System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring personal data of a user; means for analyzing the acquired personal data; means for generating a personalized prompt based on an analysis result; means for transmitting the generated prompt to a generation-related AI and acquiring an answer; and means for presenting the acquired answer to the user.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] Current AI systems have difficulty understanding the situation and context of the situation and responding appropriately, and they cannot obtain appropriate answers unless detailed information is individually input. This also requires users to have knowledge of prompt engineering, making AI difficult for anyone to use. For this reason, there is a need to develop a system that can personalize AI responses in a user-friendly manner, allowing users to obtain appropriate services and answers without having to input detailed information one by one. [Means for solving the problem]

[0005] The present invention relates to a system that acquires and analyzes a user's personal data to generate personalized prompts tailored to the user's context, and then sends the prompts to a generative AI to obtain appropriate answers. The system is equipped with a means for acquiring the user's personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app, and analyzes the data to identify the user's behavioral patterns and trends and generate optimal prompts. Furthermore, by working with multiple generative AIs, the system selects and presents optimal answers to the user, allowing the user to easily receive personalized services.

[0006] "User personal data" is information relating to an individual, such as a user's behavior, health status, purchasing history, schedule, and location information.

[0007] A "smartwatch" is a device that measures and records a user's health status, exercise history, etc.

[0008] An "electronic payment app" is application software that allows users to make cashless payments.

[0009] A "calendar app" is application software for managing a user's schedules and events.

[0010] A "map app" is application software that records a user's location information and movement history.

[0011] "Generative AI" is an artificial intelligence system that generates answers or information based on prompts from the user.

[0012] A "prompt" is input information required for processing that is sent to the generative AI.

[0013] "Analysis" is a method of processing acquired data to extract useful information.

[0014] "Personalization" means customizing information to suit a user's individual needs and circumstances.

[0015] An "answer" is information or advice that a generative AI generates in response to a user's prompt. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] System Overview

[0038] The system of the present invention acquires and analyzes a user's personal data, sends optimal prompts to a generation AI based on that data, and obtains and presents personalized answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. Next, it analyzes that data and generates optimal prompts for the user. Finally, it sends the generated prompts to the generation AI, which then presents the answers to the user.

[0039] Program processing overview

[0040] 1. Data Collection

[0041] Users link their smartwatch, electronic payment app, calendar app, and map app to the system.

[0042] The terminal periodically retrieves data from the linked data sources.

[0043] The terminal temporarily stores the acquired data and transmits it to the server at a fixed timing.

[0044] 2. Data Analysis

[0045] The server analyzes the data received from each data source.

[0046] The server analyzes the user's health condition and exercise patterns based on data from the smartwatch.

[0047] The server analyzes users' purchasing patterns based on data from the electronic payment app.

[0048] The server analyzes the user's schedule based on data from the calendar app.

[0049] The server analyzes the user's movement history based on data from the map app.

[0050] 3. Prompt Generation

[0051] The server generates the most appropriate prompt based on the analyzed data in response to the user's question or request.

[0052] The prompt reflects the user's health condition, purchase history, schedule, travel history, etc.

[0053] 4. AI Inquiry

[0054] Users enter questions and requests in natural language.

[0055] The terminal transmits the user's input to the server.

[0056] The server combines the user's input with the parsed data to construct the optimal prompt.

[0057] The server sends this constructed prompt to multiple generating AIs.

[0058] The server receives and evaluates the answers from each generated AI.

[0059] 5. Obtaining and Presenting Answers

[0060] The server evaluates the received answers and selects the most appropriate answer.

[0061] The server selects the most suitable answer for the user and sends it to the terminal.

[0062] The terminal displays the answer to the user.

[0063] Specific examples

[0064] For example, the following shows the process when a user inputs the question "What's on the schedule tomorrow?" into the system.

[0065] 1. Data Collection

[0066] Users link their everyday smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[0067] The device obtains the user's exercise history from the smartwatch, purchase history from the electronic payment app, schedule from the calendar app, and movement history from the map app.

[0068] The secured data is periodically sent to the server.

[0069] 2. Data Analysis

[0070] The server analyzes the data from the smartwatch to identify health information such as exercise history and heart rate.

[0071] The server analyzes data from the electronic payment app to identify recent purchasing behavior.

[0072] The server parses the data from the calendar app to determine the next day's schedule.

[0073] The server analyzes data from the map app to identify movement patterns.

[0074] 3. Prompt Generation

[0075] The server generates appropriate prompts for user input based on analyzed health information, purchasing behavior, schedules, and travel patterns.

[0076] "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[0077] 4. AI Inquiry

[0078] The user types the question, "What's on the schedule tomorrow?"

[0079] The terminal sends this input to the server.

[0080] The server sends the question along with the generated prompt to multiple generating AIs.

[0081] 5. Obtaining and Presenting Answers

[0082] The server evaluates the answers received from each generation AI and selects the best answer.

[0083] The server transmits the selected response to the terminal.

