system

Abstract

An object of a system according to an embodiment is to make a proposal on the basis of a schedule or a TODO list of a user and distribute the proposal as a video, thereby reducing a decision burden on the user.SOLUTION: A system includes a reception unit, a proposal unit, a generation unit, and a distribution unit. The reception unit receives a schedule and a TODO list of a user. The suggestion unit makes a suggestion on the basis of the information received by the reception unit. The generation unit generates a moving image of the information generated by the suggestion unit. The distribution unit distributes the video generated by the generation 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 had the problem that users spend a lot of time and effort on daily schedules, to-do lists, and deciding what to wear and eat.

[0005] The system according to the embodiment aims to reduce the decision-making burden on the user by making suggestions based on the user's schedule and to-do list and distributing them as videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a generation unit, and a distribution unit. The reception unit receives a user's schedule or to-do list. The proposal unit makes proposals based on the information received by the reception unit. The generation unit animates the information generated by the proposal unit. The distribution unit distributes the video generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment makes suggestions based on the user's schedule and to-do list and delivers them as videos, thereby reducing the decision-making burden on the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a solution for people who feel fatigued by daily decision-making. This system uses a generative AI to create personalized news videos every morning, delivering the user's schedule, to-do list, clothing and meal suggestions, and other related information. This reduces the burden of thinking and decision-making on the user. This system reduces the user's decision-making burden and allows them to spend their day efficiently. For example, if a user is wondering "What to wear today," the generative AI suggests the most appropriate outfit based on the weather and schedule. Similarly, if a user is wondering "What to eat," the generative AI suggests an appropriate meal, taking into account the user's preferences and health status. This system reduces the time and effort required for daily decision-making, allowing users to live more fulfilling lives. It is particularly beneficial for busy businesspeople, housewives, and other people who face numerous decisions every day.

[0029] An information provision system according to an embodiment includes a reception unit, a proposal unit, a generation unit, and a distribution unit. The reception unit accepts a user's schedule and to-do list. For example, the user inputs the next day's schedule and to-do list the night before. The proposal unit makes optimal proposals based on the information accepted by the reception unit. For example, the proposal unit generates an optimal schedule and to-do list by taking into account the user's past data, preferences, and current situation. The generation unit animates the information generated by the proposal unit. For example, the generation unit uses a generation AI to animate news personalized for each user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The distribution unit distributes the video generated by the generation unit to the user every morning. For example, the distribution unit distributes the video to the user's smartphone or tablet. As a result, the information provision system according to an embodiment efficiently manages the user's schedule and to-do list, makes optimal proposals, and distributes them as videos, thereby reducing the user's decision-making burden.

[0030] The suggestion unit can suggest clothing and meals taking into consideration the user's preferences and health condition. The suggestion unit can suggest optimal clothing and meals taking into consideration the user's preferences and health condition, for example. For example, the suggestion unit can suggest appropriate clothing based on the user's past data and current situation. The suggestion unit can also suggest nutritionally balanced meals taking into consideration the user's health condition. For example, if the user has an allergy, the suggestion unit can suggest meals that accommodate the allergy. This makes it possible to suggest more appropriate clothing and meals based on the user's preferences and health condition.

[0031] The reception unit can analyze the user's past schedule and to-do list history and select the reception method. For example, the reception unit can preferentially suggest schedule and to-do list formats that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past history. Furthermore, the reception unit can customize the optimal reception method based on the user's past feedback. In this way, the reception unit can provide the optimal reception method to the user by analyzing the past history.

[0032] The reception unit can filter schedules and to-do lists based on the user's current lifestyle and areas of interest when receiving them. For example, the reception unit preferentially receives schedules and to-do lists related to a project the user is currently working on. The reception unit can also suggest related schedules and to-do lists based on the user's areas of interest. Furthermore, the reception unit can also suggest optimal schedules and to-do lists based on the user's lifestyle (e.g., family circumstances, work situation). This makes it possible to provide more relevant schedules and to-do lists by filtering according to the user's lifestyle and areas of interest.

[0033] The reception unit can select a reception means according to the user's input method when receiving a schedule or a to-do list. For example, if the user prefers voice input, the reception unit can preferentially receive voice input. Also, if the user prefers text input, the reception unit can preferentially receive text input. Furthermore, if the user prefers image input, the reception unit can preferentially receive image input. This provides the optimal reception means according to the user's input method, thereby improving user convenience.

