System

Abstract

An object of a system according to an embodiment is to provide information according to a user's preference in real time.SOLUTION: A system according to an embodiment includes an on-device AI, a multi-modal data analyzer, and an information distributor. The on-device AI processes the user's information. The multi-modal data analysis unit integrally analyzes multi-modal data such as text, image, and voice. The information distribution unit distributes information according to the preference of the user in real time on the basis of the data analyzed by the multimodal data 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 technologies have not adequately provided information tailored to user preferences in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information according to the user's preferences in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an on-device AI, a multimodal data analysis unit, and an information distribution unit. The on-device AI processes user data. The multimodal data analysis unit comprehensively analyzes multimodal data such as text, images, and audio. The information distribution unit distributes information tailored to the user's preferences in real time based on the data analyzed by the multimodal data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information according to the user's preferences in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information providing system according to the embodiment of the present invention is a system that provides information tailored to the preferences of a user in real time. This allows the information providing system to provide information tailored to the preferences of a user in real time.

[0029] An information provision system according to an embodiment includes an on-device AI, a multimodal data analysis unit, and an information distribution unit. The on-device AI processes user data. For example, the on-device AI analyzes a user's behavioral history and identifies the user's preferences. The on-device AI can also process data while protecting the user's privacy. For example, the on-device AI processes data within the smartphone and does not transmit the data externally. The multimodal data analysis unit comprehensively analyzes multimodal data, such as text, images, and audio. For example, the multimodal data analysis unit analyzes text and images posted by the user on social media to identify the user's interests. The multimodal data analysis unit can also analyze audio data to identify the user's preferences. For example, the multimodal data analysis unit analyzes the content of a user's voice commands and provides related information. The information distribution unit distributes information tailored to the user's preferences in real time based on the data analyzed by the multimodal data analysis unit. For example, the information distribution unit notifies the user of live performance information for artists in which the user has expressed interest. The information distribution unit can also provide information on movies and merchandise that match the user's preferences in real time. For example, the information distribution unit notifies the user of release information for movies in which the user has shown interest. This allows the information provision system according to the embodiment to provide information that matches the user's preferences in real time. For example, the user can receive the latest information based on his or her interests without missing it. Furthermore, the user can receive information in an environment in which his or her privacy is protected.

[0030] On-device AI can learn a user's behavioral patterns and provide optimal information according to specific time periods and locations. For example, on-device AI can recommend music and podcasts during the user's commute and provide relaxing content at night. On-device AI can also learn a user's behavioral patterns and provide optimal information according to specific locations. For example, if the user is at a specific cafe, it can provide coupon information that can be used at that cafe. This allows the device to provide optimal information based on the user's behavioral patterns.

[0031] On-device AI can predict future interests based on a user's past search history and browsing history and provide information proactively. For example, on-device AI analyzes a user's past search history to predict future interests. For example, if a particular artist is frequently searched for, the system can proactively notify the user of new song release information for that artist. On-device AI can also analyze a user's past browsing history to predict future interests. For example, if a particular movie is frequently viewed, the system can proactively notify the user of sequel information for that movie. This allows the system to predict a user's future interests and provide information proactively.

[0032] On-device AI can provide information that can be shared, taking into account the preferences of the user's family and friends. For example, on-device AI can provide information that can be shared, taking into account the preferences of the user's family and friends. For example, it can notify the user of movie and event information that the whole family can enjoy. On-device AI can also provide information that can be shared, taking into account the preferences of the user's friends. For example, it can provide information about restaurants and activities that can be enjoyed together with friends. This allows on-device AI to provide information that can be shared, taking into account the preferences of family and friends.

[0033] On-device AI can provide health-related information based on a user's health and fitness data. For example, on-device AI can provide information useful for recovery after exercise. On-device AI can also analyze a user's health and provide health-related advice. For example, it can suggest healthy lifestyle habits based on heart rate and sleep data. This allows it to provide health-related information based on a user's health and fitness data.

[0034] The multimodal data analysis unit can analyze a user's voice commands and provide related information in real time. The multimodal data analysis unit, for example, builds a system that analyzes a user's voice commands and provides related information in real time. For example, if a user asks, "When is the next live show?", information about the artist's next live show is provided. The multimodal data analysis unit also analyzes a user's voice commands using voice recognition technology and provides related information. For example, if a user asks, "What new movies are coming out?", information about the release of those movies is provided. This makes it possible to provide related information in real time based on a user's voice commands.

