System

Abstract

An object of a system according to an embodiment is to efficiently realize that a user sends a message to the user in the future.SOLUTION: A system includes a message creation unit, a storage unit, and a distribution unit. The message creation unit creates a message of the user. The storage unit stores the message created by the message creation unit. The distribution unit distributes the message stored by the storage unit on a designated future date.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have limited the means by which users can send messages to their future selves, making it difficult to implement efficiently.

[0005] The system according to the embodiment aims to enable a user to efficiently send a message to his or her future self. [Means for solving the problem]

[0006] The system according to the embodiment includes a message creation unit, a storage unit, and a distribution unit. The message creation unit creates a message for a user. The storage unit stores the message created by the message creation unit. The distribution unit distributes the message stored by the storage unit on a specified future date. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently enable a user to send a message to his or her future self. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The time capsule service according to an embodiment of the present invention is a system in which a generation AI saves letters, video messages, goals, and dreams written by users and delivers them to the user on a specified future date. This allows the user to send messages of encouragement and support to their future selves.

[0029] The time capsule service according to the embodiment includes a message creation unit, a storage unit, and a distribution unit. The message creation unit creates a user's message. For example, the user can create a letter or a video message. The message creation unit can also record the user's goals and dreams. The storage unit saves the message created by the message creation unit. For example, the generation AI saves the letter or video message created by the user in a database. The storage unit can also save the message in the cloud. The distribution unit distributes the message saved by the storage unit at a specified future date. For example, the generation AI distributes the message via email, a messenger app, or a dedicated application at a future date specified by the user. The distribution unit can also distribute a video message at a future date specified by the user. This allows the time capsule service according to the embodiment to send messages of encouragement and support to their future selves. For example, by recording their current feelings and goals and sending encouragement to their future selves, users can maintain motivation and work hard toward achieving their goals. Recording important thoughts they don't want to forget also allows users to look back on memories that are important to their future selves.

[0030] The message creation unit can analyze the user's past messages and behavioral history and suggest message content for their future self. For example, the message creation unit uses a generation AI to analyze the user's past messages and behavioral history and suggest encouraging messages for their future self based on the user's past goals and experiences. For example, it can provide specific advice for their future self based on successful projects and goals they have achieved in the past. This makes it possible to suggest messages for their future self based on the user's past behavioral history.

[0031] The message creation unit can reference the user's past SNS posts and blog articles and automatically incorporate related content. For example, the message creation unit uses a generation AI to analyze the user's past SNS posts and automatically incorporate related content. For example, it can extract information about the user's goals and dreams from past posts and reflect it in the message. This allows messages to be created based on the user's past SNS posts and blog articles.

[0032] The storage unit can record and manage the user's goals and dreams. For example, the generation AI analyzes the user's past goal achievement history and suggests goals that are highly achievable. For example, it suggests goals that are easy for the user to achieve based on the patterns of goals achieved in the past. This allows the user to record and manage their goals and dreams.

[0033] The storage unit can analyze the user's past goal achievement history and suggest goals that are highly achievable. For example, when setting a goal, the storage unit uses the generation AI to analyze the user's current skills and resources and provide a specific achievement plan. For example, the storage unit suggests achievable steps based on the user's skill set and available resources. This makes it possible to suggest goals that are highly achievable based on the user's past goal achievement history.

[0034] The storage unit can analyze the user's current skills and resources and provide a specific goal-setting plan. For example, the storage unit can use an emotion estimation function to analyze the user's emotions in real time when setting goals and provide specific goal-setting advice to elicit positive emotions. For example, if the user is feeling anxious, the storage unit can suggest ways to relax. This allows the storage unit to provide a specific goal-setting plan based on the user's current skills and resources.

[0035] The storage unit can monitor the user's progress toward achieving their goals in real time and provide advice as needed. For example, when setting a goal, the generation AI analyzes the user's past failures and successes and suggests the optimal goal setting method. For example, it can suggest realistic goal setting based on lessons learned from past failures. This makes it possible to monitor the user's progress toward achieving their goals in real time and provide advice as needed.

[0036] The storage unit can refer to the user's past failures and success stories and suggest the optimal goal setting method. For example, the storage unit uses an emotion estimation function to analyze the user's emotions in real time when setting goals and provide reminders and motivational messages based on the emotions. For example, if the user is feeling anxious, it provides a message to help them relax. This makes it possible to suggest the optimal goal setting method based on the user's past failures and success stories.

