System

Abstract

An object of a system according to an embodiment is to propose a way of spending leisure on the basis of a user's preference.SOLUTION: A system according to an embodiment includes an information collection unit, a preference analysis unit, and a proposal generation unit. The information collection unit collects an answer to a simple question from a user. The preference analysis section analyzes the user's answer collected by the information collection section and understands the user's preference. The proposal generation unit proposes a way of spending leisure on the basis of the preference of the user analyzed by the preference 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 proposed ways to spend leisure time based on the user's preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest ways to spend leisure time based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a preference analysis unit, and a proposal generation unit. The information collection unit collects answers to simple questions from users. The preference analysis unit analyzes the user's answers collected by the information collection unit to understand the user's preferences. The proposal generation unit suggests ways to spend leisure time based on the user's preferences analyzed by the preference analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest ways to spend leisure time based on the user's preferences. [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 leisure time suggestion system according to an embodiment of the present invention is a system that suggests ways for users to spend their days off by having them answer simple questions. This system uses a generation AI to suggest optimal ways for users to spend their leisure time. This allows the leisure time suggestion system to suggest optimal ways for users to spend their leisure time based on their preferences.

[0029] The leisure time suggestion system according to the embodiment includes an information collection unit, a preference analysis unit, and a suggestion generation unit. The information collection unit collects answers to simple questions from the user. For example, the information collection unit asks the user questions such as, "How are you feeling today?", "How much time do you want to spend?", and "What is your budget?", and collects the answers. The information collection unit can also automatically collect the user's past leisure activity history. For example, it collects data on places visited and events attended in the past to understand the user's preferences. The information collection unit can also analyze public posts from the user's social media accounts to understand the user's current interests. For example, it can analyze the content of recent posts and hashtags to identify the user's interests. The preference analysis unit analyzes the user's answers collected by the information collection unit to understand the user's preferences. For example, the generation AI analyzes the user's answers and analyzes the user's desires, such as "I want to relax," "I want to be active," or "I want to try something new." The preference analysis unit can also track changes in the user's preferences by taking into account feedback on the user's past suggestions. For example, the system analyzes ratings and comments on suggestions to identify changes in preferences. Furthermore, the preference analysis unit can compare the user's preferences with those of other users to identify commonalities and differences. For example, it can identify user groups with similar preferences. The suggestion generation unit suggests ways to spend leisure time based on the user's preferences analyzed by the preference analysis unit. For example, the AI ​​generation unit may make suggestions such as "have a picnic in a nearby park," "enjoy lunch at a new restaurant," or "take an online cooking class." The suggestion generation unit can also consider the user's past suggestion history and make new suggestions that do not overlap. For example, it may store past suggestions in a database to avoid duplication. Furthermore, the suggestion generation unit can collect and reflect the latest trend information and event information in real time. For example, it may collect data from social media and news sites. This allows the leisure time suggestion system according to the embodiment to suggest optimal ways to spend leisure time based on the user's preferences. For example, if the user responds that they want to relax, the system may suggest reading in a quiet cafe.If a user says they want to stay active, they'll be offered sporting events to attend, and if they say they want to try new things, they'll be offered new online hobbies to try.

[0030] The information collection unit can automatically collect the user's past leisure activity history and input it into the generation AI. The information collection unit, for example, automatically collects the user's past leisure activity history and inputs it into the generation AI. For example, it collects data on places visited and events attended in the past to understand the user's preferences. This enables more accurate suggestions to be made based on the user's past leisure activity history.

[0031] The information collection unit can analyze posts made public from the user's SNS account to understand the user's current interests and concerns. The information collection unit, for example, analyzes posts made public from the user's SNS account to understand the user's current interests and concerns. For example, it analyzes the content of recent posts and hashtags to identify the user's interests. In this way, the user's current interests and concerns can be understood by analyzing the SNS posts.

[0032] The information collecting unit can also accept answers to questions via voice input or image input. The information collecting unit, for example, builds a system that also accepts answers to questions via voice input. For example, the system allows a user to input an answer by speaking into a microphone. The information collecting unit also builds a system that also accepts answers to questions via image input. For example, the system analyzes an image taken by the user with a camera and recognizes it as an answer. This improves user convenience by using voice input or image input.

