System

Abstract

An object of a system according to an embodiment is to automatically propose an optimal schedule that suits an individual's hobbies and preferences and register the schedule in a calendar.SOLUTION: A system according to an embodiment includes a preference analysis unit, an event search unit, a schedule proposal unit, a transportation registration unit, and a schedule selection unit. The hobby and preference analysis unit analyzes personal hobbies and preferences using the search history of the smartphone and other data. The event search unit searches for an event in consideration of weather and location. The schedule proposal unit automatically collects information on living places and various events and proposes an optimal schedule. The transportation registration unit registers transportation, a fee, and the like for participating in the proposed schedule in the calendar. The schedule selection unit allows the user to smoothly select a schedule that suits the user's hobbies and preferences from the proposed schedule candidates.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to automatically propose optimal schedules tailored to individual tastes and preferences and register them on a calendar.

[0005] The system according to the embodiment aims to automatically propose an optimal schedule that matches an individual's hobbies and preferences and register it in a calendar. [Means for solving the problem]

[0006] The system according to the embodiment includes a hobby / preference analysis unit, an event search unit, a schedule proposal unit, a transportation registration unit, and a schedule selection unit. The hobby / preference analysis unit analyzes an individual's hobby / preference using smartphone search history and other data. The event search unit searches for events taking into account weather and location. The schedule proposal unit automatically collects information about the user's place of residence and various events to propose an optimal schedule. The transportation registration unit registers the transportation means and fares for participating in the proposed schedule in a calendar. The schedule selection unit enables the user to smoothly select a schedule that suits their hobby / preference from the proposed schedule candidates. [Effects of the Invention]

[0007] The system according to the embodiment can automatically propose an optimal schedule tailored to an individual's hobbies and preferences and register it in a calendar. [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 schedule proposal system according to an embodiment of the present invention proposes an optimal schedule tailored to an individual's hobbies and preferences and automatically registers the schedule in a calendar. As a result, the schedule proposal system proposes an optimal schedule tailored to a user's hobbies and preferences and automatically registers the schedule in a calendar, thereby improving user convenience.

[0029] A schedule proposal system according to an embodiment includes a hobby / preference analysis unit, an event search unit, a schedule proposal unit, a transportation registration unit, and a schedule selection unit. The hobby / preference analysis unit analyzes an individual's hobby / preferences using smartphone search history and other data. For example, the hobby / preference analysis unit collects data such as a user's frequently searched keywords, websites visited, and apps used, and a generation AI analyzes the data to identify the user's interests. The event search unit searches for events taking into account weather and location. For example, the event search unit selects appropriate candidates for outdoor and indoor events based on weather forecast data and geographic information. The schedule proposal unit automatically collects information about the user's location and various events to propose an optimal schedule. For example, the schedule proposal unit collects event information in the user's area and creates a schedule by combining events that match the user's hobby / preferences. The transportation registration unit registers transportation methods, fares, etc. for participating in the proposed schedule in a calendar. For example, the transportation registration unit searches for the optimal transportation method to an event venue and its fare, and automatically registers the method in a calendar. The schedule selection unit allows the user to smoothly select a schedule that matches their hobbies and preferences from the proposed schedule candidates. For example, the schedule selection unit presents multiple schedule candidates and allows the user to select the schedule that most interests them. As a result, the schedule proposal system according to the embodiment can improve user convenience by proposing an optimal schedule that matches the user's hobbies and preferences and automatically registering the schedule in the calendar.

[0030] The hobby and preference analysis unit can dynamically update the hobby and preference by analyzing not only the user's past behavior history but also real-time behavioral data. The hobby and preference analysis unit updates the hobby and preference in real time based on, for example, the user's current location information. For example, it analyzes data on places the user is visiting or events the user is participating in, and updates the hobby and preference based on the user's behavior at that time. The hobby and preference analysis unit also analyzes the user's current activity data and dynamically updates the hobby and preference. For example, it analyzes the user's current activity (jogging, reading, etc.) and updates the hobby and preference based on that. The hobby and preference analysis unit also collects the user's real-time behavioral data and builds a system that dynamically updates the hobby and preference. For example, it analyzes the user's current behavior based on smartphone sensors and app usage data, and updates the hobby and preference. In this way, by analyzing the real-time behavioral data, it is possible to identify and dynamically update the user's hobby and preference more accurately.

[0031] The hobby and preference analysis unit can also analyze the hobbies and preferences of the user's friends and family and suggest events that share a common hobby. The hobby and preference analysis unit, for example, analyzes the hobbies and preferences of the user's friends and family and suggests events that share a common hobby. For example, based on social media data, it identifies events that the user's friends and family are interested in and suggests events based on that. The hobby and preference analysis unit can also analyze the behavioral history of the user's friends and family and suggest events that share a common hobby. For example, based on data on events and activities that the user has participated in together in the past, it can identify common hobbies and suggest events related to those. The hobby and preference analysis unit can also analyze real-time behavioral data of the user's friends and family and suggest events that share a common hobby. For example, it can dynamically suggest events that share a common hobby based on current location and current activity data. This makes it possible to suggest events that share a common hobby by taking into account the hobbies and preferences of the user's friends and family.

[0032] The hobby and preference analysis unit can analyze the user's hobby and preference, including data on videos viewed and music listened to by the user. The hobby and preference analysis unit, for example, analyzes data on videos viewed by the user to identify the user's hobby and preference. For example, the hobby and preference analysis unit analyzes data on music listened to by the user to identify the user's hobby and preference. For example, the hobby and preference analysis unit analyzes data on artists and genres of music that the user likes to listen to. The hobby and preference analysis unit also integrates data on videos viewed and music listened to by the user to identify the user's hobby and preference. For example, the hobby and preference analysis unit analyzes data on both videos and music and analyzes the user's hobby and preference based on common themes and genres. In this way, by including data on videos viewed and music listened to by the user, the user's hobby and preference can be identified more accurately.

[0033] The hobby and preference analysis unit collects feedback on events the user has previously attended and can analyze the user's hobby and preferences more precisely based on the collected feedback. The hobby and preference analysis unit, for example, collects feedback on events the user has previously attended and analyzes the user's hobby and preferences more precisely. For example, the user's satisfaction level and interests are identified based on questionnaires and reviews after attending the events. The hobby and preference analysis unit also analyzes data on events the user has previously attended and analyzes the user's hobby and preferences more precisely. For example, the user's interests are identified based on the types and frequency of events attended. The hobby and preference analysis unit also collects feedback on events the user has previously attended in real time and builds a system that analyzes the user's hobby and preferences more precisely. For example, the hobby and preference analysis unit dynamically updates the user's hobby and preferences based on emotional data and behavioral data during the event. In this way, the user's hobby and preferences can be analyzed more precisely by collecting feedback on past events.