[0037] The delivery unit can analyze the user's past message reception history and suggest the optimal delivery timing. For example, the generation AI analyzes the user's past message reception history and suggests the optimal delivery timing. For example, it suggests the timing that is most convenient for the user to receive messages based on the time of day or day of the week when messages were received in the past. This makes it possible to suggest the optimal delivery timing based on the user's past message reception history.

[0038] The delivery unit can analyze the user's current situation and environment and select the optimal delivery method. For example, when delivering a message, the generation AI analyzes the user's current situation and environment in real time and selects the optimal delivery method. For example, if the user is on the move, it selects a voice message. This allows the optimal delivery method to be selected based on the user's current situation and environment.

[0039] The delivery unit can refer to the user's current schedule and activity status and select the optimal delivery timing. For example, when delivering a message, the generation AI analyzes the user's current schedule in real time and selects the optimal delivery timing. For example, the message is delivered during a time when the user is not in a meeting. This makes it possible to select the optimal delivery timing based on the user's current schedule and activity status.

[0040] The delivery unit can refer to the user's past message reception history and simultaneously deliver related messages. For example, when delivering a message, the generation AI analyzes the user's past message reception history and simultaneously delivers related messages. For example, a new message can be delivered together with a previously received encouraging message. This allows related messages to be simultaneously delivered based on the user's past message reception history.

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

[0042] The time capsule service can also collect the user's health data and provide messages based on their health status. For example, it can analyze data collected from the user's fitness tracker or smartwatch to generate encouraging messages based on their health status. If the user is not exercising enough, it can provide a message encouraging them to exercise, and conversely, if they are exercising too much, it can send a message recommending rest. It can also provide advice on how to get a good night's sleep based on the user's sleep data. This allows it to provide personalized messages based on the user's health status.

[0043] The time capsule service can also provide messages based on the user's hobbies and interests. For example, it can analyze data on events the user has attended or products they have purchased in the past to generate relevant messages. If the user is a music lover, it can provide messages containing information on new album releases and concerts. If the user likes to travel, it can also send messages containing suggestions for the next travel destination or travel advice. This makes it possible to provide personalized messages based on the user's hobbies and interests.

[0044] The time capsule service can also obtain the user's past travel records and suggest the next travel destination. For example, it can analyze data on places and accommodations the user has visited in the past and suggest recommended travel destinations for the user's next visit. If the user likes nature, it can suggest tourist spots rich in nature, and if the user prefers cultural experiences, it can provide a travel plan that includes historical sites. It can also suggest travel plans that suit the user's budget and schedule. This makes it possible to make personalized travel suggestions based on the user's past travel records.

[0045] The time capsule service can also obtain a user's reading history and suggest the next book they should read. For example, it can analyze data on books the user has read in the past and suggest new books in related genres or authors. If a user likes mystery novels, it can suggest the latest mystery novels, and if a user likes self-help books, it can provide related new books. It can also suggest reading plans based on the user's reading pace and preferences. This makes it possible to make personalized reading suggestions based on the user's reading history.

[0046] The time capsule service can also obtain a user's past learning history and suggest what they should study next. For example, it can analyze data on online courses the user has taken and academic books they have read in the past to suggest new related learning resources. If the user is learning programming, it can suggest the programming language or framework they should learn next, or if they want to improve their business skills, it can provide them with related online courses. It can also suggest learning plans based on the user's learning pace and goals. This makes it possible to make personalized learning suggestions based on the user's learning history.

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

[0048] Step 1: The message creation unit creates a user's message. For example, the user can create a letter or a video message. The message creation unit also allows the user to record their goals and dreams. Step 2: The storage unit stores the message created by the message creation unit. For example, the generation AI stores letters or video messages created by users in a database. The storage unit can also store messages in the cloud. Step 3: The distribution unit distributes the message stored by the storage unit on a specified future date. For example, the generation AI distributes the message via email, a messenger app, or a dedicated application on a future date specified by the user. The distribution unit can also distribute a video message on a future date specified by the user.

[0049] (Example 2) The time capsule service according to an embodiment of the present invention is a system in which a generation AI saves letters, video messages, goals, and dreams written by users and delivers them to the user on a specified future date. This allows the user to send messages of encouragement and support to their future selves.

