System

Abstract

An object of the system according to the embodiment is to provide well-balanced information and make it easy to accept various opinions and ideas.SOLUTION: A system according to an embodiment includes an information providing unit, an emotion analysis unit, and a taste adjustment unit. The information providing unit provides balanced information using the generated AI. The emotion analysis unit adjusts the balance of the information provided by the information providing unit. The taste adjustment unit adjusts a taste of the information on the basis of the emotion analyzed by the emotion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the information provided tends to be biased, making it difficult to accept diverse opinions and ideas.

[0005] The system according to the embodiment aims to provide balanced information and make it easier to accept diverse opinions and ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, an emotion analysis unit, and a taste adjustment unit. The information providing unit provides balanced information using a generation AI. The emotion analysis unit adjusts the balance of the information provided by the information providing unit. The taste adjustment unit adjusts the taste of the information based on the emotion analyzed by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide balanced information and make it easier to accept diverse opinions and ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information provision system according to the embodiment of the present invention is a system that uses generative AI to provide balanced information and promote diverse thinking, thereby enabling the information provision system to provide users with diverse perspectives and prevent them from being trapped by biased opinions or ideas.

[0029] An information provision system according to an embodiment includes an information provision unit, an emotion analysis unit, and a taste adjustment unit. The information provision unit uses a generation AI to provide balanced information. For example, the generation AI analyzes a user's preferences and browsing history while simultaneously displaying opposing and neutral opinions. The generation AI also provides information tailored to the user's preferred taste. For example, if a user prefers strict but loving instruction, the generation AI provides information in that taste. The emotion analysis unit adjusts the balance of the information provided by the information provision unit. For example, the emotion analysis unit analyzes the user's emotional state in real time and adjusts the balance of the information according to the emotion. The taste adjustment unit adjusts the taste of the information based on the emotion analyzed by the emotion analysis unit. For example, the taste adjustment unit provides information in a taste that evokes positive emotions in the user. This allows the information provision system according to an embodiment to provide diverse perspectives to users and prevent them from being trapped by biased opinions or ideologies. For example, even if a user is biased toward a particular political opinion, the generation AI can provide opposing and neutral opinions, allowing the user to have diverse perspectives. Furthermore, by providing information tailored to the user's tastes, the user can obtain a variety of information in a format that is easy for the user to accept.

[0030] The information provision unit can provide information based not only on the user's past behavioral history but also on social trends or news. For example, the information provision unit uses a generation AI to analyze the user's past browsing history and combine it with current social trends and news to provide information. For example, it reflects the latest technological trends and market changes. The information provision unit also cross-references the user's behavioral history with current news and prioritizes displaying highly relevant information. For example, it provides the latest news in areas of interest to the user. The information provision unit also analyzes social trends in real time and customizes information to suit the user's interests. For example, it displays information related to popular topics and events. This allows the user to receive the latest information and have a variety of perspectives.

[0031] The information provision unit can automatically collect information from different cultural spheres or regions and provide users with diverse perspectives. For example, the generation AI collects information from news sites and social media sites from different cultural spheres or regions and provides it to users. For example, it displays articles about overseas news and culture. The information provision unit also cross-references information from different regions to build a system that provides users with diverse perspectives. For example, it displays different regional views on the same topic. The information provision unit also automatically collects information that takes cultural background and regional characteristics into consideration and provides it to users. For example, it displays information about the customs and traditions of different cultures. This allows users to receive information from different cultural spheres or regions and have diverse perspectives.

[0032] The information provision unit can provide a combination of information from different fields that interest the user. For example, the information provision unit uses a generative AI to analyze the user's interests and provide a combination of information from different fields. For example, it displays an article about the fusion of science and art. The information provision unit also cross-references information from different fields based on the user's interests to provide highly relevant information. For example, it displays information about the evolution of sports and technology. The information provision unit also automatically collects information from different fields to build a system that provides the user with a multifaceted perspective. For example, it displays an article about the intersection of science and culture. This makes it possible to provide the user with a multifaceted perspective.