[0034] When accepting a schedule or to-do list, the acceptance unit can prioritize accepting highly relevant information in consideration of the user's geographical location information. For example, the acceptance unit prioritizes accepting tasks to be performed in a location close to the user's current location. The acceptance unit can also suggest related events or tasks based on the user's geographical location information. Furthermore, the acceptance unit can also suggest an optimal schedule or to-do list based on the user's travel plans. This makes it possible to provide more appropriate information by preferentially accepting highly relevant information based on the user's geographical location information.

[0035] The reception unit can analyze the user's social media activity and receive related information when receiving a schedule or to-do list. For example, the reception unit prioritizes receiving events or tasks mentioned by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related schedules or to-do lists. Furthermore, the reception unit can also suggest related tasks by referring to the activities of the user's friends on social media. This allows for more appropriate information to be provided by receiving related information based on the user's social media activity.

[0036] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a schedule or to-do list. For example, the reception unit preferentially suggests reception methods that the user has preferred in the past. The reception unit can also customize the optimal reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and improve the reception method. In this way, a more appropriate reception method can be provided by reflecting the user's past feedback.

[0037] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the schedule or to-do list. For example, the suggestion unit makes detailed suggestions for tasks with high importance. The suggestion unit can also make brief suggestions for tasks with low importance. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal depending on the importance. This allows for more appropriate suggestions by adjusting the level of detail of the proposal depending on the importance of the schedule or to-do list.

[0038] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the schedule or to-do list. For example, the suggestion unit can apply an efficient suggestion algorithm to work-related tasks. The suggestion unit can also apply a relaxing suggestion algorithm to private tasks. Furthermore, the suggestion unit can select the optimal suggestion algorithm depending on the category. This allows for more appropriate suggestions by applying a suggestion algorithm depending on the category of the schedule or to-do list.

[0039] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit makes an optimal proposal based on, for example, proposals that the user has accepted in the past. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. Furthermore, the suggestion unit can also improve the accuracy of the proposal by referring to the user's past feedback. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0040] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the schedule or to-do list. For example, the suggestion unit will prioritize tasks with an approaching deadline. The suggestion unit can also postpone tasks with ample time to submit them. Furthermore, the suggestion unit can gradually adjust the priority of the proposal based on the submission time. This allows for more appropriate proposals by determining the priority of the proposal based on the submission time of the schedule or to-do list.

[0041] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the schedule or to-do list. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit can also postpone less relevant tasks. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the schedule or to-do list.

[0042] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terms. Also, if the user does not have technical expertise, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can gradually adjust the use of technical terms in the proposal according to the user's level of expertise. This allows for more appropriate proposals by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0043] When generating a video, the generation unit can adjust the level of detail of the video based on the importance of the schedule or to-do list. For example, the generation unit generates a video that includes detailed explanations for tasks with high importance. The generation unit can also generate a video that includes concise explanations for tasks with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the video depending on the importance. In this way, by adjusting the level of detail of the video according to the importance of the schedule or to-do list, more appropriate videos can be generated.

[0044] When generating a video, the generation unit can apply different video generation algorithms depending on the category of the schedule or to-do list. For example, the generation unit can apply an efficient video generation algorithm to work-related tasks. The generation unit can also apply a relaxing video generation algorithm to private tasks. Furthermore, the generation unit can select the optimal video generation algorithm depending on the category. In this way, more appropriate videos can be generated by applying a video generation algorithm depending on the category of the schedule or to-do list.

[0045] When generating a video, the generation unit can improve the accuracy of the video by referring to the user's past video viewing history. The generation unit generates an optimal video, for example, based on videos that the user has viewed in the past. The generation unit can also analyze the user's past viewing history to improve the accuracy of the video. Furthermore, the generation unit can also improve the accuracy of the video by referring to the user's past feedback. In this way, the accuracy of the video is improved by referring to the user's past video viewing history.

[0046] When generating videos, the generation unit can determine the priority of videos based on the submission time of the schedule or to-do list. For example, the generation unit can prioritize creating videos of tasks with upcoming deadlines. The generation unit can also postpone tasks with more time to submit them. Furthermore, the generation unit can gradually adjust the priority of videos based on the submission time. In this way, more appropriate videos can be generated by determining the priority of videos based on the submission time of the schedule or to-do list.

