System

Abstract

An object of a system according to an embodiment is to generate and deliver a report customized based on attribute information of a user.SOLUTION: A system according to an embodiment includes a user attribute information collection unit, an analysis unit, a report generation unit, and a distribution unit. The user attribute information collection unit collects attribute information of a user. The analysis unit analyzes the user attribute information collected by the user attribute information collection unit. The report generation part generates a customized report on the basis of the result analyzed by the analysis part. The distribution unit distributes the report generated by the report generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had a problem in that they have not been able to efficiently generate and distribute reports customized based on user attribute information.

[0005] The system according to the embodiment aims to generate and distribute a report customized based on user attribute information. [Means for solving the problem]

[0006] The system according to the embodiment includes a user attribute information collection unit, an analysis unit, a report generation unit, and a distribution unit. The user attribute information collection unit collects user attribute information. The analysis unit analyzes the user attribute information collected by the user attribute information collection unit. The report generation unit generates a customized report based on the results of the analysis by the analysis unit. The distribution unit distributes the report generated by the report generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate and deliver customized reports based on user attribute information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A customized report generation system according to an embodiment of the present invention is a system that generates and distributes a report customized based on a user's attribute information and interests. This allows the customized report generation system to provide optimal information to the user, thereby deepening their understanding of SoftBank.

[0029] A customized report generation system according to an embodiment includes a user attribute information collection unit, an analysis unit, a report generation unit, and a distribution unit. The user attribute information collection unit collects user attribute information, such as age, gender, occupation, and place of residence. The user attribute information collection unit also collects user interests, such as identifying the user's interests based on past search history and browsing history. The analysis unit analyzes the user attribute information collected by the user attribute information collection unit, using, for example, statistical analysis or a machine learning algorithm. The analysis unit also analyzes the user's interests in detail, such as identifying frequently searched keywords and frequently viewed web pages. The report generation unit generates a customized report based on the results of the analysis by the analysis unit. For example, the report generation unit provides young users with information about the latest smartphones and communication plans. The report generation unit also provides business people with information about SoftBank's business services and corporate strategy. The distribution unit distributes the report generated by the report generation unit to users, such as by sending the report via email or a messenger app. The distribution unit also allows users to view the reports through a dedicated website or app. This allows the customized report generation system according to the embodiment to provide users with optimal information and deepen their understanding of SoftBank.

[0030] The user attribute information collection unit can collect real-time behavioral data of a user and reflect it in user attribute information. The user attribute information collection unit, for example, uses GPS data from the user's smartphone to collect current location and movement history and analyze the user's behavioral patterns. For example, it identifies the user's frequently visited places and travel times. The user attribute information collection unit also collects exercise data and heart rate data from the user's smartwatch or fitness tracker to analyze the user's health condition and daily activity level. For example, it identifies the time of day the user exercises. The user attribute information collection unit also collects home appliance usage status and indoor environment data from the user's smart home device to analyze the user's lifestyle habits. For example, it identifies the time of day the user uses home appliances. In this way, the user's real-time behavioral data can be collected and reflected in attribute information.

[0031] The user attribute information collection unit analyzes the content of a user's social media posts to identify their interests in more detail. For example, the user attribute information collection unit analyzes the content of a user's Twitter or Facebook posts to extract frequently used keywords and hashtags. For example, it identifies topics and events that the user is interested in. The user attribute information collection unit also analyzes images posted by the user on Instagram and uses image recognition technology to identify places and things that the user is interested in. For example, it identifies tourist spots visited by the user and products purchased by the user. The user attribute information collection unit also analyzes the user's LinkedIn profile and posts to identify occupational and career-related interests. For example, it identifies the industries and skills in which the user is interested. This allows the user's social media posts to be analyzed to identify their interests in more detail.

[0032] The user attribute information collection unit analyzes the user's voice data and can identify the user's interests from the speaking style and tone of voice. The user attribute information collection unit, for example, analyzes the voice data of the user's smart speaker or voice assistant and infers emotions from the speaking style and tone of voice. For example, it identifies topics in which the user is interested and questions asked. The user attribute information collection unit also analyzes the voice data of the user's telephone calls and can identify the user's interests from the content of the conversation and tone of voice. For example, it can identify themes and concerns that the user frequently talks about. The user attribute information collection unit also analyzes the user's voice memos and recorded data and can use voice recognition technology to identify the content and emotions recorded by the user. For example, it can identify ideas and plans that the user has noted down. This makes it possible to analyze the user's voice data and identify the user's interests from the speaking style and tone of voice.

[0033] The user attribute information collection unit analyzes the user's purchasing history and can identify the user's interests from the categories and brands of purchased products. The user attribute information collection unit, for example, analyzes the user's purchasing history on an online shopping site and identifies the categories and brands of purchased products. For example, it identifies the products and brands that the user frequently purchases. The user attribute information collection unit also analyzes the user's credit card usage history and identifies the categories of purchased products and spending patterns. For example, it identifies which categories the user spends most on. The user attribute information collection unit also analyzes the user's purchasing history at stores and identifies the categories and brands of purchased products. For example, it identifies the stores the user frequently uses and the products that the user purchases. In this way, it is possible to analyze the user's purchasing history and identify the user's interests from the categories and brands of purchased products.

