System

Abstract

An object of a system according to an embodiment is to propose specific advice or activity for a user to deepen a relationship with an important person.SOLUTION: A system according to an embodiment includes an advice suggestion unit, a reminder unit, and a message generation unit. The advice suggestion unit suggests advice or an activity for fostering a relationship with an important person in the daily life of the user. The reminder unit reminds of the anniversary. The message generation unit generates a message of gratitude or a surprise idea.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of lacking specific advice and activity suggestions to help users deepen their relationships with their loved ones.

[0005] The system according to the embodiment aims to suggest specific advice and activities that will help the user deepen their relationship with their loved ones. [Means for solving the problem]

[0006] The system according to the embodiment includes an advice suggestion unit, a reminder unit, and a message generation unit. The advice suggestion unit suggests advice or activities to foster relationships with important people in the user's daily life. The reminder unit reminds the user of anniversaries. The message generation unit generates thank-you messages or surprise ideas. [Effects of the Invention]

[0007] The system according to the embodiment can suggest specific advice and activities to help the user deepen their relationship with their loved ones. [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 Life Charger AI system according to an embodiment of the present invention supports people who have experienced regrets over a deceased loved one by helping them transform their current relationships into deeper, more meaningful ones. The system uses generative AI to suggest advice and activities to foster relationships with loved ones in the user's daily life, generating anniversary reminders, thank-you messages, and surprise ideas. This allows the Life Charger AI system to strengthen bonds with family and friends, enabling users to live life to the fullest with their loved ones.

[0029] The Life Charger AI system according to the embodiment includes an advice suggestion unit, a reminder unit, and a message generation unit. The advice suggestion unit suggests advice and activities to foster relationships with important people in the user's daily life. For example, if a user inputs a question such as "How should I spend today with my family?" into the generation AI, the generation AI will suggest specific activities such as "cooking together," "going for a walk," and "writing a thank-you letter." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates advice and activities based on the prompt. The reminder unit reminds the user of anniversaries of important people. For example, if a user inputs an instruction such as "Please remind me so I don't forget my anniversary next week," the generation AI will register the anniversary on a calendar and send a reminder at the appropriate time. This allows the user to remember their important people's anniversaries and make appropriate preparations. The message generation unit generates messages and surprise ideas for the user to express their gratitude to their important people. For example, if a user inputs an instruction such as "Please think of a message of gratitude" into the generation AI, it will generate a message of gratitude such as "Thank you for always supporting me" or "Thanks to you, every day is fun." It will also suggest surprise ideas such as "Prepare a handmade gift" or "Plan a special dinner." In this way, the Life Charger AI system according to the embodiment can deepen relationships with important people in the user's daily life.

[0030] The advice suggestion unit can analyze the user's past behavioral history and suggest the most effective activity. For example, the generation AI analyzes the user's past behavioral history and extracts activities that have generated particularly positive responses. For example, the generation AI can suggest similar activities based on data on past trips and events taken with family. The generation AI analyzes the user's behavioral history and identifies the most effective activity based on past event participation history and behavioral patterns. This makes it possible to suggest optimal activities based on the user's past behavioral history.

[0031] The advice suggestion unit can learn the hobbies or interests of the user's loved ones and suggest activities based on them. For example, the generation AI analyzes the SNS posts and message content of the user's loved ones to identify their hobbies and interests. For example, it can suggest concert tickets to someone who likes music. The generation AI learns the hobbies and interests of the user's loved ones and suggests activities based on them. For example, it can suggest tickets to sporting events to someone who likes sports. This makes it possible to suggest optimal activities based on the hobbies and interests of the user's loved ones.

[0032] The reminder section can automatically scan the user's calendar and automatically register important anniversaries. For example, the generation AI scans the user's calendar and automatically detects and registers past anniversaries and events. For example, it sets the next anniversary based on past birthdays or wedding anniversaries. The generation AI automatically scans the calendar and automatically registers important anniversaries using calendar app integration and OCR technology. This allows the reminder section to automatically scan the user's calendar and automatically register important anniversaries.