[0047] When generating videos, the generation unit can adjust the order of the videos based on the relevance of the schedule or to-do list. For example, the generation unit can prioritize highly relevant tasks to create videos. The generation unit can also postpone less relevant tasks. Furthermore, the generation unit can gradually adjust the order of the videos based on the relevance. In this way, more appropriate videos can be generated by adjusting the order of the videos based on the relevance of the schedule or to-do list.

[0048] When generating a video, the generation unit can adjust the use of technical terms in the video according to the user's level of expertise. For example, if the user has technical knowledge, the generation unit generates a video that uses a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate a concise and easy-to-understand video. Furthermore, the generation unit can gradually adjust the use of technical terms in the video according to the user's level of expertise. In this way, by adjusting the use of technical terms in the video according to the user's level of expertise, a more appropriate video can be generated.

[0049] When distributing a video, the distribution unit can analyze the user's past viewing history and select the optimal distribution method. For example, the distribution unit preferentially suggests distribution methods that the user has previously preferred. The distribution unit can also customize the optimal distribution method based on the user's past viewing history. Furthermore, the distribution unit can also improve the distribution method by referring to the user's past feedback. In this way, the optimal distribution method can be provided by analyzing the user's past viewing history.

[0050] The distribution unit can filter videos based on the user's current living situation and areas of interest when distributing videos. For example, the distribution unit prioritizes the distribution of videos related to a project the user is currently working on. The distribution unit can also suggest related videos based on the user's areas of interest. Furthermore, the distribution unit can also suggest optimal videos depending on the user's living situation (e.g., family circumstances, work situation). This makes it possible to provide more relevant videos by filtering according to the user's living situation and areas of interest.

[0051] When distributing video, the distribution unit can select the optimal distribution means according to the user's device information. For example, if the user is using a smartphone, the distribution unit distributes video optimized for the smartphone. Furthermore, if the user is using a tablet, the distribution unit can also distribute video optimized for the tablet. Furthermore, if the user is using a smartwatch, the distribution unit can also distribute concise, highly visible video. This improves user convenience by providing the optimal distribution means according to the user's device information.

[0052] When distributing videos, the distribution unit can prioritize distribution of highly relevant videos taking into account the user's geographical location information. For example, the distribution unit prioritizes distribution of videos related to tasks the user is performing in a location close to his or her current location. The distribution unit can also suggest videos related to related events or tasks based on the user's geographical location information. Furthermore, the distribution unit can also suggest optimal videos based on the user's travel plans. This enables more appropriate information to be provided by preferentially distributing highly relevant videos based on the user's geographical location information.

[0053] The distribution unit can analyze the user's social media activity and distribute related videos when distributing videos. For example, the distribution unit prioritizes the distribution of videos related to events or tasks mentioned by the user on social media. The distribution unit can also analyze the content posted by the user on social media and suggest related videos. Furthermore, the distribution unit can also suggest related videos by taking into account the activities of the user's friends on social media. This makes it possible to provide more appropriate information by distributing related videos based on the user's social media activity.

[0054] The distribution unit can customize the distribution method by reflecting the user's past feedback when distributing a video. For example, the distribution unit preferentially suggests distribution methods that the user has previously preferred. The distribution unit can also customize the optimal distribution method based on the user's past feedback. Furthermore, the distribution unit can analyze the user's past feedback and improve the distribution method. In this way, a more appropriate distribution method can be provided by reflecting the user's past feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The suggestion unit can analyze the user's past behavioral patterns and improve the accuracy of the suggestions. For example, the suggestion unit makes optimal suggestions based on suggestions that the user has accepted in the past. The suggestion unit can also analyze the user's past behavioral patterns and adjust the timing of the suggestions. Furthermore, the suggestion unit can also customize the content of the suggestions by referring to the user's past feedback. In this way, the accuracy of the suggestions can be improved by referring to the user's past behavioral patterns.

[0057] The generation unit can adjust the resolution of the video based on the user's device information. For example, if the user is using a smartphone, the generation unit can generate a video with a resolution optimized for the smartphone. Also, if the user is using a tablet, the generation unit can generate a video with a resolution optimized for the tablet. Furthermore, if the user is using a smartwatch, the generation unit can generate a video with a concise and highly visible resolution. This improves user convenience by providing a video with an optimal resolution according to the user's device information.