[0034] The event search unit can suggest more appropriate events by taking into account the user's past participation history and ratings. The event search unit, for example, analyzes the user's past event participation history to suggest appropriate events. For example, it searches for events that the user is likely to be interested in based on the types and frequency of events that the user has participated in in the past. The event search unit also suggests appropriate events based on the user's past event rating data. For example, it prioritizes searching for events related to events that the user has given high ratings to. The event search unit also integrates the user's past participation history and rating data to build a system that suggests more appropriate events. For example, it analyzes the participation history and rating data to search for events that match the user's interests. This makes it possible to suggest more appropriate events by taking into account the user's past participation history and ratings.

[0035] The event search unit can optimize the search results for events to match the free time in the user's schedule. The event search unit, for example, analyzes the user's schedule data and suggests events that match the free time. For example, it searches for events that are held during free time slots based on the user's calendar. The event search unit also compares the user's schedule with the event times and builds a system that suggests the most suitable events. For example, it automatically filters events to match the user's free time. The event search unit also analyzes the user's schedule data in real time and suggests events that match the free time. For example, if the user's schedule changes, it immediately suggests new events. This enables efficient schedule management by optimizing events to match the free time in the user's schedule.

[0036] The event search unit can suggest healthy events by taking into account the user's health condition and fitness data. The event search unit, for example, analyzes the user's health condition data and suggests healthy events. For example, it searches for appropriate exercise events or health events based on the user's fitness data. The event search unit also builds a system that suggests healthy events based on the user's fitness data. For example, it suggests events based on the user's exercise history and health goals. The event search unit also integrates the user's health condition and fitness data to suggest healthy events. For example, it searches for and suggests appropriate events according to the user's health condition. In this way, healthy events can be suggested by taking into account the user's health condition and fitness data.

[0037] The event search unit can share the results of event search with the user's friends and family and suggest events that can be attended jointly. The event search unit, for example, builds a system that shares the results of event search with the user's friends and family and suggests events that can be attended jointly. For example, it suggests events that friends and family might be interested in based on social media data. The event search unit also analyzes the schedule data of the user's friends and family and suggests events that can be attended jointly. For example, it searches for events that are held at a time that suits everyone's schedules. The event search unit also shares the results of event search in real time and suggests events that can be attended jointly with friends and family. For example, it provides a function to share detailed information about an event and check whether participation is possible. In this way, by sharing the results of event search, it is possible to suggest events that can be attended jointly.

[0038] The schedule suggestion unit can analyze the user's past schedule history and propose an optimal schedule pattern. The schedule suggestion unit, for example, analyzes the user's past schedule history and proposes an optimal schedule pattern. For example, it creates a schedule that is likely to interest the user based on data on events and activities that the user has participated in in the past. The schedule suggestion unit also builds a system that proposes an optimal schedule pattern based on the user's past schedule data. For example, it prioritizes the incorporation of events and activities that the user has given high ratings. The schedule suggestion unit also integrates the user's past schedule history and evaluation data to propose an optimal schedule pattern. For example, it analyzes the participation history and evaluation data to create a schedule that matches the user's interests. In this way, it is possible to propose an optimal schedule pattern by analyzing the past schedule history.

[0039] The schedule suggestion unit can propose a balanced schedule by taking into account the user's work and academic schedules. The schedule suggestion unit, for example, analyzes the user's work and academic schedule data and proposes a balanced schedule. For example, it schedules events and activities to avoid work or academic hours. The schedule suggestion unit also compares the user's work and academic schedule with the event times to build a system that proposes a balanced schedule. For example, it automatically filters events to match the work and academic schedule. The schedule suggestion unit also analyzes the user's work and academic schedule data in real time and proposes a balanced schedule. For example, if the user's schedule changes, it immediately proposes new events. In this way, it is possible to propose a balanced schedule by taking into account the user's work and academic schedule.

[0040] The schedule suggestion unit can propose a healthy schedule by taking into account the user's dietary and sleep data. The schedule suggestion unit, for example, analyzes the user's dietary data and proposes a healthy schedule. For example, it incorporates appropriate events and activities based on the user's meal times and nutritional balance. The schedule suggestion unit also builds a system that proposes a healthy schedule based on the user's sleep data. For example, it proposes events that match the user's sleep patterns. The schedule suggestion unit also integrates the user's dietary and sleep data to propose a healthy schedule. For example, it searches for and proposes appropriate events and activities according to the user's health condition. In this way, a healthy schedule can be proposed by taking into account the dietary and sleep data.

[0041] The schedule suggestion unit can share the schedule suggestion results with the user's friends and family, and suggest schedules that can be participated in jointly. The schedule suggestion unit, for example, builds a system that shares the schedule suggestion results with the user's friends and family, and suggests schedules that can be participated in jointly. For example, it suggests events that friends and family are likely to be interested in based on social media data. The schedule suggestion unit also analyzes the schedule data of the user's friends and family, and suggests schedules that can be participated in jointly. For example, it searches for events that are held at time periods that fit everyone's schedules. The schedule suggestion unit also shares the schedule suggestion results in real time, and suggests schedules that can be participated in jointly with friends and family. For example, it provides a function for sharing detailed information about an event and confirming whether participation is possible. In this way, by sharing the schedule suggestion results, it is possible to suggest schedules that can be participated in jointly.

[0042] The transportation means registration unit can propose more comfortable transportation means by taking into account the user's past travel history and evaluations. The transportation means registration unit, for example, analyzes the user's past travel history and proposes comfortable transportation means. For example, it proposes transportation means that the user is likely to be interested in based on the types and frequency of transportation means used in the past. The transportation means registration unit also proposes comfortable transportation means based on the user's past travel evaluation data. For example, it preferentially proposes transportation means related to transportation means that the user has given high ratings. The transportation means registration unit also integrates the user's past travel history and evaluation data to build a system that proposes comfortable transportation means. For example, it analyzes the travel history and evaluation data and proposes transportation means that match the user's interests. In this way, it is possible to propose more comfortable transportation means by taking into account the user's past travel history and evaluations.