[0050] The time capsule service according to the embodiment includes a message creation unit, a storage unit, and a distribution unit. The message creation unit creates a user's message. For example, the user can create a letter or a video message. The message creation unit can also record the user's goals and dreams. The storage unit saves the message created by the message creation unit. For example, the generation AI saves the letter or video message created by the user in a database. The storage unit can also save the message in the cloud. The distribution unit distributes the message saved by the storage unit at a specified future date. For example, the generation AI distributes the message via email, a messenger app, or a dedicated application at a future date specified by the user. The distribution unit can also distribute a video message at a future date specified by the user. This allows the time capsule service according to the embodiment to send messages of encouragement and support to their future selves. For example, by recording their current feelings and goals and sending encouragement to their future selves, users can maintain motivation and work hard toward achieving their goals. Recording important thoughts they don't want to forget also allows users to look back on memories that are important to their future selves.

[0051] The message creation unit can analyze the user's past messages and behavioral history and suggest message content for their future self. For example, the message creation unit uses a generation AI to analyze the user's past messages and behavioral history and suggest encouraging messages for their future self based on the user's past goals and experiences. For example, it can provide specific advice for their future self based on successful projects and goals they have achieved in the past. This makes it possible to suggest messages for their future self based on the user's past behavioral history.

[0052] The message creation unit can analyze the user's emotional state in real time and automatically generate words of encouragement or messages of support that correspond to the user's emotions. For example, the message creation unit uses a generation AI to analyze the user's facial expressions and voice tone in real time to estimate the user's emotional state. For example, if the user is feeling down, it can automatically generate words of encouragement and provide a positive message. This makes it possible to automatically generate messages that correspond to the user's emotional state.

[0053] The message creation unit can analyze the user's emotions and provide advice to elicit positive emotions. For example, the message creation unit uses an emotion estimation function to analyze the emotions of the user when creating a message in real time and provide specific advice to elicit positive emotions. For example, if the user is feeling anxious, the message creation unit can suggest ways to relax. This makes it possible to provide advice to elicit positive emotions from the user.

[0054] The message creation unit can analyze the user's tone of voice and facial expression to suggest the most appropriate message content. For example, the message creation unit uses a generation AI to analyze the user's tone of voice in real time and estimate the user's emotional state. For example, if the user is feeling down, the unit can automatically generate words of encouragement and provide a positive message. This allows the system to suggest the most appropriate message content based on the user's tone of voice and facial expression.

[0055] The message creation unit can reference the user's past SNS posts and blog articles and automatically incorporate related content. For example, the message creation unit uses a generation AI to analyze the user's past SNS posts and automatically incorporate related content. For example, it can extract information about the user's goals and dreams from past posts and reflect it in the message. This allows messages to be created based on the user's past SNS posts and blog articles.

[0056] The message creation unit can analyze the user's emotions and provide a message template based on the emotions. For example, the message creation unit uses an emotion estimation function to analyze the emotions of the user when creating a message in real time and provide a message template based on the emotions. For example, if the user is feeling down, the message creation unit suggests a template including words of encouragement. This makes it possible to provide a message template based on the user's emotions.

[0057] The storage unit can record and manage the user's goals and dreams. For example, the generation AI analyzes the user's past goal achievement history and suggests goals that are highly achievable. For example, it suggests goals that are easy for the user to achieve based on the patterns of goals achieved in the past. This allows the user to record and manage their goals and dreams.

[0058] The storage unit can analyze the user's past goal achievement history and suggest goals that are highly achievable. For example, when setting a goal, the storage unit uses the generation AI to analyze the user's current skills and resources and provide a specific achievement plan. For example, the storage unit suggests achievable steps based on the user's skill set and available resources. This makes it possible to suggest goals that are highly achievable based on the user's past goal achievement history.

[0059] The storage unit can analyze the user's current skills and resources and provide a specific goal-setting plan. For example, the storage unit can use an emotion estimation function to analyze the user's emotions in real time when setting goals and provide specific goal-setting advice to elicit positive emotions. For example, if the user is feeling anxious, the storage unit can suggest ways to relax. This allows the storage unit to provide a specific goal-setting plan based on the user's current skills and resources.

[0060] The storage unit can analyze the user's emotions and provide goal-setting advice to elicit positive emotions. For example, the storage unit allows the generation AI to monitor the user's progress toward achieving their goals in real time and provide specific advice as needed. For example, if progress is lagging, the storage unit can suggest efficient methods. This makes it possible to provide goal-setting advice to elicit positive emotions from the user.

