System

Abstract

An object of the system according to the embodiment is to analyze diagnosis information of a user and display an optimal proposal and advertisement.SOLUTION: A system includes a diagnostic information collection part, an analysis part, a proposal part, and an advertisement display part. The diagnostic information collection unit collects diagnostic information of a user. The analysis unit analyzes the diagnostic information collected by the diagnostic information collection unit. The proposal unit makes an optimal proposal to the user on the basis of the result analyzed by the analysis unit. The advertisement display unit displays an optimum advertisement based on the content proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide personalized suggestions based on the user's diagnostic information, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the diagnostic information of the user and display optimal suggestions and advertisements. [Means for solving the problem]

[0006] The system according to the embodiment includes a diagnostic information collection unit, an analysis unit, a suggestion unit, and an advertisement display unit. The diagnostic information collection unit collects diagnostic information about a user. The analysis unit analyzes the diagnostic information collected by the diagnostic information collection unit. The suggestion unit makes optimal suggestions to the user based on the results of the analysis by the analysis unit. The advertisement display unit displays optimal advertisements based on the content suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's diagnostic information and display optimal suggestions and advertisements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The proposal system according to the embodiment of the present invention aggregates users' diagnostic information, analyzes it using a generation AI, and makes personalized proposals. This allows the proposal system to make optimal proposals to users, and also allows publishers to earn revenue.

[0029] The proposal system according to the embodiment includes a diagnostic information collection unit, an analysis unit, a suggestion unit, and an advertisement display unit. The diagnostic information collection unit collects diagnostic information about a user. For example, it collects answers to questions such as an MBTI test. The diagnostic information collection unit can also collect past career information. For example, it prompts the user to input the user's work history and skill set. The diagnostic information collection unit can also collect personal information such as family structure and place of residence. For example, it prompts the user to input the user's family structure and place of residence. The analysis unit analyzes the diagnostic information collected by the diagnostic information collection unit. For example, the generation AI analyzes the user's personality and career trends. The analysis unit can also analyze the user's personal information. For example, the generation AI performs analysis based on the user's family structure and place of residence. The suggestion unit makes optimal suggestions to the user based on the results of the analysis by the analysis unit. For example, the generation AI suggests occupations based on the user's personality and career. The suggestion unit can also suggest books and movies that match the user's interests and personality. For example, the generation AI suggests books and movies based on the user's hobbies. The advertisement display unit displays the optimal advertisement based on the content proposed by the suggestion unit. For example, the generation AI displays an advertisement for a job-hunting site that is optimal for the user. The advertisement display unit can also display an advertisement for a matching app that is optimal for the user. For example, the generation AI displays an advertisement for a matching app based on the user's personality. This allows the suggestion system according to the embodiment to aggregate diagnostic information about the user and make optimal suggestions and display advertisements. For example, the user can find a job or hobby that suits them, improving their quality of life. Furthermore, the publisher can earn revenue through effective advertisements.

[0030] The diagnostic information collection unit can analyze a user's past online activity history and reflect it in the diagnostic information. For example, the diagnostic information collection unit can analyze a user's social media posts and infer a user's personality and interests from the content and frequency of the posts. For example, a user who frequently posts travel photos can be determined to be adventurous, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's search history and infer a user's interests. For example, a user who frequently searches for cooking recipes can be determined to be a cooking enthusiast, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's website browsing history and infer a user's interests. For example, a user who frequently visits sports-related websites can be determined to be a sports enthusiast, and this can be reflected in the diagnostic results. In this way, a user's past online activity history can be reflected in the diagnostic information.

[0031] The diagnostic information collection unit incorporates the user's heart rate and electrodermal activity to perform a more accurate diagnosis. The diagnostic information collection unit, for example, measures the user's heart rate and analyzes the stress level when answering diagnostic questions. For example, if the heart rate is elevated, it is determined that the user is feeling stressed, and this is reflected in the diagnostic result. The diagnostic information collection unit can also measure electrodermal activity and analyze the user's emotional state. For example, if the electrodermal activity is high, it is determined that the user is tense, and this is reflected in the diagnostic result. The diagnostic information collection unit can also combine data on the heart rate and electrodermal activity to comprehensively analyze the user's emotional state. For example, the user's emotional state is reflected in the diagnostic result based on fluctuations in the heart rate and changes in electrodermal activity. This allows for more accurate diagnosis by incorporating the user's physical data.