[0043] The transportation means registration unit can optimize the results of transportation means selection to match the free time in the user's schedule. The transportation means registration unit, for example, analyzes the user's schedule data and suggests transportation means that match the free time. For example, it searches for transportation means that can be used during free time periods based on the user's calendar. The transportation means registration unit also compares the user's schedule with the usage times of transportation means to build a system that suggests the optimal transportation means. For example, it automatically filters transportation means that match the user's free time. The transportation means registration unit also analyzes the user's schedule data in real time and suggests transportation means that match the free time. For example, if the user's schedule changes, it immediately suggests a new transportation means. This enables efficient schedule management by optimizing transportation means that match the free time in the user's schedule.

[0044] The transportation means registration unit can suggest transportation means that are good for the health by taking into account the user's health condition and fitness data. The transportation means registration unit, for example, analyzes the user's health condition data and suggests transportation means that are good for the health. For example, it suggests appropriate transportation means based on the user's fitness data. The transportation means registration unit also builds a system that suggests transportation means that are good for the health based on the user's fitness data. For example, it suggests transportation means that are good for the health based on the user's exercise history and health goals. The transportation means registration unit also integrates the user's health condition and fitness data to suggest transportation means that are good for the health. For example, it searches for and suggests appropriate transportation means according to the user's health condition. In this way, it is possible to suggest transportation means that are good for the health by taking into account the user's health condition and fitness data.

[0045] The transportation means registration unit can share the results of the transportation means selection with the user's friends and family and suggest transportation means that can be used jointly. The transportation means registration unit, for example, builds a system that shares the results of the transportation means selection with the user's friends and family and suggests transportation means that can be used jointly. For example, it suggests transportation means that friends and family are likely to be interested in based on social media data. The transportation means registration unit also analyzes schedule data of the user's friends and family and suggests transportation means that can be used jointly. For example, it searches for transportation means that can be used during a time period that suits everyone's schedules. The transportation means registration unit also shares the results of the transportation means selection in real time and suggests transportation means that can be used jointly with friends and family. For example, it provides a function to share detailed information about transportation means and check whether they are available. In this way, by sharing the results of the transportation means selection, transportation means that can be used jointly can be suggested.

[0046] The schedule selection unit can analyze the user's past schedule selection history and propose an optimal schedule selection pattern. The schedule selection unit, for example, analyzes the user's past schedule selection history and proposes an optimal schedule selection pattern. For example, it proposes schedules that the user is likely to be interested in based on data on events and activities selected in the past. The schedule selection unit also builds a system that proposes an optimal schedule selection pattern based on the user's past schedule selection data. For example, it prioritizes the proposal of events and activities that the user has given high ratings. The schedule selection unit also integrates the user's past schedule selection history and evaluation data to propose an optimal schedule selection pattern. For example, it analyzes the selection history and evaluation data to propose a schedule that matches the user's interests. In this way, it is possible to propose an optimal schedule selection pattern by analyzing the past schedule selection history.

[0047] The schedule selection unit can select a balanced schedule by taking into consideration the user's work and academic schedules. The schedule selection unit, for example, analyzes the user's work and academic schedule data to select a balanced schedule. For example, it selects events and activities that avoid work and academic hours. The schedule selection unit also compares the user's work and academic schedule with the event times to build a system that selects a balanced schedule. For example, it automatically filters events to match the work and academic schedule. The schedule selection unit also analyzes the user's work and academic schedule data in real time to select a balanced schedule. For example, it immediately suggests new events when the user's schedule changes. This makes it possible to select a balanced schedule by taking into consideration the user's work and academic schedule.

[0048] The schedule selection unit can select a healthy schedule by taking into account the user's dietary and sleep data when selecting a schedule. The schedule selection unit, for example, analyzes the user's dietary data and selects a healthy schedule. For example, it selects appropriate events and activities based on the user's meal times and nutritional balance. The schedule selection unit also builds a system that selects a healthy schedule based on the user's sleep data. For example, it selects events that match the user's sleep patterns. The schedule selection unit also integrates the user's dietary and sleep data to select a healthy schedule. For example, it searches for and selects appropriate events and activities according to the user's health condition. In this way, a healthy schedule can be selected by taking into account the dietary and sleep data.

[0049] The schedule selection unit can share the schedule selection results with the user's friends and family, and select a schedule that can be participated in jointly. The schedule selection unit, for example, builds a system that shares the schedule selection results with the user's friends and family, and selects a schedule that can be participated in jointly. For example, it selects events that friends and family are likely to be interested in based on social media data. The schedule selection unit also analyzes schedule data of the user's friends and family, and selects a schedule that can be participated in jointly. For example, it searches for events that are held at a time that suits everyone's schedules. The schedule selection unit also shares the schedule selection results in real time, and selects a schedule that can be participated in jointly with friends and family. For example, it provides a function for sharing detailed information about an event and confirming whether participation is possible. In this way, by sharing the schedule selection results, a schedule that can be participated in jointly can be selected.

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

[0051] The schedule suggestion system can also monitor the user's health and suggest a schedule that takes health into consideration. For example, it can analyze the user's fitness data and incorporate appropriate exercise and rest times into the schedule. It can also suggest nutritionally balanced meal times based on the user's dietary data. It can also analyze the user's sleep data and suggest a schedule that ensures sufficient sleep. This can improve the user's overall quality of life by providing a schedule that takes their health into consideration.

[0052] The schedule suggestion system can also suggest events that can be attended jointly, taking into account the schedules of the user's friends and family. For example, it can analyze the schedule data of friends and family and suggest events that are held at times when all are available. It can also suggest events that friends and family might be interested in based on social media data. It can also provide a function to share detailed event information and check whether or not participants can attend. This allows the system to suggest events that the user and their friends and family can enjoy together, deepening bonds.

[0053] The schedule suggestion system can also analyze a user's past event participation history and suggest new events related to events that gave the user particularly high satisfaction. For example, it can suggest events with a similar theme or genre based on data on events that the user has previously given high ratings. It can also analyze the types and frequency of events that the user frequently attends and suggest new events that the user may be interested in. Furthermore, it can integrate the user's past event participation history with rating data to make more accurate event suggestions. This can suggest events that match the user's interests and improve satisfaction.

[0054] The schedule suggestion system can also propose a balanced schedule by taking into account the user's work and academic schedules. For example, it can schedule events and activities to avoid work or academic hours. It is also possible to build a system that compares the user's work and academic schedule with the times of events to propose an optimal schedule. Furthermore, it can instantly suggest new events if the user's schedule changes. This allows the system to provide a balanced schedule by taking into account work and academic schedules, supporting efficient time management.