[0033] The reminder unit can analyze the user's past anniversary data and suggest the optimal reminder timing. For example, the generation AI analyzes the user's past anniversary data and suggests the optimal reminder timing. For example, the generation AI calculates the optimal reminder timing based on past reminder settings and the user's responses. The generation AI analyzes the anniversary data history and related event information to identify the optimal reminder timing. This makes it possible to suggest the optimal reminder timing based on the user's past anniversary data.

[0034] The message generation unit can analyze the user's past message history and generate a new message based on the most moving message. For example, the message generation unit uses a generation AI to analyze the user's past message history and extract particularly moving messages. For example, a new message can be generated based on thank you messages or surprise messages sent in the past. The generation AI analyzes the content and sending date and time of the message history and generates a new message based on the most moving message. This makes it possible to generate moving messages based on the user's past message history.

[0035] The message generation unit can learn the hobbies or interests of the user's loved ones and generate thank-you messages or surprise ideas based on that. For example, the message generation unit uses a generation AI to analyze the SNS posts and message content of the user's loved ones and identify their hobbies and interests. For example, for someone who likes music, it generates a music-related thank-you message. The generation AI learns the hobbies and interests of the user's loved ones and generates thank-you messages or surprise ideas based on that. For example, for someone who likes sports, it generates a message related to sporting events. This makes it possible to generate thank-you messages and surprise ideas based on the hobbies and interests of the user's loved ones.

[0036] The advice suggestion unit can analyze the SNS posts of the user's loved ones and suggest activities based on the information obtained therefrom. For example, the generation AI in the advice suggestion unit analyzes the SNS posts of the user's loved ones and identifies topics of interest from the content of recent posts. For example, it can suggest the next travel destination based on recent travel photos. The generation AI analyzes the content of SNS posts and suggests activities based on the information obtained therefrom. For example, it can suggest activities based on interest in specific themes or fields. This makes it possible to suggest optimal activities based on the SNS posts of the user's loved ones.

[0037] The advice suggestion unit can analyze the content of past messages from the user's loved ones and suggest activities based on the information obtained from that. For example, the generation AI analyzes the content of past messages between the user and their loved ones and suggests activities based on topics that have generated particularly positive responses. For example, it may suggest visiting a restaurant that has been talked about in the past. The generation AI analyzes the history of message content and suggests activities based on the information obtained from that analysis. For example, it may suggest activities based on interest in a particular theme or field. This makes it possible to suggest optimal activities based on the content of past messages from the user's loved ones.

[0038] The reminder section also links with the calendars of the user's loved ones, allowing for integrated reminders of both anniversaries. For example, the reminder section builds a system in which the generation AI links with the calendars of the user's loved ones, integrating both anniversaries and reminders. For example, the birthdays of all family members can be integrated into one calendar. The generation AI links the calendars using the calendar app's API integration or data synchronization method, integrating anniversaries and reminders. This allows for the calendars of the user and loved ones to be linked, allowing for integrated reminders of anniversaries.

[0039] The reminder section can analyze the SNS posts of the user's loved ones and remind them of anniversaries based on the information obtained from them. For example, the reminder section uses a generation AI to analyze the SNS posts of the user's loved ones and extract information about anniversaries and events to remind them. For example, a reminder can be set based on posts about birthdays or wedding anniversaries. The generation AI analyzes the content of the SNS posts and reminds them of anniversaries based on the information obtained from them. This allows the user to be reminded of anniversaries based on the SNS posts of their loved ones.

[0040] The message generation unit can analyze the SNS posts of the user's loved ones and generate thank-you messages or surprise ideas based on the information obtained therefrom. For example, the message generation unit uses a generation AI to analyze the SNS posts of the user's loved ones and identify topics of interest from the content of recent posts. For example, the message generation unit generates thank-you messages or surprise ideas based on recent travel photos. The generation AI analyzes the content of SNS posts and generates thank-you messages or surprise ideas based on the information obtained therefrom. This makes it possible to generate thank-you messages or surprise ideas based on the SNS posts of the user's loved ones.

[0041] The message generation unit can analyze the content of past messages from the user's loved ones and generate thank-you messages or surprise ideas based on the information obtained from that. For example, the message generation unit uses a generation AI to analyze the content of past messages between the user and loved ones and generate new thank-you messages based on particularly touching messages. For example, it reuses thank-you messages sent in the past. The generation AI analyzes the message content history and generates thank-you messages or surprise ideas based on the information obtained from that. This makes it possible to generate thank-you messages or surprise ideas based on the content of past messages from the user's loved ones.