[0035] The multimodal data analysis unit can analyze images taken by a user and provide information related to the images. For example, the multimodal data analysis unit builds a system that analyzes images taken by a user and provides related information. For example, the multimodal data analysis unit analyzes images of an artist taken by a user and provides the latest information on that artist. The multimodal data analysis unit also analyzes images taken by a user using image recognition technology and provides related information. For example, the multimodal data analysis unit analyzes images of a landscape taken by a user and provides tourist information about that location. This makes it possible to provide related information based on images taken by a user.

[0036] The multimodal data analysis unit can analyze a user's purchasing history and provide related product information. The multimodal data analysis unit, for example, analyzes a user's purchasing history and builds a system that provides related product information. For example, if a user purchases merchandise from a specific artist, information on new products from that artist is provided. The multimodal data analysis unit also analyzes the purchasing history and provides product information based on the user's preferences. For example, it analyzes trends in products that the user has purchased in the past and recommends related new products. This makes it possible to provide related product information based on the user's purchasing history.

[0037] The multimodal data analysis unit can provide information about nearby events based on the user's location information. The multimodal data analysis unit, for example, builds a system that provides information about nearby events based on the user's location information. For example, if the user is in a specific city, the system notifies the user of information about live shows being held in that city. The multimodal data analysis unit also analyzes the location information and provides event information based on the user's current location. For example, if the user is in a specific location, the system provides information about events being held in that location. This makes it possible to provide information about nearby events based on the user's location information.

[0038] The information distribution unit can analyze the content of a user's emails and messages and provide related information. For example, the information distribution unit builds a system that analyzes the content of a user's emails and messages and provides related information. For example, if a user mentions a particular artist, the information distribution unit notifies the user of the latest information on that artist. The information distribution unit also analyzes the content of emails and messages and provides information based on the user's interests. For example, if a user talks about a particular event, the information distribution unit provides detailed information about that event. This makes it possible to provide related information based on the content of a user's emails and messages.

[0039] The information distribution unit can suggest new hobbies and activities based on the user's hobbies and interests. The information distribution unit, for example, analyzes the user's hobbies and interests and builds a system that suggests new hobbies and activities. For example, if the user is interested in music, the information distribution unit suggests playing a new instrument. The information distribution unit also suggests new activities based on the user's interests. For example, if the user is interested in sports, the information distribution unit suggests new sports experiences. This makes it possible to suggest new hobbies and activities based on the user's hobbies and interests.

[0040] The information distribution unit can provide information about the next travel destination based on the user's past travel history. The information distribution unit, for example, analyzes the user's past travel history and builds a system that provides information about the next travel destination. For example, the information distribution unit suggests a new travel destination based on places the user has visited in the past. The information distribution unit also provides information related to the next travel destination based on the user's travel history. For example, the information distribution unit suggests new tourist spots in a city based on a city the user has visited in the past. This makes it possible to provide information about the next travel destination based on the user's past travel history.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The information providing system may further include a purchase history analysis unit that analyzes a user's purchase history and provides related product information. For example, if a user frequently purchases products from a particular brand, the system may notify the user of new product information from that brand. The purchase history analysis unit may also analyze a user's purchasing trends and recommend related products. For example, related new products may be recommended based on the user's past purchase trends. This allows the system to provide related product information based on the user's purchase history.

[0043] The information providing system may further include a health analysis unit that provides health-related information based on the user's health condition and fitness data. For example, it may notify the user of information useful for recovery after exercise. The health analysis unit may also analyze the user's health condition and provide health-related advice. For example, it may suggest healthy lifestyle habits based on heart rate and sleep data. This allows the system to provide health-related information based on the user's health condition and fitness data.

[0044] The information providing system may further include a location information analysis unit that provides information about nearby events based on the user's location information. For example, if the user is in a specific city, the system notifies the user of information about live performances being held in that city. The location information analysis unit may also provide event information based on the user's current location. For example, if the user is in a specific location, the system provides information about events being held in that location. This makes it possible to provide information about nearby events based on the user's location information.

