specific action plan to the user based on the analysis results. The analysis unit, for example, builds a system that proposes a specific action plan to the user based on the analysis results. For example, the analysis unit proposes an action plan based on solutions tried by people with the same problem. The analysis unit also develops an algorithm that proposes a specific action plan to the user based on the analysis results of word-of-mouth information and personal experiences. For example, the analysis unit proposes an action plan based on success stories. The analysis unit also develops a system that proposes a specific action plan to the user based on the analysis results. For example, the analysis unit proposes an action plan based on solutions tried by people in the same situation. In this way, a specific action plan can be proposed based on the analysis results.

[0067] The analysis unit can use the emotion estimation function to analyze the user's emotional response to the analysis results and provide emotionally positive information preferentially. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to the analysis results and build a system that provides positive information preferentially. For example, it re-provides information to which the user has a positive response. The analysis unit also analyzes the user's emotional response to the analysis results in real time and develops an algorithm that provides positive information preferentially. For example, it re-provides information that the user found enjoyable. The analysis unit also uses the emotion estimation function to analyze the user's emotional response to the analysis results and develops a system that provides emotionally positive information preferentially. For example, it re-provides information that the user found satisfying. This makes it possible to provide positive information that corresponds to the user's emotions.

[0068] The analysis unit visualizes the analysis results in different formats, allowing the user to intuitively understand. The analysis unit, for example, builds a system that displays the analysis results in visual formats such as graphs and charts. For example, success rates and satisfaction levels are shown in graphs. The analysis unit also develops algorithms to visualize the analysis results, allowing the user to intuitively understand. For example, the effectiveness of solutions is shown in charts. The analysis unit also develops systems that visualize the analysis results in different formats, allowing the user to intuitively understand. For example, a comparison of solutions is shown in a graph. In this way, by visualizing the analysis results, the user can intuitively understand.

[0069] The analysis department can integrate the analysis results with other datasets to gain new insights. For example, the analysis department will build a system that integrates the analysis results with patent data to gain new insights. For example, it will analyze technological trends and the competitive situation. The analysis department will also integrate the analysis results with market data to discover new business opportunities. For example, it will analyze solutions that address specific market needs. The analysis department will also develop a system that integrates the analysis results with other datasets to gain new insights. For example, it will integrate with a database of academic papers to analyze the latest research trends. In this way, new insights can be gained by integrating the analysis results with other datasets.

[0070] The analysis unit uses the emotion estimation function to monitor the user's emotional response to the analysis results in real time and provide optimal information. The analysis unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the analysis results in real time. For example, it prioritizes providing information to which the user has a positive response. The analysis unit also monitors the user's emotional response to the analysis results in real time and develops an algorithm that provides optimal information. For example, it eliminates information to which the user has a negative response. The analysis unit also uses the emotion estimation function to monitor the user's emotional response to the analysis results in real time and develops a system that provides optimal information. For example, it re-provides information that the user is satisfied with. In this way, it is possible to monitor the user's emotional response in real time and provide optimal information.

[0071] The consultation providing unit can predict future consultation content based on the user's past consultation history and prepare for it in advance. The consultation providing unit, for example, analyzes the user's past consultation history and builds a system that predicts future consultation content. For example, it prepares advice in advance for problems that are expected at a specific time. The consultation providing unit also develops an algorithm that predicts future consultation content based on the past consultation history. For example, it predicts what kind of problem the user will have next based on past data and provides advice in advance. The consultation providing unit also analyzes the user's past consultation history and develops a system that predicts future consultation content. For example, it predicts what kind of support the user will need next based on past consultation content and provides advice at an appropriate time. In this way, by predicting future consultation content and preparing in advance, it is possible to provide quick and appropriate advice.

[0072] The consultation providing unit can introduce an algorithm that dynamically changes the priority of advice depending on the individual situation of the user. The consultation providing unit, for example, develops an algorithm that dynamically changes the priority of advice depending on the individual situation of the user. For example, advice is provided depending on urgency or importance. The consultation providing unit also builds a system that dynamically changes the priority of advice depending on the individual situation. For example, advice is provided based on the user's current situation and past history. The consultation providing unit also develops an algorithm that dynamically changes the priority of advice depending on the individual situation of the user and incorporates it into the system. For example, optimal advice is provided depending on the user's situation. This makes it possible to dynamically change the priority of advice depending on the individual situation of the user.

[0073] The consultation providing unit can use the emotion estimation function to monitor the emotional state of the user in real time and provide emotionally positive advice. The consultation providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the user in real time. For example, it preferentially provides advice that the user has positive emotions for. The consultation providing unit also develops an algorithm that monitors the emotional state of the user in real time and provides emotionally positive advice. For example, it provides advice that makes the user feel happy. The consultation providing unit also uses the emotion estimation function to develop a system that monitors the emotional state of the user in real time and provides emotionally positive advice. For example, it provides advice that satisfies the user. In this way, it is possible to monitor the emotional state of the user in real time and provide emotionally positive advice.