[0058] The distribution unit can adjust the video distribution time based on the user's geographical location information. For example, if the user is in a different time zone, the distribution time can be set to match that time zone. Also, if the user is traveling, the distribution time can be set to match the time zone of the user's destination. Furthermore, if the user is in a specific location, the distribution unit can distribute a video containing information related to that location. This makes it possible to provide more appropriate information by providing the optimal distribution time based on the user's geographical location information.

[0059] The suggestion unit can analyze the user's social media activity and customize the content of the suggestions. For example, it can make suggestions related to events or tasks mentioned by the user on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest related schedules or to-do lists. Furthermore, the suggestion unit can also suggest related tasks based on the activity of the user's friends on social media. This makes it possible to provide more appropriate information by making related suggestions based on the user's social media activity.

[0060] The generation unit can adjust the content of the video according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can generate a video containing specialized content. On the other hand, if the user does not have specialized knowledge, the generation unit can generate a video containing simple, easy-to-understand content. Furthermore, the generation unit can gradually adjust the content of the video according to the user's level of expertise. This makes it possible to provide more appropriate information by providing the optimal video according to the user's level of expertise.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives the user's schedule and to-do list. For example, the user inputs the next day's schedule and to-do list the night before. Step 2: The suggestion unit makes optimal suggestions based on the information received by the reception unit. For example, the suggestion unit generates an optimal schedule or to-do list by taking into account the user's past data, preferences, and current situation. Step 3: The generator animates the information generated by the suggester. For example, the generator uses a generation AI to animate news tailored to each individual user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The distribution unit distributes the video generated by the generation unit to the user every morning. For example, the distribution unit distributes the video to the user's smartphone or tablet.

[0063] (Example 2) A system according to an embodiment of the present invention is a solution for people who feel fatigued by daily decision-making. This system uses a generative AI to create personalized news videos every morning, delivering the user's schedule, to-do list, clothing and meal suggestions, and other related information. This reduces the burden of thinking and decision-making on the user. This system reduces the user's decision-making burden and allows them to spend their day efficiently. For example, if a user is wondering "What to wear today," the generative AI suggests the most appropriate outfit based on the weather and schedule. Similarly, if a user is wondering "What to eat," the generative AI suggests an appropriate meal, taking into account the user's preferences and health status. This system reduces the time and effort required for daily decision-making, allowing users to live more fulfilling lives. It is particularly beneficial for busy businesspeople, housewives, and other people who face numerous decisions every day.

[0064] An information provision system according to an embodiment includes a reception unit, a proposal unit, a generation unit, and a distribution unit. The reception unit accepts a user's schedule and to-do list. For example, the user inputs the next day's schedule and to-do list the night before. The proposal unit makes optimal proposals based on the information accepted by the reception unit. For example, the proposal unit generates an optimal schedule and to-do list by taking into account the user's past data, preferences, and current situation. The generation unit animates the information generated by the proposal unit. For example, the generation unit uses a generation AI to animate news personalized for each user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The distribution unit distributes the video generated by the generation unit to the user every morning. For example, the distribution unit distributes the video to the user's smartphone or tablet. As a result, the information provision system according to an embodiment efficiently manages the user's schedule and to-do list, makes optimal proposals, and distributes them as videos, thereby reducing the user's decision-making burden.

[0065] The suggestion unit can suggest clothing and meals taking into consideration the user's preferences and health condition. The suggestion unit can suggest optimal clothing and meals taking into consideration the user's preferences and health condition, for example. For example, the suggestion unit can suggest appropriate clothing based on the user's past data and current situation. The suggestion unit can also suggest nutritionally balanced meals taking into consideration the user's health condition. For example, if the user has an allergy, the suggestion unit can suggest meals that accommodate the allergy. This makes it possible to suggest more appropriate clothing and meals based on the user's preferences and health condition.

[0066] The reception unit can estimate the user's emotions and adjust the timing of receiving schedules and to-do lists based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive schedules and to-do lists during times when the user is able to relax. The reception unit can also receive detailed schedules and to-do lists when the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also receive concise schedules and to-do lists. This allows for more appropriate timing for receiving schedules and to-do lists by adjusting the timing of receiving them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the user's past schedule and to-do list history and select the reception method. For example, the reception unit can preferentially suggest schedule and to-do list formats that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past history. Furthermore, the reception unit can customize the optimal reception method based on the user's past feedback. In this way, the reception unit can provide the optimal reception method to the user by analyzing the past history.