[0032] The diagnostic information collection unit can incorporate feedback from the user's family and friends to reflect a third-party perspective. For example, the diagnostic information collection unit can conduct a survey of the user's family and friends to request feedback on the user's personality and behavior. For example, the family and friends evaluate the user's strengths and weaknesses. The diagnostic information collection unit can also collect feedback from the user's family and friends to reflect a third-party perspective. For example, the family and friends provide their opinions on the user's personality and behavior. The diagnostic information collection unit can also comprehensively evaluate the user's personality and behavior based on the feedback from the user's family and friends. For example, the user's personality and behavioral tendencies can be reflected in the diagnostic results based on the opinions of the family and friends. This makes it possible to incorporate feedback from the user's family and friends and reflect a third-party perspective.

[0033] The suggestion unit can analyze the user's past suggestion history and subsequent behavior to improve the accuracy of suggestions. The suggestion unit, for example, analyzes the user's past suggestion history and tracks subsequent behavior. For example, it checks whether the user has applied for a job that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and improve the accuracy of suggestions based on subsequent behavior. For example, it checks whether the user has actually purchased a book or movie that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and provide feedback to improve the accuracy of suggestions based on subsequent behavior. For example, it checks whether the user has participated in a community that was suggested in the past, thereby improving the accuracy of suggestions. In this way, it is possible to analyze the user's past suggestion history and subsequent behavior to improve the accuracy of suggestions.

[0034] The suggestion unit can reflect the user's health condition and fitness data in the suggestions and suggest a healthy lifestyle. The suggestion unit, for example, collects the user's health condition and fitness data and reflects it in the suggestions. For example, the suggestion unit can suggest a healthy lifestyle based on the user's exercise habits and dietary details. The suggestion unit can also suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can suggest an exercise plan based on the user's heart rate and calorie consumption. The suggestion unit can also provide feedback to suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can provide advice on nutritional balance based on the user's dietary details. In this way, a healthy lifestyle can be suggested that reflects the user's health condition and fitness data.

[0035] The suggestion unit can reflect the user's cultural background and regional characteristics in the content of the suggestions, thereby making more personalized suggestions. The suggestion unit, for example, collects the user's cultural background and regional characteristics and reflects them in the content of the suggestions. For example, the suggestion unit makes suggestions that take into account the culture and customs of the region in which the user lives. The suggestion unit can also make more personalized suggestions based on the user's cultural background and regional characteristics. For example, the suggestion unit makes suggestions that take into account the user's religion and language. The suggestion unit can also provide feedback to make more personalized suggestions based on the user's cultural background and regional characteristics. For example, the suggestion unit makes suggestions that take into account the climate and economic situation of the user's region. This makes it possible to make more personalized suggestions that reflect the user's cultural background and regional characteristics.

[0036] The advertisement display unit can analyze the click rate and conversion rate of the advertisement and optimize the advertisement content based on the user's behavioral patterns. The advertisement display unit, for example, analyzes the click rate and conversion rate of the advertisement and builds a system that optimizes the advertisement content based on the user's behavioral patterns. For example, the advertisement content is adjusted based on past click data. The advertisement display unit can also analyze the click rate and conversion rate of the advertisement and optimize the advertisement content based on the user's behavioral patterns. For example, the advertisement content is adjusted based on past conversion data. The advertisement display unit can also analyze the click rate and conversion rate of the advertisement and provide feedback to optimize the advertisement content based on the user's behavioral patterns. For example, advice on adjusting the advertisement content is provided based on past click data. This makes it possible to optimize the advertisement content based on the user's behavioral patterns.

[0037] The advertisement display unit can reflect the user's past purchasing history and interests in the advertisement display, thereby displaying more relevant advertisements. The advertisement display unit, for example, analyzes the user's past purchasing history and builds a system that displays highly relevant advertisements. For example, advertisements related to products purchased in the past are displayed. The advertisement display unit can also analyze the user's past purchasing history and display highly relevant advertisements. For example, advertisements related to products purchased in the past are displayed. The advertisement display unit can also analyze the user's interests and display highly relevant advertisements. For example, advertisements related to the user's hobbies and interests are displayed. This allows the user's past purchasing history and interests to be reflected, thereby displaying more relevant advertisements.

[0038] The advertisement display unit incorporates information about the user's social network into the advertisement display, and can display advertisements that will interest friends and family. The advertisement display unit, for example, analyzes information about the user's social network and builds a system that displays advertisements that will interest friends and family. For example, it displays advertisements for products and services that friends are interested in. The advertisement display unit can also analyze information about the user's social network and display advertisements that will interest friends and family. For example, it displays advertisements for products and services that friends are interested in. The advertisement display unit can also analyze information about the user's social network and provide feedback for displaying advertisements that will interest friends and family. For example, it provides advice on displaying advertisements for products and services that friends are interested in. In this way, it is possible to incorporate information about the user's social network and display advertisements that will interest friends and family.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The recommendation system can also collect the user's health data and make suggestions based on the user's health condition. For example, the system can collect the user's sleep data and provide advice on how to improve sleep quality. The system can also collect the user's dietary data and suggest nutritionally balanced meal plans. The system can also collect the user's exercise data and suggest appropriate exercise plans. This allows for personalized suggestions based on the user's health condition.