[0055] The schedule suggestion system can also analyze data on videos viewed and music listened to by users to suggest related events and activities. For example, it can suggest related events based on videos of genres or themes that users frequently watch. It can also suggest music events or concerts based on the artists and genres of music that users like to listen to. It can also integrate video and music data to suggest events based on common themes or genres. This makes it possible to utilize users' viewing and listening data to provide events that match their interests.

[0056] The schedule suggestion system can also analyze a user's past travel history and ratings to suggest more comfortable means of transportation. For example, it can suggest new means of transportation based on means of transportation that the user has previously rated highly. It can also analyze a user's past travel history to suggest means of transportation that the user may be interested in. Furthermore, it can integrate a user's travel history and rating data to suggest more accurate means of transportation. This can improve the user's travel experience and provide a more comfortable means of transportation.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The hobby and preference analysis unit analyzes an individual's hobbies and preferences using smartphone search history and other data. For example, the hobby and preference analysis unit collects data such as the user's frequently searched keywords, websites visited, and apps used, and the generation AI analyzes this data to identify the user's interests. Step 2: The event search unit searches for events taking into account the weather and location. For example, the event search unit selects appropriate candidates for outdoor and indoor events based on weather forecast data and geographic information. Step 3: The schedule suggestion unit automatically collects information about the user's place of residence and various events and proposes an optimal schedule. For example, the schedule suggestion unit collects event information in the user's area and creates a schedule by combining events that match the user's hobbies and preferences. Step 4: The transportation registration unit registers the transportation means and fare for participating in the proposed schedule in the calendar. For example, the transportation registration unit searches for the optimal transportation means to the event venue and its fare, and automatically registers it in the calendar. Step 5: The schedule selection unit allows the user to smoothly select a schedule that suits their interests and preferences from the proposed schedule candidates. For example, the schedule selection unit may present multiple schedule candidates and allow the user to select the schedule that interests them most.

[0059] (Example 2) A schedule proposal system according to an embodiment of the present invention proposes an optimal schedule tailored to an individual's hobbies and preferences and automatically registers the schedule in a calendar. As a result, the schedule proposal system proposes an optimal schedule tailored to a user's hobbies and preferences and automatically registers the schedule in a calendar, thereby improving user convenience.

[0060] A schedule proposal system according to an embodiment includes a hobby / preference analysis unit, an event search unit, a schedule proposal unit, a transportation registration unit, and a schedule selection unit. The hobby / preference analysis unit analyzes an individual's hobby / preferences using smartphone search history and other data. For example, the hobby / preference analysis unit collects data such as a user's frequently searched keywords, websites visited, and apps used, and a generation AI analyzes the data to identify the user's interests. The event search unit searches for events taking into account weather and location. For example, the event search unit selects appropriate candidates for outdoor and indoor events based on weather forecast data and geographic information. The schedule proposal unit automatically collects information about the user's location and various events to propose an optimal schedule. For example, the schedule proposal unit collects event information in the user's area and creates a schedule by combining events that match the user's hobby / preferences. The transportation registration unit registers transportation methods, fares, etc. for participating in the proposed schedule in a calendar. For example, the transportation registration unit searches for the optimal transportation method to an event venue and its fare, and automatically registers the method in a calendar. The schedule selection unit allows the user to smoothly select a schedule that matches their hobbies and preferences from the proposed schedule candidates. For example, the schedule selection unit presents multiple schedule candidates and allows the user to select the schedule that most interests them. As a result, the schedule proposal system according to the embodiment can improve user convenience by proposing an optimal schedule that matches the user's hobbies and preferences and automatically registering the schedule in the calendar.

[0061] The hobby and preference analysis unit can estimate a user's emotions and identify hobbies and preferences that elicit positive emotions. For example, the generative AI analyzes a user's search history and social media posts to identify hobbies and preferences that elicit positive emotions. For example, it analyzes posts in which the user frequently expresses emotions such as "fun" and "happiness" and identifies related hobbies. The hobby and preference analysis unit also identifies hobbies and preferences that elicit positive emotions based on the user's past behavioral data. For example, it analyzes events and activities that the user has participated in in the past that elicited particularly high satisfaction and identifies hobbies related to them. The hobby and preference analysis unit also analyzes the user's real-time emotional data to identify hobbies and preferences that elicit positive emotions. For example, it analyzes the emotions the user feels during their current activity and dynamically updates the hobbies and preferences based on that. This allows the system to identify hobbies and preferences that elicit positive emotions and propose a more satisfying schedule.

[0062] The hobby and preference analysis unit can dynamically update the hobby and preference by analyzing not only the user's past behavior history but also real-time behavioral data. The hobby and preference analysis unit updates the hobby and preference in real time based on, for example, the user's current location information. For example, it analyzes data on places the user is visiting or events the user is participating in, and updates the hobby and preference based on the user's behavior at that time. The hobby and preference analysis unit also analyzes the user's current activity data and dynamically updates the hobby and preference. For example, it analyzes the user's current activity (jogging, reading, etc.) and updates the hobby and preference based on that. The hobby and preference analysis unit also collects the user's real-time behavioral data and builds a system that dynamically updates the hobby and preference. For example, it analyzes the user's current behavior based on smartphone sensors and app usage data, and updates the hobby and preference. In this way, by analyzing the real-time behavioral data, it is possible to identify and dynamically update the user's hobby and preference more accurately.

[0063] The hobby and preference analysis unit can also analyze the hobbies and preferences of the user's friends and family and suggest events that share a common hobby. The hobby and preference analysis unit, for example, analyzes the hobbies and preferences of the user's friends and family and suggests events that share a common hobby. For example, based on social media data, it identifies events that the user's friends and family are interested in and suggests events based on that. The hobby and preference analysis unit can also analyze the behavioral history of the user's friends and family and suggest events that share a common hobby. For example, based on data on events and activities that the user has participated in together in the past, it can identify common hobbies and suggest events related to those. The hobby and preference analysis unit can also analyze real-time behavioral data of the user's friends and family and suggest events that share a common hobby. For example, it can dynamically suggest events that share a common hobby based on current location and current activity data. This makes it possible to suggest events that share a common hobby by taking into account the hobbies and preferences of the user's friends and family.