[0084] The terminal displays the answer to the user, "I have a meeting tomorrow at 10 o'clock, after which I plan to travel from the office to my home."

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, and once the link is established, personal information from these data sources can be collected.

[0088] Step 2:

[0089] The device periodically retrieves data from linked data sources: health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and location information and movement history from a map app.

[0090] Step 3:

[0091] The device temporarily stores the collected data locally, and the stored data is sent to the server at regular intervals.

[0092] Step 4:

[0093] The server receives the data sent from the terminal and stores the received data in a database.

[0094] Step 5:

[0095] The server analyzes the information received from each data source: Analyzing data from a smartwatch to identify the user's health and exercise patterns Analyzing data from an electronic payment app to identify purchasing patterns and spending trends Analyzing data from a calendar app to identify the user's schedule Analyzing data from a map app to identify movement history and places visited

[0096] Step 6:

[0097] The server uses the analyzed data to generate personalized prompts based on the user's questions and requests, including specific information such as the user's health status, purchase history, schedule, and travel history.

[0098] Step 7:

[0099] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[0100] Step 8:

[0101] The terminal sends the user's input to the server, where it is processed.

[0102] Step 9:

[0103] The server combines the user's input with previously generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[0104] Step 10:

[0105] The server collects the answers received from the AI ​​generators. The answers from each AI generator are evaluated by the server.

[0106] Step 11:

[0107] The server evaluates the collected answers and selects the most appropriate answer, which is then stored back in the database.

[0108] Step 12:

[0109] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[0110] Example 1

[0111] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0112] Conventional systems have had difficulty efficiently collecting and analyzing users' personal data and providing personalized answers. Furthermore, mechanisms for effectively integrating data collected from multiple data sources and quickly generating optimal answers to users' questions and requests have not been fully established. Furthermore, the process of selecting the optimal answer using multiple generative AI systems is complicated, making it impossible to provide the most appropriate information to users.

[0113] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0114] In this invention, the server includes means for acquiring personal data of the user, means for periodically transmitting the acquired personal data to the server, means for analyzing the received data and identifying user behavior and patterns, means for generating personalized prompts based on the analysis results in response to the user's questions and requests, means for transmitting the generated prompts to multiple generative AIs, acquiring and evaluating answers, and means for presenting the optimal answer to the user. This makes it possible to integrate information acquired from the user's various data sources and provide personalized answers efficiently and quickly.

[0115] "User personal data" refers to data generated by a user that can identify or identify an individual, such as biometric information, purchase history, schedule, and location information.

[0116] A "server" is a centralized computer system that receives data from users' terminals or devices and analyzes and processes that data.

[0117] "Means of acquisition" refers to the organization of software and hardware for collecting data from external data sources such as smart devices and applications.

[0118] The "periodic transmission means" refers to a protocol and mechanism for transmitting acquired data to a server at regular intervals.

[0119] The "analysis means" refers to an algorithm and data processing method for identifying the user's behavioral patterns, health status, schedule details, etc. based on the received data.

[0120] A "prompt" is a specific inquiry that is generated based on a user's question or request and reflects the analysis results.

[0121] "Generative AI" is an artificial intelligence model that generates appropriate answers to prompts it receives.

[0122] "Means for obtaining and evaluating answers" refers to the algorithms and methods for evaluating the answers returned by the generative AI and selecting the most appropriate one.

[0123] "Multiple data sources" refers to various information sources that provide data, such as smart devices, electronic trading apps, schedule management apps, and location information apps.

[0124] The following detailed description is provided as a mode for implementing the invention.

[0125] System Overview

[0126] The system of the present invention obtains a user's personal data from multiple data sources, analyzes it to generate personalized prompts, and sends the prompts to a generation AI to provide the user with the most appropriate answer. The system consists of the following main components:

[0127] 1. How we collect your personal data

[0128] Smart devices (e.g. smartwatches)

[0129] E-commerce app

[0130] Schedule management apps (e.g., calendar apps)

[0131] Location-based apps (e.g., map apps)

[0132] 2. Means for periodically sending acquired personal data to a server

[0133] Data transmission protocol and dedicated software

[0134] 3. A means of analyzing the received data and identifying user behavior and patterns.

[0135] Data Analysis Algorithms

[0136] 4. A way to use the analysis results to generate personalized prompts based on the user's questions and requests.

[0137] Prompt Generation Engine

[0138] 5. A means to send the generated prompts to multiple generative AIs, obtain responses, and evaluate them.

[0139] Multiple generative AI models and an interface to control them

[0140] 6. How to present the best answer to the user

[0141] User Interface

[0142] Hardware and Software Details

[0143] Users link their everyday smartwatches and mobile applications to the system, integrating their biometric information, purchase history, schedule, location information, and more into the system. Smartwatches (e.g., health management devices) measure heart rate and exercise volume and send the data to the device.

[0144] The device uses a data transmission protocol to temporarily store this data and periodically transmit it to a server. The server receives the transmitted data and uses a data analysis algorithm to analyze the user's behavior and patterns, including health status, purchasing behavior, schedule, and movement history.