[0041] The diagnostic information collection unit can analyze a user's past online activity history and reflect it in the diagnostic information. For example, it can analyze a user's social media posts and infer their personality and interests from the content and frequency of their posts. For example, a user who frequently posts travel photos can be determined to be adventurous, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's search history and infer their interests. For example, a user who frequently searches for cooking recipes can be determined to be a fan of cooking, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's website browsing history and infer their interests. For example, a user who frequently visits sports-related websites can be determined to be a fan of sports, and this can be reflected in the diagnostic results. In this way, the user's past online activity history can be reflected in the diagnostic information.

[0042] The diagnostic information collection unit can incorporate feedback from the user's family and friends to reflect a third-party perspective. For example, a survey can be conducted on the user's family and friends to request feedback on the user's personality and behavior. For example, the family and friends can evaluate the user's strengths and weaknesses. The diagnostic information collection unit can also collect feedback from the user's family and friends to reflect a third-party perspective. For example, the family and friends can provide their opinions on the user's personality and behavior. The diagnostic information collection unit can also comprehensively evaluate the user's personality and behavior based on the feedback from the user's family and friends. For example, the user's personality and behavioral tendencies can be reflected in the diagnostic results based on the opinions of the family and friends. This makes it possible to incorporate feedback from the user's family and friends and reflect a third-party perspective.

[0043] The suggestion unit can analyze the user's real-time reaction to the suggestion content and dynamically adjust the suggestion content. For example, the suggestion unit analyzes the user's real-time reaction when receiving the suggestion content and dynamically adjusts the suggestion content. For example, if the user shows no interest, a different suggestion is made. The suggestion unit can also analyze the user's real-time reaction and dynamically adjust the suggestion content. For example, if the user shows interest, a related suggestion is made. The suggestion unit can also analyze the user's real-time reaction and provide feedback to dynamically adjust the suggestion content. For example, if the user shows interest, the reason is analyzed and reflected in the suggestion content. In this way, the suggestion content can be dynamically adjusted based on the user's real-time reaction.

[0044] The suggestion unit can analyze the user's past suggestion history and subsequent behavior to improve the accuracy of suggestions. For example, the suggestion unit can analyze the user's past suggestion history and track subsequent behavior. For example, it can check whether the user has applied for a job that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and improve the accuracy of suggestions based on subsequent behavior. For example, it can check whether the user has actually purchased a book or movie that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and provide feedback to improve the accuracy of suggestions based on subsequent behavior. For example, it can check whether the user has participated in a community that was suggested in the past, thereby improving the accuracy of suggestions. In this way, the user's past suggestion history and subsequent behavior can be analyzed to improve the accuracy of suggestions.

[0045] The suggestion unit can reflect the user's health condition and fitness data in the suggestions and suggest a healthy lifestyle. For example, the suggestion unit collects the user's health condition and fitness data and reflects it in the suggestions. For example, the suggestion unit can suggest a healthy lifestyle based on the user's exercise habits and dietary details. The suggestion unit can also suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can suggest an exercise plan based on the user's heart rate and calorie consumption. The suggestion unit can also provide feedback to suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can provide advice on nutritional balance based on the user's dietary details. In this way, a healthy lifestyle can be suggested that reflects the user's health condition and fitness data.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The diagnostic information collection unit collects diagnostic information about the user. For example, it collects answers to questions such as those in an MBTI test. The diagnostic information collection unit can also collect past career information. For example, it prompts the user to input their work history and skill set. The diagnostic information collection unit can also collect personal information such as family structure and place of residence. For example, it prompts the user to input their family structure and place of residence. Step 2: The analysis unit analyzes the diagnostic information collected by the diagnostic information collection unit. For example, the generation AI analyzes the user's personality and career trends. The analysis unit also allows the generation AI to analyze the user's personal information. For example, the generation AI performs analysis based on the user's family structure and place of residence. Step 3: The suggestion unit makes optimal suggestions to the user based on the results of the analysis by the analysis unit. For example, the generation AI may suggest occupations based on the user's personality and career. The suggestion unit may also suggest books or movies that match the user's interests and personality. For example, the generation AI may suggest books or movies based on the user's hobbies. Step 4: The advertisement display unit displays the optimal advertisement based on the content proposed by the suggestion unit. For example, the generation AI displays an advertisement for a job-hunting site that is optimal for the user. The advertisement display unit can also display an advertisement for a matching app that is optimal for the user. For example, the generation AI displays an advertisement for a matching app based on the user's personality.