[0034] The report generation unit can customize not only the report content but also the report format based on user attribute information. The report generation unit customizes the report format based on, for example, the user's age and occupation. For example, it provides video-format reports to young people and text-format reports to business people. The report generation unit also customizes the report content and format based on the user's interests. For example, it provides technical explanation videos to users who are interested in technology and reports that make extensive use of images and illustrations to users who are interested in entertainment. The report generation unit also customizes the report format based on the user's device usage. For example, it provides mobile-friendly reports to users who mainly use smartphones and detailed text reports to users who mainly use PCs. This makes it possible to customize the report content and format based on user attribute information.

[0035] The report generation unit can analyze the user's past feedback and continuously optimize the content of the report. For example, the report generation unit analyzes feedback provided by the user in the past and improves the content of the report. For example, it prioritizes the incorporation of content that the user has given a high rating. The report generation unit also optimizes the format and delivery method of the report based on the user's feedback. For example, it identifies the user's preferred format and delivery timing and provides the report accordingly. The report generation unit also collects the user's feedback in real time and reflects it in the next report. For example, it updates the content of the report every time the user provides a comment or rating. This allows the user's past feedback to be analyzed and the content of the report to be continuously optimized.

[0036] The report generation unit can generate reports in different languages ​​and accommodate international users. For example, the report generation unit can automatically translate reports and provide them in different languages ​​based on the user's language setting. For example, the report generation unit supports multiple languages ​​such as English, French, and Chinese. The report generation unit also builds a system that delivers reports generated in different languages ​​to international users. For example, the report is provided in an appropriate language based on the user's place of residence and language setting. The report generation unit also develops a multilingual report generation system that allows users to view reports in the language of their choice. For example, the report generation unit provides an interface that allows users to freely select the language of the report. This allows reports to be generated in different languages ​​and accommodate international users.

[0037] The report generation unit can optimize the timing of report delivery based on user attribute information. The report generation unit, for example, analyzes the user's lifestyle and device usage status to identify the optimal delivery timing. For example, the report is delivered during the time period when the user is most active. The report generation unit also analyzes the user's past report viewing history to identify the optimal delivery timing. For example, the report is delivered during the time period when the user frequently views reports. The report generation unit also analyzes the user's schedule and calendar information to identify the optimal delivery timing. For example, the report is delivered during the time period when the user is free or during breaks. This makes it possible to optimize the timing of report delivery based on user attribute information.

[0038] The distribution unit can analyze the user's device usage and deliver reports to the optimal device. For example, the distribution unit analyzes the user's device usage history and identifies the device used most frequently. For example, a report for a smartphone is delivered to a user who mainly uses a smartphone. The distribution unit also analyzes the user's device settings and screen size and delivers reports in the optimal format. For example, a report for a large screen is provided to a user who uses a tablet. The distribution unit also monitors the user's device usage in real time and delivers reports to the optimal device. For example, a report is delivered according to the device the user is currently using. This makes it possible to analyze the user's device usage and deliver reports to the optimal device.

[0039] The distribution unit can analyze the user's schedule and distribute reports at the optimal timing. The distribution unit, for example, analyzes the user's calendar app or schedule management tool to identify the optimal distribution timing. For example, reports are distributed during times when the user is free or during breaks. The distribution unit also analyzes the user's past report viewing history to identify the optimal distribution timing. For example, reports are distributed during times when the user frequently views reports. The distribution unit also analyzes the user's lifestyle and device usage to identify the optimal distribution timing. For example, reports are distributed during times when the user is most active. This makes it possible to analyze the user's schedule and distribute reports at the optimal timing.

[0040] The distribution unit can distribute the report in audio format, allowing the user to obtain information through hearing. For example, the distribution unit converts the content of the report into audio format using speech synthesis technology and distributes it to the user. For example, the report can be listened to through a smart speaker or a voice assistant. The distribution unit can also analyze the user's device usage and distribute the report in the optimal audio format. For example, the report can be distributed as a voice message to a user who uses a smartphone. The distribution unit can also distribute the content of the report in audio format, allowing the user to listen to it during their commute or exercise. For example, the report can be distributed in podcast format. In this way, the report can be distributed in audio format, allowing the user to obtain information through hearing.

[0041] The distribution unit can distribute the report in chatbot format, allowing the user to obtain information in an interactive format. The distribution unit, for example, distributes the contents of the report in chatbot format, allowing the user to obtain information in an interactive format. For example, the distribution unit distributes the report through a messenger app. The distribution unit also builds a system in which the chatbot answers user questions in real time. For example, the user can send questions about the report to the chatbot and receive an immediate answer. The distribution unit also builds a system in which the user can provide feedback on the report distributed in chatbot format. For example, the user can send ratings and comments to the chatbot. This allows the report to be distributed in chatbot format, allowing the user to obtain information in an interactive format.