[0045] The information providing system may further include a travel history analysis unit that provides information about the next travel destination based on the user's past travel history. For example, a new travel destination may be suggested based on places the user has visited in the past. The travel history analysis unit may also provide information related to the next travel destination based on the user's travel history. For example, a new tourist spot in a city may be suggested based on a city the user has visited in the past. This allows information about the next travel destination to be provided based on the user's past travel history.

[0046] The information providing system may further include a hobby analysis unit that suggests new hobbies and activities based on the user's hobbies and interests. For example, if the user is interested in music, the hobby analysis unit may suggest playing a new instrument. The hobby analysis unit may also suggest new activities based on the user's interests. For example, if the user is interested in sports, the hobby analysis unit may suggest new sports experiences. This allows the system to suggest new hobbies and activities based on the user's hobbies and interests.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: On-device AI processes user data. For example, it analyzes the user's behavioral history and identifies the user's preferences. On-device AI can also process data while protecting the user's privacy. For example, the data is processed within the smartphone and not transmitted externally. Step 2: The multimodal data analysis unit performs an integrated analysis of multimodal data, including text, images, and audio. For example, it analyzes text and images posted by users on social media to understand their interests. It can also analyze audio data to identify user preferences. For example, it analyzes voice commands and provides related information. Step 3: The information distribution unit distributes information tailored to the user's preferences in real time based on the data analyzed by the multimodal data analysis unit. For example, it notifies the user of live performance information for artists in which the user has shown interest or movie release information. It can also provide real-time information on movies and merchandise tailored to the user's preferences.

[0049] (Example 2) The information providing system according to the embodiment of the present invention is a system that provides information tailored to the preferences of a user in real time. This allows the information providing system to provide information tailored to the preferences of a user in real time.

[0050] An information provision system according to an embodiment includes an on-device AI, a multimodal data analysis unit, and an information distribution unit. The on-device AI processes user data. For example, the on-device AI analyzes a user's behavioral history and identifies the user's preferences. The on-device AI can also process data while protecting the user's privacy. For example, the on-device AI processes data within the smartphone and does not transmit the data externally. The multimodal data analysis unit comprehensively analyzes multimodal data, such as text, images, and audio. For example, the multimodal data analysis unit analyzes text and images posted by the user on social media to identify the user's interests. The multimodal data analysis unit can also analyze audio data to identify the user's preferences. For example, the multimodal data analysis unit analyzes the content of a user's voice commands and provides related information. The information distribution unit distributes information tailored to the user's preferences in real time based on the data analyzed by the multimodal data analysis unit. For example, the information distribution unit notifies the user of live performance information for artists in which the user has expressed interest. The information distribution unit can also provide information on movies and merchandise that match the user's preferences in real time. For example, the information distribution unit notifies the user of release information for movies in which the user has shown interest. This allows the information provision system according to the embodiment to provide information that matches the user's preferences in real time. For example, the user can receive the latest information based on his or her interests without missing it. Furthermore, the user can receive information in an environment in which his or her privacy is protected.

[0051] On-device AI can learn a user's behavioral patterns and provide optimal information according to specific time periods and locations. For example, on-device AI can recommend music and podcasts during the user's commute and provide relaxing content at night. On-device AI can also learn a user's behavioral patterns and provide optimal information according to specific locations. For example, if the user is at a specific cafe, it can provide coupon information that can be used at that cafe. This allows the device to provide optimal information based on the user's behavioral patterns.

[0052] On-device AI can predict future interests based on a user's past search history and browsing history and provide information proactively. For example, on-device AI analyzes a user's past search history to predict future interests. For example, if a particular artist is frequently searched for, the system can proactively notify the user of new song release information for that artist. On-device AI can also analyze a user's past browsing history to predict future interests. For example, if a particular movie is frequently viewed, the system can proactively notify the user of sequel information for that movie. This allows the system to predict a user's future interests and provide information proactively.