[0064] The hobby and preference analysis unit can analyze the user's hobby and preference, including data on videos viewed and music listened to by the user. The hobby and preference analysis unit, for example, analyzes data on videos viewed by the user to identify the user's hobby and preference. For example, the hobby and preference analysis unit analyzes data on music listened to by the user to identify the user's hobby and preference. For example, the hobby and preference analysis unit analyzes data on artists and genres of music that the user likes to listen to. The hobby and preference analysis unit also integrates data on videos viewed and music listened to by the user to identify the user's hobby and preference. For example, the hobby and preference analysis unit analyzes data on both videos and music and analyzes the user's hobby and preference based on common themes and genres. In this way, by including data on videos viewed and music listened to by the user, the user's hobby and preference can be identified more accurately.

[0065] The hobby and preference analysis unit collects feedback on events the user has previously attended and can analyze the user's hobby and preferences more precisely based on the collected feedback. The hobby and preference analysis unit, for example, collects feedback on events the user has previously attended and analyzes the user's hobby and preferences more precisely. For example, the user's satisfaction level and interests are identified based on questionnaires and reviews after attending the events. The hobby and preference analysis unit also analyzes data on events the user has previously attended and analyzes the user's hobby and preferences more precisely. For example, the user's interests are identified based on the types and frequency of events attended. The hobby and preference analysis unit also collects feedback on events the user has previously attended in real time and builds a system that analyzes the user's hobby and preferences more precisely. For example, the hobby and preference analysis unit dynamically updates the user's hobby and preferences based on emotional data and behavioral data during the event. In this way, the user's hobby and preferences can be analyzed more precisely by collecting feedback on past events.

[0066] The hobby and preference analysis unit uses the emotion estimation function to analyze how a user feels about a specific hobby and can suggest hobby and preference suggestions based on the emotions. The hobby and preference analysis unit, for example, uses the emotion estimation function to analyze how a user feels about a specific hobby. For example, it analyzes the emotion scores of posts and comments about the user's hobby and identifies the hobby and preference based on the emotion scores. The hobby and preference analysis unit also analyzes the user's real-time emotion data and suggests hobby and preference suggestions based on the emotions. For example, it analyzes the emotion the user feels about the activity they are currently performing and suggests hobby and preference suggestions based on the emotion scores. The hobby and preference analysis unit also uses the emotion estimation function to analyze the emotion the user feels about events and activities they have participated in in the past and suggests hobby and preference suggestions based on the emotions. For example, it suggests hobbies related to events that generated high satisfaction. In this way, the emotion estimation function makes it possible to suggest hobby and preference suggestions based on the user's emotions.

[0067] The event search unit can estimate the user's emotions and prioritize searching for events that elicit positive emotions. For example, the event search unit uses a generation AI to analyze the user's emotional data and prioritize searching for events that elicit positive emotions. For example, it suggests events related to events that the user has previously participated in and felt a high level of satisfaction. The event search unit also searches for events that elicit positive emotions based on the user's real-time emotional data. For example, it suggests appropriate events based on the emotions the user is currently feeling. The event search unit also uses an emotion estimation function to analyze the user's emotions toward specific events and prioritize searching for events that elicit positive emotions. For example, it suggests events related to events that the user felt positive about in the past. This prioritizes searching for events that elicit positive emotions, thereby improving user satisfaction.

[0068] The event search unit can suggest more appropriate events by taking into account the user's past participation history and ratings. The event search unit, for example, analyzes the user's past event participation history to suggest appropriate events. For example, it searches for events that the user is likely to be interested in based on the types and frequency of events that the user has participated in in the past. The event search unit also suggests appropriate events based on the user's past event rating data. For example, it prioritizes searching for events related to events that the user has given high ratings to. The event search unit also integrates the user's past participation history and rating data to build a system that suggests more appropriate events. For example, it analyzes the participation history and rating data to search for events that match the user's interests. This makes it possible to suggest more appropriate events by taking into account the user's past participation history and ratings.

[0069] The event search unit can optimize the search results for events to match the free time in the user's schedule. The event search unit, for example, analyzes the user's schedule data and suggests events that match the free time. For example, it searches for events that are held during free time slots based on the user's calendar. The event search unit also compares the user's schedule with the event times and builds a system that suggests the most suitable events. For example, it automatically filters events to match the user's free time. The event search unit also analyzes the user's schedule data in real time and suggests events that match the free time. For example, if the user's schedule changes, it immediately suggests new events. This enables efficient schedule management by optimizing events to match the free time in the user's schedule.

[0070] The event search unit can suggest healthy events by taking into account the user's health condition and fitness data. The event search unit, for example, analyzes the user's health condition data and suggests healthy events. For example, it searches for appropriate exercise events or health events based on the user's fitness data. The event search unit also builds a system that suggests healthy events based on the user's fitness data. For example, it suggests events based on the user's exercise history and health goals. The event search unit also integrates the user's health condition and fitness data to suggest healthy events. For example, it searches for and suggests appropriate events according to the user's health condition. In this way, healthy events can be suggested by taking into account the user's health condition and fitness data.

[0071] The event search unit can share the results of event search with the user's friends and family and suggest events that can be attended jointly. The event search unit, for example, builds a system that shares the results of event search with the user's friends and family and suggests events that can be attended jointly. For example, it suggests events that friends and family might be interested in based on social media data. The event search unit also analyzes the schedule data of the user's friends and family and suggests events that can be attended jointly. For example, it searches for events that are held at a time that suits everyone's schedules. The event search unit also shares the results of event search in real time and suggests events that can be attended jointly with friends and family. For example, it provides a function to share detailed information about an event and check whether participation is possible. In this way, by sharing the results of event search, it is possible to suggest events that can be attended jointly.

[0072] The event search unit uses the emotion estimation function to analyze how a user feels about a specific event and can suggest events based on the emotions. The event search unit, for example, uses the emotion estimation function to analyze how a user feels about a specific event. For example, it suggests events based on emotions based on emotion data from when the user participated in past events. The event search unit also analyzes real-time emotion data from the user and suggests events based on emotions. For example, it suggests appropriate events based on the emotions the user is currently feeling. The event search unit also uses the emotion estimation function to analyze how the user feels about events that the user participated in in the past and suggests events based on emotions. For example, it suggests events related to events that the user felt positive about in the past. In this way, by using the emotion estimation function, it is possible to suggest events based on the user's emotions.

[0073] The schedule suggestion unit can estimate the user's emotions and prioritize suggesting schedules that elicit positive emotions. For example, the schedule suggestion unit uses a generation AI to analyze the user's emotional data and prioritize suggest schedules that elicit positive emotions. For example, the schedule suggestion unit creates a schedule based on events and activities that the user has found highly satisfying in the past. The schedule suggestion unit also proposes schedules that elicit positive emotions based on the user's real-time emotional data. For example, it combines appropriate events and activities based on the user's current emotions. The schedule suggestion unit also uses an emotion estimation function to analyze the user's emotions regarding a specific schedule and prioritize suggest schedules that elicit positive emotions. For example, it suggests events related to schedules that the user has felt positive about in the past. This prioritizes suggesting schedules that elicit positive emotions, thereby improving user satisfaction.