[0042] The report generation unit can collect user feedback in real time and immediately reflect it in the content of the next report. For example, the report generation unit can collect feedback provided by users on a report in real time and reflect it in the content of the next report. For example, the report content is updated every time a user provides a comment or rating. The report generation unit can also analyze user feedback in real time and build a system that optimizes the content of the next report. For example, it can prioritize content that users have given high ratings to. The report generation unit can also adjust the format and delivery method of the report based on the user feedback. For example, it can identify the user's preferred format and delivery timing and provide a report accordingly. This allows user feedback to be collected in real time and immediately reflected in the content of the next report.

[0043] The report generation unit can share user feedback with other users and collect community-based improvement suggestions. The report generation unit, for example, builds a system that shares feedback provided by a user with other users and collects community-based improvement suggestions. For example, it allows users to comment on and rate the feedback. The report generation unit also collects community-based improvement suggestions and reflects them in the content of the next report. For example, it incorporates improvements proposed by multiple users. The report generation unit also builds a system that promotes community-based discussions based on user feedback. For example, it allows users to exchange opinions on the feedback. This makes it possible to share user feedback with other users and collect community-based improvement suggestions.

[0044] The report generation unit can improve not only the content of the report but also the format and delivery method of the report based on the feedback. The report generation unit improves the content of the report based on, for example, feedback provided by the user. For example, it provides a report that reflects problems and requests pointed out by the user. The report generation unit also improves the format and delivery method of the report based on the user's feedback. For example, it identifies the format and delivery timing preferred by the user and provides a report accordingly. The report generation unit also collects user feedback in real time and continuously improves the content, format, and delivery method of the report. For example, it updates the content of the report every time the user provides a comment or rating. In this way, not only the content of the report but also the format and delivery method can be improved based on the feedback.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The user attribute information collection unit can analyze the user's voice data and identify the user's interests from the speaking style and tone of voice. For example, it can analyze the voice data of the user's smart speaker or voice assistant and infer emotions from the speaking style and tone of voice. For example, it can identify topics that the user is interested in and questions asked. The user attribute information collection unit can also analyze the voice data of the user's telephone calls and identify the user's interests from the content of the conversation and tone of voice. For example, it can identify the themes and concerns that the user frequently talks about. The user attribute information collection unit can also analyze the user's voice memos and recording data and use voice recognition technology to identify the content and emotions recorded by the user. For example, it can identify ideas and plans that the user has noted down. This makes it possible to analyze the user's voice data and identify the user's interests from the speaking style and tone of voice.

[0047] The report generation unit can customize not only the report content but also the report format based on user attribute information. For example, the report format can be customized based on the user's age and occupation. For example, video-format reports can be provided to young people, and text-format reports can be provided to business people. The report generation unit also customizes the report content and format based on the user's interests. For example, technical explanation videos can be provided to users interested in technology, and reports with many images and illustrations can be provided to users interested in entertainment. The report generation unit also customizes the report format based on the user's device usage. For example, mobile-friendly reports can be provided to users who primarily use smartphones, and detailed text reports can be provided to users who primarily use PCs. This makes it possible to customize the report content and format based on user attribute information.

[0048] The report generation unit can analyze the user's past feedback and continuously optimize the content of the report. For example, it analyzes feedback provided by the user in the past and improves the content of the report. For example, it prioritizes the incorporation of content that the user has given a high rating. The report generation unit also optimizes the format and delivery method of the report based on the user's feedback. For example, it identifies the user's preferred format and delivery timing and provides the report accordingly. The report generation unit also collects user feedback in real time and reflects it in the next report. For example, it updates the content of the report every time the user provides a comment or rating. This allows the user's past feedback to be analyzed and the content of the report to be continuously optimized.

[0049] The report generation unit can generate reports in different languages ​​and accommodate international users. For example, the report can be automatically translated and provided in different languages ​​based on the user's language setting. For example, multiple languages ​​such as English, French, and Chinese are supported. The report generation unit also builds a system that delivers reports generated in different languages ​​to international users. For example, the report can be provided in an appropriate language based on the user's location and language setting. The report generation unit also develops a multilingual report generation system that allows users to view reports in the language of their choice. For example, it provides an interface that allows users to freely select the language of the report. This allows reports to be generated in different languages ​​and accommodate international users.

[0050] The report generation unit can optimize the timing of report delivery based on user attribute information. For example, it analyzes the user's lifestyle and device usage to identify the optimal delivery timing. For example, it delivers reports during the time periods when the user is most active. The report generation unit also analyzes the user's past report viewing history to identify the optimal delivery timing. For example, it delivers reports during the time periods when the user frequently views reports. The report generation unit also analyzes the user's schedule and calendar information to identify the optimal delivery timing. For example, it delivers reports during times when the user is free or during breaks. This makes it possible to optimize the timing of report delivery based on user attribute information.