[0053] On-device AI can use emotion estimation to provide information tailored to a user's emotional state, helping to reduce stress. For example, on-device AI can use emotion estimation to analyze a user's emotional state in real time and provide information to reduce stress. For example, if a user is feeling stressed, it can recommend relaxing music or videos. On-device AI can also use emotion estimation to analyze a user's emotional state and provide information that elicits positive emotions. For example, if a user is feeling down, it can provide encouraging messages or fun content. This provides information tailored to a user's emotional state, helping to reduce stress.

[0054] On-device AI can provide information that can be shared, taking into account the preferences of the user's family and friends. For example, on-device AI can provide information that can be shared, taking into account the preferences of the user's family and friends. For example, it can notify the user of movie and event information that the whole family can enjoy. On-device AI can also provide information that can be shared, taking into account the preferences of the user's friends. For example, it can provide information about restaurants and activities that can be enjoyed together with friends. This allows on-device AI to provide information that can be shared, taking into account the preferences of family and friends.

[0055] On-device AI can provide health-related information based on a user's health and fitness data. For example, on-device AI can provide information useful for recovery after exercise. On-device AI can also analyze a user's health and provide health-related advice. For example, it can suggest healthy lifestyle habits based on heart rate and sleep data. This allows it to provide health-related information based on a user's health and fitness data.

[0056] On-device AI can use its emotion estimation function to provide information to users at the timing when they are most relaxed. For example, on-device AI can use its emotion estimation function to provide information to users at the timing when they are most relaxed. For example, it can notify the user of entertainment information when they are relaxing. On-device AI can also analyze the timing when the user is most relaxed and provide information at that timing. For example, it can provide content that helps the user relax during their break. This allows information to be provided to users at the timing when they are most relaxed.

[0057] The multimodal data analysis unit can analyze a user's voice commands and provide related information in real time. The multimodal data analysis unit, for example, builds a system that analyzes a user's voice commands and provides related information in real time. For example, if a user asks, "When is the next live show?", information about the artist's next live show is provided. The multimodal data analysis unit also analyzes a user's voice commands using voice recognition technology and provides related information. For example, if a user asks, "What new movies are coming out?", information about the release of those movies is provided. This makes it possible to provide related information in real time based on a user's voice commands.

[0058] The multimodal data analysis unit can analyze images taken by a user and provide information related to the images. For example, the multimodal data analysis unit builds a system that analyzes images taken by a user and provides related information. For example, the multimodal data analysis unit analyzes images of an artist taken by a user and provides the latest information on that artist. The multimodal data analysis unit also analyzes images taken by a user using image recognition technology and provides related information. For example, the multimodal data analysis unit analyzes images of a landscape taken by a user and provides tourist information about that location. This makes it possible to provide related information based on images taken by a user.

[0059] The multimodal data analysis unit can use the emotion estimation function to estimate emotions from the content posted by a user on social media and provide information corresponding to the emotions. The multimodal data analysis unit, for example, uses the emotion estimation function to estimate emotions from the content posted by a user on social media and builds a system that provides information corresponding to the emotions. For example, if a user posts something positive, related entertainment information is provided. The multimodal data analysis unit also analyzes the content of the SNS post to estimate the user's emotions. For example, if a user posts something negative, an encouraging message or relaxing content is provided. This makes it possible to estimate emotions based on the user's SNS posts and provide information corresponding to the emotions.

[0060] The multimodal data analysis unit can analyze a user's purchasing history and provide related product information. The multimodal data analysis unit, for example, analyzes a user's purchasing history and builds a system that provides related product information. For example, if a user purchases merchandise from a specific artist, information on new products from that artist is provided. The multimodal data analysis unit also analyzes the purchasing history and provides product information based on the user's preferences. For example, it analyzes trends in products that the user has purchased in the past and recommends related new products. This makes it possible to provide related product information based on the user's purchasing history.

[0061] The multimodal data analysis unit can provide information about nearby events based on the user's location information. The multimodal data analysis unit, for example, builds a system that provides information about nearby events based on the user's location information. For example, if the user is in a specific city, the system notifies the user of information about live shows being held in that city. The multimodal data analysis unit also analyzes the location information and provides event information based on the user's current location. For example, if the user is in a specific location, the system provides information about events being held in that location. This makes it possible to provide information about nearby events based on the user's location information.