[0042] The advice suggestion unit can analyze the past activity history of the user's family or friends and suggest the most effective activity. For example, the advice suggestion unit uses a generation AI to analyze the past activity history of the user's family and friends and extract activities that received particularly positive responses. For example, the generation AI can suggest similar activities based on data on past trips and events that the entire family took together. The generation AI analyzes the activity history of family and friends and identifies the most effective activity based on past event participation history and behavioral patterns. This makes it possible to suggest optimal activities based on the past activity history of the user's family and friends.

[0043] The advice suggestion unit can learn the hobbies or interests of the user's family or friends and suggest activities based on that. For example, the generation AI analyzes the content of social media posts and messages from the user's family and friends to identify their hobbies and interests. For example, it can suggest concert tickets to someone who likes music. The generation AI learns the hobbies and interests of family and friends and suggests activities based on that. For example, it can suggest tickets to sporting events to someone who likes sports. This makes it possible to suggest optimal activities based on the hobbies and interests of the user's family and friends.

[0044] The advice suggestion unit can analyze the SNS posts of the user's family and friends and suggest activities based on the information obtained therefrom. For example, the generation AI in the advice suggestion unit analyzes the SNS posts of the user's family and friends and identifies topics of interest from the content of recent posts. For example, it may suggest the next travel destination based on recent travel photos. The generation AI analyzes the content of SNS posts and suggests activities based on the information obtained therefrom. For example, it may suggest activities based on interest in a particular theme or field. This makes it possible to suggest optimal activities based on the SNS posts of the user's family and friends.

[0045] The advice suggestion unit can analyze the content of past messages from the user's family and friends and suggest activities based on the information obtained therefrom. For example, the generation AI analyzes the content of past messages between the user and family and friends and suggests activities based on topics that have generated particularly positive responses. For example, it may suggest visiting a restaurant that has been talked about in the past. The generation AI analyzes the history of message content and suggests activities based on the information obtained therefrom. For example, it suggests activities based on interest in a particular theme or field. This makes it possible to suggest optimal activities based on the content of past messages from the user's family and friends.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The Life Charger AI system can also be equipped with a health management unit. The health management unit monitors the user's health status and provides appropriate health advice. For example, it analyzes the user's sleep patterns and dietary content and suggests healthy lifestyle habits. If the user is feeling stressed, it can also suggest relaxing activities and meals. This can provide support for maintaining the user's health and building better relationships.

[0048] The Life Charger AI system can also be equipped with a learning support unit. The learning support unit analyzes the user's learning history and suggests optimal learning methods and learning materials. For example, it can suggest a new learning plan based on learning methods that have been effective for the user in the past. If the user is interested in a particular field, it can also suggest learning materials and activities related to that field. This can improve the user's learning efficiency and support their personal growth.

[0049] The Life Charger AI system can also be equipped with a hobby discovery unit. The hobby discovery unit analyzes the user's past behavioral history and interests to suggest new hobbies and activities. For example, it can suggest new hobbies based on activities the user has enjoyed in the past. If the user is interested in a particular field, it can also suggest activities and events related to that field. This can provide new enjoyment to the user's life and help them spend their time more fulfillingly.

[0050] The Life Charger AI system can further be equipped with a travel planning unit. The travel planning unit analyzes the user's past travel history and interests to propose optimal travel plans. For example, it can suggest new travel destinations based on places the user has visited in the past and areas in which the user is interested. If the user is interested in a specific theme, it can also suggest travel plans related to that theme. This can enrich the user's travel experience and support a memorable trip.