[0051] The distribution unit can analyze a user's device usage and deliver reports to the optimal device. For example, it can analyze a user's device usage history and identify the device that is used most frequently. For example, it can deliver reports for smartphones to a user who primarily uses smartphones. The distribution unit can also analyze a user's device settings and screen size and deliver reports in the optimal format. For example, it can provide reports for large screens to users who use tablets. The distribution unit can also monitor a user's device usage in real time and deliver reports to the optimal device. For example, it can deliver reports that match the device the user is currently using. This makes it possible to analyze a user's device usage and deliver reports to the optimal device.

[0052] The distribution unit can analyze the user's schedule and distribute reports at the optimal timing. For example, it can analyze the user's calendar app or schedule management tool to identify the optimal distribution timing. For example, it can distribute reports during times when the user is free or during breaks. The distribution unit can also analyze the user's past report viewing history to identify the optimal distribution timing. For example, it can distribute reports during times when the user frequently views reports. The distribution unit can also analyze the user's lifestyle and device usage to identify the optimal distribution timing. For example, it can distribute reports during times when the user is most active. This makes it possible to analyze the user's schedule and distribute reports at the optimal timing.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The user attribute information collection unit collects user attribute information, such as age, gender, occupation, and place of residence. It also identifies user interests based on past search and browsing history to gather information on user interests. Step 2: The analysis unit analyzes the user attribute information collected by the user attribute information collection unit. For example, the analysis is performed using statistical analysis or machine learning algorithms. The analysis unit also analyzes the user's interests in detail to identify frequently searched keywords and frequently viewed web pages. Step 3: The report generation unit generates a customized report based on the results of the analysis by the analysis unit. For example, it provides information on the latest smartphones and communication plans to young users, and information on business services and corporate strategies to business people. Step 4: The distribution unit distributes the report generated by the report generation unit to the user. For example, the distribution unit may send the report via email or a messenger app, or allow the user to view the report via a dedicated website or app.

[0055] (Example 2) A customized report generation system according to an embodiment of the present invention is a system that generates and distributes a report customized based on a user's attribute information and interests. This allows the customized report generation system to provide optimal information to the user, thereby deepening their understanding of SoftBank.

[0056] A customized report generation system according to an embodiment includes a user attribute information collection unit, an analysis unit, a report generation unit, and a distribution unit. The user attribute information collection unit collects user attribute information, such as age, gender, occupation, and place of residence. The user attribute information collection unit also collects user interests, such as identifying the user's interests based on past search history and browsing history. The analysis unit analyzes the user attribute information collected by the user attribute information collection unit, using, for example, statistical analysis or a machine learning algorithm. The analysis unit also analyzes the user's interests in detail, such as identifying frequently searched keywords and frequently viewed web pages. The report generation unit generates a customized report based on the results of the analysis by the analysis unit. For example, the report generation unit provides young users with information about the latest smartphones and communication plans. The report generation unit also provides business people with information about SoftBank's business services and corporate strategy. The distribution unit distributes the report generated by the report generation unit to users, such as by sending the report via email or a messenger app. The distribution unit also allows users to view the reports through a dedicated website or app. This allows the customized report generation system according to the embodiment to provide users with optimal information and deepen their understanding of SoftBank.

[0057] The user attribute information collection unit can collect real-time behavioral data of a user and reflect it in user attribute information. The user attribute information collection unit, for example, uses GPS data from the user's smartphone to collect current location and movement history and analyze the user's behavioral patterns. For example, it identifies the user's frequently visited places and travel times. The user attribute information collection unit also collects exercise data and heart rate data from the user's smartwatch or fitness tracker to analyze the user's health condition and daily activity level. For example, it identifies the time of day the user exercises. The user attribute information collection unit also collects home appliance usage status and indoor environment data from the user's smart home device to analyze the user's lifestyle habits. For example, it identifies the time of day the user uses home appliances. In this way, the user's real-time behavioral data can be collected and reflected in attribute information.

[0058] The user attribute information collection unit analyzes the content of a user's social media posts to identify their interests in more detail. For example, the user attribute information collection unit analyzes the content of a user's Twitter or Facebook posts to extract frequently used keywords and hashtags. For example, it identifies topics and events that the user is interested in. The user attribute information collection unit also analyzes images posted by the user on Instagram and uses image recognition technology to identify places and things that the user is interested in. For example, it identifies tourist spots visited by the user and products purchased by the user. The user attribute information collection unit also analyzes the user's LinkedIn profile and posts to identify occupational and career-related interests. For example, it identifies the industries and skills in which the user is interested. This allows the user's social media posts to be analyzed to identify their interests in more detail.

[0059] The user attribute information collection unit can use the emotion estimation function to analyze emotional fluctuations from a user's past search history and browsing history and identify changes in interests. The user attribute information collection unit, for example, analyzes the user's past search history and calculates an emotion score for each search keyword. For example, it identifies keywords associated with positive emotions and keywords associated with negative emotions. The user attribute information collection unit also analyzes the user's webpage browsing history and estimates the user's emotional response to the viewed content. For example, it identifies pages the user viewed for a long time and sites the user frequently visited. The user attribute information collection unit also analyzes the user's online shopping history and identifies emotional fluctuations based on reviews and ratings of purchased products. For example, it identifies products the user was satisfied with and products the user was dissatisfied with. This makes it possible to analyze emotional fluctuations from the user's past search history and browsing history and identify changes in interests.