[0033] The information collecting unit can dynamically change the content of the questions depending on the season and the weather. The information collecting unit, for example, builds a system that dynamically changes the content of the questions depending on the season. For example, questions suggesting beach and outdoor activities in the summer and skiing and hot springs in the winter. The information collecting unit also builds a system that dynamically changes the content of the questions depending on the weather. For example, questions suggesting indoor activities on rainy days and outdoor activities on sunny days. This makes it possible to make appropriate suggestions depending on the season and the weather.

[0034] The preference analysis unit can track changes in preferences by taking into account feedback on past user suggestions. The preference analysis unit, for example, collects feedback on past user suggestions and builds a system that tracks changes in preferences. For example, it analyzes ratings and comments on suggestions to identify changes in preferences. This makes it possible to track changes in preferences based on past feedback.

[0035] The preference analysis unit can compare the preference data with that of other users to find commonalities and differences. The preference analysis unit, for example, builds a system that compares the preference data with that of other users to find commonalities and differences. For example, it identifies a user group with the same preferences. This makes it possible to find commonalities and differences by comparing the preference data with that of other users.

[0036] The preference analysis unit can refer to data from different cultural spheres or regions and make suggestions from a global perspective. The preference analysis unit, for example, references data from different cultural spheres or regions to build a system that analyzes user preferences. For example, it makes suggestions that take into account the culture and customs of each region. This makes it possible to make suggestions from a global perspective by referring to data from different cultural spheres or regions.

[0037] The preference analysis unit can integrate data from different devices and analyze user preferences. The preference analysis unit, for example, builds a system that integrates data from different devices and analyzes user preferences. For example, it centrally manages data from smartphones, tablets, and PCs. This allows for more accurate analysis of user preferences by integrating data from different devices.

[0038] The proposal generation unit can make new proposals that do not overlap, taking into account the user's past proposal history. The proposal generation unit, for example, builds a system that makes new proposals that do not overlap, taking into account the user's past proposal history. For example, the contents of past proposals are stored in a database to avoid duplication. In this way, by taking into account the past proposal history, new proposals that do not overlap can be made.

[0039] The proposal generation unit can collect and reflect the latest trend information and event information in real time. The proposal generation unit, for example, builds a system that collects the latest trend information and event information in real time and reflects it in proposal content. For example, it collects data from social media and news sites. This allows for more attractive proposals by reflecting the latest trend information and event information.

[0040] The proposal generation unit can combine proposals from different genres to propose a composite way of spending leisure time. The proposal generation unit, for example, builds a system that combines proposals from different genres to propose a composite way of spending leisure time. For example, it makes a proposal that combines sports and cultural activities. This makes it possible to combine proposals from different genres to propose a composite way of spending leisure time.

[0041] The suggestion generation unit can make suggestions that can be enjoyed by a group, taking into account the preferences of the user's friends and family. The suggestion generation unit, for example, builds a system that makes suggestions that can be enjoyed by a group, taking into account the preferences of the user's friends and family. For example, it collects preference data of friends and family and reflects it in the suggestion content. This makes it possible to make suggestions that can be enjoyed by a group, taking into account the preferences of friends and family.

[0042] The proposal generation unit can present the proposal content in the most likely format by taking into account the user's past selection history. The proposal generation unit, for example, builds a system that presents the proposal content in the most likely format by taking into account the user's past selection history. For example, the proposal content is adjusted based on the format selected in the past. In this way, the proposal content can be presented in the most likely format by taking into account the user's past selection history.

[0043] The proposal generation unit can present proposal contents using an interactive UI so that the user can intuitively select. For example, the proposal generation unit constructs a system that uses an interactive UI to present proposal contents so that the user can intuitively select. For example, a UI using drag-and-drop or swipe operations is provided. In this way, the interactive UI allows the user to intuitively select proposal contents.

[0044] The proposal generation unit can present the proposal content in a visually appealing manner using AR technology. For example, when presenting the proposal content, the proposal generation unit constructs a system that presents the proposal content in a visually appealing manner using AR technology. For example, the proposal content is displayed through a smartphone camera. In this way, by using AR technology, the proposal content can be presented in a visually appealing manner.