[0068] The reception unit can filter schedules and to-do lists based on the user's current lifestyle and areas of interest when receiving them. For example, the reception unit preferentially receives schedules and to-do lists related to a project the user is currently working on. The reception unit can also suggest related schedules and to-do lists based on the user's areas of interest. Furthermore, the reception unit can also suggest optimal schedules and to-do lists based on the user's lifestyle (e.g., family circumstances, work situation). This makes it possible to provide more relevant schedules and to-do lists by filtering according to the user's lifestyle and areas of interest.

[0069] The reception unit can select a reception means according to the user's input method when receiving a schedule or a to-do list. For example, if the user prefers voice input, the reception unit can preferentially receive voice input. Also, if the user prefers text input, the reception unit can preferentially receive text input. Furthermore, if the user prefers image input, the reception unit can preferentially receive image input. This provides the optimal reception means according to the user's input method, thereby improving user convenience.

[0070] The reception unit can estimate the user's emotions and determine the priority of the schedule or to-do list to be received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can postpone less important tasks. Furthermore, if the user is relaxed, the reception unit can also prioritize more important tasks. Furthermore, if the user is in a hurry, the reception unit can prioritize the most important tasks. This enables more appropriate prioritization by determining the priority of the schedule or to-do list according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] When accepting a schedule or to-do list, the acceptance unit can prioritize accepting highly relevant information in consideration of the user's geographical location information. For example, the acceptance unit prioritizes accepting tasks to be performed in a location close to the user's current location. The acceptance unit can also suggest related events or tasks based on the user's geographical location information. Furthermore, the acceptance unit can also suggest an optimal schedule or to-do list based on the user's travel plans. This makes it possible to provide more appropriate information by preferentially accepting highly relevant information based on the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity and receive related information when receiving a schedule or to-do list. For example, the reception unit prioritizes receiving events or tasks mentioned by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related schedules or to-do lists. Furthermore, the reception unit can also suggest related tasks by referring to the activities of the user's friends on social media. This allows for more appropriate information to be provided by receiving related information based on the user's social media activity.

[0073] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a schedule or to-do list. For example, the reception unit preferentially suggests reception methods that the user has preferred in the past. The reception unit can also customize the optimal reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and improve the reception method. In this way, a more appropriate reception method can be provided by reflecting the user's past feedback.

[0074] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make simple and intuitive suggestions. If the user is relaxed, the suggestion unit can also make detailed suggestions. If the user is in a hurry, the suggestion unit can also make suggestions that focus on the main points. This allows for more appropriate suggestions by adjusting the way suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the schedule or to-do list. For example, the suggestion unit makes detailed suggestions for tasks with high importance. The suggestion unit can also make brief suggestions for tasks with low importance. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal depending on the importance. This allows for more appropriate suggestions by adjusting the level of detail of the proposal depending on the importance of the schedule or to-do list.

[0076] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the schedule or to-do list. For example, the suggestion unit can apply an efficient suggestion algorithm to work-related tasks. The suggestion unit can also apply a relaxing suggestion algorithm to private tasks. Furthermore, the suggestion unit can select the optimal suggestion algorithm depending on the category. This allows for more appropriate suggestions by applying a suggestion algorithm depending on the category of the schedule or to-do list.

[0077] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit makes an optimal proposal based on, for example, proposals that the user has accepted in the past. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. Furthermore, the suggestion unit can also improve the accuracy of the proposal by referring to the user's past feedback. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0078] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make short, to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make concise suggestions. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the schedule or to-do list. For example, the suggestion unit will prioritize tasks with an approaching deadline. The suggestion unit can also postpone tasks with ample time to submit them. Furthermore, the suggestion unit can gradually adjust the priority of the proposal based on the submission time. This allows for more appropriate proposals by determining the priority of the proposal based on the submission time of the schedule or to-do list.

[0080] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the schedule or to-do list. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit can also postpone less relevant tasks. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the schedule or to-do list.