[0051] The Life Charger AI system can further include a feedback collection unit. The feedback collection unit collects feedback from users and uses it to improve the system. For example, if the user inputs their thoughts on suggested activities or messages, the system can improve the suggestions based on that feedback. If the user has a positive reaction to a particular activity, the system can also suggest similar activities. This allows the system to make optimal suggestions tailored to the user's needs.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The advice suggestion unit suggests advice and activities to foster relationships with important people in the user's daily life. For example, if the user inputs a question such as "How should I spend today with my family?" into the generation AI, the AI ​​will suggest specific activities such as "cook together," "go for a walk," or "write a thank you letter." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates advice and activities based on that prompt. Step 2: The reminder section reminds the user of anniversaries of loved ones. For example, if the user inputs an instruction such as "Please remind me so I don't forget my anniversary next week," the AI ​​will register that anniversary in the calendar and send a reminder at the appropriate time. This allows the user to make appropriate preparations without forgetting the anniversary of their loved one. Step 3: The message generator generates messages and surprise ideas for users to express their gratitude to loved ones. For example, if a user inputs instructions such as "Please think of a message of gratitude" into the AI ​​generator, it will generate messages of gratitude such as "Thank you for always supporting me" or "Thanks to you, every day is fun." It also suggests surprise ideas such as "Prepare a handmade gift" or "Plan a special dinner."

[0054] (Example 2) The Life Charger AI system according to an embodiment of the present invention supports people who have experienced regrets over a deceased loved one by helping them transform their current relationships into deeper, more meaningful ones. The system uses generative AI to suggest advice and activities to foster relationships with loved ones in the user's daily life, generating anniversary reminders, thank-you messages, and surprise ideas. This allows the Life Charger AI system to strengthen bonds with family and friends, enabling users to live life to the fullest with their loved ones.

[0055] The Life Charger AI system according to the embodiment includes an advice suggestion unit, a reminder unit, and a message generation unit. The advice suggestion unit suggests advice and activities to foster relationships with important people in the user's daily life. For example, if a user inputs a question such as "How should I spend today with my family?" into the generation AI, the generation AI will suggest specific activities such as "cooking together," "going for a walk," and "writing a thank-you letter." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates advice and activities based on the prompt. The reminder unit reminds the user of anniversaries of important people. For example, if a user inputs an instruction such as "Please remind me so I don't forget my anniversary next week," the generation AI will register the anniversary on a calendar and send a reminder at the appropriate time. This allows the user to remember their important people's anniversaries and make appropriate preparations. The message generation unit generates messages and surprise ideas for the user to express their gratitude to their important people. For example, if a user inputs an instruction such as "Please think of a message of gratitude" into the generation AI, it will generate a message of gratitude such as "Thank you for always supporting me" or "Thanks to you, every day is fun." It will also suggest surprise ideas such as "Prepare a handmade gift" or "Plan a special dinner." In this way, the Life Charger AI system according to the embodiment can deepen relationships with important people in the user's daily life.

[0056] The advice suggestion unit can analyze the user's past behavioral history and suggest the most effective activity. For example, the generation AI analyzes the user's past behavioral history and extracts activities that have generated particularly positive responses. For example, the generation AI can suggest similar activities based on data on past trips and events taken with family. The generation AI analyzes the user's behavioral history and identifies the most effective activity based on past event participation history and behavioral patterns. This makes it possible to suggest optimal activities based on the user's past behavioral history.

[0057] The advice suggestion unit can learn the hobbies or interests of the user's loved ones and suggest activities based on them. For example, the generation AI analyzes the SNS posts and message content of the user's loved ones to identify their hobbies and interests. For example, it can suggest concert tickets to someone who likes music. The generation AI learns the hobbies and interests of the user's loved ones and suggests activities based on them. For example, it can suggest tickets to sporting events to someone who likes sports. This makes it possible to suggest optimal activities based on the hobbies and interests of the user's loved ones.

[0058] The advice suggestion unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest an activity that is optimal for the user's emotions at that time. For example, the advice suggestion unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest an activity that will help the user relax when stress levels are high. For example, the advice suggestion unit can suggest a meditation or yoga session. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and suggest an activity that is optimal for the user's emotions at that time. This makes it possible to suggest optimal activities based on the user's emotional state.

[0059] The reminder section can automatically scan the user's calendar and automatically register important anniversaries. For example, the generation AI scans the user's calendar and automatically detects and registers past anniversaries and events. For example, it sets the next anniversary based on past birthdays or wedding anniversaries. The generation AI automatically scans the calendar and automatically registers important anniversaries using calendar app integration and OCR technology. This allows the reminder section to automatically scan the user's calendar and automatically register important anniversaries.