[0145] Next, the prompt generation engine uses the analysis results to generate a prompt that matches the user's question or request, such as "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[0146] The generated prompts are sent from the server to multiple generative AI models, which then return answers. The server evaluates these answers and selects the best answer, which is then presented to the user through a user interface.

[0147] Specific examples

[0148] As a concrete example, the process when the user inputs the question "What's the schedule for tomorrow?" is shown below.

[0149] 1. Data Collection

[0150] It collects data from the user's linked smartwatch, electronic payment app, calendar app, and map app.

[0151] 2. Data Analysis

[0152] The server analyzes this data to determine, for example, health information and schedules.

[0153] 3. Prompt Generation

[0154] It generates prompts such as, "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[0155] 4. AI Inquiry

[0156] This prompt is sent to a generative AI model, which chooses the best answer.

[0157] 5. Answer presentation

[0158] Ultimately, the user will see an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1: Data collection

[0161] Users link their smartwatches, e-commerce apps, schedule management apps, and location apps to the system.

[0162] The device periodically collects personal data (e.g., heart rate, purchase history, schedule, movement history) from linked devices and apps.

[0163] Input: Personal data from smart devices and applications

[0164] Data processing: Using the API of each device or application, the data is formatted and temporarily stored.

[0165] Output: Formatted personal data

[0166] Step 2: Send data

[0167] The terminal transmits the temporarily stored data to the server at a fixed timing.

[0168] Input: Temporarily saved formatted personal data

[0169] Data processing: Using a data transmission protocol, the data is sent to the server.

[0170] Output: Personal data sent to the server

[0171] Step 3: Receiving data

[0172] The server receives the personal data transmitted from the terminal.

[0173] Input: Personal data sent from the device

[0174] Data processing: The received data is stored in the server's database.

[0175] Output: Personal data stored on the server

[0176] Step 4: Data analysis

[0177] The server analyzes the received data and identifies user behavior and patterns.

[0178] Input: Personal data stored on the server

[0179] Data computation: Applying data analysis algorithms to identify health conditions, purchasing behavior, schedules, and travel history.

[0180] Output: Analyzed user behavior and patterns (e.g., exercise patterns, purchase history, schedule, travel patterns)

[0181] Step 5: Prompt generation

[0182] The server uses the analysis results to generate personalized prompts that respond to the user's questions and requests.

[0183] Input: Analyzed user behavior and patterns

[0184] Data Calculation: The analysis results are converted into prompts using a prompt generation engine.

[0185] Output: Generated prompt (e.g., "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'")

[0186] Step 6: User enters question

[0187] Users enter questions and requests in natural language.

[0188] Input: A user question or request (e.g., "What's on the schedule tomorrow?")

[0189] Output: User's natural language input

[0190] Step 7: Submit your question

[0191] The terminal transmits the user's questions and requests to the server.

[0192] Input: User's natural language input

[0193] Data processing: The query or request is formatted according to the protocol and sent to the server.

[0194] Output: Formatted questions and requests to the server

[0195] Step 8: Send prompts

[0196] The server combines the generated prompts with the user's question and sends them to multiple generative AI models.

[0197] Input: User question, generated prompt

[0198] Data computation: Prompts and questions are combined and sent to each generative AI model.

[0199] Output: The prompts and questions sent to the generative AI model

[0200] Step 9: Obtaining and Evaluating Answers

[0201] The server receives the answers from the generative AI model and selects the best answer.

[0202] Input: Answer from a generative AI model

[0203] Data calculation: Evaluate each answer and select the most appropriate answer.

[0204] Output: Best answer

[0205] Step 10: Submit and present your answers

[0206] The server transmits the selected optimal answer to the terminal.

[0207] Input: Best Answer

[0208] Data processing: The responses are formatted for the user interface and sent to the device.

[0209] The terminal displays the answer to the user.

[0210] Output: The answer shown to the user (e.g., "I have a meeting tomorrow at 10:00, after which I plan to travel from my office to my home.")

[0211] (Application example 1)

[0212] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0213] Conventional personalization systems that use personal data have the problem of being unable to effectively utilize a user's diverse data sources, resulting in services and content that do not adequately address the user's interests and activities. It is also difficult to integrate data collected from different devices and applications and provide optimal content based on individual analysis results. Furthermore, there is also the issue of how to effectively utilize generative AI to provide optimal answers and recommended content to users.

[0214] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0215] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI and acquiring answers, means for presenting the acquired answers to the user, and means for recommending optimal content based on the user's interests and activities. This makes it possible to provide personalized content by integrating various data sources of the user and sending prompt sentences to the generative AI model.

[0216] "User personal data" refers to information related to an individual obtained from the digital devices and applications that a user uses on a daily basis, including data on health status, purchasing history, schedule, travel history, etc.

[0217] "Means of acquisition" refers to the functions and methods for collecting personal data from multiple data sources, such as linking a smartwatch with an electronic payment application, a calendar application, and a map application to acquire data.