[0081] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terms. Also, if the user does not have technical expertise, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can gradually adjust the use of technical terms in the proposal according to the user's level of expertise. This allows for more appropriate proposals by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0082] The generation unit can estimate the user's emotions and adjust the way the video is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a video using relaxing music and colors. If the user is relaxed, the generation unit can also generate a video containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a concise video that focuses on the main points. In this way, by adjusting the way the video is presented according to the user's emotions, a more appropriate video is generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] When generating a video, the generation unit can adjust the level of detail of the video based on the importance of the schedule or to-do list. For example, the generation unit generates a video that includes detailed explanations for tasks with high importance. The generation unit can also generate a video that includes concise explanations for tasks with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the video depending on the importance. In this way, by adjusting the level of detail of the video according to the importance of the schedule or to-do list, more appropriate videos can be generated.

[0084] When generating a video, the generation unit can apply different video generation algorithms depending on the category of the schedule or to-do list. For example, the generation unit can apply an efficient video generation algorithm to work-related tasks. The generation unit can also apply a relaxing video generation algorithm to private tasks. Furthermore, the generation unit can select the optimal video generation algorithm depending on the category. In this way, more appropriate videos can be generated by applying a video generation algorithm depending on the category of the schedule or to-do list.

[0085] When generating a video, the generation unit can improve the accuracy of the video by referring to the user's past video viewing history. The generation unit generates an optimal video, for example, based on videos that the user has viewed in the past. The generation unit can also analyze the user's past viewing history to improve the accuracy of the video. Furthermore, the generation unit can also improve the accuracy of the video by referring to the user's past feedback. In this way, the accuracy of the video is improved by referring to the user's past video viewing history.

[0086] The generation unit can estimate the user's emotions and adjust the length of the video based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a short, to-the-point video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can also generate a concise, quick video. In this way, by adjusting the length of the video according to the user's emotions, a more appropriate video is generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] When generating videos, the generation unit can determine the priority of videos based on the submission time of the schedule or to-do list. For example, the generation unit can prioritize creating videos of tasks with upcoming deadlines. The generation unit can also postpone tasks with more time to submit them. Furthermore, the generation unit can gradually adjust the priority of videos based on the submission time. In this way, more appropriate videos can be generated by determining the priority of videos based on the submission time of the schedule or to-do list.

[0088] When generating videos, the generation unit can adjust the order of the videos based on the relevance of the schedule or to-do list. For example, the generation unit can prioritize highly relevant tasks to create videos. The generation unit can also postpone less relevant tasks. Furthermore, the generation unit can gradually adjust the order of the videos based on the relevance. In this way, more appropriate videos can be generated by adjusting the order of the videos based on the relevance of the schedule or to-do list.

[0089] When generating a video, the generation unit can adjust the use of technical terms in the video according to the user's level of expertise. For example, if the user has technical knowledge, the generation unit generates a video that uses a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate a concise and easy-to-understand video. Furthermore, the generation unit can gradually adjust the use of technical terms in the video according to the user's level of expertise. In this way, by adjusting the use of technical terms in the video according to the user's level of expertise, a more appropriate video can be generated.

[0090] The distribution unit can estimate the user's emotions and adjust the timing of video distribution based on the estimated user emotions. For example, if the user is feeling stressed, the distribution unit distributes videos at a time when the user is able to relax. Furthermore, if the user is relaxed, the distribution unit can distribute videos containing detailed information. Furthermore, if the user is in a hurry, the distribution unit can distribute videos that are concise and to the point. This allows for more appropriate timing by adjusting the timing of video distribution according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When distributing a video, the distribution unit can analyze the user's past viewing history and select the optimal distribution method. For example, the distribution unit preferentially suggests distribution methods that the user has previously preferred. The distribution unit can also customize the optimal distribution method based on the user's past viewing history. Furthermore, the distribution unit can also improve the distribution method by referring to the user's past feedback. In this way, the optimal distribution method can be provided by analyzing the user's past viewing history.

[0092] The distribution unit can filter videos based on the user's current living situation and areas of interest when distributing videos. For example, the distribution unit prioritizes the distribution of videos related to a project the user is currently working on. The distribution unit can also suggest related videos based on the user's areas of interest. Furthermore, the distribution unit can also suggest optimal videos depending on the user's living situation (e.g., family circumstances, work situation). This makes it possible to provide more relevant videos by filtering according to the user's living situation and areas of interest.

[0093] When distributing video, the distribution unit can select the optimal distribution means according to the user's device information. For example, if the user is using a smartphone, the distribution unit distributes video optimized for the smartphone. Furthermore, if the user is using a tablet, the distribution unit can also distribute video optimized for the tablet. Furthermore, if the user is using a smartwatch, the distribution unit can also distribute concise, highly visible video. This improves user convenience by providing the optimal distribution means according to the user's device information.