[0048] (Example 2) The proposal system according to the embodiment of the present invention aggregates users' diagnostic information, analyzes it using a generation AI, and makes personalized proposals. This allows the proposal system to make optimal proposals to users, and also allows publishers to earn revenue.

[0049] The proposal system according to the embodiment includes a diagnostic information collection unit, an analysis unit, a suggestion unit, and an advertisement display unit. The diagnostic information collection unit collects diagnostic information about a user. For example, it collects answers to questions such as an MBTI test. The diagnostic information collection unit can also collect past career information. For example, it prompts the user to input the user's work history and skill set. The diagnostic information collection unit can also collect personal information such as family structure and place of residence. For example, it prompts the user to input the user's family structure and place of residence. The analysis unit analyzes the diagnostic information collected by the diagnostic information collection unit. For example, the generation AI analyzes the user's personality and career trends. The analysis unit can also analyze the user's personal information. For example, the generation AI performs analysis based on the user's family structure and place of residence. The suggestion unit makes optimal suggestions to the user based on the results of the analysis by the analysis unit. For example, the generation AI suggests occupations based on the user's personality and career. The suggestion unit can also suggest books and movies that match the user's interests and personality. For example, the generation AI suggests books and movies based on the user's hobbies. The advertisement display unit displays the optimal advertisement based on the content proposed by the suggestion unit. For example, the generation AI displays an advertisement for a job-hunting site that is optimal for the user. The advertisement display unit can also display an advertisement for a matching app that is optimal for the user. For example, the generation AI displays an advertisement for a matching app based on the user's personality. This allows the suggestion system according to the embodiment to aggregate diagnostic information about the user and make optimal suggestions and display advertisements. For example, the user can find a job or hobby that suits them, improving their quality of life. Furthermore, the publisher can earn revenue through effective advertisements.

[0050] The diagnostic information collection unit can analyze the user's voice and facial expressions and generate a diagnostic result that reflects the user's emotional state. For example, the diagnostic information collection unit uses a camera and a microphone to analyze the user's voice and facial expressions in real time when the user answers diagnostic questions. For example, the diagnostic information collection unit analyzes the user's tone of voice and facial expressions when answering questions and reflects the emotional state in the diagnostic result. The diagnostic information collection unit can also analyze the user's emotional state using voice recognition technology. For example, the diagnostic information collection unit can analyze the user's tone of voice and speed using voice recognition technology and reflect the emotional state in the diagnostic result. The diagnostic information collection unit can also analyze the user's emotional state using facial expression recognition technology. For example, the diagnostic information collection unit can analyze the user's facial expressions using facial expression recognition technology and reflect the emotional state in the diagnostic result. This makes it possible to generate a diagnostic result that reflects the user's emotional state.

[0051] The diagnostic information collection unit can analyze a user's past online activity history and reflect it in the diagnostic information. For example, the diagnostic information collection unit can analyze a user's social media posts and infer a user's personality and interests from the content and frequency of the posts. For example, a user who frequently posts travel photos can be determined to be adventurous, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's search history and infer a user's interests. For example, a user who frequently searches for cooking recipes can be determined to be a cooking enthusiast, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's website browsing history and infer a user's interests. For example, a user who frequently visits sports-related websites can be determined to be a sports enthusiast, and this can be reflected in the diagnostic results. In this way, a user's past online activity history can be reflected in the diagnostic information.

[0052] The diagnostic information collection unit uses the emotion estimation function to analyze the emotions of the user when answering the diagnostic questions in real time, thereby improving the reliability of the answers. For example, the diagnostic information collection unit uses the emotion estimation function to analyze the emotions of the user when answering the diagnostic questions in real time. For example, it determines whether the user is answering seriously and prioritizes answers with high reliability. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and improve the reliability of the answers. For example, it prioritizes answers given when the user is relaxed. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and provide feedback to improve the reliability of the answers. For example, if the user is nervous, it provides advice to relax. In this way, the user's emotions can be analyzed in real time and the reliability of the answers can be improved.

[0053] The diagnostic information collection unit incorporates the user's heart rate and electrodermal activity to perform a more accurate diagnosis. The diagnostic information collection unit, for example, measures the user's heart rate and analyzes the stress level when answering diagnostic questions. For example, if the heart rate is elevated, it is determined that the user is feeling stressed, and this is reflected in the diagnostic result. The diagnostic information collection unit can also measure electrodermal activity and analyze the user's emotional state. For example, if the electrodermal activity is high, it is determined that the user is tense, and this is reflected in the diagnostic result. The diagnostic information collection unit can also combine data on the heart rate and electrodermal activity to comprehensively analyze the user's emotional state. For example, the user's emotional state is reflected in the diagnostic result based on fluctuations in the heart rate and changes in electrodermal activity. This allows for more accurate diagnosis by incorporating the user's physical data.