[0060] The reminder unit can analyze the user's past anniversary data and suggest the optimal reminder timing. For example, the generation AI analyzes the user's past anniversary data and suggests the optimal reminder timing. For example, the generation AI calculates the optimal reminder timing based on past reminder settings and the user's responses. The generation AI analyzes the anniversary data history and related event information to identify the optimal reminder timing. This makes it possible to suggest the optimal reminder timing based on the user's past anniversary data.

[0061] The reminder unit can use the emotion estimation function to send a reminder based on the user's emotional state. For example, the reminder unit uses the emotion estimation function to analyze the user's emotional state in real time and send a reminder at an appropriate time. For example, the reminder unit sends a reminder when the user is relaxed. The emotion estimation function estimates the user's emotional state using facial expression recognition or voice analysis and sends a reminder based on that emotional state. This allows the optimal reminder to be sent based on the user's emotional state.

[0062] The message generation unit can analyze the user's past message history and generate a new message based on the most moving message. For example, the message generation unit uses a generation AI to analyze the user's past message history and extract particularly moving messages. For example, a new message can be generated based on thank you messages or surprise messages sent in the past. The generation AI analyzes the content and sending date and time of the message history and generates a new message based on the most moving message. This makes it possible to generate moving messages based on the user's past message history.

[0063] The message generation unit can learn the hobbies or interests of the user's loved ones and generate thank-you messages or surprise ideas based on that. For example, the message generation unit uses a generation AI to analyze the SNS posts and message content of the user's loved ones and identify their hobbies and interests. For example, for someone who likes music, it generates a music-related thank-you message. The generation AI learns the hobbies and interests of the user's loved ones and generates thank-you messages or surprise ideas based on that. For example, for someone who likes sports, it generates a message related to sporting events. This makes it possible to generate thank-you messages and surprise ideas based on the hobbies and interests of the user's loved ones.

[0064] The message generation unit can use the emotion estimation function to analyze the user's emotional state in real time and generate a message or surprise idea that is optimal for the user's emotions at that time. For example, the message generation unit can use the emotion estimation function to analyze the user's emotional state in real time and generate a message or surprise idea that will help the user relax when they are under high stress. For example, it can suggest an encouraging message or a relaxing activity. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and generate a message or surprise idea that is optimal for the user's emotions at that time. This makes it possible to generate an optimal message or surprise idea based on the user's emotional state.

[0065] The advice suggestion unit can analyze the SNS posts of the user's loved ones and suggest activities based on the information obtained therefrom. For example, the generation AI in the advice suggestion unit analyzes the SNS posts of the user's loved ones and identifies topics of interest from the content of recent posts. For example, it can suggest the next travel destination based on recent travel photos. The generation AI analyzes the content of SNS posts and suggests activities based on the information obtained therefrom. For example, it can suggest activities based on interest in specific themes or fields. This makes it possible to suggest optimal activities based on the SNS posts of the user's loved ones.

[0066] The advice suggestion unit can analyze the content of past messages from the user's loved ones and suggest activities based on the information obtained from that. For example, the generation AI analyzes the content of past messages between the user and their loved ones and suggests activities based on topics that have generated particularly positive responses. For example, it may suggest visiting a restaurant that has been talked about in the past. The generation AI analyzes the history of message content and suggests activities based on the information obtained from that analysis. For example, it may suggest activities based on interest in a particular theme or field. This makes it possible to suggest optimal activities based on the content of past messages from the user's loved ones.

[0067] The advice suggestion unit can use the emotion estimation function to estimate the emotional state of the user's loved one and suggest an activity that is optimal for that emotion. For example, the advice suggestion unit can use the emotion estimation function to analyze the emotional state of the user's loved one in real time and suggest an activity that will help them relax when they are under a lot of stress. For example, it can suggest a meditation or yoga session. The emotion estimation function uses facial expression recognition and voice analysis to estimate the emotional state of the user's loved one and suggest an activity that is optimal for that emotion. This makes it possible to suggest an optimal activity based on the emotional state of the user's loved one.