[0033] The information provision unit can provide information based not only on the user's past behavioral history but also on their mood or emotions. For example, the information provision unit provides information by using a generation AI to analyze the user's past browsing history and current emotional state. For example, if the user feels like relaxing, it provides information that will help them relax. The information provision unit also cross-references the user's behavioral history with their current emotions to build a system that provides highly relevant information. For example, it provides positive information in areas that interest the user. The information provision unit also analyzes the emotional state in real time and provides information tailored to the user's mood. For example, if the user feels like cheering up, it provides an encouraging message. This makes it possible to provide information that corresponds to the user's current mood and emotions.

[0034] The information provision unit can automatically collect tastes from different cultural spheres and regions and provide diverse tastes to users. For example, the information provision unit uses a generation AI to collect information from different cultural spheres and regions and provide diverse tastes to users. For example, it displays information about foreign cuisine and music. The information provision unit also cross-references tastes from different regions to build a system that provides diverse perspectives to users. For example, it displays different regional views on the same theme. The information provision unit also automatically collects tastes that take cultural background and regional characteristics into consideration and provides them to users. For example, it displays information about the customs and traditions of different cultures. This allows the user to be provided with tastes from different cultural spheres and regions and have diverse perspectives.

[0035] The information provision unit can provide information by combining tastes from different fields that interest the user. For example, the information provision unit uses a generative AI to analyze the user's interests and provide a combination of information from different fields. For example, it displays an article about the fusion of science and art. The information provision unit also cross-references information from different fields based on the user's interests to provide highly relevant information. For example, it displays information about the evolution of sports and technology. The information provision unit also automatically collects information from different fields and builds a system that provides users with a multifaceted perspective. For example, it displays an article about the intersection of science and culture. This makes it possible to provide users with a multifaceted perspective.

[0036] The information providing unit can provide diversity information based not only on the user's past behavioral history but also on social trends or news. For example, the information providing unit provides diversity information by using a generation AI to analyze the user's past browsing history and current social trends. For example, the information providing unit displays the latest diversity-related news and events. The information providing unit also cross-references the user's behavioral history with current news to build a system that provides highly relevant diversity information. For example, the information providing unit provides information on diversity in areas that interest the user. The information providing unit also analyzes social trends in real time to provide diversity information tailored to the user's interests. For example, the information providing unit displays popular diversity-related topics and events. This allows the user to be provided with the latest diversity information and gain diverse perspectives.

[0037] The information provision unit can automatically collect information on diversity from different cultural spheres and regions, and provide users with diverse perspectives. For example, the generation AI collects diversity information from news sites and social media sites from different cultural spheres and regions, and provides it to users. For example, it displays articles and examples related to diversity overseas. The information provision unit also cross-references diversity information from different regions, building a system that provides users with diverse perspectives. For example, it displays different regional views on the same topic. The information provision unit also automatically collects diversity information that takes cultural background and regional characteristics into consideration, and provides it to users. For example, it displays diversity information related to the customs and traditions of different cultures. This allows users to receive information on diversity from different cultural spheres and regions, and gain diverse perspectives.

[0038] The information providing unit can provide a combination of diversity information from different fields that interest the user. For example, the information providing unit uses a generative AI to analyze the user's interests and provide a combination of diversity information from different fields. For example, it displays examples of diversity related to the fusion of science and art. The information providing unit also cross-references diversity information from different fields based on the user's interests to provide highly relevant information. For example, it displays diversity information related to the evolution of sports and technology. The information providing unit also automatically collects diversity information from different fields to build a system that provides the user with a multifaceted perspective. For example, it displays examples of diversity related to the intersection of science and culture. This makes it possible to provide the user with a multifaceted perspective.

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

[0040] The information provision system may further include a health management unit that monitors the user's health condition. The health management unit collects biometric data such as the user's heart rate and sleep patterns and analyzes the user's health condition in real time. For example, if the user is feeling stressed, the health management unit may suggest information or activities that will help them relax. The health management unit may also provide appropriate dietary and exercise advice based on the user's health condition. This supports the user's health and promotes a more balanced lifestyle.

[0041] The information provision system can further include a learning support unit that analyzes the user's learning history. The learning support unit analyzes the content the user has learned in the past and the areas of interest, and provides related new information and learning resources. For example, if the user is interested in a particular technology, it can suggest the latest research papers and online courses related to that technology. The learning support unit can also track the user's learning progress and provide review and additional learning resources at appropriate times. This can improve the user's learning efficiency and support continuous learning.