[0054] The diagnostic information collection unit can incorporate feedback from the user's family and friends to reflect a third-party perspective. For example, the diagnostic information collection unit can conduct a survey of the user's family and friends to request feedback on the user's personality and behavior. For example, the family and friends evaluate the user's strengths and weaknesses. The diagnostic information collection unit can also collect feedback from the user's family and friends to reflect a third-party perspective. For example, the family and friends provide their opinions on the user's personality and behavior. The diagnostic information collection unit can also comprehensively evaluate the user's personality and behavior based on the feedback from the user's family and friends. For example, the user's personality and behavioral tendencies can be reflected in the diagnostic results based on the opinions of the family and friends. This makes it possible to incorporate feedback from the user's family and friends and reflect a third-party perspective.

[0055] The diagnostic information collection unit can use the emotion estimation function to analyze the emotions of the user when answering the diagnostic questions and suggest a question format that elicits positive emotions. The diagnostic information collection unit, for example, uses the emotion estimation function to analyze the emotions of the user when answering the diagnostic questions in real time and suggest a question format that elicits positive emotions. For example, a question format that allows the user to relax is adopted. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and suggest a question format that elicits positive emotions. For example, a question format that makes the user feel happy is adopted. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and provide feedback to elicit positive emotions. For example, advice is provided that helps the user relax. This makes it possible to suggest a question format that elicits positive emotions from the user.

[0056] The suggestion unit can analyze the user's real-time reaction to the suggestion content and dynamically adjust the suggestion content. For example, the suggestion unit analyzes the user's real-time reaction when receiving the suggestion content and dynamically adjusts the suggestion content. For example, if the user shows no interest, a different suggestion is made. The suggestion unit can also analyze the user's real-time reaction and dynamically adjust the suggestion content. For example, if the user shows interest, a related suggestion is made. The suggestion unit can also analyze the user's real-time reaction and provide feedback to dynamically adjust the suggestion content. For example, if the user shows interest, the reason is analyzed and reflected in the suggestion content. In this way, the suggestion content can be dynamically adjusted based on the user's real-time reaction.

[0057] The suggestion unit can analyze the user's past suggestion history and subsequent behavior to improve the accuracy of suggestions. The suggestion unit, for example, analyzes the user's past suggestion history and tracks subsequent behavior. For example, it checks whether the user has applied for a job that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and improve the accuracy of suggestions based on subsequent behavior. For example, it checks whether the user has actually purchased a book or movie that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and provide feedback to improve the accuracy of suggestions based on subsequent behavior. For example, it checks whether the user has participated in a community that was suggested in the past, thereby improving the accuracy of suggestions. In this way, it is possible to analyze the user's past suggestion history and subsequent behavior to improve the accuracy of suggestions.

[0058] The suggestion unit can reflect the user's health condition and fitness data in the suggestions and suggest a healthy lifestyle. The suggestion unit, for example, collects the user's health condition and fitness data and reflects it in the suggestions. For example, the suggestion unit can suggest a healthy lifestyle based on the user's exercise habits and dietary details. The suggestion unit can also suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can suggest an exercise plan based on the user's heart rate and calorie consumption. The suggestion unit can also provide feedback to suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can provide advice on nutritional balance based on the user's dietary details. In this way, a healthy lifestyle can be suggested that reflects the user's health condition and fitness data.

[0059] The suggestion unit can reflect the user's cultural background and regional characteristics in the content of the suggestions, thereby making more personalized suggestions. The suggestion unit, for example, collects the user's cultural background and regional characteristics and reflects them in the content of the suggestions. For example, the suggestion unit makes suggestions that take into account the culture and customs of the region in which the user lives. The suggestion unit can also make more personalized suggestions based on the user's cultural background and regional characteristics. For example, the suggestion unit makes suggestions that take into account the user's religion and language. The suggestion unit can also provide feedback to make more personalized suggestions based on the user's cultural background and regional characteristics. For example, the suggestion unit makes suggestions that take into account the climate and economic situation of the user's region. This makes it possible to make more personalized suggestions that reflect the user's cultural background and regional characteristics.

[0060] The suggestion unit can use the emotion estimation function to analyze the emotion the user felt when receiving the suggestion and make a suggestion to avoid negative emotions. The suggestion unit can, for example, use the emotion estimation function to analyze the emotion the user felt when receiving the suggestion in real time and make a suggestion to avoid negative emotions. For example, it can avoid suggestions that make the user feel uncomfortable. The suggestion unit can also use the emotion estimation function to analyze the emotional state of the user and make a suggestion to avoid negative emotions. For example, it can avoid suggestions that make the user feel stressed. The suggestion unit can also use the emotion estimation function to analyze the emotional state of the user and provide feedback to avoid negative emotions. For example, it can provide advice to help the user relax. This makes it possible to make a suggestion to avoid the user's negative emotions.