[0074] The schedule suggestion unit can analyze the user's past schedule history and propose an optimal schedule pattern. The schedule suggestion unit, for example, analyzes the user's past schedule history and proposes an optimal schedule pattern. For example, it creates a schedule that is likely to interest the user based on data on events and activities that the user has participated in in the past. The schedule suggestion unit also builds a system that proposes an optimal schedule pattern based on the user's past schedule data. For example, it prioritizes the incorporation of events and activities that the user has given high ratings. The schedule suggestion unit also integrates the user's past schedule history and evaluation data to propose an optimal schedule pattern. For example, it analyzes the participation history and evaluation data to create a schedule that matches the user's interests. In this way, it is possible to propose an optimal schedule pattern by analyzing the past schedule history.

[0075] The schedule suggestion unit can propose a balanced schedule by taking into account the user's work and academic schedules. The schedule suggestion unit, for example, analyzes the user's work and academic schedule data and proposes a balanced schedule. For example, it schedules events and activities to avoid work or academic hours. The schedule suggestion unit also compares the user's work and academic schedule with the event times to build a system that proposes a balanced schedule. For example, it automatically filters events to match the work and academic schedule. The schedule suggestion unit also analyzes the user's work and academic schedule data in real time and proposes a balanced schedule. For example, if the user's schedule changes, it immediately proposes new events. In this way, it is possible to propose a balanced schedule by taking into account the user's work and academic schedule.

[0076] The schedule suggestion unit can propose a healthy schedule by taking into account the user's dietary and sleep data. The schedule suggestion unit, for example, analyzes the user's dietary data and proposes a healthy schedule. For example, it incorporates appropriate events and activities based on the user's meal times and nutritional balance. The schedule suggestion unit also builds a system that proposes a healthy schedule based on the user's sleep data. For example, it proposes events that match the user's sleep patterns. The schedule suggestion unit also integrates the user's dietary and sleep data to propose a healthy schedule. For example, it searches for and proposes appropriate events and activities according to the user's health condition. In this way, a healthy schedule can be proposed by taking into account the dietary and sleep data.

[0077] The schedule suggestion unit can share the schedule suggestion results with the user's friends and family, and suggest schedules that can be participated in jointly. The schedule suggestion unit, for example, builds a system that shares the schedule suggestion results with the user's friends and family, and suggests schedules that can be participated in jointly. For example, it suggests events that friends and family are likely to be interested in based on social media data. The schedule suggestion unit also analyzes the schedule data of the user's friends and family, and suggests schedules that can be participated in jointly. For example, it searches for events that are held at time periods that fit everyone's schedules. The schedule suggestion unit also shares the schedule suggestion results in real time, and suggests schedules that can be participated in jointly with friends and family. For example, it provides a function for sharing detailed information about an event and confirming whether participation is possible. In this way, by sharing the schedule suggestion results, it is possible to suggest schedules that can be participated in jointly.

[0078] The schedule suggestion unit uses the emotion estimation function to analyze how the user feels about a specific schedule and propose a schedule based on the emotion. The schedule suggestion unit, for example, uses the emotion estimation function to analyze how the user feels about a specific schedule. For example, the schedule suggestion unit proposes a schedule based on the emotion based on the user's emotion data about past schedules. The schedule suggestion unit also analyzes the user's real-time emotion data and proposes a schedule based on the emotion. For example, the schedule suggestion unit combines appropriate events and activities based on the emotion the user is currently feeling. The schedule suggestion unit also uses the emotion estimation function to analyze the emotion the user felt about events and activities that the user participated in in the past and proposes a schedule based on the emotion. For example, the schedule suggestion unit proposes events related to schedules that the user felt positive about in the past. In this way, by using the emotion estimation function, it is possible to propose a schedule based on the user's emotion.

[0079] The transportation means registration unit can estimate the user's emotions and preferentially suggest transportation means that elicit positive emotions. For example, the transportation means registration unit uses a generation AI to analyze the user's emotional data and preferentially suggest transportation means that elicit positive emotions. For example, suggestions are made based on transportation means that the user has had high satisfaction with in the past. The transportation means registration unit also suggests transportation means that elicit positive emotions based on the user's real-time emotional data. For example, it suggests appropriate transportation means based on the emotions the user is currently feeling. The transportation means registration unit also uses an emotion estimation function to analyze the user's emotions toward specific transportation means and preferentially suggest transportation means that elicit positive emotions. For example, it suggests transportation means that the user has had positive emotions about in the past. In this way, user satisfaction is improved by preferentially suggesting transportation means that elicit positive emotions.

[0080] The transportation means registration unit can propose more comfortable transportation means by taking into account the user's past travel history and evaluations. The transportation means registration unit, for example, analyzes the user's past travel history and proposes comfortable transportation means. For example, it proposes transportation means that the user is likely to be interested in based on the types and frequency of transportation means used in the past. The transportation means registration unit also proposes comfortable transportation means based on the user's past travel evaluation data. For example, it preferentially proposes transportation means related to transportation means that the user has given high ratings. The transportation means registration unit also integrates the user's past travel history and evaluation data to build a system that proposes comfortable transportation means. For example, it analyzes the travel history and evaluation data and proposes transportation means that match the user's interests. In this way, it is possible to propose more comfortable transportation means by taking into account the user's past travel history and evaluations.

[0081] The transportation means registration unit can optimize the results of transportation means selection to match the free time in the user's schedule. The transportation means registration unit, for example, analyzes the user's schedule data and suggests transportation means that match the free time. For example, it searches for transportation means that can be used during free time periods based on the user's calendar. The transportation means registration unit also compares the user's schedule with the usage times of transportation means to build a system that suggests the optimal transportation means. For example, it automatically filters transportation means that match the user's free time. The transportation means registration unit also analyzes the user's schedule data in real time and suggests transportation means that match the free time. For example, if the user's schedule changes, it immediately suggests a new transportation means. This enables efficient schedule management by optimizing transportation means that match the free time in the user's schedule.