[0218] "Means for analysis" refers to the functions and methods for processing collected personal data and analyzing the user's behavioral patterns and status, such as analyzing health status, purchasing patterns, schedules, and movement history.

[0219] A "personalized prompt" is a prompt that, based on the analysis results, creates a query or request that best suits the user's specific interests and needs.

[0220] "Generative AI" refers to an artificial intelligence model or system that generates answers to questions or requests in natural language based on given prompts. It may include multiple generative AIs.

[0221] "Means of obtaining answers" refers to the function or method for sending the generated prompts to the generative AI and collecting the answers obtained from them.

[0222] "Means for presenting to the user" refers to the functions and methods for displaying the obtained answers and recommended content to the user. For example, providing information to the user via a smartphone application.

[0223] "Means for recommending content" refers to functions or methods that suggest optimal content (videos, articles, music, events, etc.) based on the user's interests and activities, based on the results of an analysis of the user's personal data.

[0224] This invention relates to a system for recommending optimal content based on a user's interests and activities. The system acquires and analyzes the user's personal data, and based on the data, sends optimal prompts to a generating AI to present personalized answers and recommended content.

[0225] The system uses the following hardware and software:

[0226] Hardware: Smartphone (user device), server

[0227] Software: Smartwatch app, electronic payment app, calendar app, map app, Python, Django (cloud server side), generative AI (e.g., OpenAI GPT model)

[0228] The system works as follows:

[0229] First, the user's device links the smartwatch, electronic payment app, calendar app, and map app to collect personal data from each data source. The collected data is temporarily stored in the device's local storage and periodically sent to a cloud server. This allows data related to the user's daily activities and interests to be accumulated.

[0230] Next, the cloud server analyzes the received data. Specifically, the following analysis is performed:

[0231] Health analysis from smartwatches (e.g., steps, heart rate, etc.)

[0232] Analysis of purchasing patterns from electronic payment apps (e.g., products purchased and frequency of purchases)

[0233] Analyzing schedules from calendar apps (e.g., upcoming meetings and events)

[0234] Analysis of travel history from map apps (e.g., places visited, distance traveled, etc.)

[0235] Based on the analyzed data, the cloud server generates the optimal prompt for the user. This prompt is sent to the AI ​​generator in response to the user's specific question or request. For example, in response to the question, "What video do you want to watch now?", the following prompt sentence is generated:

[0236] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[0237] The generated prompts are sent to the generation AI to obtain the answers. The cloud server evaluates the answers received from multiple generation AIs and selects the optimal answer or recommended content. The selected answer or content is then sent to the user's device and presented to the user.

[0238] For example, if a user types the question, "What video should I watch tonight?", the cloud server generates a message that reads, "Based on your recent activity, we recommend a cooking video to help you relax," and displays it on the user's smartphone.

[0239] In this way, a system is realized that can integrate a user's various data sources and provide personalized content.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] Data collection

[0243] Users link their smartwatches, electronic payment applications, calendar applications, and map applications to the system. The devices periodically acquire personal data (health information, purchase history, schedules, and movement history) from each linked data source. The acquired data is temporarily stored in the device's local storage and then sent to a cloud server at regular intervals.

[0244] Input: Personal data from smartwatches, electronic payment applications, calendar applications, and map applications

[0245] Output: Save data to local storage and send data to cloud server

[0246] Step 2:

[0247] Data analysis

[0248] The cloud server analyzes the data received from each data source, and in the process identifies the user's health status (number of steps, heart rate, etc.), purchasing patterns (items purchased, purchase frequency, etc.), schedule (meetings and event schedules), movement history, etc.

[0249] Input: Personal data obtained

[0250] Output: Analysis results of health status, purchasing patterns, schedules, and travel history

[0251] Step 3:

[0252] Prompt Generation

[0253] The cloud server uses the analyzed data to generate optimal prompts based on the user's questions and requests. These prompts reflect the user's health status, purchase history, schedule, and travel history. For example, the following prompts may be generated:

[0254] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[0255] Input: Analysis results

[0256] Output: personalized prompt

[0257] Step 4:

[0258] AI Inquiry

[0259] The user inputs a question or request in natural language. The device sends the user's input to a cloud server. The cloud server then constructs an optimal prompt based on the user's input and analyzed data, and sends this prompt to multiple generation AIs.

[0260] Input: User questions, requests, analysis results

[0261] Output: Prompt sent to the generation AI

[0262] Step 5:

[0263] Obtaining and evaluating answers

[0264] The cloud server receives the answers from each generation AI, evaluates each answer, and selects the most appropriate answer.

[0265] Input: Answer from the generative AI

[0266] Output: The best answer evaluated

[0267] Step 6:

[0268] Providing an answer

[0269] The cloud server then sends the selected optimal answer to the user's device, which then displays the answer to the user. For example, the user might receive a message such as, "Based on your recent activities, we recommend some cooking videos to help you relax."

[0270] Input: Best Answer

[0271] Output: Send and display the answer to the user's terminal

[0272] By repeating the above steps, a system is realized that can integrate a user's various data sources and provide personalized content.