[0061] The advertisement display unit can analyze the user's real-time emotional state when displaying an advertisement, and display the advertisement at the optimal timing. The advertisement display unit, for example, builds a system that analyzes the user's real-time emotional state and displays the advertisement at the optimal timing. For example, the advertisement is displayed when the user is relaxed. The advertisement display unit can also analyze the user's real-time emotional state and display the advertisement at the optimal timing. For example, the advertisement is displayed when the user is showing interest. The advertisement display unit can also analyze the user's real-time emotional state and provide feedback for displaying the advertisement at the optimal timing. For example, the advertisement display unit provides advice to display the advertisement when the user is relaxed. This makes it possible to display the advertisement at the optimal timing based on the user's real-time emotional state.

[0062] The advertisement display unit can analyze the click rate and conversion rate of the advertisement and optimize the advertisement content based on the user's behavioral patterns. The advertisement display unit, for example, analyzes the click rate and conversion rate of the advertisement and builds a system that optimizes the advertisement content based on the user's behavioral patterns. For example, the advertisement content is adjusted based on past click data. The advertisement display unit can also analyze the click rate and conversion rate of the advertisement and optimize the advertisement content based on the user's behavioral patterns. For example, the advertisement content is adjusted based on past conversion data. The advertisement display unit can also analyze the click rate and conversion rate of the advertisement and provide feedback to optimize the advertisement content based on the user's behavioral patterns. For example, advice on adjusting the advertisement content is provided based on past click data. This makes it possible to optimize the advertisement content based on the user's behavioral patterns.

[0063] The advertisement display unit can use the emotion estimation function to analyze the emotion a user feels when viewing an advertisement, and prioritize displaying advertisements that elicit positive emotions. The advertisement display unit can, for example, use the emotion estimation function to analyze the emotion a user feels when viewing an advertisement in real time, and prioritize displaying advertisements that elicit positive emotions. For example, it prioritizes advertisements that make the user feel happy. The advertisement display unit can also use the emotion estimation function to analyze the user's emotional state, and prioritize displaying advertisements that elicit positive emotions. For example, it prioritizes advertisements that make the user feel satisfied. The advertisement display unit can also use the emotion estimation function to analyze the user's emotional state, and provide feedback to elicit positive emotions. For example, it can provide advice to help the user relax. This makes it possible to prioritize displaying advertisements that elicit positive emotions in the user.

[0064] The advertisement display unit can reflect the user's past purchasing history and interests in the advertisement display, thereby displaying more relevant advertisements. The advertisement display unit, for example, analyzes the user's past purchasing history and builds a system that displays highly relevant advertisements. For example, advertisements related to products purchased in the past are displayed. The advertisement display unit can also analyze the user's past purchasing history and display highly relevant advertisements. For example, advertisements related to products purchased in the past are displayed. The advertisement display unit can also analyze the user's interests and display highly relevant advertisements. For example, advertisements related to the user's hobbies and interests are displayed. This allows the user's past purchasing history and interests to be reflected, thereby displaying more relevant advertisements.

[0065] The advertisement display unit incorporates information about the user's social network into the advertisement display, and can display advertisements that will interest friends and family. The advertisement display unit, for example, analyzes information about the user's social network and builds a system that displays advertisements that will interest friends and family. For example, it displays advertisements for products and services that friends are interested in. The advertisement display unit can also analyze information about the user's social network and display advertisements that will interest friends and family. For example, it displays advertisements for products and services that friends are interested in. The advertisement display unit can also analyze information about the user's social network and provide feedback for displaying advertisements that will interest friends and family. For example, it provides advice on displaying advertisements for products and services that friends are interested in. In this way, it is possible to incorporate information about the user's social network and display advertisements that will interest friends and family.

[0066] The advertisement display unit can use the emotion estimation function to analyze the emotion a user feels when viewing an advertisement and display an advertisement that avoids negative emotions. The advertisement display unit can, for example, use the emotion estimation function to analyze the emotion a user feels when viewing an advertisement in real time and display an advertisement that avoids negative emotions. For example, it can avoid advertisements that make the user feel uncomfortable. The advertisement display unit can also use the emotion estimation function to analyze the user's emotional state and display an advertisement that avoids negative emotions. For example, it can avoid advertisements that make the user feel stressed. The advertisement display unit can also use the emotion estimation function to analyze the user's emotional state and provide feedback to avoid negative emotions. For example, it can provide advice to help the user relax. This makes it possible to display an advertisement that avoids negative emotions for the user.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The recommendation system can also collect the user's health data and make suggestions based on the user's health condition. For example, the system can collect the user's sleep data and provide advice on how to improve sleep quality. The system can also collect the user's dietary data and suggest nutritionally balanced meal plans. The system can also collect the user's exercise data and suggest appropriate exercise plans. This allows for personalized suggestions based on the user's health condition.