[0045] The proposal generation unit can automatically select the optimal display format according to the user's device. For example, the proposal generation unit constructs a system that automatically selects the optimal display format according to the user's device when presenting proposal content. For example, it provides display formats optimized for smartphones, tablets, and PCs. This makes it possible to automatically select the optimal display format according to the user's device.

[0046] The proposal generation unit can perform more accurate customization by taking into account the user's past feedback. The proposal generation unit, for example, builds a system that customizes proposal content by taking into account the user's past feedback. For example, the proposal content is adjusted based on past feedback data. This allows for more accurate customization by taking into account the past feedback.

[0047] The proposal generation unit can provide optimal proposals by taking into account the user's real-time location information. The proposal generation unit, for example, builds a system that customizes proposal content by taking into account the user's real-time location information. For example, the proposal generation unit suggests nearby restaurants and events based on the user's current location. This allows optimal proposals to be provided by taking into account the real-time location information.

[0048] The suggestion generation unit can consider feedback from the user's friends and family to make suggestions that can be enjoyed by a group. The suggestion generation unit, for example, considers feedback from the user's friends and family to build a system that customizes the suggestion content. For example, it collects feedback data from friends and family and reflects it in the suggestion content. This makes it possible to make suggestions that can be enjoyed by a group by considering feedback from friends and family.

[0049] The proposal generation unit can integrate feedback from different devices and perform comprehensive customization. The proposal generation unit, for example, builds a system that integrates feedback from different devices and customizes proposal content. For example, it centrally manages feedback from smartphones, tablets, and PCs. This makes it possible to perform comprehensive customization by integrating feedback from different devices.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The information collection unit can collect the user's health data and reflect it in the suggestions. For example, it can collect data from a smartwatch or fitness tracker to understand the user's exercise volume and sleep status. It can also suggest appropriate leisure activities based on the user's health condition. For example, it can suggest walking or yoga to a user who is not getting enough exercise, and suggest a relaxing spa or massage to a user who is tired. This makes it possible to suggest the most appropriate way to spend leisure time according to the user's health condition.

[0052] The suggestion generation unit can suggest leisure activities to improve specific skills based on the user's hobbies and special skills. For example, a user who enjoys music can be suggested to practice an instrument or participate in a music event, and a user who is good at cooking can be suggested to take a cooking class or try a new recipe. It can also suggest training programs or participation in sporting events to a user who likes sports. This makes it possible to suggest ways to spend leisure time that make use of the user's hobbies and special skills.

[0053] The information collection unit can collect the user's travel history and reflect it in the suggestions. For example, it can collect data on countries and cities visited in the past to understand the user's travel preferences. It can also suggest new travel destinations based on places the user has visited in the past. For example, if a user has visited a beach resort in the past, other beach resorts and seaside activities can be suggested, and if a user has visited a historical city, other historical tourist spots can be suggested. This makes it possible to suggest optimal travel destinations according to the user's travel preferences.

[0054] The suggestion generator can take into account the user's learning history and suggest leisure activities to deepen their knowledge. For example, it can collect data on online courses taken in the past and books read to understand the user's learning preferences. It can also suggest new learning opportunities based on the user's areas of interest. For example, it can suggest a visit to a science museum or an online seminar to a user interested in science, and suggest historical documentaries or lectures to a user interested in history. This makes it possible to suggest optimal learning opportunities according to the user's learning preferences.