[0068] The reminder section also links with the calendars of the user's loved ones, allowing for integrated reminders of both anniversaries. For example, the reminder section builds a system in which the generation AI links with the calendars of the user's loved ones, integrating both anniversaries and reminders. For example, the birthdays of all family members can be integrated into one calendar. The generation AI links the calendars using the calendar app's API integration or data synchronization method, integrating anniversaries and reminders. This allows for the calendars of the user and loved ones to be linked, allowing for integrated reminders of anniversaries.

[0069] The reminder section can analyze the SNS posts of the user's loved ones and remind them of anniversaries based on the information obtained from them. For example, the reminder section uses a generation AI to analyze the SNS posts of the user's loved ones and extract information about anniversaries and events to remind them. For example, a reminder can be set based on posts about birthdays or wedding anniversaries. The generation AI analyzes the content of the SNS posts and reminds them of anniversaries based on the information obtained from them. This allows the user to be reminded of anniversaries based on the SNS posts of their loved ones.

[0070] The reminder unit can use the emotion estimation function to estimate the emotional state of the user's loved one and provide a reminder notification that is optimal for that emotion. For example, the reminder unit can use the emotion estimation function to analyze the emotional state of the user's loved one in real time and provide a reminder notification at an appropriate time. For example, the reminder unit can send a reminder when the loved one is relaxed. The emotion estimation function can estimate the emotional state of the user's loved one using facial expression recognition and voice analysis and provide a reminder notification based on that emotional state. This allows the reminder notification to be optimal based on the emotional state of the user's loved one.

[0071] The message generation unit can analyze the SNS posts of the user's loved ones and generate thank-you messages or surprise ideas based on the information obtained therefrom. For example, the message generation unit uses a generation AI to analyze the SNS posts of the user's loved ones and identify topics of interest from the content of recent posts. For example, the message generation unit generates thank-you messages or surprise ideas based on recent travel photos. The generation AI analyzes the content of SNS posts and generates thank-you messages or surprise ideas based on the information obtained therefrom. This makes it possible to generate thank-you messages or surprise ideas based on the SNS posts of the user's loved ones.

[0072] The message generation unit can analyze the content of past messages from the user's loved ones and generate thank-you messages or surprise ideas based on the information obtained from that. For example, the message generation unit uses a generation AI to analyze the content of past messages between the user and loved ones and generate new thank-you messages based on particularly touching messages. For example, it reuses thank-you messages sent in the past. The generation AI analyzes the message content history and generates thank-you messages or surprise ideas based on the information obtained from that. This makes it possible to generate thank-you messages or surprise ideas based on the content of past messages from the user's loved ones.

[0073] The message generation unit can use the emotion estimation function to estimate the emotional state of the user's loved one and generate a message or surprise idea that is optimal for that emotion. For example, the message generation unit can use the emotion estimation function to analyze the emotional state of the user's loved one in real time and generate a message or surprise idea that will help them relax when they are under stress. For example, it can suggest an encouraging message or a relaxing activity. The emotion estimation function uses facial expression recognition and voice analysis to estimate the emotional state of the user's loved one and generate a message or surprise idea that is optimal for that emotion. This makes it possible to generate an optimal message or surprise idea based on the emotional state of the user's loved one.

[0074] The advice suggestion unit can analyze the past activity history of the user's family or friends and suggest the most effective activity. For example, the advice suggestion unit uses a generation AI to analyze the past activity history of the user's family and friends and extract activities that received particularly positive responses. For example, the generation AI can suggest similar activities based on data on past trips and events that the entire family took together. The generation AI analyzes the activity history of family and friends and identifies the most effective activity based on past event participation history and behavioral patterns. This makes it possible to suggest optimal activities based on the past activity history of the user's family and friends.

[0075] The advice suggestion unit can learn the hobbies or interests of the user's family or friends and suggest activities based on that. For example, the generation AI analyzes the content of social media posts and messages from the user's family and friends to identify their hobbies and interests. For example, it can suggest concert tickets to someone who likes music. The generation AI learns the hobbies and interests of family and friends and suggests activities based on that. For example, it can suggest tickets to sporting events to someone who likes sports. This makes it possible to suggest optimal activities based on the hobbies and interests of the user's family and friends.