[0273] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0274] System Overview

[0275] The system of the present invention acquires and analyzes a user's personal data and emotional data, generates personalized prompts based on that data, and sends the optimal prompts to the generation AI to obtain and present answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, electronic payment app, calendar app, and map app. Furthermore, by combining an emotion engine, it is possible to recognize the user's emotions from voice analysis, facial recognition, and text analysis. This allows the user's emotional data to be reflected in the analysis results, providing more appropriate prompts and answers.

[0276] Program processing overview

[0277] 1. Data Collection

[0278] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources.

[0279] The device periodically retrieves data from the linked data source and temporarily stores it.

[0280] The terminal periodically transmits the collected data to the server.

[0281] 2. Emotional Data Collection

[0282] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[0283] The device temporarily stores the collected emotion data and periodically transmits it to the server.

[0284] 3. Data Analysis

[0285] The server receives the personal data and emotion data transmitted from the terminal.

[0286] The server analyzes the information received from each data source: health status and exercise patterns are identified from smartwatch data, purchasing patterns from electronic payment app data, user schedules from calendar app data, and movement history from map app data.

[0287] The server also analyzes emotional data obtained through voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[0288] 4. Prompt Generation

[0289] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[0290] 5. AI Inquiry

[0291] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[0292] The terminal transmits the user's input to the server.

[0293] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[0294] 6. Obtaining and Presenting Answers

[0295] The server collects and evaluates the answers received from the generation AI.

[0296] The server selects the most appropriate answer, which is then stored in the database again.

[0297] The server sends the selected answer to the terminal.

[0298] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[0299] Specific examples

[0300] For example, if a user inputs a question into the system such as "What's on the schedule for tomorrow?", the process is as follows:

[0301] 1. Data Collection

[0302] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[0303] The device collects health data from the smartwatch, purchase history from the electronic payment app, schedules from the calendar app, and movement history from the map app, and sends this to a server.

[0304] 2. Emotional Data Collection

[0305] The device acquires emotional data through voice analysis, facial recognition, and text analysis of the user and sends it to the server.

[0306] 3. Data Analysis

[0307] The server receives and analyzes the personal data and emotional data to determine the user's health status, purchase history, schedule, and travel history, and to determine the user's current emotional state from the emotional data.

[0308] 4. Prompt Generation

[0309] The server uses the analyzed information to generate the most appropriate prompt for the user's question, which reflects the user's state and emotions.

[0310] 5. AI Inquiry

[0311] The user types, "What's on my schedule tomorrow?"

[0312] The terminal sends this input to the server.

[0313] The server combines the prompts and inputs and sends them to the generated AI.

[0314] 6. Obtaining and Presenting Answers

[0315] The server receives the answers from the generation AI, evaluates them, and selects the most appropriate answer.

[0316] The server sends the selected answer to the terminal.

[0317] The device will display an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[0318] The processing flow will be explained below.

[0319] Step 1:

[0320] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these devices and apps.

[0321] Step 2:

[0322] The device periodically retrieves data from each linked data source and temporarily stores it locally. For example, it retrieves health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and movement history from a map app.

[0323] Step 3:

[0324] The device periodically sends the collected data to the server, allowing the server to always have the most up-to-date personal data.

[0325] Step 4:

[0326] The device can analyze the user's voice, recognize their face, analyze their writing, and collect emotional data in real time. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice.

[0327] Step 5:

[0328] The device temporarily stores the collected emotion data and periodically transmits it to the server, which centrally manages the emotion data in the same way as personal data.

[0329] Step 6:

[0330] The server receives the personal data and emotion data sent from the device, which is then stored in a database for subsequent analysis.

[0331] Step 7:

[0332] The server analyzes the received personal data and emotional data. For example, it analyzes smartwatch data to identify the user's health condition and exercise patterns, analyzes electronic payment app data to identify purchasing patterns, analyzes calendar app data to identify the user's schedule, and analyzes map app data to identify movement history. Furthermore, it analyzes emotional data obtained through voice analysis, face recognition, and text analysis to identify the user's current emotional state.

[0333] Step 8:

[0334] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests. The generated prompts reflect the user's health status, purchasing history, schedule, travel history, emotional state, and other identified information.

[0335] Step 9:

[0336] A user inputs a question or request into a terminal in natural language, for example, "What's the schedule for tomorrow?"

[0337] Step 10:

[0338] The terminal sends the user's input to the server, where the user's natural language input is processed appropriately.

[0339] Step 11:

[0340] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[0341] Step 12:

[0342] The server collects and evaluates the answers received from the AI ​​generators, compares the answers from multiple AI generators, and selects the most appropriate answer.

[0343] Step 13:

[0344] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[0345] Example 2

[0346] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] Conventional systems have had difficulty efficiently collecting and analyzing users' personal and emotional data to generate personalized prompts. Furthermore, they lacked a mechanism for linking multiple generative AI models to select optimal answers and provide them to users. As a result, they were unable to provide appropriate services that could meet the diverse needs of users.