[0060] The user attribute information collection unit analyzes the user's voice data and can identify the user's interests from the speaking style and tone of voice. The user attribute information collection unit, for example, analyzes the voice data of the user's smart speaker or voice assistant and infers emotions from the speaking style and tone of voice. For example, it identifies topics in which the user is interested and questions asked. The user attribute information collection unit also analyzes the voice data of the user's telephone calls and can identify the user's interests from the content of the conversation and tone of voice. For example, it can identify themes and concerns that the user frequently talks about. The user attribute information collection unit also analyzes the user's voice memos and recorded data and can use voice recognition technology to identify the content and emotions recorded by the user. For example, it can identify ideas and plans that the user has noted down. This makes it possible to analyze the user's voice data and identify the user's interests from the speaking style and tone of voice.

[0061] The user attribute information collection unit analyzes the user's purchasing history and can identify the user's interests from the categories and brands of purchased products. The user attribute information collection unit, for example, analyzes the user's purchasing history on an online shopping site and identifies the categories and brands of purchased products. For example, it identifies the products and brands that the user frequently purchases. The user attribute information collection unit also analyzes the user's credit card usage history and identifies the categories of purchased products and spending patterns. For example, it identifies which categories the user spends most on. The user attribute information collection unit also analyzes the user's purchasing history at stores and identifies the categories and brands of purchased products. For example, it identifies the stores the user frequently uses and the products that the user purchases. In this way, it is possible to analyze the user's purchasing history and identify the user's interests from the categories and brands of purchased products.

[0062] The user attribute information collection unit can use the emotion estimation function to analyze the emotional response of the video content viewed by the user and identify the user's interests. The user attribute information collection unit, for example, analyzes the viewing history of the video content viewed by the user and estimates the emotional response from the facial expressions and voices of the user while viewing. For example, it identifies videos in which the user has positive emotions and videos in which the user has negative emotions. The user attribute information collection unit also analyzes comments and ratings of the videos viewed by the user and calculates an emotion score. For example, it identifies videos in which the user has given high ratings and videos in which the user has given low ratings. The user attribute information collection unit also analyzes the playback time and viewing frequency of the videos viewed by the user to estimate the emotional response. For example, it identifies videos that the user watches multiple times or videos that the user watches for a long time. This makes it possible to analyze the emotional response of the video content viewed by the user and identify the user's interests.

[0063] The report generation unit can customize not only the report content but also the report format based on user attribute information. The report generation unit customizes the report format based on, for example, the user's age and occupation. For example, it provides video-format reports to young people and text-format reports to business people. The report generation unit also customizes the report content and format based on the user's interests. For example, it provides technical explanation videos to users who are interested in technology and reports that make extensive use of images and illustrations to users who are interested in entertainment. The report generation unit also customizes the report format based on the user's device usage. For example, it provides mobile-friendly reports to users who mainly use smartphones and detailed text reports to users who mainly use PCs. This makes it possible to customize the report content and format based on user attribute information.

[0064] The report generation unit can analyze the user's past feedback and continuously optimize the content of the report. For example, the report generation unit analyzes feedback provided by the user in the past and improves the content of the report. For example, it prioritizes the incorporation of content that the user has given a high rating. The report generation unit also optimizes the format and delivery method of the report based on the user's feedback. For example, it identifies the user's preferred format and delivery timing and provides the report accordingly. The report generation unit also collects the user's feedback in real time and reflects it in the next report. For example, it updates the content of the report every time the user provides a comment or rating. This allows the user's past feedback to be analyzed and the content of the report to be continuously optimized.

[0065] The report generation unit can generate reports in different languages ​​and accommodate international users. For example, the report generation unit can automatically translate reports and provide them in different languages ​​based on the user's language setting. For example, the report generation unit supports multiple languages ​​such as English, French, and Chinese. The report generation unit also builds a system that delivers reports generated in different languages ​​to international users. For example, the report is provided in an appropriate language based on the user's place of residence and language setting. The report generation unit also develops a multilingual report generation system that allows users to view reports in the language of their choice. For example, the report generation unit provides an interface that allows users to freely select the language of the report. This allows reports to be generated in different languages ​​and accommodate international users.

[0066] The report generation unit can optimize the timing of report delivery based on user attribute information. The report generation unit, for example, analyzes the user's lifestyle and device usage status to identify the optimal delivery timing. For example, the report is delivered during the time period when the user is most active. The report generation unit also analyzes the user's past report viewing history to identify the optimal delivery timing. For example, the report is delivered during the time period when the user frequently views reports. The report generation unit also analyzes the user's schedule and calendar information to identify the optimal delivery timing. For example, the report is delivered during the time period when the user is free or during breaks. This makes it possible to optimize the timing of report delivery based on user attribute information.