[0061] The storage unit can monitor the user's progress toward achieving their goals in real time and provide advice as needed. For example, when setting a goal, the generation AI analyzes the user's past failures and successes and suggests the optimal goal setting method. For example, it can suggest realistic goal setting based on lessons learned from past failures. This makes it possible to monitor the user's progress toward achieving their goals in real time and provide advice as needed.

[0062] The storage unit can refer to the user's past failures and success stories and suggest the optimal goal setting method. For example, the storage unit uses an emotion estimation function to analyze the user's emotions in real time when setting goals and provide reminders and motivational messages based on the emotions. For example, if the user is feeling anxious, it provides a message to help them relax. This makes it possible to suggest the optimal goal setting method based on the user's past failures and success stories.

[0063] The storage unit can analyze the user's emotions and provide reminders and motivational messages based on the emotions. For example, the storage unit uses an emotion estimation function to analyze the emotions of the user when setting goals in real time and provide reminders and motivational messages based on the emotions. For example, if the user is feeling anxious, a message to relax is provided. In this way, reminders and motivational messages based on the user's emotions can be provided.

[0064] The delivery unit can analyze the user's past message reception history and suggest the optimal delivery timing. For example, the generation AI analyzes the user's past message reception history and suggests the optimal delivery timing. For example, it suggests the timing that is most convenient for the user to receive messages based on the time of day or day of the week when messages were received in the past. This makes it possible to suggest the optimal delivery timing based on the user's past message reception history.

[0065] The delivery unit can analyze the user's current situation and environment and select the optimal delivery method. For example, when delivering a message, the generation AI analyzes the user's current situation and environment in real time and selects the optimal delivery method. For example, if the user is on the move, it selects a voice message. This allows the optimal delivery method to be selected based on the user's current situation and environment.

[0066] The delivery unit can analyze the user's emotions and suggest delivery timing that will elicit positive emotions. For example, the delivery unit uses an emotion estimation function to analyze the user's emotions when receiving a message in real time and suggest the optimal delivery timing that will elicit positive emotions. For example, the delivery unit delivers a message during a time period when the user is relaxed. This makes it possible to suggest the optimal delivery timing based on the user's emotions.

[0067] The delivery unit can refer to the user's current schedule and activity status and select the optimal delivery timing. For example, when delivering a message, the generation AI analyzes the user's current schedule in real time and selects the optimal delivery timing. For example, the message is delivered during a time when the user is not in a meeting. This makes it possible to select the optimal delivery timing based on the user's current schedule and activity status.

[0068] The delivery unit can refer to the user's past message reception history and simultaneously deliver related messages. For example, when delivering a message, the generation AI analyzes the user's past message reception history and simultaneously delivers related messages. For example, a new message can be delivered together with a previously received encouraging message. This allows related messages to be simultaneously delivered based on the user's past message reception history.

[0069] The delivery unit can analyze the user's emotions and propose a delivery method based on the emotions. For example, the delivery unit uses an emotion estimation function to analyze the user's emotions when receiving a message in real time and propose an optimal delivery method based on the emotions. For example, a video message is delivered during a time when the user is relaxing. This makes it possible to propose an optimal delivery method based on the user's emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The time capsule service can also collect the user's health data and provide messages based on their health status. For example, it can analyze data collected from the user's fitness tracker or smartwatch to generate encouraging messages based on their health status. If the user is not exercising enough, it can provide a message encouraging them to exercise, and conversely, if they are exercising too much, it can send a message recommending rest. It can also provide advice on how to get a good night's sleep based on the user's sleep data. This allows it to provide personalized messages based on the user's health status.

[0072] The time capsule service can also provide messages based on the user's hobbies and interests. For example, it can analyze data on events the user has attended or products they have purchased in the past to generate relevant messages. If the user is a music lover, it can provide messages containing information on new album releases and concerts. If the user likes to travel, it can also send messages containing suggestions for the next travel destination or travel advice. This makes it possible to provide personalized messages based on the user's hobbies and interests.

[0073] The time capsule service can also estimate the user's emotions and provide messages to reduce the user's stress level based on the estimated emotions. For example, if the user is feeling stressed, a message suggesting relaxation methods or activities for relieving stress can be generated. If the user is relaxed, advice on how to maintain that state can be provided. Also, if the user is emotionally unstable, a message urging the user to seek professional advice can be sent. This makes it possible to provide messages for stress management that correspond to the user's emotional state.