[0055] The information collection unit can collect the user's consumption history and reflect it in the content of suggestions. For example, data on products and services purchased in the past can be collected to understand the user's consumption preferences. New products and services can also be suggested based on the user's consumption history. For example, new outdoor activities and camping equipment can be suggested to a user who has previously purchased outdoor equipment, and new recipes and cooking classes can be suggested to a user who has purchased cooking equipment. This makes it possible to suggest optimal products and services according to the user's consumption preferences.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information collection unit collects answers to simple questions from the user. For example, it asks questions such as, "How are you feeling today?", "How much time do you want to spend?", and "What is your budget?" and collects the answers. It also collects the user's past leisure activity history and public posts from their social media accounts to understand the user's preferences. Step 2: The preference analysis unit analyzes the user's responses collected by the information collection unit to understand the user's preferences. For example, by analyzing the user's responses, it can identify preferences such as "I want to relax," "I want to be active," or "I want to try new things." It also compares the responses with feedback from past suggestions and the preference data of other users to identify changes in preferences and commonalities. Step 3: The proposal generator suggests ways to spend leisure time based on the user's preferences analyzed by the preference analyzer. For example, it may make suggestions such as "have a picnic in a nearby park," "enjoy lunch at a new restaurant," or "take an online cooking class." It also takes into account past proposal history to make new, unique proposals and reflects the latest trend and event information collected in real time.

[0058] (Example 2) The leisure time suggestion system according to an embodiment of the present invention is a system that suggests ways for users to spend their days off by having them answer simple questions. This system uses a generation AI to suggest optimal ways for users to spend their leisure time. This allows the leisure time suggestion system to suggest optimal ways for users to spend their leisure time based on their preferences.

[0059] The leisure time suggestion system according to the embodiment includes an information collection unit, a preference analysis unit, and a suggestion generation unit. The information collection unit collects answers to simple questions from the user. For example, the information collection unit asks the user questions such as, "How are you feeling today?", "How much time do you want to spend?", and "What is your budget?", and collects the answers. The information collection unit can also automatically collect the user's past leisure activity history. For example, it collects data on places visited and events attended in the past to understand the user's preferences. The information collection unit can also analyze public posts from the user's social media accounts to understand the user's current interests. For example, it can analyze the content of recent posts and hashtags to identify the user's interests. The preference analysis unit analyzes the user's answers collected by the information collection unit to understand the user's preferences. For example, the generation AI analyzes the user's answers and analyzes the user's desires, such as "I want to relax," "I want to be active," or "I want to try something new." The preference analysis unit can also track changes in the user's preferences by taking into account feedback on the user's past suggestions. For example, the system analyzes ratings and comments on suggestions to identify changes in preferences. Furthermore, the preference analysis unit can compare the user's preferences with those of other users to identify commonalities and differences. For example, it can identify user groups with similar preferences. The suggestion generation unit suggests ways to spend leisure time based on the user's preferences analyzed by the preference analysis unit. For example, the AI ​​generation unit may make suggestions such as "have a picnic in a nearby park," "enjoy lunch at a new restaurant," or "take an online cooking class." The suggestion generation unit can also consider the user's past suggestion history and make new suggestions that do not overlap. For example, it may store past suggestions in a database to avoid duplication. Furthermore, the suggestion generation unit can collect and reflect the latest trend information and event information in real time. For example, it may collect data from social media and news sites. This allows the leisure time suggestion system according to the embodiment to suggest optimal ways to spend leisure time based on the user's preferences. For example, if the user responds that they want to relax, the system may suggest reading in a quiet cafe.If a user says they want to stay active, they'll be offered sporting events to attend, and if they say they want to try new things, they'll be offered new online hobbies to try.

[0060] The information collection unit can automatically collect the user's past leisure activity history and input it into the generation AI. The information collection unit, for example, automatically collects the user's past leisure activity history and inputs it into the generation AI. For example, it collects data on places visited and events attended in the past to understand the user's preferences. This enables more accurate suggestions to be made based on the user's past leisure activity history.

[0061] The information collection unit can analyze posts made public from the user's SNS account to understand the user's current interests and concerns. The information collection unit, for example, analyzes posts made public from the user's SNS account to understand the user's current interests and concerns. For example, it analyzes the content of recent posts and hashtags to identify the user's interests. In this way, the user's current interests and concerns can be understood by analyzing the SNS posts.

[0062] The information collection unit can use the emotion estimation function to analyze the emotion of the user when answering a question in real time and dynamically change the content of the question based on the emotion. The information collection unit, for example, uses the emotion estimation function to analyze the emotion of the user when answering a question in real time. For example, it analyzes the user's facial expression and voice and calculates an emotion score. The information collection unit also dynamically changes the content of the question based on the emotion. For example, if the user is feeling stressed, it changes the question to one that makes suggestions to help the user relax. This allows the content of the question to be dynamically changed based on the user's emotion.