[0067] The report generation unit can use the emotion estimation function to identify topics that interest the user most and generate detailed reports on those topics. The report generation unit, for example, analyzes the user's emotional responses and identifies topics that elicit the most positive emotions. For example, it incorporates themes and topics that interest the user into the report. The report generation unit also uses the emotion estimation function to generate detailed reports on topics that interest the user most. For example, it provides information on fields and industries that interest the user. The report generation unit also adjusts the content of the report based on the user's emotion data and composes the report around the most interesting topics. For example, it includes news and trends that interest the user in the report. This makes it possible to identify topics that interest the user most and generate detailed reports on those topics.

[0068] The distribution unit can analyze the user's device usage and deliver reports to the optimal device. For example, the distribution unit analyzes the user's device usage history and identifies the device used most frequently. For example, a report for a smartphone is delivered to a user who mainly uses a smartphone. The distribution unit also analyzes the user's device settings and screen size and delivers reports in the optimal format. For example, a report for a large screen is provided to a user who uses a tablet. The distribution unit also monitors the user's device usage in real time and delivers reports to the optimal device. For example, a report is delivered according to the device the user is currently using. This makes it possible to analyze the user's device usage and deliver reports to the optimal device.

[0069] The distribution unit can analyze the user's schedule and distribute reports at the optimal timing. The distribution unit, for example, analyzes the user's calendar app or schedule management tool to identify the optimal distribution timing. For example, reports are distributed during times when the user is free or during breaks. The distribution unit also analyzes the user's past report viewing history to identify the optimal distribution timing. For example, reports are distributed during times when the user frequently views reports. The distribution unit also analyzes the user's lifestyle and device usage to identify the optimal distribution timing. For example, reports are distributed during times when the user is most active. This makes it possible to analyze the user's schedule and distribute reports at the optimal timing.

[0070] The distribution unit can use the emotion estimation function to distribute reports during times when the user is most relaxed. The distribution unit, for example, analyzes the user's emotion data to identify times when the user is most relaxed. For example, the distribution unit distributes reports according to times when the user is most relaxed. The distribution unit also uses the emotion estimation function to build a system that distributes reports during times when the user is most relaxed. For example, the distribution unit analyzes the user's heart rate and stress level to identify the optimal distribution timing. The distribution unit also analyzes the user's daily rhythm and device usage status to distribute reports during times when the user is most relaxed. For example, the distribution unit distributes reports according to times when the user is most relaxed. This makes it possible to distribute reports during times when the user is most relaxed.

[0071] The distribution unit can distribute the report in audio format, allowing the user to obtain information through hearing. For example, the distribution unit converts the content of the report into audio format using speech synthesis technology and distributes it to the user. For example, the report can be listened to through a smart speaker or a voice assistant. The distribution unit can also analyze the user's device usage and distribute the report in the optimal audio format. For example, the report can be distributed as a voice message to a user who uses a smartphone. The distribution unit can also distribute the content of the report in audio format, allowing the user to listen to it during their commute or exercise. For example, the report can be distributed in podcast format. In this way, the report can be distributed in audio format, allowing the user to obtain information through hearing.

[0072] The distribution unit can distribute the report in chatbot format, allowing the user to obtain information in an interactive format. The distribution unit, for example, distributes the contents of the report in chatbot format, allowing the user to obtain information in an interactive format. For example, the distribution unit distributes the report through a messenger app. The distribution unit also builds a system in which the chatbot answers user questions in real time. For example, the user can send questions about the report to the chatbot and receive an immediate answer. The distribution unit also builds a system in which the user can provide feedback on the report distributed in chatbot format. For example, the user can send ratings and comments to the chatbot. This allows the report to be distributed in chatbot format, allowing the user to obtain information in an interactive format.

[0073] The delivery unit can use the emotion estimation function to identify the delivery method that elicits the most positive emotion from the user and deliver the report using that method. The delivery unit, for example, analyzes the user's emotion data and identifies the delivery method that elicits the most positive emotion. For example, it identifies the delivery method that the user prefers and delivers the report using that method. The delivery unit also uses the emotion estimation function to build a system that identifies the delivery method that elicits the most positive emotion from the user. For example, it identifies the optimal delivery method based on the user's emotion score. The delivery unit also adjusts the report delivery method based on the user's emotion data. For example, it delivers the report in audio format during a time period when the user is relaxed. This makes it possible to identify the delivery method that elicits the most positive emotion from the user and deliver the report using that method.

[0074] The report generation unit can collect user feedback in real time and immediately reflect it in the content of the next report. For example, the report generation unit can collect feedback provided by users on a report in real time and reflect it in the content of the next report. For example, the report content is updated every time a user provides a comment or rating. The report generation unit can also analyze user feedback in real time and build a system that optimizes the content of the next report. For example, it can prioritize content that users have given high ratings to. The report generation unit can also adjust the format and delivery method of the report based on the user feedback. For example, it can identify the user's preferred format and delivery timing and provide a report accordingly. This allows user feedback to be collected in real time and immediately reflected in the content of the next report.