[0076] The advice suggestion unit can use the emotion estimation function to analyze the emotional state of the user's family and friends in real time and suggest activities that are optimal for their emotions at that time. For example, the advice suggestion unit can use the emotion estimation function to analyze the emotional state of the user's family and friends in real time and suggest activities that will help them relax when they are under a lot of stress. For example, it can suggest meditation or yoga sessions. The emotion estimation function estimates the emotional state of family and friends using facial expression recognition and voice analysis and suggests activities that are optimal for their emotions at that time. This makes it possible to suggest optimal activities based on the emotional state of the user's family and friends.

[0077] The advice suggestion unit can analyze the SNS posts of the user's family and friends and suggest activities based on the information obtained therefrom. For example, the generation AI in the advice suggestion unit analyzes the SNS posts of the user's family and friends and identifies topics of interest from the content of recent posts. For example, it may suggest the next travel destination based on recent travel photos. The generation AI analyzes the content of SNS posts and suggests activities based on the information obtained therefrom. For example, it may suggest activities based on interest in a particular theme or field. This makes it possible to suggest optimal activities based on the SNS posts of the user's family and friends.

[0078] The advice suggestion unit can analyze the content of past messages from the user's family and friends and suggest activities based on the information obtained therefrom. For example, the generation AI analyzes the content of past messages between the user and family and friends and suggests activities based on topics that have generated particularly positive responses. For example, it may suggest visiting a restaurant that has been talked about in the past. The generation AI analyzes the history of message content and suggests activities based on the information obtained therefrom. For example, it suggests activities based on interest in a particular theme or field. This makes it possible to suggest optimal activities based on the content of past messages from the user's family and friends.

[0079] The advice suggestion unit can use the emotion estimation function to estimate the emotional state of the user's family and friends and suggest activities that are optimal for those emotions. For example, the advice suggestion unit can use the emotion estimation function to analyze the emotional state of the user's family and friends in real time and suggest activities that will help them relax when they are under a lot of stress. For example, the advice suggestion unit can suggest meditation or yoga sessions. The emotion estimation function uses facial expression recognition and voice analysis to estimate the emotional state of family and friends and suggest activities that are optimal for those emotions. This makes it possible to suggest optimal activities based on the emotional state of the user's family and friends.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The Life Charger AI system can also be equipped with a health management unit. The health management unit monitors the user's health status and provides appropriate health advice. For example, it analyzes the user's sleep patterns and dietary content and suggests healthy lifestyle habits. If the user is feeling stressed, it can also suggest relaxing activities and meals. This can provide support for maintaining the user's health and building better relationships.

[0082] The Life Charger AI system can also be equipped with a learning support unit. The learning support unit analyzes the user's learning history and suggests optimal learning methods and learning materials. For example, it can suggest a new learning plan based on learning methods that have been effective for the user in the past. If the user is interested in a particular field, it can also suggest learning materials and activities related to that field. This can improve the user's learning efficiency and support their personal growth.

[0083] The Life Charger AI system can also be equipped with a hobby discovery unit. The hobby discovery unit analyzes the user's past behavioral history and interests to suggest new hobbies and activities. For example, it can suggest new hobbies based on activities the user has enjoyed in the past. If the user is interested in a particular field, it can also suggest activities and events related to that field. This can provide new enjoyment to the user's life and help them spend their time more fulfillingly.

[0084] The Life Charger AI system can further be equipped with a travel planning unit. The travel planning unit analyzes the user's past travel history and interests to propose optimal travel plans. For example, it can suggest new travel destinations based on places the user has visited in the past and areas in which the user is interested. If the user is interested in a specific theme, it can also suggest travel plans related to that theme. This can enrich the user's travel experience and support a memorable trip.

[0085] The Life Charger AI system can further include a feedback collection unit. The feedback collection unit collects feedback from users and uses it to improve the system. For example, if the user inputs their thoughts on suggested activities or messages, the system can improve the suggestions based on that feedback. If the user has a positive reaction to a particular activity, the system can also suggest similar activities. This allows the system to make optimal suggestions tailored to the user's needs.