[0042] The information provision system may further include a purchasing support unit that analyzes the user's purchasing history. The purchasing support unit analyzes the products and services the user has purchased in the past and suggests new related products and services. For example, if the user prefers products from a particular brand, the purchasing support unit may provide information about new products and sales from that brand. The purchasing support unit may also provide repeat purchase suggestions and coupons at appropriate times based on the user's purchasing history. This improves the user's purchasing experience and supports more convenient shopping.

[0043] The information provision system can further include a travel support unit that analyzes the user's travel history. The travel support unit analyzes places the user has visited in the past and tourist spots that interest the user, and suggests new related travel destinations and tourist information. For example, if the user likes nature, it will suggest tourist spots and activities rich in nature. The travel support unit can also provide travel plans and accommodation information at appropriate times based on the user's travel history. This can improve the user's travel experience and support a more fulfilling trip.

[0044] The information provision system can further include a hobby support unit that analyzes the user's hobbies and interests. The hobby support unit analyzes the activities the user has enjoyed in the past and the areas of interest to suggest new related hobbies and activities. For example, if the user likes cooking, it can provide information on new recipes and cooking classes. The hobby support unit can also provide information on events and workshops at appropriate times based on the user's hobbies. This can enrich the user's hobbies and support a richer life.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The information provider uses the generation AI to provide balanced information. For example, the generation AI analyzes the user's preferences and past browsing history, while simultaneously displaying opposing and neutral opinions. The generation AI also provides information tailored to the user's taste. For example, if the user prefers instruction that is strict but loving, the information provided will be tailored to that taste. Step 2: The emotion analysis unit adjusts the balance of the information provided by the information providing unit. For example, the emotion analysis unit analyzes the user's emotional state in real time and adjusts the balance of the information according to the emotion. Step 3: The taste adjustment unit adjusts the taste of the information based on the emotions analyzed by the emotion analysis unit. For example, the information is provided in a taste that evokes positive emotions in the user.

[0047] (Example 2) The information provision system according to the embodiment of the present invention is a system that uses generative AI to provide balanced information and promote diverse thinking, thereby enabling the information provision system to provide users with diverse perspectives and prevent them from being trapped by biased opinions or ideas.

[0048] An information provision system according to an embodiment includes an information provision unit, an emotion analysis unit, and a taste adjustment unit. The information provision unit uses a generation AI to provide balanced information. For example, the generation AI analyzes a user's preferences and browsing history while simultaneously displaying opposing and neutral opinions. The generation AI also provides information tailored to the user's preferred taste. For example, if a user prefers strict but loving instruction, the generation AI provides information in that taste. The emotion analysis unit adjusts the balance of the information provided by the information provision unit. For example, the emotion analysis unit analyzes the user's emotional state in real time and adjusts the balance of the information according to the emotion. The taste adjustment unit adjusts the taste of the information based on the emotion analyzed by the emotion analysis unit. For example, the taste adjustment unit provides information in a taste that evokes positive emotions in the user. This allows the information provision system according to an embodiment to provide diverse perspectives to users and prevent them from being trapped by biased opinions or ideologies. For example, even if a user is biased toward a particular political opinion, the generation AI can provide opposing and neutral opinions, allowing the user to have diverse perspectives. Furthermore, by providing information tailored to the user's tastes, the user can obtain a variety of information in a format that is easy for the user to accept.

[0049] The information provision unit can provide information based not only on the user's past behavioral history but also on social trends or news. For example, the information provision unit uses a generation AI to analyze the user's past browsing history and combine it with current social trends and news to provide information. For example, it reflects the latest technological trends and market changes. The information provision unit also cross-references the user's behavioral history with current news and prioritizes displaying highly relevant information. For example, it provides the latest news in areas of interest to the user. The information provision unit also analyzes social trends in real time and customizes information to suit the user's interests. For example, it displays information related to popular topics and events. This allows the user to receive the latest information and have a variety of perspectives.