[0075] The report generation unit uses the emotion estimation function to analyze the user's emotion when providing feedback and can prioritize reflect emotionally positive feedback. The report generation unit, for example, analyzes the user's emotion when providing feedback and prioritizes reflecting feedback with positive emotions. For example, it prioritizes incorporating content that the user has given a high rating. The report generation unit also uses the emotion estimation function to build a system that analyzes the user's emotion when providing feedback. For example, it evaluates the feedback based on the user's emotion score. The report generation unit also determines the priority of feedback based on the user's emotion data. For example, it prioritizes reflecting feedback with positive emotions. This makes it possible to analyze the user's emotion when providing feedback and prioritize reflecting emotionally positive feedback.

[0076] The report generation unit can share user feedback with other users and collect community-based improvement suggestions. The report generation unit, for example, builds a system that shares feedback provided by a user with other users and collects community-based improvement suggestions. For example, it allows users to comment on and rate the feedback. The report generation unit also collects community-based improvement suggestions and reflects them in the content of the next report. For example, it incorporates improvements proposed by multiple users. The report generation unit also builds a system that promotes community-based discussions based on user feedback. For example, it allows users to exchange opinions on the feedback. This makes it possible to share user feedback with other users and collect community-based improvement suggestions.

[0077] The report generation unit can improve not only the content of the report but also the format and delivery method of the report based on the feedback. The report generation unit improves the content of the report based on, for example, feedback provided by the user. For example, it provides a report that reflects problems and requests pointed out by the user. The report generation unit also improves the format and delivery method of the report based on the user's feedback. For example, it identifies the format and delivery timing preferred by the user and provides a report accordingly. The report generation unit also collects user feedback in real time and continuously improves the content, format, and delivery method of the report. For example, it updates the content of the report every time the user provides a comment or rating. In this way, not only the content of the report but also the format and delivery method can be improved based on the feedback.

[0078] The report generation unit can use the emotion estimation function to identify the feedback method that elicits the most positive emotions from the user and collect feedback using that method. The report generation unit, for example, analyzes the user's emotion data and identifies the feedback method that elicits the most positive emotions. For example, it identifies the feedback method that the user prefers and collects feedback using that method. The report generation unit also uses the emotion estimation function to build a system that identifies the feedback method that elicits the most positive emotions from the user. For example, it identifies the optimal feedback method based on the user's emotion score. The report generation unit also adjusts the feedback collection method based on the user's emotion data. For example, it collects feedback during times when the user is relaxed. This makes it possible to identify the feedback method that elicits the most positive emotions from the user and collect feedback using that method.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The user attribute information collection unit can analyze the user's voice data and identify the user's interests from the speaking style and tone of voice. For example, it can analyze the voice data of the user's smart speaker or voice assistant and infer emotions from the speaking style and tone of voice. For example, it can identify topics that the user is interested in and questions asked. The user attribute information collection unit can also analyze the voice data of the user's telephone calls and identify the user's interests from the content of the conversation and tone of voice. For example, it can identify the themes and concerns that the user frequently talks about. The user attribute information collection unit can also analyze the user's voice memos and recording data and use voice recognition technology to identify the content and emotions recorded by the user. For example, it can identify ideas and plans that the user has noted down. This makes it possible to analyze the user's voice data and identify the user's interests from the speaking style and tone of voice.

[0081] The report generation unit can customize not only the report content but also the report format based on user attribute information. For example, the report format can be customized based on the user's age and occupation. For example, video-format reports can be provided to young people, and text-format reports can be provided to business people. The report generation unit also customizes the report content and format based on the user's interests. For example, technical explanation videos can be provided to users interested in technology, and reports with many images and illustrations can be provided to users interested in entertainment. The report generation unit also customizes the report format based on the user's device usage. For example, mobile-friendly reports can be provided to users who primarily use smartphones, and detailed text reports can be provided to users who primarily use PCs. This makes it possible to customize the report content and format based on user attribute information.

[0082] The report generation unit can analyze the user's past feedback and continuously optimize the content of the report. For example, it analyzes feedback provided by the user in the past and improves the content of the report. For example, it prioritizes the incorporation of content that the user has given a high rating. The report generation unit also optimizes the format and delivery method of the report based on the user's feedback. For example, it identifies the user's preferred format and delivery timing and provides the report accordingly. The report generation unit also collects user feedback in real time and reflects it in the next report. For example, it updates the content of the report every time the user provides a comment or rating. This allows the user's past feedback to be analyzed and the content of the report to be continuously optimized.

[0083] The report generation unit can generate reports in different languages ​​and accommodate international users. For example, the report can be automatically translated and provided in different languages ​​based on the user's language setting. For example, multiple languages ​​such as English, French, and Chinese are supported. The report generation unit also builds a system that delivers reports generated in different languages ​​to international users. For example, the report can be provided in an appropriate language based on the user's location and language setting. The report generation unit also develops a multilingual report generation system that allows users to view reports in the language of their choice. For example, it provides an interface that allows users to freely select the language of the report. This allows reports to be generated in different languages ​​and accommodate international users.