[0063] The information collecting unit can also accept answers to questions via voice input or image input. The information collecting unit, for example, builds a system that also accepts answers to questions via voice input. For example, the system allows a user to input an answer by speaking into a microphone. The information collecting unit also builds a system that also accepts answers to questions via image input. For example, the system analyzes an image taken by the user with a camera and recognizes it as an answer. This improves user convenience by using voice input or image input.

[0064] The information collecting unit can dynamically change the content of the questions depending on the season and the weather. The information collecting unit, for example, builds a system that dynamically changes the content of the questions depending on the season. For example, questions suggesting beach and outdoor activities in the summer and skiing and hot springs in the winter. The information collecting unit also builds a system that dynamically changes the content of the questions depending on the weather. For example, questions suggesting indoor activities on rainy days and outdoor activities on sunny days. This makes it possible to make appropriate suggestions depending on the season and the weather.

[0065] The information collection unit can use the emotion estimation function to analyze the emotion a user has when answering questions and present questions that elicit positive emotions. The information collection unit, for example, uses the emotion estimation function to analyze the emotion a user has when answering questions in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information collection unit also presents questions that elicit positive emotions. For example, if the user feels like relaxing, it presents questions that suggest ways to help the user relax. This makes it possible to present questions that elicit positive emotions.

[0066] The preference analysis unit can track changes in preferences by taking into account feedback on past user suggestions. The preference analysis unit, for example, collects feedback on past user suggestions and builds a system that tracks changes in preferences. For example, it analyzes ratings and comments on suggestions to identify changes in preferences. This makes it possible to track changes in preferences based on past feedback.

[0067] The preference analysis unit can compare the preference data with that of other users to find commonalities and differences. The preference analysis unit, for example, builds a system that compares the preference data with that of other users to find commonalities and differences. For example, it identifies a user group with the same preferences. This makes it possible to find commonalities and differences by comparing the preference data with that of other users.

[0068] The preference analysis unit can take into account emotional fluctuations when analyzing a user's preferences using the emotion estimation function. The preference analysis unit, for example, uses the emotion estimation function to build a system that takes into account emotional fluctuations when analyzing a user's preferences. For example, the preference analysis unit analyzes the user's preferences based on the user's emotion score. This allows for more accurate preference analysis by taking into account emotional fluctuations.

[0069] The preference analysis unit can refer to data from different cultural spheres or regions and make suggestions from a global perspective. The preference analysis unit, for example, references data from different cultural spheres or regions to build a system that analyzes user preferences. For example, it makes suggestions that take into account the culture and customs of each region. This makes it possible to make suggestions from a global perspective by referring to data from different cultural spheres or regions.

[0070] The preference analysis unit can integrate data from different devices and analyze user preferences. The preference analysis unit, for example, builds a system that integrates data from different devices and analyzes user preferences. For example, it centrally manages data from smartphones, tablets, and PCs. This allows for more accurate analysis of user preferences by integrating data from different devices.

[0071] The preference analysis unit uses the emotion estimation function to monitor emotional fluctuations in real time when analyzing a user's preferences, and can respond promptly to changes in preferences. The preference analysis unit, for example, uses the emotion estimation function to build a system that monitors emotional fluctuations in real time when analyzing a user's preferences. For example, it analyzes the user's emotion score in real time. The preference analysis unit also builds a system that responds promptly to changes in preferences. For example, it adjusts the content of suggestions based on changes in the user's emotion score. This makes it possible to respond promptly to changes in preferences by monitoring emotional fluctuations in real time.

[0072] The proposal generation unit can make new proposals that do not overlap, taking into account the user's past proposal history. The proposal generation unit, for example, builds a system that makes new proposals that do not overlap, taking into account the user's past proposal history. For example, the contents of past proposals are stored in a database to avoid duplication. In this way, by taking into account the past proposal history, new proposals that do not overlap can be made.

[0073] The proposal generation unit can collect and reflect the latest trend information and event information in real time. The proposal generation unit, for example, builds a system that collects the latest trend information and event information in real time and reflects it in proposal content. For example, it collects data from social media and news sites. This allows for more attractive proposals by reflecting the latest trend information and event information.