[0050] The emotion analysis unit can analyze the user's emotional state in real time and adjust the balance of information according to their emotions. For example, the emotion analysis unit uses a generative AI to analyze the user's facial expressions and voice and estimate their emotions in real time. For example, if the user is feeling stressed, it will prioritize providing relaxing information. The emotion analysis unit also adjusts the tone and content of the information based on the user's emotional state. For example, if the user is sad, it will display positive news or encouraging messages. The emotion analysis unit also uses emotion analysis to predict how the user will feel about specific information and prioritize displaying emotionally neutral information. This makes it possible to provide information according to the user's emotions.

[0051] The taste adjustment unit can provide information tailored to the user's preferred taste. For example, the taste adjustment unit uses a generation AI to analyze the user's facial expressions and voice and estimate their emotions in real time. For example, if the user is feeling stressed, it provides information with a relaxing taste. The taste adjustment unit also adjusts the tone and content of the information based on the user's emotional state. For example, if the user is sad, it provides information with a positive tone. The taste adjustment unit also uses emotion analysis to predict how the user will feel in response to a specific taste and provides information with an emotionally positive taste. This makes it possible to provide information tailored to the user's preferences.

[0052] The information provision unit can automatically collect information from different cultural spheres or regions and provide users with diverse perspectives. For example, the generation AI collects information from news sites and social media sites from different cultural spheres or regions and provides it to users. For example, it displays articles about overseas news and culture. The information provision unit also cross-references information from different regions to build a system that provides users with diverse perspectives. For example, it displays different regional views on the same topic. The information provision unit also automatically collects information that takes cultural background and regional characteristics into consideration and provides it to users. For example, it displays information about the customs and traditions of different cultures. This allows users to receive information from different cultural spheres or regions and have diverse perspectives.

[0053] The information provision unit can provide a combination of information from different fields that interest the user. For example, the information provision unit uses a generative AI to analyze the user's interests and provide a combination of information from different fields. For example, it displays an article about the fusion of science and art. The information provision unit also cross-references information from different fields based on the user's interests to provide highly relevant information. For example, it displays information about the evolution of sports and technology. The information provision unit also automatically collects information from different fields to build a system that provides the user with a multifaceted perspective. For example, it displays an article about the intersection of science and culture. This makes it possible to provide the user with a multifaceted perspective.

[0054] The emotion analysis unit can predict how a user will feel about specific information and prioritize displaying emotionally neutral information. The emotion analysis unit, for example, uses an emotion estimation function to predict how a user will feel about specific news or articles. For example, it prioritizes displaying information with an emotion score close to neutral. The emotion analysis unit also analyzes the user's emotional response in real time and builds a system that selects emotionally neutral information. For example, it displays information that is neither positive nor negative. The emotion analysis unit also provides information that does not bias the user emotionally based on the emotion estimation data. For example, it prioritizes displaying information with an emotion score that is not extremely high. This makes it possible to provide information that is not biased toward the user's emotions.

[0055] The information providing unit can prioritize displaying information that evokes the most positive emotions in the user, thereby promoting balanced information provision. The information providing unit, for example, uses an emotion estimation function to identify information that evokes the most positive emotions in the user and display it preferentially. For example, news or articles with a high emotion score are displayed. The information providing unit also analyzes the user's emotional response in real time to build a system for providing information that elicits positive emotions. For example, information that makes the user feel joyful is displayed. The information providing unit also prioritizes displaying information that evokes positive emotions in the user based on the emotion estimation data, thereby promoting balanced information provision. For example, information with a high emotion score is displayed preferentially. This makes it possible to provide information that elicits positive emotions in the user.

[0056] The information provision unit can analyze the user's emotional state in real time and adjust the tone of the information according to the emotion. For example, the information provision unit uses a generation AI to analyze the user's facial expressions and voice and estimate the emotion in real time. For example, if the user is feeling stressed, it provides information with a relaxing tone. The information provision unit also adjusts the tone and content of the information based on the user's emotional state. For example, if the user is sad, it provides information with a positive tone. The information provision unit also uses emotion analysis to predict how the user will feel in response to a specific tone and provides information with an emotionally positive tone. This makes it possible to provide information in a tone that corresponds to the user's emotion.