[0348] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring personal data of the user, means for analyzing the acquired personal data, means for collecting and analyzing emotion data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, and means for presenting the acquired answers to the user. This makes it possible to extract deep insights based on diverse user data and provide optimal prompts and answers for each individual user.

[0349] "Personal data" refers to information such as heart rate, step count, purchase history, schedule, and movement history obtained from a user's smartwatch, electronic payment app, calendar app, map app, etc.

[0350] "Emotional data" refers to data that indicates a user's emotional state obtained through voice analysis, facial recognition, and text analysis.

[0351] "Analysis" refers to the process of identifying a user's health status, purchasing patterns, schedule, travel history, and emotional state based on the acquired personal and emotional data.

[0352] "Personalized prompting" refers to the process of creating the optimal query for a user's specific question or request based on the analysis results.

[0353] "Generative AI models" refer to artificial intelligence algorithms and systems that generate appropriate answers based on user input and generated prompts.

[0354] "Presenting" refers to the act of displaying or notifying the user of the obtained answer.

[0355] "Data Source" refers to devices or applications that provide personal data and emotional data, such as smartwatches, electronic payment apps, calendar apps, and map apps.

[0356] MODE FOR CARRYING OUT THE INVENTION

[0357] The system of the present invention acquires a user's personal data and emotional data, analyzes this data to generate personalized prompts, and obtains and presents optimal answers using a generative AI model. Specific hardware and software configurations for implementing this system are described below.

[0358] Hardware and Software

[0359] 1. User device: smartphone, tablet, etc.

[0360] 2. Data Collection Devices:

[0361] Smartwatch: Collects health data such as heart rate and steps

[0362] Electronic payment app: Collecting purchase history

[0363] Calendar app: collects user schedules

[0364] Map app: Collects travel history

[0365] 3. Emotion analysis device:

[0366] Camera: Facial expression analysis through facial recognition

[0367] Microphone: Audio analysis

[0368] Text analysis software: Sentiment analysis of user-entered text

[0369] 4. Server: Receives data, analyzes it, generates prompts, and queries the AI ​​model

[0370] Data collection and analysis

[0371] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service on their devices, which allows personal data to be collected. The devices periodically retrieve data from these sources and temporarily store it in local storage. The devices then periodically transmit the data to a server. The devices also analyze the user's voice, face, and writing in real time to collect emotional data.

[0372] The server receives personal data and emotional data sent from the user's device and passes them to the analysis engine. The analysis engine identifies the user's health condition and exercise patterns from the smartwatch data, purchasing patterns from the electronic payment app data, schedules from the calendar app data, and movement history from the map app data. It also identifies the user's current emotional state based on the emotional data.

[0373] Prompt generation and AI queries

[0374] Based on the analyzed data, the server generates the most appropriate prompt based on the user's question or request. For example, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt such as, "I have a meeting tomorrow at 10 o'clock, after which I'm scheduled to travel from the office to my home."

[0375] The device then sends the user's input to the server, which uses a generative AI model to find the optimal answer. During this process, the server works with multiple generative AI models to evaluate the quality of the answers and select the most appropriate answer.

[0376] Obtaining and presenting answers

[0377] The server collects and evaluates the answers received from the generative AI model, selects the most appropriate answer, and sends it to the device, which displays it to the user.

[0378] For example, if a user types the question "What's on my schedule tomorrow?", the system will provide an answer such as "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[0379] keyword

[0380] Generative AI model, prompt sentence

[0381] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0382] Step 1:

[0383] Data collection

[0384] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from those devices and apps.

[0385] Input: Data from smartwatch, electronic payment app, calendar app, map app

[0386] Output: Personal data stored on the device

[0387] The device periodically collects data from linked devices and apps and temporarily stores it: specifically, it collects heart rate and step count from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and movement history from a map app.

[0388] Specific operation: The device periodically calls the API to obtain data and saves it in local storage.

[0389] Step 2:

[0390] Emotional Data Collection

[0391] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[0392] Input: User's voice, face, and text

[0393] Output: Emotion data stored on the device

[0394] Voice analysis extracts emotions from the tone and pitch of a user's voice, facial recognition determines emotions from changes in facial expressions, and text analysis uses a dictionary of emotion words to analyze emotions such as positive, negative, and neutral.

[0395] Specific operation: The device uses a camera to analyze facial expressions, a microphone to analyze voices, and performs text analysis of sentences entered by the user.

[0396] The device transmits the collected emotional data and personal data to a server at regular intervals.

[0397] Step 3:

[0398] Data analysis

[0399] The server receives the personal data and emotion data transmitted from the terminal.

[0400] Input: Personal data and emotional data sent from the device

[0401] Output: Data analyzed by the analysis engine

[0402] The server passes the received data to the analysis engine, which performs the following specific data processing and calculations:

[0403] Health data analysis: Analyzes heart rate and step count data from smartwatches to identify the user's health status and exercise patterns.