[0062] The multimodal data analysis unit can use the emotion estimation function to provide preferentially information that is likely to interest the user. For example, the multimodal data analysis unit uses the emotion estimation function to build a system that preferentially provides information that is likely to interest the user. For example, entertainment information is notified when the user is excited. The multimodal data analysis unit also analyzes the user's emotional state and provides information that is likely to interest the user. For example, related event information is provided when the user is feeling positive emotions. This allows the information that is likely to interest the user to be provided preferentially.

[0063] The information distribution unit can analyze the content of a user's emails and messages and provide related information. For example, the information distribution unit builds a system that analyzes the content of a user's emails and messages and provides related information. For example, if a user mentions a particular artist, the information distribution unit notifies the user of the latest information on that artist. The information distribution unit also analyzes the content of emails and messages and provides information based on the user's interests. For example, if a user talks about a particular event, the information distribution unit provides detailed information about that event. This makes it possible to provide related information based on the content of a user's emails and messages.

[0064] The information distribution unit can use the emotion estimation function to provide information that evokes the most positive emotion in the user. For example, the information distribution unit uses the emotion estimation function to build a system that provides information that evokes the most positive emotion in the user. For example, entertainment information is notified when the user is feeling joyful. The information distribution unit also analyzes the user's emotional state and provides information that elicits positive emotions. For example, when the user is feeling satisfied, related event or product information is provided. This makes it possible to provide information that evokes the most positive emotion in the user.

[0065] The information distribution unit can suggest new hobbies and activities based on the user's hobbies and interests. The information distribution unit, for example, analyzes the user's hobbies and interests and builds a system that suggests new hobbies and activities. For example, if the user is interested in music, the information distribution unit suggests playing a new instrument. The information distribution unit also suggests new activities based on the user's interests. For example, if the user is interested in sports, the information distribution unit suggests new sports experiences. This makes it possible to suggest new hobbies and activities based on the user's hobbies and interests.

[0066] The information distribution unit can provide information about the next travel destination based on the user's past travel history. The information distribution unit, for example, analyzes the user's past travel history and builds a system that provides information about the next travel destination. For example, the information distribution unit suggests a new travel destination based on places the user has visited in the past. The information distribution unit also provides information related to the next travel destination based on the user's travel history. For example, the information distribution unit suggests new tourist spots in a city based on a city the user has visited in the past. This makes it possible to provide information about the next travel destination based on the user's past travel history.

[0067] The information distribution unit can use the emotion estimation function to provide information that allows the user to be most relaxed. The information distribution unit, for example, builds a system that uses the emotion estimation function to provide information that allows the user to be most relaxed. For example, when the user is relaxing, the information distribution unit recommends relaxing music or videos. The information distribution unit also analyzes the user's emotional state and provides information that helps the user to relax. For example, when the user is feeling stressed, the information distribution unit provides relaxation music or a meditation guide. This allows the user to be provided with information that allows the user to be most relaxed.

[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 providing system may further include a purchase history analysis unit that analyzes a user's purchase history and provides related product information. For example, if a user frequently purchases products from a particular brand, the system may notify the user of new product information from that brand. The purchase history analysis unit may also analyze a user's purchasing trends and recommend related products. For example, related new products may be recommended based on the user's past purchase trends. This allows the system to provide related product information based on the user's purchase history.

[0070] The information providing system may further include a health analysis unit that provides health-related information based on the user's health condition and fitness data. For example, it may notify the user of information useful for recovery after exercise. The health analysis unit may also analyze the user's health condition and provide health-related advice. For example, it may suggest healthy lifestyle habits based on heart rate and sleep data. This allows the system to provide health-related information based on the user's health condition and fitness data.

[0071] The information providing system may further include a location information analysis unit that provides information about nearby events based on the user's location information. For example, if the user is in a specific city, the system notifies the user of information about live performances being held in that city. The location information analysis unit may also provide event information based on the user's current location. For example, if the user is in a specific location, the system provides information about events being held in that location. This makes it possible to provide information about nearby events based on the user's location information.

[0072] The information providing system may further include a travel history analysis unit that provides information about the next travel destination based on the user's past travel history. For example, a new travel destination may be suggested based on places the user has visited in the past. The travel history analysis unit may also provide information related to the next travel destination based on the user's travel history. For example, a new tourist spot in a city may be suggested based on a city the user has visited in the past. This allows information about the next travel destination to be provided based on the user's past travel history.