[0074] The suggestion generation unit can use the emotion estimation function to generate suggestions that are most suitable for the user's current emotional state. The suggestion generation unit, for example, uses the emotion estimation function to build a system that generates suggestions that are most suitable for the user's current emotional state. For example, the suggestion generation unit adjusts the suggestion content based on the user's emotion score. This makes it possible to provide suggestions that are most suitable for the user's current emotional state.

[0075] The proposal generation unit can combine proposals from different genres to propose a composite way of spending leisure time. The proposal generation unit, for example, builds a system that combines proposals from different genres to propose a composite way of spending leisure time. For example, it makes a proposal that combines sports and cultural activities. This makes it possible to combine proposals from different genres to propose a composite way of spending leisure time.

[0076] The suggestion generation unit can make suggestions that can be enjoyed by a group, taking into account the preferences of the user's friends and family. The suggestion generation unit, for example, builds a system that makes suggestions that can be enjoyed by a group, taking into account the preferences of the user's friends and family. For example, it collects preference data of friends and family and reflects it in the suggestion content. This makes it possible to make suggestions that can be enjoyed by a group, taking into account the preferences of friends and family.

[0077] The proposal generation unit can use the emotion estimation function to customize the proposal content based on the user's emotion and make proposals that elicit positive emotions. The proposal generation unit, for example, uses the emotion estimation function to build a system that customizes the proposal content based on the user's emotion. For example, the proposal content is adjusted based on the user's emotion score. This makes it possible to customize the proposal content based on the user's emotion and make proposals that elicit positive emotions.

[0078] The proposal generation unit can present the proposal content in the most likely format by taking into account the user's past selection history. The proposal generation unit, for example, builds a system that presents the proposal content in the most likely format by taking into account the user's past selection history. For example, the proposal content is adjusted based on the format selected in the past. In this way, the proposal content can be presented in the most likely format by taking into account the user's past selection history.

[0079] The proposal generation unit can present proposal contents using an interactive UI so that the user can intuitively select. For example, the proposal generation unit constructs a system that uses an interactive UI to present proposal contents so that the user can intuitively select. For example, a UI using drag-and-drop or swipe operations is provided. In this way, the interactive UI allows the user to intuitively select proposal contents.

[0080] The proposal generation unit can use the emotion estimation function to dynamically change the presentation method of the proposal content based on the user's emotion. The proposal generation unit, for example, uses the emotion estimation function to build a system that dynamically changes the presentation method of the proposal content based on the user's emotion. For example, the presentation method is adjusted based on the user's emotion score. This makes it possible to dynamically change the presentation method of the proposal content based on the user's emotion.

[0081] The proposal generation unit can present the proposal content in a visually appealing manner using AR technology. For example, when presenting the proposal content, the proposal generation unit constructs a system that presents the proposal content in a visually appealing manner using AR technology. For example, the proposal content is displayed through a smartphone camera. In this way, by using AR technology, the proposal content can be presented in a visually appealing manner.

[0082] The proposal generation unit can automatically select the optimal display format according to the user's device. For example, the proposal generation unit constructs a system that automatically selects the optimal display format according to the user's device when presenting proposal content. For example, it provides display formats optimized for smartphones, tablets, and PCs. This makes it possible to automatically select the optimal display format according to the user's device.

[0083] The suggestion generation unit can use the emotion estimation function to change the presentation order of the suggestion contents based on the user's emotion and present them in the most interesting order. The suggestion generation unit, for example, uses the emotion estimation function to build a system that changes the presentation order of the suggestion contents based on the user's emotion. For example, the presentation order is adjusted based on the user's emotion score. In this way, by changing the presentation order of the suggestion contents based on the user's emotion, it is possible to present them in the most interesting order.

[0084] The proposal generation unit can perform more accurate customization by taking into account the user's past feedback. The proposal generation unit, for example, builds a system that customizes proposal content by taking into account the user's past feedback. For example, the proposal content is adjusted based on past feedback data. This allows for more accurate customization by taking into account the past feedback.