[0057] The information provision unit can provide information based not only on the user's past behavioral history but also on their mood or emotions. For example, the information provision unit provides information by using a generation AI to analyze the user's past browsing history and current emotional state. For example, if the user feels like relaxing, it provides information that will help them relax. The information provision unit also cross-references the user's behavioral history with their current emotions to build a system that provides highly relevant information. For example, it provides positive information in areas that interest the user. The information provision unit also analyzes the emotional state in real time and provides information tailored to the user's mood. For example, if the user feels like cheering up, it provides an encouraging message. This makes it possible to provide information that corresponds to the user's current mood and emotions.

[0058] The information providing unit can use the emotion estimation function to predict what emotion a user will have in response to a specific taste, and provide information with an emotionally positive taste. The information providing unit, for example, uses the emotion estimation function to predict what emotion a user will have in response to a specific taste. For example, it preferentially provides information with a taste with a high emotion score. The information providing unit also analyzes the user's emotional response in real time, and builds a system that provides information with an emotionally positive taste. For example, it provides information with a taste that makes the user feel joy. The information providing unit also preferentially provides information with a taste that makes the user feel positive emotion, based on the emotion estimation data. For example, it preferentially provides information with a taste that makes the user feel positive emotion. This makes it possible to provide information with a taste that makes the user feel positive emotion.

[0059] The information provision unit can automatically collect tastes from different cultural spheres and regions and provide diverse tastes to users. For example, the information provision unit uses a generation AI to collect information from different cultural spheres and regions and provide diverse tastes to users. For example, it displays information about foreign cuisine and music. The information provision unit also cross-references tastes from different regions to build a system that provides diverse perspectives to users. For example, it displays different regional views on the same theme. The information provision unit also automatically collects tastes that take cultural background and regional characteristics into consideration and provides them to users. For example, it displays information about the customs and traditions of different cultures. This allows the user to be provided with tastes from different cultural spheres and regions and have diverse perspectives.

[0060] The information provision unit can provide information by combining tastes from different fields that interest the user. For example, the information provision unit uses a generative AI to analyze the user's interests and provide a combination of information from different fields. For example, it displays an article about the fusion of science and art. The information provision unit also cross-references information from different fields based on the user's interests to provide highly relevant information. For example, it displays information about the evolution of sports and technology. The information provision unit also automatically collects information from different fields and builds a system that provides users with a multifaceted perspective. For example, it displays an article about the intersection of science and culture. This makes it possible to provide users with a multifaceted perspective.

[0061] The information providing unit can use the emotion estimation function to provide information in a style that evokes the most positive emotion for the user, thereby promoting the provision of information in a preferred style. The information providing unit, for example, uses the emotion estimation function to identify the style that evokes the most positive emotion for the user and provide it preferentially. For example, information in a style with a high emotion score is provided. The information providing unit also analyzes the user's emotional response in real time and builds a system that provides information in a style that elicits positive emotions. For example, information in a style that makes the user feel joy is provided. The information providing unit also prioritizes providing information in a style that evokes positive emotions for the user based on the emotion estimation data, promoting the provision of information in a preferred style. For example, information in a style with a high emotion score is provided preferentially. This makes it possible to provide information in a style that evokes positive emotions for the user.

[0062] The information provision unit can analyze the user's emotional state in real time and adjust the information on respect for diversity and fair opportunities for success according to the emotion. For example, the information provision unit uses a generation AI to analyze the user's facial expressions and voice and estimate the emotion in real time. For example, if the user is feeling stressed, it provides relaxing diversity information. The information provision unit also adjusts the tone and content of the diversity information based on the user's emotional state. For example, if the user is sad, it provides diversity information with a positive tone. The information provision unit also uses emotion analysis to predict how the user will feel in response to specific diversity information and provides emotionally positive diversity information. This makes it possible to provide diversity information according to the user's emotions.

[0063] The information providing unit can provide diversity information based not only on the user's past behavioral history but also on social trends or news. For example, the information providing unit provides diversity information by using a generation AI to analyze the user's past browsing history and current social trends. For example, the information providing unit displays the latest diversity-related news and events. The information providing unit also cross-references the user's behavioral history with current news to build a system that provides highly relevant diversity information. For example, the information providing unit provides information on diversity in areas that interest the user. The information providing unit also analyzes social trends in real time to provide diversity information tailored to the user's interests. For example, the information providing unit displays popular diversity-related topics and events. This allows the user to be provided with the latest diversity information and gain diverse perspectives.