[0073] The information providing system may further include a hobby analysis unit that suggests new hobbies and activities based on the user's hobbies and interests. For example, if the user is interested in music, the hobby analysis unit may suggest playing a new instrument. The hobby analysis unit may also suggest new activities based on the user's interests. For example, if the user is interested in sports, the hobby analysis unit may suggest new sports experiences. This allows the system to suggest new hobbies and activities based on the user's hobbies and interests.

[0074] The information provision system can also estimate the user's emotions and provide information at the timing when the user is most relaxed based on the estimated user emotions. For example, entertainment information can be notified when the user is relaxing. The information provision system can also analyze the timing when the user is most relaxed and provide information at that timing. For example, content that allows the user to relax during a break can be provided. This allows information to be provided at the timing when the user is most relaxed.

[0075] The information provision system can also estimate the user's emotions and provide information that elicits positive emotions based on the estimated user emotions. For example, if the user is feeling down, it can provide encouraging messages or fun content. Also, if the user is feeling stressed, it can recommend relaxing music or videos. In this way, it is possible to provide information that corresponds to the user's emotional state and elicit positive emotions.

[0076] The information provision system can also estimate the user's emotions and provide information that is likely to be of interest to the user based on the estimated user emotions. For example, entertainment information can be notified when the user is excited. The information provision system can also analyze the user's emotional state and provide information that is likely to be of interest to the user. For example, when the user is feeling positive, related event information can be provided. This allows the information that is likely to be of interest to the user to be provided with priority.

[0077] The information provision system can also estimate the user's emotions and provide information that will evoke the most positive emotions based on the estimated user emotions. For example, entertainment information can be provided when the user is feeling happy. The information provision system can also analyze the user's emotional state and provide information that will evoke positive emotions. For example, when the user is feeling satisfied, related event or product information can be provided. This allows the information that will evoke the most positive emotions to be provided to the user.

[0078] The information provision system can also estimate the user's emotions and provide information to reduce stress based on the estimated user emotions. For example, if the user is feeling stressed, the system can recommend relaxing music or videos. If the user is feeling depressed, the system can provide encouraging messages or fun content. This allows the system to provide information according to the user's emotional state and reduce stress.

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

[0080] Step 1: On-device AI processes user data. For example, it analyzes the user's behavioral history and identifies the user's preferences. On-device AI can also process data while protecting the user's privacy. For example, the data is processed within the smartphone and not transmitted externally. Step 2: The multimodal data analysis unit performs an integrated analysis of multimodal data, including text, images, and audio. For example, it analyzes text and images posted by users on social media to understand their interests. It can also analyze audio data to identify user preferences. For example, it analyzes voice commands and provides related information. Step 3: The information distribution unit distributes information tailored to the user's preferences in real time based on the data analyzed by the multimodal data analysis unit. For example, it notifies the user of live performance information for artists in which the user has shown interest or movie release information. It can also provide real-time information on movies and merchandise tailored to the user's preferences.

[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 a 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Equipped with on-device AI, The on-device AI processes the user's data; A multimodal data analysis unit that comprehensively analyzes multimodal data such as text, images, and audio; an information distribution unit that distributes information according to the user's preferences in real time based on the data analyzed by the multimodal data analysis unit. A system characterized by:

2. The on-device AI is Learn the user's behavioral patterns and provide optimal information according to specific time periods and locations 2. The system of claim 1.

3. The on-device AI is Provide information that can be shared, taking into account the preferences of the user's family and friends 2. The system of claim 1.

4. The multimodal data analysis unit Analyzing the user's voice commands and providing relevant information in real time 2. The system of claim 1.

5. The information distribution unit Analyze the user's calendar information and provide information tailored to their schedule 2. The system of claim 1.

6. The on-device AI is Providing information according to the user's emotional state to reduce stress 2. The system of claim 1.

7. The multimodal data analysis unit The system estimates the user's emotions from the content posted on social media and provides information according to the emotions.

2. The system of claim 1.

8. The information distribution unit Provide information that gives the user the most positive feelings 2. The system of claim 1.

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