[0094] The delivery unit can estimate the user's emotions and determine the priority of videos to be delivered based on the estimated user emotions. For example, if the user is feeling stressed, the delivery unit can prioritize delivering videos that are relaxing. Furthermore, if the user is relaxed, the delivery unit can also prioritize delivering videos that include detailed information. Furthermore, if the user is in a hurry, the delivery unit can also prioritize delivering videos that are concise and to the point. In this way, by determining the priority of videos according to the user's emotions, more appropriate videos can be delivered. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] When distributing videos, the distribution unit can prioritize distribution of highly relevant videos taking into account the user's geographical location information. For example, the distribution unit prioritizes distribution of videos related to tasks the user is performing in a location close to his or her current location. The distribution unit can also suggest videos related to related events or tasks based on the user's geographical location information. Furthermore, the distribution unit can also suggest optimal videos based on the user's travel plans. This enables more appropriate information to be provided by preferentially distributing highly relevant videos based on the user's geographical location information.

[0096] The distribution unit can analyze the user's social media activity and distribute related videos when distributing videos. For example, the distribution unit prioritizes the distribution of videos related to events or tasks mentioned by the user on social media. The distribution unit can also analyze the content posted by the user on social media and suggest related videos. Furthermore, the distribution unit can also suggest related videos by taking into account the activities of the user's friends on social media. This makes it possible to provide more appropriate information by distributing related videos based on the user's social media activity.

[0097] The distribution unit can customize the distribution method by reflecting the user's past feedback when distributing a video. For example, the distribution unit preferentially suggests distribution methods that the user has previously preferred. The distribution unit can also customize the optimal distribution method based on the user's past feedback. Furthermore, the distribution unit can analyze the user's past feedback and improve the distribution method. In this way, a more appropriate distribution method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, suggestion unit, generation unit, and distribution unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts the user's schedule and to-do list. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes optimal suggestions by taking into account the user's past data, preferences, and current situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates personalized news videos for each user using a generation AI. The distribution unit is realized, for example, by the output device 40 of the smart device 14 and distributes the generated videos to the user every morning. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, suggestion unit, generation unit, and distribution unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts the user's schedule and to-do list. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes optimal suggestions by taking into account the user's past data, preferences, and current situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to create personalized news videos for each user. The distribution unit is realized, for example, by the speaker 240 of the smart glasses 214 and distributes the generated videos to the user every morning. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, generation unit, and distribution unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and accepts the user's schedule and to-do list. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes optimal suggestions by taking into account the user's past data, preferences, and current situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to create animations of news tailored to each individual user. The distribution unit is realized, for example, by the display 343 of the headset-type terminal 314 and distributes the generated animations to the user every morning. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, generation unit, and distribution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts the user's schedule and to-do list. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes optimal suggestions by taking into account the user's past data, preferences, and current situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to create animations of news tailored to each individual user. The distribution unit is realized, for example, by the speaker 240 of the robot 414 and distributes the generated animations to the user every morning.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The suggestion unit can also estimate the user's emotions and adjust the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing activities or break times. If the user is relaxed, the suggestion unit can suggest challenging tasks or new hobbies. Furthermore, if the user is in a hurry, the suggestion unit can suggest time-saving techniques to complete tasks efficiently. This allows for more appropriate support by providing suggestions based on the user's emotions.

[0100] The generation unit can also estimate the user's emotions and adjust the tempo of the video based on the estimated user's emotions. For example, if the user is feeling stressed, a video with a slow tempo can be generated. If the user is relaxed, a video with a normal tempo can be generated. Furthermore, if the user is in a hurry, a short, fast-paced video can be generated. In this way, by adjusting the tempo of the video according to the user's emotions, more appropriate videos can be generated.

[0101] The distribution unit can also estimate the user's emotions and adjust the frequency of video distribution based on the estimated user emotions. For example, if the user is feeling stressed, the frequency can be reduced to allow more time for relaxation. Also, if the user is relaxed, videos can be distributed at a normal frequency. Furthermore, if the user is in a hurry, only important information can be distributed to prevent information overload. This makes it possible to provide more appropriate information by adjusting the distribution frequency according to the user's emotions.