[0069] The diagnostic information collection unit can analyze the user's voice and facial expressions and generate a diagnostic result that reflects the user's emotional state. For example, when the user answers diagnostic questions, the voice and facial expressions are analyzed in real time using a camera and a microphone. For example, the tone of voice and facial expressions of the user when answering questions are analyzed, and the emotional state is reflected in the diagnostic result. The diagnostic information collection unit can also analyze the user's emotional state using voice recognition technology. For example, the voice recognition technology can be used to analyze the tone and speed of the user's voice, and the emotional state is reflected in the diagnostic result. The diagnostic information collection unit can also analyze the user's emotional state using facial expression recognition technology. For example, the facial expression recognition technology can be used to analyze the user's facial expressions, and the emotional state is reflected in the diagnostic result. In this way, a diagnostic result that reflects the user's emotional state can be generated.

[0070] The diagnostic information collection unit can analyze a user's past online activity history and reflect it in the diagnostic information. For example, it can analyze a user's social media posts and infer their personality and interests from the content and frequency of their posts. For example, a user who frequently posts travel photos can be determined to be adventurous, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's search history and infer their interests. For example, a user who frequently searches for cooking recipes can be determined to be a fan of cooking, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also analyze a user's website browsing history and infer their interests. For example, a user who frequently visits sports-related websites can be determined to be a fan of sports, and this can be reflected in the diagnostic results. In this way, the user's past online activity history can be reflected in the diagnostic information.

[0071] The diagnostic information collection unit can use the emotion estimation function to analyze the emotions of a user when answering a diagnostic question in real time, thereby improving the reliability of the answer. For example, when a user answers a diagnostic question, the emotion estimation function is used to analyze the emotions in real time. For example, it determines whether the user is answering seriously and prioritizes answers with higher reliability. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and improve the reliability of the answer. For example, it prioritizes answers given when the user is relaxed. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and provide feedback to improve the reliability of the answer. For example, if the user is nervous, it provides advice to relax. In this way, the user's emotions can be analyzed in real time, thereby improving the reliability of the answer.

[0072] The diagnostic information collection unit can incorporate the user's heart rate and electrodermal activity to perform a more accurate diagnosis. For example, it can measure the user's heart rate and analyze the stress level when answering diagnostic questions. For example, if the heart rate is elevated, it can be determined that the user is feeling stressed, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also measure electrodermal activity to analyze the user's emotional state. For example, if the electrodermal activity is high, it can be determined that the user is tense, and this can be reflected in the diagnostic results. The diagnostic information collection unit can also combine the heart rate and electrodermal activity data to comprehensively analyze the user's emotional state. For example, the user's emotional state can be reflected in the diagnostic results based on fluctuations in heart rate and electrodermal activity. This allows for more accurate diagnosis by incorporating the user's physical data.

[0073] The diagnostic information collection unit can incorporate feedback from the user's family and friends to reflect a third-party perspective. For example, a survey can be conducted on the user's family and friends to request feedback on the user's personality and behavior. For example, the family and friends can evaluate the user's strengths and weaknesses. The diagnostic information collection unit can also collect feedback from the user's family and friends to reflect a third-party perspective. For example, the family and friends can provide their opinions on the user's personality and behavior. The diagnostic information collection unit can also comprehensively evaluate the user's personality and behavior based on the feedback from the user's family and friends. For example, the user's personality and behavioral tendencies can be reflected in the diagnostic results based on the opinions of the family and friends. This makes it possible to incorporate feedback from the user's family and friends and reflect a third-party perspective.

[0074] The diagnostic information collection unit can use the emotion estimation function to analyze the emotions of the user when answering the diagnostic questions and suggest a question format that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions of the user when answering the diagnostic questions in real time and suggest a question format that elicits positive emotions. For example, a question format that helps the user relax can be adopted. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and suggest a question format that elicits positive emotions. For example, a question format that helps the user feel happy can be adopted. The diagnostic information collection unit can also use the emotion estimation function to analyze the user's emotional state and provide feedback to elicit positive emotions. For example, advice can be provided that helps the user relax. This makes it possible to suggest a question format that elicits positive emotions from the user.