[0074] The consultation providing unit can provide personalized consultation content in different formats. For example, the consultation providing unit builds a system that provides personalized consultation content in different formats, such as text, audio, or video. For example, it allows the user to select a format according to their preferences. The consultation providing unit also develops an algorithm that provides consultation content in different formats. For example, it adds a function that converts text to audio. The consultation providing unit also develops a system that provides personalized consultation content in different formats. For example, it provides advice in video format. As a result, by providing personalized consultation content in different formats, it becomes possible to provide information according to the user's preferences.

[0075] The consultation providing unit can link the user's consultation content with other AI systems and provide multifaceted advice by integrating knowledge from different fields. For example, the consultation providing unit builds a system that links the user's consultation content with other AI systems and provides multifaceted advice by integrating knowledge from different fields. For example, it links with medical AI to provide health advice. The consultation providing unit also links with other AI systems to develop algorithms that integrate knowledge from different fields. For example, it links with financial AI to provide economic advice. The consultation providing unit also links the user's consultation content with other AI systems and develops a system that integrates knowledge from different fields to provide multifaceted advice. For example, it links with education AI to provide advice on learning. This makes it possible to provide multifaceted advice by integrating knowledge from different fields.

[0076] The consultation providing unit can use the emotion estimation function to monitor the emotional state of the user in real time and provide optimal advice. The consultation providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the user in real time. For example, it preferentially provides advice that the user has positive emotions for. The consultation providing unit also develops an algorithm that monitors the emotional state of the user in real time and provides optimal advice. For example, it provides advice that makes the user feel happy. The consultation providing unit also uses the emotion estimation function to develop a system that monitors the emotional state of the user in real time and provides optimal advice. For example, it provides advice that satisfies the user. In this way, it is possible to monitor the emotional state of the user in real time and provide optimal advice.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The database utilization unit can predict future behavior based on a user's past behavioral data. For example, it can analyze a user's past consultation history and find specific patterns. If stress tends to increase at certain times, it can suggest preventative measures at that time. The database utilization unit can also develop an algorithm to predict future behavior based on a user's past behavioral data. It can predict what kind of worries the user will have next from past data and provide advice in advance. Furthermore, the database utilization unit can analyze a user's past behavioral patterns and build a system to predict future behavior. It can predict what kind of support the user will need next from the content of past consultations and provide advice at the appropriate time. This makes it possible to predict a user's future behavior and provide appropriate advice in advance.

[0079] The database utilization unit can improve reliability by cross-referencing information in the database and extracting points of agreement from multiple information sources. For example, an algorithm can be developed that cross-references multiple information sources in the database and extracts points of agreement. If advice from different experts matches, the advice can be determined to be highly reliable. The database utilization unit can also use cross-referencing to build a system that improves the reliability of information in the database. If multiple users attempt the same solution and are successful, that solution can be provided preferentially. Furthermore, the database utilization unit can improve reliability by cross-referencing information in the database and extracting points of agreement. The same advice obtained from different information sources can be provided preferentially to users. This improves the reliability of the information in the database.

[0080] The database utilization unit can use the emotion estimation function to analyze the user's emotional response to past consultation content and provide emotionally positive answers preferentially. For example, it can analyze the user's emotional response to past consultation content and provide answers that elicit positive emotions preferentially. It can re-provide advice to which the user previously responded positively. Furthermore, the database utilization unit can use the emotion estimation function to analyze the user's emotional response to past consultation content in real time and provide positive answers preferentially. It can re-provide advice that the user previously found enjoyable. Furthermore, the database utilization unit can build a system that analyzes the user's emotional response to past consultation content and provide emotionally positive answers preferentially. It can re-provide advice that the user previously found satisfying. This makes it possible to provide positive answers that correspond to the user's emotions.

[0081] The information collection unit can collect information not only from online communities and social networking sites, but also from expert blogs and forums. For example, a system can be built that collects information not only from online communities and social networking sites, but also from expert blogs and forums. Health-related information can be collected from blogs written by medical experts. The information collection unit can also develop an algorithm that collects information from expert blogs and forums. The latest technology information can be collected from forums written by technical experts. Still further, the information collection unit can develop a system that collects information not only from online communities and social networking sites, but also from expert blogs and forums. Learning-related information can be collected from blogs written by education experts. This makes it possible to collect information from a variety of information sources.

[0082] The information collection unit can introduce an algorithm that evaluates the reliability of information when collecting information, and prioritize the collection of highly reliable information. For example, an algorithm that evaluates the reliability of information can be introduced when collecting information. A reliability score of an information source can be calculated, and information with a high score can be preferentially collected. The information collection unit can also build a system that preferentially collects highly reliable information. Information from information sources that have been certified by experts can be preferentially collected. Furthermore, the information collection unit can develop an algorithm that evaluates the reliability of information, and prioritize the collection of highly reliable information. Reliability can be evaluated based on the information source's past performance and evaluation. This allows highly reliable information to be preferentially collected.