[0404] Purchasing data analysis: Identifying user purchasing patterns and recently purchased items from data from electronic payment apps.

[0405] Schedule analysis: Analyzes the user's schedule from data from the calendar app.

[0406] Movement history analysis: Identify movement history from map app data and extract frequently visited places, etc.

[0407] Specific operation: The analysis engine in the server filters the data stored in the database and analyzes the data using various analysis algorithms.

[0408] Step 4:

[0409] Prompt Generation

[0410] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests.

[0411] Input: Analysis results (personal data and emotional data)

[0412] Output: Generated prompt

[0413] Specifically, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt saying, "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[0414] Specific operation: The server passes the parameters for prompt generation to the generative AI model and creates the optimal prompt.

[0415] Step 5:

[0416] AI Inquiry

[0417] Users input questions or requests in natural language, for example, "What's on the schedule for tomorrow?"

[0418] Input: User questions or requests

[0419] Output: User input sent to the server

[0420] The terminal transmits the user's input to the server.

[0421] The server combines the user's input with the generated prompts to pose the optimal query to the generative AI model.

[0422] Input: User input and generated prompts

[0423] Output: The query sent to the generative AI model

[0424] How it works: The server combines user input and prompts and queries the generative AI model using an API.

[0425] Step 6:

[0426] Obtaining and presenting answers

[0427] The server collects and evaluates the answers received from the generative AI models, and works with multiple generative AI models to select the optimal answer.

[0428] Input: Answer from a generative AI model

[0429] Output: The best answer selected

[0430] The server sends the best answer to the terminal.

[0431] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[0432] Specific operation: The server compares multiple answers based on the answer evaluation criteria, selects the best one, sends it to the terminal, and displays it to the user.

[0433] (Application example 2)

[0434] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0435] Conventional virtual store systems have difficulty in making product recommendations that reflect the user's individual needs and emotional state. Furthermore, a lack of personalization has led to a poor user experience. Therefore, there is a need for a system that can utilize the user's personal data and emotional data to make highly accurate personalized product recommendations.

[0436] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0437] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, means for presenting the acquired answers to the user, means for acquiring personal data from multiple data sources such as a smartwatch, an electronic payment app, a calendar app, and a map app, means for collecting emotional data through voice analysis, face recognition, and text analysis, means for generating prompt sentences based on the analyzed personal data and emotional data, means for selecting an optimal answer in cooperation with multiple generative AI models, and means for presenting the selected answer to the user as a personalized product proposal. This enables highly accurate personalized product proposals that reflect the user's individual needs and emotional state.

[0438] "User's personal data" refers to information about the user, such as health status, purchase history, schedule, and travel history.

[0439] "Means of acquisition" refers to the means of collecting data from smartwatches, electronic payment apps, calendar apps, map apps, etc.

[0440] "Means of analysis" refers to the means for analyzing the acquired personal data and understanding the user's behavior and status.

[0441] "Personalized prompts" refer to questions or requests that are tailored to a user's individual characteristics and needs, based on analyzed personal and emotional data.

[0442] "Generative AI model" refers to an artificial intelligence model that generates appropriate answers to input prompts.

[0443] "Means for obtaining answers" refers to the means for collecting the answers generated from the generative AI model.

[0444] The "means for presenting to the user" refers to a means for visually or audibly presenting the obtained answer to the user.

[0445] "Voice analysis" refers to the technology of analyzing a user's emotions and state from voice data.

[0446] "Facial recognition" refers to the technology of recognizing a user's face from image data and analyzing their facial expressions.

[0447] "Text analysis" refers to the technology of analyzing a user's emotions and intentions from text data.

[0448] "Personalized product suggestions" refer to suggestions that recommend the most suitable products and services to a user based on the user's personal data and emotional data.

[0449] The system of the present invention collects and analyzes a user's personal data and emotional data, generates prompts based on the collected data, and sends the optimal prompts to a generative AI model to obtain and present answers. This system acquires personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. It can also collect emotional data through voice analysis, face recognition, and text analysis. Specific embodiments are described below.

[0450] 1. Data collection method

[0451] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources. The device periodically retrieves data from the linked data sources and temporarily stores it. The device periodically transmits the collected data to the server.

[0452] 2. Emotion data collection method

[0453] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis, and the collected emotional data is temporarily stored and periodically sent to a server.

[0454] 3. Data Analysis

[0455] The server receives and analyzes the personal and emotional data sent from the device. It identifies health conditions and exercise patterns from smartwatch data, purchasing patterns from electronic payment app data, the user's schedule from calendar app data, and movement history from map app data. Furthermore, it analyzes the emotional data based on voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[0456] 4. Prompt Generation

[0457] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[0458] 5. AI Inquiry

[0459] A user inputs a question or request in natural language. For example, they might input a question like, "What product best suits my mood right now?" The device sends this input to a server, which combines the user's input with generated prompts to form an optimal query and send it to multiple generative AI models.