[0064] The information providing unit can use the emotion estimation function to predict what emotion a user will feel in response to information of a specific variety, and preferentially display emotionally positive information. The information providing unit, for example, uses the emotion estimation function to predict what emotion a user will feel in response to information of a specific variety. For example, it preferentially provides information of a variety with a high emotion score. The information providing unit also analyzes the user's emotional response in real time, and builds a system that provides information of a variety with a positive emotion. For example, it provides information of a variety that makes the user feel happy. The information providing unit also preferentially provides information of a variety that makes the user feel positive emotion based on the emotion estimation data. For example, it preferentially provides information of a variety with a high emotion score. This makes it possible to provide information of a variety that makes the user feel positive emotion.

[0065] The information provision unit can automatically collect information on diversity from different cultural spheres and regions, and provide users with diverse perspectives. For example, the generation AI collects diversity information from news sites and social media sites from different cultural spheres and regions, and provides it to users. For example, it displays articles and examples related to diversity overseas. The information provision unit also cross-references diversity information from different regions, building a system that provides users with diverse perspectives. For example, it displays different regional views on the same topic. The information provision unit also automatically collects diversity information that takes cultural background and regional characteristics into consideration, and provides it to users. For example, it displays diversity information related to the customs and traditions of different cultures. This allows users to receive information on diversity from different cultural spheres and regions, and gain diverse perspectives.

[0066] The information providing unit can provide a combination of diversity information from different fields that interest the user. For example, the information providing unit uses a generative AI to analyze the user's interests and provide a combination of diversity information from different fields. For example, it displays examples of diversity related to the fusion of science and art. The information providing unit also cross-references diversity information from different fields based on the user's interests to provide highly relevant information. For example, it displays diversity information related to the evolution of sports and technology. The information providing unit also automatically collects diversity information from different fields to build a system that provides the user with a multifaceted perspective. For example, it displays examples of diversity related to the intersection of science and culture. This makes it possible to provide the user with a multifaceted perspective.

[0067] The information providing unit uses the emotion estimation function to preferentially display information about diversity that evokes the most positive emotions in the user, thereby promoting respect for diversity and providing fair opportunities for success. The information providing unit, for example, uses the emotion estimation function to identify information about diversity that evokes the most positive emotions in the user and provide it preferentially. For example, it provides examples of diversity with high emotion scores. The information providing unit also analyzes the user's emotional responses in real time and builds a system that provides information about diversity that elicits positive emotions. For example, it provides examples of diversity that bring joy to the user. The information providing unit also preferentially provides information about diversity that evokes positive emotions in the user based on the emotion estimation data, thereby promoting respect for diversity and providing fair opportunities for success. For example, it provides information about diversity with high emotion scores. This makes it possible to provide information about diversity that evokes positive emotions in the user.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The information provision system may further include a health management unit that monitors the user's health condition. The health management unit collects biometric data such as the user's heart rate and sleep patterns and analyzes the user's health condition in real time. For example, if the user is feeling stressed, the health management unit may suggest information or activities that will help them relax. The health management unit may also provide appropriate dietary and exercise advice based on the user's health condition. This supports the user's health and promotes a more balanced lifestyle.

[0070] The information provision system can further include a learning support unit that analyzes the user's learning history. The learning support unit analyzes the content the user has learned in the past and the areas of interest, and provides related new information and learning resources. For example, if the user is interested in a particular technology, it can suggest the latest research papers and online courses related to that technology. The learning support unit can also track the user's learning progress and provide review and additional learning resources at appropriate times. This can improve the user's learning efficiency and support continuous learning.

[0071] The information provision system may further include a purchasing support unit that analyzes the user's purchasing history. The purchasing support unit analyzes the products and services the user has purchased in the past and suggests new related products and services. For example, if the user prefers products from a particular brand, the purchasing support unit may provide information about new products and sales from that brand. The purchasing support unit may also provide repeat purchase suggestions and coupons at appropriate times based on the user's purchasing history. This improves the user's purchasing experience and supports more convenient shopping.