[0102] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, suggestions can be made during times when the user is able to relax. Also, if the user is relaxed, detailed suggestions can be made. Furthermore, if the user is in a hurry, concise suggestions can be made that get to the point. In this way, more appropriate suggestions can be made by adjusting the timing of suggestions according to the user's emotions.

[0103] The generation unit can also estimate the user's emotions and customize the content of the video based on the estimated user's emotions. For example, if the user is feeling stressed, a video containing relaxing content can be generated. If the user is relaxed, a video containing detailed information can be generated. Furthermore, if the user is in a hurry, a concise video that gets straight to the point can be generated. In this way, more appropriate videos can be generated by customizing the content of the video according to the user's emotions.

[0104] The suggestion unit can analyze the user's past behavioral patterns and improve the accuracy of the suggestions. For example, the suggestion unit makes optimal suggestions based on suggestions that the user has accepted in the past. The suggestion unit can also analyze the user's past behavioral patterns and adjust the timing of the suggestions. Furthermore, the suggestion unit can also customize the content of the suggestions by referring to the user's past feedback. In this way, the accuracy of the suggestions can be improved by referring to the user's past behavioral patterns.

[0105] The generation unit can adjust the resolution of the video based on the user's device information. For example, if the user is using a smartphone, the generation unit can generate a video with a resolution optimized for the smartphone. Also, if the user is using a tablet, the generation unit can generate a video with a resolution optimized for the tablet. Furthermore, if the user is using a smartwatch, the generation unit can generate a video with a concise and highly visible resolution. This improves user convenience by providing a video with an optimal resolution according to the user's device information.

[0106] The distribution unit can adjust the video distribution time based on the user's geographical location information. For example, if the user is in a different time zone, the distribution time can be set to match that time zone. Also, if the user is traveling, the distribution time can be set to match the time zone of the user's destination. Furthermore, if the user is in a specific location, the distribution unit can distribute a video containing information related to that location. This makes it possible to provide more appropriate information by providing the optimal distribution time based on the user's geographical location information.

[0107] The suggestion unit can analyze the user's social media activity and customize the content of the suggestions. For example, it can make suggestions related to events or tasks mentioned by the user on social media. The suggestion unit can also analyze the content of the user's social media posts and suggest related schedules or to-do lists. Furthermore, the suggestion unit can also suggest related tasks based on the activity of the user's friends on social media. This makes it possible to provide more appropriate information by making related suggestions based on the user's social media activity.

[0108] The generation unit can adjust the content of the video according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can generate a video containing specialized content. On the other hand, if the user does not have specialized knowledge, the generation unit can generate a video containing simple, easy-to-understand content. Furthermore, the generation unit can gradually adjust the content of the video according to the user's level of expertise. This makes it possible to provide more appropriate information by providing the optimal video according to the user's level of expertise.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit receives the user's schedule and to-do list. For example, the user inputs the next day's schedule and to-do list the night before. Step 2: The suggestion unit makes optimal suggestions based on the information received by the reception unit. For example, the suggestion unit generates an optimal schedule or to-do list by taking into account the user's past data, preferences, and current situation. Step 3: The generator animates the information generated by the suggester. For example, the generator uses a generation AI to animate news tailored to each individual user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The distribution unit distributes the video generated by the generation unit to the user every morning. For example, the distribution unit distributes the video to the user's smartphone or tablet.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 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.

[0130] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 AI 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 AI 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section that accepts users' schedules and to-do lists; a proposal unit that makes a proposal based on the information received by the reception unit; a generating unit that animates the information generated by the suggesting unit; a distribution unit that distributes the video generated by the generation unit. A system characterized by:

2. The proposal unit Suggest clothing and meals based on the user's preferences and health condition 2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the timing of schedule and to-do list entries based on the estimated user emotions.

2. The system of claim 1.

4. The reception unit Analyze the user's past schedule and to-do list history to determine the reception method 2. The system of claim 1.

5. The reception unit When accepting schedules and to-do lists, filter based on the user's current life situation and areas of interest.

2. The system of claim 1.

6. The reception unit When accepting schedules or to-do lists, select the acceptance method according to the user's input method.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize the schedule and to-do list based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When accepting schedules and to-do lists, the system prioritizes relevant information by taking into account the user's geographic location.

2. The system of claim 1.

Images