[0084] The report generation unit can optimize the timing of report delivery based on user attribute information. For example, it analyzes the user's lifestyle and device usage to identify the optimal delivery timing. For example, it delivers reports during the time periods when the user is most active. The report generation unit also analyzes the user's past report viewing history to identify the optimal delivery timing. For example, it delivers reports during the time periods when the user frequently views reports. The report generation unit also analyzes the user's schedule and calendar information to identify the optimal delivery timing. For example, it delivers reports during times when the user is free or during breaks. This makes it possible to optimize the timing of report delivery based on user attribute information.

[0085] The report generation unit can use the emotion estimation function to identify topics that interest the user most and generate detailed reports on those topics. For example, it can analyze the user's emotional responses and identify topics that elicit the most positive emotions. For example, it can incorporate themes and topics that interest the user into the report. The report generation unit also uses the emotion estimation function to generate detailed reports on topics that interest the user most. For example, it can provide information on fields and industries that interest the user. The report generation unit can also adjust the content of the report based on the user's emotion data and compose the report around the most interesting topics. For example, it can include news and trends that interest the user in the report. This makes it possible to identify topics that interest the user most and generate detailed reports on those topics.

[0086] The distribution unit can analyze a user's device usage and deliver reports to the optimal device. For example, it can analyze a user's device usage history and identify the device that is used most frequently. For example, it can deliver reports for smartphones to a user who primarily uses smartphones. The distribution unit can also analyze a user's device settings and screen size and deliver reports in the optimal format. For example, it can provide reports for large screens to users who use tablets. The distribution unit can also monitor a user's device usage in real time and deliver reports to the optimal device. For example, it can deliver reports that match the device the user is currently using. This makes it possible to analyze a user's device usage and deliver reports to the optimal device.

[0087] The distribution unit can analyze the user's schedule and distribute reports at the optimal timing. For example, it can analyze the user's calendar app or schedule management tool to identify the optimal distribution timing. For example, it can distribute reports during times when the user is free or during breaks. The distribution unit can also analyze the user's past report viewing history to identify the optimal distribution timing. For example, it can distribute reports during times when the user frequently views reports. The distribution unit can also analyze the user's lifestyle and device usage to identify the optimal distribution timing. For example, it can distribute reports during times when the user is most active. This makes it possible to analyze the user's schedule and distribute reports at the optimal timing.

[0088] The distribution unit can use the emotion estimation function to distribute reports during times when the user is most relaxed. For example, the distribution unit analyzes the user's emotion data to identify times when the user is most relaxed. For example, the distribution unit distributes reports according to times when the user is most relaxed. The distribution unit also uses the emotion estimation function to build a system that distributes reports during times when the user is most relaxed. For example, the distribution unit analyzes the user's heart rate and stress level to identify the optimal distribution timing. The distribution unit also analyzes the user's daily rhythm and device usage status to distribute reports during times when the user is most relaxed. For example, the distribution unit distributes reports according to times when the user is most relaxed. This makes it possible to distribute reports during times when the user is most relaxed.

[0089] The delivery unit can use the emotion estimation function to identify the delivery method that elicits the most positive emotion from the user and deliver the report using that method. For example, the delivery unit analyzes the user's emotion data and identifies the delivery method that elicits the most positive emotion. For example, the delivery method that the user prefers is identified and the report is delivered using that method. The delivery unit also uses the emotion estimation function to build a system that identifies the delivery method that elicits the most positive emotion from the user. For example, the delivery unit identifies the optimal delivery method based on the user's emotion score. The delivery unit also adjusts the report delivery method based on the user's emotion data. For example, the report is delivered in audio format during a time period when the user is relaxed. This allows the delivery method that elicits the most positive emotion from the user to be identified and the report to be delivered using that method.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The user attribute information collection unit collects user attribute information, such as age, gender, occupation, and place of residence. It also identifies user interests based on past search and browsing history to gather information on user interests. Step 2: The analysis unit analyzes the user attribute information collected by the user attribute information collection unit. For example, the analysis is performed using statistical analysis or machine learning algorithms. The analysis unit also analyzes the user's interests in detail to identify frequently searched keywords and frequently viewed web pages. Step 3: The report generation unit generates a customized report based on the results of the analysis by the analysis unit. For example, it provides information on the latest smartphones and communication plans to young users, and information on business services and corporate strategies to business people. Step 4: The distribution unit distributes the report generated by the report generation unit to the user. For example, the distribution unit may send the report via email or a messenger app, or allow the user to view the report via a dedicated website or app.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0124] The data processing system 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 user attribute information collection unit that collects user attribute information; an analysis unit that analyzes the user attribute information collected by the user attribute information collection unit; a report generation unit that generates a customized report based on the results of the analysis by the analysis unit; a distribution unit that distributes the report generated by the report generation unit to a user. A system characterized by:

2. The user attribute information collection unit Collecting real-time user behavior data and reflecting it in the user attribute information 2. The system of claim 1.

3. The user attribute information collection unit Analyzing your social media posts to better identify your interests 2. The system of claim 1.

4. The user attribute information collection unit Analyzes emotional fluctuations based on a user's past search and browsing history to identify changes in interests 2. The system of claim 1.

5. The user attribute information collection unit Analyzes user voice data to identify interests based on speaking style and tone of voice 2. The system of claim 1.

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