[0083] The information collection unit can use the emotion estimation function to analyze the emotional tone of the collected information and provide positive information preferentially. For example, a system can be built using the emotion estimation function to analyze the emotional tone of the collected information. Information with a positive tone can be provided preferentially. The information collection unit can also develop an algorithm that analyzes the emotional tone of the collected information and provides positive information preferentially. Information with a high emotion score can be provided preferentially. Furthermore, the information collection unit can use the emotion estimation function to develop a system that analyzes the emotional tone of the collected information and provides positive information preferentially. Information to which the user has a positive reaction can be provided preferentially. This makes it possible to provide information that takes the user's emotions into consideration by providing positive information preferentially.

[0084] The information gathering unit can expand the scope of information gathering to include information from different cultural spheres and regions. For example, it can expand the scope of information gathering and build a system that collects information from different cultural spheres and regions. It can collect information from overseas online communities. The information gathering unit can also develop algorithms that collect information from different cultural spheres and regions. It can collect information from sources in different languages, translate it, and provide it. Furthermore, the information gathering unit can expand the scope of information gathering and develop a system that collects information from different cultural spheres and regions. It can collect information on issues specific to the region. By collecting information from different cultural spheres and regions, it becomes possible to provide information from diverse perspectives.

[0085] The information collection unit can simultaneously collect audio and video multimedia information when collecting information. For example, a system can be built that simultaneously collects multimedia information such as audio and video when collecting information. Videos of expert lectures can be collected. The information collection unit can also develop an algorithm that simultaneously collects multimedia information. Audio data can be analyzed and important information can be extracted. Furthermore, the information collection unit can develop a system that simultaneously collects multimedia information such as audio and video when collecting information. Videos of user interviews can be collected. In this way, by collecting multimedia information such as audio and video, it becomes possible to provide information from a more multifaceted perspective.

[0086] The information collection unit can use the emotion estimation function to monitor the user's emotional response to the collected information in real time and provide optimal information. For example, the emotion estimation function can be used to build a system that monitors the user's emotional response to the collected information in real time. Information to which the user has a positive response can be preferentially provided. The information collection unit can also monitor the user's emotional response to the collected information in real time and develop an algorithm that provides optimal information. Information to which the user has a negative response can be eliminated. Furthermore, the information collection unit can use the emotion estimation function to develop a system that monitors the user's emotional response to the collected information in real time and provides optimal information. Information that satisfies the user can be provided again. This makes it possible to monitor the user's emotional response in real time and provide optimal information.

[0087] The analysis unit can introduce an algorithm that evaluates the reliability of information when analyzing word-of-mouth information and personal experiences. For example, an algorithm that evaluates the reliability of information can be introduced when analyzing word-of-mouth information and personal experiences. A reliability score for the information source can be calculated, and information with a high score can be preferentially analyzed. The analysis unit can also develop an algorithm that evaluates the reliability of information and use it in analyzing word-of-mouth information and personal experiences. Reliability can be evaluated based on the information source's past performance and evaluations. Furthermore, the analysis unit can build a system that evaluates the reliability of word-of-mouth information and personal experiences, and prioritize analysis of highly reliable information. Information from information sources that have been certified by experts can be prioritized in analysis. This makes it possible to evaluate the reliability of word-of-mouth information and personal experiences, and provide highly reliable information.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The database utilization unit utilizes the accumulated data. For example, the database utilization unit provides general solutions based on the user's behavioral data and past consultation history. The database utilization unit also uses generation AI (for example, text generation AI or multimodal generation AI) to generate answers to the user's consultation. Step 2: The information gathering unit actively gathers information from people around you who have similar experiences. For example, the information gathering unit gathers relevant information from online communities and social networking sites. The information gathering unit can also gather information from specific sources based on the user's request. Step 3: The analysis unit analyzes the collected word-of-mouth information and personal experiences. For example, the analysis unit uses generative AI to analyze the collected information and extract information that is useful to users. The analysis unit can also introduce an algorithm to evaluate the reliability of the information and prioritize analysis of highly reliable information. Step 4: The advice providing unit provides personalized advice. For example, the advice providing unit considers the user's past consultation history and current situation to provide optimal advice. The advice providing unit can also use an emotion estimation function to monitor the user's emotional state in real time and provide emotionally positive advice.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The database utilization department utilizes the accumulated data, An information gathering department that actively gathers information from people around them who have had similar experiences, An analysis department that analyzes the collected word-of-mouth information and experiences; a consultation providing unit that provides personalized consultation; A system characterized by:

2. The database utilization unit Analyze users' past behavior patterns and predict future behavior 2. The system of claim 1.

3. The database utilization unit Cross-reference information in databases to extract matches from multiple sources for increased reliability 2. The system of claim 1.

4. The database utilization unit Analyzes the user's emotional response to past inquiries and prioritizes providing emotionally positive answers 2. The system of claim 1.

5. The information collecting unit Gather information from online communities and social networks, as well as expert blogs and forums 2. The system of claim 1.

Images