[0082] The transportation means registration unit can suggest transportation means that are good for the health by taking into account the user's health condition and fitness data. The transportation means registration unit, for example, analyzes the user's health condition data and suggests transportation means that are good for the health. For example, it suggests appropriate transportation means based on the user's fitness data. The transportation means registration unit also builds a system that suggests transportation means that are good for the health based on the user's fitness data. For example, it suggests transportation means that are good for the health based on the user's exercise history and health goals. The transportation means registration unit also integrates the user's health condition and fitness data to suggest transportation means that are good for the health. For example, it searches for and suggests appropriate transportation means according to the user's health condition. In this way, it is possible to suggest transportation means that are good for the health by taking into account the user's health condition and fitness data.

[0083] The transportation means registration unit can share the results of the transportation means selection with the user's friends and family and suggest transportation means that can be used jointly. The transportation means registration unit, for example, builds a system that shares the results of the transportation means selection with the user's friends and family and suggests transportation means that can be used jointly. For example, it suggests transportation means that friends and family are likely to be interested in based on social media data. The transportation means registration unit also analyzes schedule data of the user's friends and family and suggests transportation means that can be used jointly. For example, it searches for transportation means that can be used during a time period that suits everyone's schedules. The transportation means registration unit also shares the results of the transportation means selection in real time and suggests transportation means that can be used jointly with friends and family. For example, it provides a function to share detailed information about transportation means and check whether they are available. In this way, by sharing the results of the transportation means selection, transportation means that can be used jointly can be suggested.

[0084] The transportation means registration unit uses the emotion estimation function to analyze what emotions the user has toward a specific transportation means and can suggest transportation means based on the emotions. The transportation means registration unit, for example, uses the emotion estimation function to analyze what emotions the user has toward a specific transportation means. For example, it suggests transportation means based on emotions based on emotion data of the user toward transportation means in the past. The transportation means registration unit also analyzes real-time emotion data of the user and suggests transportation means based on emotions. For example, it suggests appropriate transportation means based on emotions currently felt by the user. The transportation means registration unit also uses the emotion estimation function to analyze emotions toward transportation means used by the user in the past and suggests transportation means based on emotions. For example, it suggests transportation means that the user has felt positive about in the past. In this way, by using the emotion estimation function, it is possible to suggest transportation means based on the user's emotions.

[0085] The schedule selection unit can estimate the user's emotions and prioritize selecting schedules that elicit positive emotions. For example, the schedule selection unit uses a generation AI to analyze the user's emotional data and prioritize selecting schedules that elicit positive emotions. For example, the schedule selection unit selects a schedule based on events and activities that the user has previously found highly satisfying. The schedule selection unit also selects a schedule that elicits positive emotions based on the user's real-time emotional data. For example, the schedule selection unit selects appropriate events and activities based on the user's current emotions. The schedule selection unit also uses an emotion estimation function to analyze the user's emotions regarding a specific schedule and prioritize selecting schedules that elicit positive emotions. For example, the schedule selection unit selects events related to schedules that the user has previously found positive. This prioritizes the selection of schedules that elicit positive emotions, thereby improving user satisfaction.

[0086] The schedule selection unit can analyze the user's past schedule selection history and propose an optimal schedule selection pattern. The schedule selection unit, for example, analyzes the user's past schedule selection history and proposes an optimal schedule selection pattern. For example, it proposes schedules that the user is likely to be interested in based on data on events and activities selected in the past. The schedule selection unit also builds a system that proposes an optimal schedule selection pattern based on the user's past schedule selection data. For example, it prioritizes the proposal of events and activities that the user has given high ratings. The schedule selection unit also integrates the user's past schedule selection history and evaluation data to propose an optimal schedule selection pattern. For example, it analyzes the selection history and evaluation data to propose a schedule that matches the user's interests. In this way, it is possible to propose an optimal schedule selection pattern by analyzing the past schedule selection history.

[0087] The schedule selection unit can select a balanced schedule by taking into consideration the user's work and academic schedules. The schedule selection unit, for example, analyzes the user's work and academic schedule data to select a balanced schedule. For example, it selects events and activities that avoid work and academic hours. The schedule selection unit also compares the user's work and academic schedule with the event times to build a system that selects a balanced schedule. For example, it automatically filters events to match the work and academic schedule. The schedule selection unit also analyzes the user's work and academic schedule data in real time to select a balanced schedule. For example, it immediately suggests new events when the user's schedule changes. This makes it possible to select a balanced schedule by taking into consideration the user's work and academic schedule.

[0088] The schedule selection unit can select a healthy schedule by taking into account the user's dietary and sleep data when selecting a schedule. The schedule selection unit, for example, analyzes the user's dietary data and selects a healthy schedule. For example, it selects appropriate events and activities based on the user's meal times and nutritional balance. The schedule selection unit also builds a system that selects a healthy schedule based on the user's sleep data. For example, it selects events that match the user's sleep patterns. The schedule selection unit also integrates the user's dietary and sleep data to select a healthy schedule. For example, it searches for and selects appropriate events and activities according to the user's health condition. In this way, a healthy schedule can be selected by taking into account the dietary and sleep data.

[0089] The schedule selection unit can share the schedule selection results with the user's friends and family, and select a schedule that can be participated in jointly. The schedule selection unit, for example, builds a system that shares the schedule selection results with the user's friends and family, and selects a schedule that can be participated in jointly. For example, it selects events that friends and family are likely to be interested in based on social media data. The schedule selection unit also analyzes schedule data of the user's friends and family, and selects a schedule that can be participated in jointly. For example, it searches for events that are held at a time that suits everyone's schedules. The schedule selection unit also shares the schedule selection results in real time, and selects a schedule that can be participated in jointly with friends and family. For example, it provides a function for sharing detailed information about an event and confirming whether participation is possible. In this way, by sharing the schedule selection results, a schedule that can be participated in jointly can be selected.

[0090] The schedule selection unit uses the emotion estimation function to analyze how the user feels about a specific schedule and select a schedule based on the emotion. The schedule selection unit, for example, uses the emotion estimation function to analyze how the user feels about a specific schedule. For example, it selects a schedule based on the emotion based on emotion data of the user regarding past schedules. The schedule selection unit also analyzes the user's real-time emotion data and selects a schedule based on the emotion. For example, it selects an appropriate event or activity based on the emotion the user is currently feeling. The schedule selection unit also uses the emotion estimation function to analyze the emotion of the user regarding events or activities that the user has participated in in the past and selects a schedule based on the emotion. For example, it selects an event related to a schedule that the user felt positive about in the past. In this way, by using the emotion estimation function, it is possible to select a schedule based on the user's emotion.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The schedule suggestion system can also monitor the user's health and suggest a schedule that takes health into consideration. For example, it can analyze the user's fitness data and incorporate appropriate exercise and rest times into the schedule. It can also suggest nutritionally balanced meal times based on the user's dietary data. It can also analyze the user's sleep data and suggest a schedule that ensures sufficient sleep. This can improve the user's overall quality of life by providing a schedule that takes their health into consideration.