[0086] The Life Charger AI system also uses an emotion estimation function to analyze the user's emotional state in real time and suggest music that best suits their emotions at that time. For example, when a user wants to relax, it suggests relaxing music. When a user wants to cheer up, it suggests energetic music. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and suggests music that best suits their emotions at that time. This allows it to provide the best music based on the user's emotional state.

[0087] The Life Charger AI system also uses its emotion estimation function to analyze the user's emotional state in real time and suggest movies and dramas that best suit their emotions at that time. For example, when a user wants to relax, it will suggest relaxing movies and dramas. When a user wants to cheer up, it will suggest energetic movies and dramas. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and suggest movies and dramas that best suit their emotions at that time. This allows it to provide the optimal entertainment based on the user's emotional state.

[0088] The Life Charger AI system also uses its emotion estimation function to analyze the user's emotional state in real time and suggest a reading list that best suits their emotions at that time. For example, when a user wants to relax, it will suggest relaxing books. When a user wants to cheer up, it will suggest energetic books. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and suggest a reading list that best suits their emotions at that time. This allows the system to provide an optimal reading experience based on the user's emotional state.

[0089] The Life Charger AI system also uses its emotion estimation function to analyze the user's emotional state in real time and suggest an exercise plan that is optimal for that emotion at that time. For example, when a user wants to relax, it suggests relaxing yoga or stretching. When a user wants to cheer up, it suggests energetic running or dancing. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and suggest an exercise plan that is optimal for that emotion at that time. This allows the system to provide an optimal exercise experience based on the user's emotional state.

[0090] The Life Charger AI system also uses its emotion estimation function to analyze the user's emotional state in real time and suggest a meal plan that is optimal for that emotion at that time. For example, when a user wants to relax, it will suggest a relaxing meal. When a user wants to feel energized, it will suggest an energizing meal. The emotion estimation function uses facial expression recognition and voice analysis to estimate the user's emotional state and suggest a meal plan that is optimal for that emotion at that time. This allows the system to provide the optimal dining experience based on the user's emotional state.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The advice suggestion unit suggests advice and activities to foster relationships with important people in the user's daily life. For example, if the user inputs a question such as "How should I spend today with my family?" into the generation AI, the AI ​​will suggest specific activities such as "cook together," "go for a walk," or "write a thank you letter." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates advice and activities based on that prompt. Step 2: The reminder section reminds the user of anniversaries of loved ones. For example, if the user inputs an instruction such as "Please remind me so I don't forget my anniversary next week," the AI ​​will register that anniversary in the calendar and send a reminder at the appropriate time. This allows the user to make appropriate preparations without forgetting the anniversary of their loved one. Step 3: The message generator generates messages and surprise ideas for users to express their gratitude to loved ones. For example, if a user inputs instructions such as "Please think of a message of gratitude" into the AI ​​generator, it will generate messages of gratitude such as "Thank you for always supporting me" or "Thanks to you, every day is fun." It also suggests surprise ideas such as "Prepare a handmade gift" or "Plan a special dinner."

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an advice suggestion unit that suggests advice or activities for fostering relationships with important people in the user's daily life; A reminder section to remind you of anniversaries, A message generation unit that generates thank-you messages and surprise ideas. A system characterized by:

2. The advice suggestion unit Analyze the user's past behavior history and suggest the most effective activity 2. The system of claim 1.

3. The reminder unit Automatically scanning the user's calendar and automatically registering important anniversaries.

2. The system of claim 1.

4. The message generation unit Analyzing the user's past message history and generating new messages based on the most inspiring messages 2. The system of claim 1.

5. The advice suggestion unit Analyzing the SNS posts of the user's loved ones and suggesting the activity based on information obtained therefrom.

2. The system of claim 1.

6. The reminder unit Using emotion estimation function, a reminder notification is given based on the user's emotional state.

2. The system of claim 1.

7. The message generation unit Using an emotion estimation function, the emotional state of the user is analyzed in real time, and the message or surprise idea that best suits the emotion at that time is generated.

2. The system of claim 1.

8. The advice suggestion unit Using an emotion estimation function, the emotional state of the user's family and friends is analyzed in real time, and the activity that best suits the user's emotions at that time is suggested.

2. The system of claim 1.

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