[0072] The information provision system can further include a travel support unit that analyzes the user's travel history. The travel support unit analyzes places the user has visited in the past and tourist spots that interest the user, and suggests new related travel destinations and tourist information. For example, if the user likes nature, it will suggest tourist spots and activities rich in nature. The travel support unit can also provide travel plans and accommodation information at appropriate times based on the user's travel history. This can improve the user's travel experience and support a more fulfilling trip.

[0073] The information provision system can further include a hobby support unit that analyzes the user's hobbies and interests. The hobby support unit analyzes the activities the user has enjoyed in the past and the areas of interest to suggest new related hobbies and activities. For example, if the user likes cooking, it can provide information on new recipes and cooking classes. The hobby support unit can also provide information on events and workshops at appropriate times based on the user's hobbies. This can enrich the user's hobbies and support a richer life.

[0074] The information provision system can also analyze the user's emotional state in real time and adjust the tone and content of the information according to their emotions. For example, if a user is feeling stressed, it can provide relaxing information or activities. If a user is sad, it can display positive news or encouraging messages. This allows the system to provide information according to the user's emotions and support their mental health.

[0075] The information provision system can also analyze the user's emotional state in real time and adjust the balance of information according to their emotions. For example, if the user is feeling positive, providing balanced information can broaden the user's perspective. On the other hand, if the user is feeling negative, emotionally neutral information can be displayed preferentially. This makes it possible to provide information that is not biased towards the user's emotions, thereby supporting the user's mental health.

[0076] The information provision system can also analyze the user's emotional state in real time and adjust the tone of the information according to their emotions. For example, if the user is feeling stressed, it can provide information with a relaxing tone. If the user is sad, it can provide information with a positive tone. This makes it possible to provide information in a tone that matches the user's emotions, thereby supporting the user's mental health.

[0077] The information provision system can also analyze the user's emotional state in real time and adjust the priority of information according to the user's emotions. For example, if the user is feeling positive, information with a high emotional score can be displayed preferentially. On the other hand, if the user is feeling negative, emotionally neutral information can be displayed preferentially. This makes it possible to provide information according to the user's emotions and support the user's mental health.

[0078] The information provision system can also analyze the user's emotional state in real time and filter information according to the emotion. For example, if the user is feeling stressed, negative information can be filtered out and positive information can be displayed preferentially. Also, if the user is feeling positive, balanced information can be provided. This makes it possible to provide information according to the user's emotions and support the user's mental health.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The information provider uses the generation AI to provide balanced information. For example, the generation AI analyzes the user's preferences and past browsing history, while simultaneously displaying opposing and neutral opinions. The generation AI also provides information tailored to the user's taste. For example, if the user prefers instruction that is strict but loving, the information provided will be tailored to that taste. Step 2: The emotion analysis unit adjusts the balance of the information provided by the information providing unit. For example, the emotion analysis unit analyzes the user's emotional state in real time and adjusts the balance of the information according to the emotion. Step 3: The taste adjustment unit adjusts the taste of the information based on the emotions analyzed by the emotion analysis unit. For example, the information is provided in a taste that evokes positive emotions in the user.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. An information provider that uses generative AI to provide balanced information; an emotion analysis unit that adjusts the balance of information provided by the information providing unit; a taste adjustment unit that adjusts the taste of information based on the emotion analyzed by the emotion analysis unit. A system characterized by:

2. The information providing unit Providing information based on users' past behavioral history as well as social trends or news 2. The system of claim 1.

3. The emotion analysis unit Analyzing the user's emotional state in real time and adjusting the balance of the information according to the emotion 2. The system of claim 1.

4. The taste adjustment unit Providing the information according to the user's preferences 2. The system of claim 1.

5. The information providing unit Automatically collect the above information from different cultures or regions to provide users with diverse perspectives 2. The system of claim 1.

6. The information providing unit Combining and providing the information from the different fields that interest the user 2. The system of claim 1.

7. The emotion analysis unit Predicting the emotion a user will have toward specific information, and preferentially displaying emotionally neutral information.

2. The system of claim 1.

8. The information providing unit The information that gives the user the most positive feelings is preferentially displayed, and the provision of balanced information is promoted.

2. The system of claim 1.

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