[0075] The suggestion unit can analyze the user's real-time reaction to the suggestion content and dynamically adjust the suggestion content. For example, the suggestion unit analyzes the user's real-time reaction when receiving the suggestion content and dynamically adjusts the suggestion content. For example, if the user shows no interest, a different suggestion is made. The suggestion unit can also analyze the user's real-time reaction and dynamically adjust the suggestion content. For example, if the user shows interest, a related suggestion is made. The suggestion unit can also analyze the user's real-time reaction and provide feedback to dynamically adjust the suggestion content. For example, if the user shows interest, the reason is analyzed and reflected in the suggestion content. In this way, the suggestion content can be dynamically adjusted based on the user's real-time reaction.

[0076] The suggestion unit can analyze the user's past suggestion history and subsequent behavior to improve the accuracy of suggestions. For example, the suggestion unit can analyze the user's past suggestion history and track subsequent behavior. For example, it can check whether the user has applied for a job that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and improve the accuracy of suggestions based on subsequent behavior. For example, it can check whether the user has actually purchased a book or movie that was suggested in the past, thereby improving the accuracy of suggestions. The suggestion unit can also analyze the user's past suggestion history and provide feedback to improve the accuracy of suggestions based on subsequent behavior. For example, it can check whether the user has participated in a community that was suggested in the past, thereby improving the accuracy of suggestions. In this way, the user's past suggestion history and subsequent behavior can be analyzed to improve the accuracy of suggestions.

[0077] The suggestion unit can reflect the user's health condition and fitness data in the suggestions and suggest a healthy lifestyle. For example, the suggestion unit collects the user's health condition and fitness data and reflects it in the suggestions. For example, the suggestion unit can suggest a healthy lifestyle based on the user's exercise habits and dietary details. The suggestion unit can also suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can suggest an exercise plan based on the user's heart rate and calorie consumption. The suggestion unit can also provide feedback to suggest a healthy lifestyle based on the user's health condition and fitness data. For example, the suggestion unit can provide advice on nutritional balance based on the user's dietary details. In this way, a healthy lifestyle can be suggested that reflects the user's health condition and fitness data.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The diagnostic information collection unit collects diagnostic information about the user. For example, it collects answers to questions such as those in an MBTI test. The diagnostic information collection unit can also collect past career information. For example, it prompts the user to input their work history and skill set. The diagnostic information collection unit can also collect personal information such as family structure and place of residence. For example, it prompts the user to input their family structure and place of residence. Step 2: The analysis unit analyzes the diagnostic information collected by the diagnostic information collection unit. For example, the generation AI analyzes the user's personality and career trends. The analysis unit also allows the generation AI to analyze the user's personal information. For example, the generation AI performs analysis based on the user's family structure and place of residence. Step 3: The suggestion unit makes optimal suggestions to the user based on the results of the analysis by the analysis unit. For example, the generation AI may suggest occupations based on the user's personality and career. The suggestion unit may also suggest books or movies that match the user's interests and personality. For example, the generation AI may suggest books or movies based on the user's hobbies. Step 4: The advertisement display unit displays the optimal advertisement based on the content proposed by the suggestion unit. For example, the generation AI displays an advertisement for a job-hunting site that is optimal for the user. The advertisement display unit can also display an advertisement for a matching app that is optimal for the user. For example, the generation AI displays an advertisement for a matching app based on the user's personality.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] 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]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a diagnostic information collection unit that collects diagnostic information of a user; an analysis unit that analyzes the diagnostic information collected by the diagnostic information collection unit; a suggestion unit that makes an optimal suggestion to a user based on the results of the analysis by the analysis unit; an advertisement display unit that displays an optimal advertisement based on the content proposed by the proposal unit; A system characterized by:

2. The diagnostic information collection unit Analyzing the user's voice and facial expressions to generate a diagnosis that reflects the user's emotional state 2. The system of claim 1.

3. The diagnostic information collection unit Incorporates the user's heart rate and electrodermal activity to provide a more accurate diagnosis 2. The system of claim 1.

4. The proposal unit Analyzing the user's real-time response to the suggestions and dynamically adjusting the suggestions.

2. The system of claim 1.

5. The advertisement display unit Analyzing the user's real-time emotional state when displaying an advertisement and displaying the advertisement at the optimal timing 2. The system of claim 1.

6. The diagnostic information collection unit Analyzing the emotions of the user when answering diagnostic questions in real time to improve the reliability of the answers 2. The system of claim 1.

7. The proposal unit Analyzing the emotions felt by the user when receiving the suggestions and prioritizing the suggestions that elicit positive emotions.

2. The system of claim 1.

8. The advertisement display unit Analyzing the emotions felt by the user when viewing the advertisement, and preferentially displaying the advertisement that elicits positive emotions 2. The system of claim 1.

Images