[0093] The schedule suggestion system can also estimate the user's emotions and suggest relaxation events to reduce stress. For example, if the user is feeling stressed, it can suggest events such as relaxation yoga or meditation sessions. It can also suggest activities with a high relaxing effect, such as nature walks or hot spring trips. Furthermore, based on the user's emotional data, it can suggest music or art therapy events that are effective in reducing stress. This can help reduce the user's stress and support their physical and mental health.

[0094] The schedule suggestion system can also suggest events that can be attended jointly, taking into account the schedules of the user's friends and family. For example, it can analyze the schedule data of friends and family and suggest events that are held at times when all are available. It can also suggest events that friends and family might be interested in based on social media data. It can also provide a function to share detailed event information and check whether or not participants can attend. This allows the system to suggest events that the user and their friends and family can enjoy together, deepening bonds.

[0095] The schedule suggestion system can also analyze a user's past event participation history and suggest new events related to events that gave the user particularly high satisfaction. For example, it can suggest events with a similar theme or genre based on data on events that the user has previously given high ratings. It can also analyze the types and frequency of events that the user frequently attends and suggest new events that the user may be interested in. Furthermore, it can integrate the user's past event participation history with rating data to make more accurate event suggestions. This can suggest events that match the user's interests and improve satisfaction.

[0096] The schedule suggestion system can also estimate the user's emotions and suggest hobbies and activities that elicit positive emotions. For example, new suggestions can be made based on hobbies and activities that the user has previously experienced positive emotions. It can also analyze the user's real-time emotional data to suggest hobbies and activities that match their current mood. Furthermore, it can discover new hobbies and activities that elicit positive emotions based on the user's emotional data. This makes it possible to make suggestions based on the user's emotions and improve satisfaction in daily life.

[0097] The schedule suggestion system can also propose a balanced schedule by taking into account the user's work and academic schedules. For example, it can schedule events and activities to avoid work or academic hours. It is also possible to build a system that compares the user's work and academic schedule with the times of events to propose an optimal schedule. Furthermore, it can instantly suggest new events if the user's schedule changes. This allows the system to provide a balanced schedule by taking into account work and academic schedules, supporting efficient time management.

[0098] The schedule suggestion system can also estimate the user's emotions and suggest events and activities to alleviate negative emotions. For example, if the user is feeling stressed or anxious, it can suggest events related to relaxation and mental health. It can also suggest hobbies and activities to alleviate negative emotions based on the user's emotional data. Furthermore, it can analyze the user's real-time emotional data and suggest ways to refresh that suit their current mood. This can help alleviate the user's negative emotions and support their physical and mental health.

[0099] The schedule suggestion system can also analyze data on videos viewed and music listened to by users to suggest related events and activities. For example, it can suggest related events based on videos of genres or themes that users frequently watch. It can also suggest music events or concerts based on the artists and genres of music that users like to listen to. It can also integrate video and music data to suggest events based on common themes or genres. This makes it possible to utilize users' viewing and listening data to provide events that match their interests.

[0100] The schedule suggestion system can also estimate the user's emotions and suggest transportation methods based on the user's emotions. For example, if the user wants to relax, it can suggest comfortable transportation methods. If the user is feeling active, it can also suggest active transportation methods such as cycling or walking. Furthermore, it can suggest entertainment and relaxation methods that can be enjoyed while traveling based on the user's emotional data. This makes it possible to provide transportation methods based on the user's emotions and improve the user's experience while traveling.

[0101] The schedule suggestion system can also analyze a user's past travel history and ratings to suggest more comfortable means of transportation. For example, it can suggest new means of transportation based on means of transportation that the user has previously rated highly. It can also analyze a user's past travel history to suggest means of transportation that the user may be interested in. Furthermore, it can integrate a user's travel history and rating data to suggest more accurate means of transportation. This can improve the user's travel experience and provide a more comfortable means of transportation.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The hobby and preference analysis unit analyzes an individual's hobbies and preferences using smartphone search history and other data. For example, the hobby and preference analysis unit collects data such as the user's frequently searched keywords, websites visited, and apps used, and the generation AI analyzes this data to identify the user's interests. Step 2: The event search unit searches for events taking into account the weather and location. For example, the event search unit selects appropriate candidates for outdoor and indoor events based on weather forecast data and geographic information. Step 3: The schedule suggestion unit automatically collects information about the user's place of residence and various events and proposes an optimal schedule. For example, the schedule suggestion unit collects event information in the user's area and creates a schedule by combining events that match the user's hobbies and preferences. Step 4: The transportation registration unit registers the transportation means and fare for participating in the proposed schedule in the calendar. For example, the transportation registration unit searches for the optimal transportation means to the event venue and its fare, and automatically registers it in the calendar. Step 5: The schedule selection unit allows the user to smoothly select a schedule that suits their interests and preferences from the proposed schedule candidates. For example, the schedule selection unit may present multiple schedule candidates and allow the user to select the schedule that interests them most.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 hobby and preference analysis section that analyzes personal hobby and preference using smartphone search history and other data; An event search section that searches for events taking into account weather and location; A schedule suggestion section that automatically collects information about where you live and various events to suggest the best schedule, and a transportation means registration unit that registers transportation means, fares, etc. for participating in the proposed schedule in a calendar; A schedule selection unit that enables a user to smoothly select a schedule that matches their hobbies and preferences from the proposed schedule candidates is provided. A system characterized by:

2. The hobby and preference analysis unit Estimating the user's emotions and identifying the user's preferences to elicit positive emotions 2. The system of claim 1.

3. The hobby and preference analysis unit The user's past behavior history as well as real-time behavior data are analyzed to dynamically update the user's hobbies and preferences.

2. The system of claim 1.

4. The hobby and preference analysis unit The hobbies and preferences of the user's friends and family are also analyzed, and events with common hobbies are suggested.

2. The system of claim 1.

5. The hobby and preference analysis unit The user's tastes and preferences are analyzed, including the data on videos watched and music listened to by the user.

2. The system of claim 1.

6. The hobby and preference analysis unit Feedback from the events that the user has previously participated in is collected, and the user's interests and preferences are analyzed in more detail based on the feedback.

2. The system of claim 1.

Images