[0074] The time capsule service can also estimate the user's emotions and provide messages to motivate the user based on the estimated emotions. For example, if the user is feeling unmotivated, it can generate a message that reflects on past successes or suggests specific steps to achieve a goal. If the user is highly motivated, it can provide an encouraging message to help maintain their momentum. It can also send a message containing advice for success when the user starts a new challenge. This allows it to provide messages to motivate the user based on their emotional state.

[0075] The time capsule service can also estimate the user's emotions and provide messages to increase the user's self-esteem based on the estimated emotions. For example, if the user feels that they have low self-esteem, it can generate a message that reflects on past achievements and positive feedback from others. If the user wants to increase their self-esteem, it can provide specific advice for self-improvement. Furthermore, if the user maintains a high level of self-esteem, it can also send an encouraging message to help them maintain that state. This makes it possible to provide messages to increase self-esteem that correspond to the user's emotional state.

[0076] The time capsule service can also obtain the user's past travel records and suggest the next travel destination. For example, it can analyze data on places and accommodations the user has visited in the past and suggest recommended travel destinations for the user's next visit. If the user likes nature, it can suggest tourist spots rich in nature, and if the user prefers cultural experiences, it can provide a travel plan that includes historical sites. It can also suggest travel plans that suit the user's budget and schedule. This makes it possible to make personalized travel suggestions based on the user's past travel records.

[0077] The time capsule service can also obtain a user's reading history and suggest the next book they should read. For example, it can analyze data on books the user has read in the past and suggest new books in related genres or authors. If a user likes mystery novels, it can suggest the latest mystery novels, and if a user likes self-help books, it can provide related new books. It can also suggest reading plans based on the user's reading pace and preferences. This makes it possible to make personalized reading suggestions based on the user's reading history.

[0078] The time capsule service can also estimate a user's emotions and provide messages to stimulate their creativity based on the estimated emotions. For example, if a user shows interest in creative activities, it can generate messages offering hints and inspiration for generating new ideas. If a user is facing a creative block, it can suggest activities to refresh themselves. Furthermore, if a user is working on a creative project, it can send messages offering specific advice and resources. In this way, it is possible to provide messages to improve creativity according to the user's emotional state.

[0079] The time capsule service can also obtain a user's past learning history and suggest what they should study next. For example, it can analyze data on online courses the user has taken and academic books they have read in the past to suggest new related learning resources. If the user is learning programming, it can suggest the programming language or framework they should learn next, or if they want to improve their business skills, it can provide them with related online courses. It can also suggest learning plans based on the user's learning pace and goals. This makes it possible to make personalized learning suggestions based on the user's learning history.

[0080] The time capsule service can also estimate a user's emotions and provide messages to improve the user's relationships based on the estimated emotions. For example, if a user is having trouble with their relationships, a message offering advice on improving communication skills can be generated. If a user wants to strengthen their relationships with friends or family, specific activities or events can be suggested. Furthermore, when a user is building new relationships, a message including effective communication methods for people they meet for the first time can be sent. This allows the service to provide messages for improving relationships that correspond to the user's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The message creation unit creates a user's message. For example, the user can create a letter or a video message. The message creation unit also allows the user to record their goals and dreams. Step 2: The storage unit stores the message created by the message creation unit. For example, the generation AI stores letters or video messages created by users in a database. The storage unit can also store messages in the cloud. Step 3: The distribution unit distributes the message stored by the storage unit on a specified future date. For example, the generation AI distributes the message via email, a messenger app, or a dedicated application on a future date specified by the user. The distribution unit can also distribute a video message on a future date specified by the user.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 message creation unit that creates a message for a user; a storage unit for storing the message created by the message creation unit; a delivery unit that delivers the message stored by the storage unit on a specified future date. A system characterized by:

2. The message creation unit Analyze the user's emotional state in real time and automatically generate words of encouragement or messages of support according to the user's emotions.

2. The system of claim 1.

3. The message creation unit Refer to the user's past social media posts and blog posts and automatically import related content 2. The system of claim 1.

4. The storage unit Record and manage the user's goals and dreams 2. The system of claim 1.

5. The distribution unit Analyze the user's past message reception history and suggest the optimal delivery timing 2. The system of claim 1.

6. The storage unit Analyze the user's emotions and provide goal setting advice to elicit positive emotions 2. The system of claim 1.

7. The distribution unit Analyze the user's emotions and suggest delivery timing to elicit positive emotions 2. The system of claim 1.

8. The distribution unit Analyze the user's emotions and propose a delivery method based on those emotions 2. The system of claim 1.

Images