[0085] The proposal generation unit can provide optimal proposals by taking into account the user's real-time location information. The proposal generation unit, for example, builds a system that customizes proposal content by taking into account the user's real-time location information. For example, the proposal generation unit suggests nearby restaurants and events based on the user's current location. This allows optimal proposals to be provided by taking into account the real-time location information.

[0086] The proposal generation unit can use the emotion estimation function to dynamically customize the proposal content based on the user's emotion. The proposal generation unit, for example, uses the emotion estimation function to build a system that dynamically customizes the proposal content based on the user's emotion. For example, the proposal content is adjusted based on the user's emotion score. This allows the proposal content to be dynamically customized based on the user's emotion.

[0087] The suggestion generation unit can consider feedback from the user's friends and family to make suggestions that can be enjoyed by a group. The suggestion generation unit, for example, considers feedback from the user's friends and family to build a system that customizes the suggestion content. For example, it collects feedback data from friends and family and reflects it in the suggestion content. This makes it possible to make suggestions that can be enjoyed by a group by considering feedback from friends and family.

[0088] The proposal generation unit can integrate feedback from different devices and perform comprehensive customization. The proposal generation unit, for example, builds a system that integrates feedback from different devices and customizes proposal content. For example, it centrally manages feedback from smartphones, tablets, and PCs. This makes it possible to perform comprehensive customization by integrating feedback from different devices.

[0089] The proposal generation unit can use the emotion estimation function to customize the proposal content based on the user's emotion in real time and provide optimal proposals. The proposal generation unit, for example, uses the emotion estimation function to build a system that customizes the proposal content based on the user's emotion in real time. For example, the proposal content is adjusted based on the user's emotion score. This makes it possible to provide optimal proposals by customizing the proposal content based on the user's emotion in real time.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The information collection unit can collect the user's health data and reflect it in the suggestions. For example, it can collect data from a smartwatch or fitness tracker to understand the user's exercise volume and sleep status. It can also suggest appropriate leisure activities based on the user's health condition. For example, it can suggest walking or yoga to a user who is not getting enough exercise, and suggest a relaxing spa or massage to a user who is tired. This makes it possible to suggest the most appropriate way to spend leisure time according to the user's health condition.

[0092] The suggestion generation unit can suggest leisure activities to improve specific skills based on the user's hobbies and special skills. For example, a user who enjoys music can be suggested to practice an instrument or participate in a music event, and a user who is good at cooking can be suggested to take a cooking class or try a new recipe. It can also suggest training programs or participation in sporting events to a user who likes sports. This makes it possible to suggest ways to spend leisure time that make use of the user's hobbies and special skills.

[0093] The information collection unit can collect the user's travel history and reflect it in the suggestions. For example, it can collect data on countries and cities visited in the past to understand the user's travel preferences. It can also suggest new travel destinations based on places the user has visited in the past. For example, if a user has visited a beach resort in the past, other beach resorts and seaside activities can be suggested, and if a user has visited a historical city, other historical tourist spots can be suggested. This makes it possible to suggest optimal travel destinations according to the user's travel preferences.

[0094] The suggestion generator can take into account the user's learning history and suggest leisure activities to deepen their knowledge. For example, it can collect data on online courses taken in the past and books read to understand the user's learning preferences. It can also suggest new learning opportunities based on the user's areas of interest. For example, it can suggest a visit to a science museum or an online seminar to a user interested in science, and suggest historical documentaries or lectures to a user interested in history. This makes it possible to suggest optimal learning opportunities according to the user's learning preferences.

[0095] The information collection unit can collect the user's consumption history and reflect it in the content of suggestions. For example, data on products and services purchased in the past can be collected to understand the user's consumption preferences. New products and services can also be suggested based on the user's consumption history. For example, new outdoor activities and camping equipment can be suggested to a user who has previously purchased outdoor equipment, and new recipes and cooking classes can be suggested to a user who has purchased cooking equipment. This makes it possible to suggest optimal products and services according to the user's consumption preferences.

[0096] The suggestion generation unit can estimate the user's emotions and suggest relaxing music or podcasts based on the estimated emotions. For example, if the user is feeling stressed, the suggestion generation unit can suggest relaxing music or meditation podcasts, and if the user wants to cheer up, the suggestion generation unit can suggest up-tempo music or motivational podcasts. Also, if the user wants to concentrate, the suggestion generation unit can suggest music that improves concentration or podcasts that are useful for studying. This makes it possible to suggest optimal music or podcasts according to the user's emotions.

[0097] The information collecting unit can estimate the user's emotions and suggest movies and dramas that the user may be interested in based on the estimated emotions. For example, when the user is feeling sad, comedy movies and dramas that will brighten the mood can be suggested, and when the user wants to relax, relaxing and healing movies and dramas can be suggested. Also, when the user wants to get excited, action movies and thrillers can be suggested. In this way, it is possible to suggest movies and dramas that are optimal for the user's emotions.

[0098] The suggestion generator can estimate the user's emotions and suggest activities that the user can enjoy based on the estimated emotions. For example, if the user is tired, the suggestion generator can suggest relaxing spa or massage, and if the user is feeling energetic, the suggestion generator can suggest sports or outdoor activities. Also, if the user is feeling creative, the suggestion generator can suggest arts and crafts workshops. In this way, the suggestion generator can suggest activities that are optimal for the user's emotions.

[0099] The information collection unit can estimate the user's emotions and suggest an environment in which the user can relax based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a quiet cafe or a walk in nature, and if the user wants to relax, it can suggest a hot spring or spa. It can also suggest a quiet library or cafe if the user wants to concentrate. In this way, it is possible to suggest the optimal relaxation environment according to the user's emotions.

[0100] The suggestion generation unit can estimate the user's emotions and suggest games and apps that the user can enjoy based on the estimated emotions. For example, if the user wants to relax, the suggestion generation unit can suggest a puzzle game or meditation app that has a relaxing effect, and if the user is feeling energetic, the suggestion generation unit can suggest an action game or fitness app. Also, if the user is feeling creative, the suggestion generation unit can suggest an art or music production app. In this way, the suggestion generation unit can suggest the most suitable games and apps according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information collection unit collects answers to simple questions from the user. For example, it asks questions such as, "How are you feeling today?", "How much time do you want to spend?", and "What is your budget?" and collects the answers. It also collects the user's past leisure activity history and public posts from their social media accounts to understand the user's preferences. Step 2: The preference analysis unit analyzes the user's responses collected by the information collection unit to understand the user's preferences. For example, by analyzing the user's responses, it can identify preferences such as "I want to relax," "I want to be active," or "I want to try new things." It also compares the responses with feedback from past suggestions and the preference data of other users to identify changes in preferences and commonalities. Step 3: The proposal generator suggests ways to spend leisure time based on the user's preferences analyzed by the preference analyzer. For example, it may make suggestions such as "have a picnic in a nearby park," "enjoy lunch at a new restaurant," or "take an online cooking class." It also takes into account past proposal history to make new, unique proposals and reflects the latest trend and event information collected in real time.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 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 collection unit that collects answers to simple questions from users; a preference analysis unit that analyzes the user's responses collected by the information collection unit and understands the user's preferences; a suggestion generation unit that suggests ways to spend leisure time based on the preferences of the user analyzed by the preference analysis unit. A system characterized by:

2. The information collecting unit The user's past leisure activity history is automatically collected and input into the generation AI.

2. The system of claim 1.

3. The preference analysis unit Considering the user's feedback on past suggestions and tracking changes in preferences 2. The system of claim 1.

4. The proposal generation unit Consider the user's past suggestion history and make new, non-duplicate suggestions.

2. The system of claim 1.

5. The proposal generation unit Considering the user's past selection history, present the proposal in the format that is most likely to be selected.

2. The system of claim 1.

6. The information collecting unit Using an emotion estimation function, the emotions of the user when answering questions are analyzed in real time, and the content of the questions is dynamically changed based on the emotions.

2. The system of claim 1.

7. The preference analysis unit Using an emotion estimation function, emotional fluctuations are taken into account when analyzing the user's preferences.

2. The system of claim 1.

8. The proposal generation unit Using emotion estimation capabilities to generate recommendations that best fit the user's current emotional state 2. The system of claim 1.

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