[0460] 6. Obtaining and Presenting Answers

[0461] The server collects and evaluates the answers received from the generative AI model to select the most appropriate answer. The selected answer is then saved in the database again. The server then sends the selected answer to the device, which then presents the answer to the user. For example, the answer displayed might be, "The relaxing product that best suits your current mood is an aroma candle. It costs 1,500 yen, and more details are available here."

[0462] Specific examples

[0463] For example, if User A inputs the question "What product best suits my mood right now?" into the system, the following prompt sentence will be generated:

[0464] Based on User A's health data, purchase history, schedule, and movement history, please suggest products that suit their current emotional state (relaxation). Based on their recent purchase history, please make a list of recommended products, including new products that will help them relax.

[0465] These prompts are input into a generative AI model, and the answers obtained provide optimal product suggestions to the user.

[0466] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0467] Step 1:

[0468] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows for the collection of personal data from the user. The input is the user-provided credentials for each application, and the output is the readiness to collect personal data from linked data sources.

[0469] Step 2:

[0470] The device periodically retrieves and temporarily stores data from linked data sources, including health data from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and travel history from a map app. The input is data retrieved from each application, and the output is temporarily stored personal data.

[0471] Step 3:

[0472] The device periodically transmits the collected data to the server. The input is the temporarily stored personal data, and the output is the personal data transmitted to the server. This allows the data to be analyzed on the server.

[0473] Step 4:

[0474] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis. The input is the user's voice, facial image, and text data (chat, email, etc.), and the output is analyzed emotional data.

[0475] Step 5:

[0476] The device temporarily stores the collected emotion data and periodically transmits it to the server. The input is the analyzed emotion data, and the output is the emotion data transmitted to the server.

[0477] Step 6:

[0478] The server receives and analyzes the personal data and emotional data sent from the device. The input is the user's personal data and emotional data, and the output is the analysis results. Based on this data, the server identifies the user's health condition, purchasing patterns, schedule, movement history, and emotional state.

[0479] Step 7:

[0480] The server generates personalized prompts based on the analysis results. The input is the analysis results, and the output is the optimal prompt text. The prompt text reflects the user's state and emotions.

[0481] Step 8:

[0482] The user inputs a question or request in natural language. The input is the user's question or request (e.g., "What product would suit my mood right now?"), and the output is receiving and displaying the user's question or request.

[0483] Step 9:

[0484] The terminal sends the user's input to the server. The input is the user's question or request, and the output is the user's question or request sent to the server.

[0485] Step 10:

[0486] The server combines the user input and generated prompts to form an optimal query, where the input is the user input and prompt, and the output is the completed query, which is then sent to multiple generative AI models.

[0487] Step 11:

[0488] The server collects and evaluates the answers received from the generative AI model. The input is the answer of the generative AI model, and the output is the most appropriate answer. The server selects the best answer and stores it in the database again.

[0489] Step 12:

[0490] The server sends the selected answer to the terminal, and the terminal presents the answer to the user. The input is the selected answer, and the output is the answer presented to the user. For example, an answer such as "The relaxing product that is perfect for your current mood is an aroma candle. It costs 1,500 yen, and details are here" is displayed.

[0491] 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.

[0492] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0493] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0494] [Second embodiment]

[0495] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0496] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0497] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0498] 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.

[0499] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0500] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0501] 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.

[0502] 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.

[0503] 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 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.

[0504] 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.

[0505] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0506] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0507] System Overview

[0508] The system of the present invention acquires and analyzes a user's personal data, sends optimal prompts to a generation AI based on that data, and obtains and presents personalized answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. Next, it analyzes that data and generates optimal prompts for the user. Finally, it sends the generated prompts to the generation AI, which then presents the answers to the user.

[0509] Program processing overview

[0510] 1. Data Collection

[0511] Users link their smartwatch, electronic payment app, calendar app, and map app to the system.

[0512] The terminal periodically retrieves data from the linked data sources.

[0513] The terminal temporarily stores the acquired data and transmits it to the server at a fixed timing.

[0514] 2. Data Analysis

[0515] The server analyzes the data received from each data source.

[0516] The server analyzes the user's health condition and exercise patterns based on data from the smartwatch.

[0517] The server analyzes users' purchasing patterns based on data from the electronic payment app.

[0518] The server analyzes the user's schedule based on data from the calendar app.

[0519] The server analyzes the user's movement history based on data from the map app.

[0520] 3. Prompt Generation

[0521] The server generates the most appropriate prompt based on the analyzed data in response to the user's question or request.

[0522] The prompt reflects the user's health condition, purchase history, schedule, travel history, etc.

[0523] 4. AI Inquiry

[0524] Users enter questions and requests in natural language.

[0525] The terminal transmits the user's input to the server.

[0526] The server combines the user's input with the parsed data to construct the optimal prompt.

[0527] The server sends this constructed prompt to multiple generating AIs.

[0528] The server receives and evaluates the answers from each generated AI.

[0529] 5. Obtaining and Presenting Answers

[0530] The server evaluates the received answers and selects the most appropriate answer.

[0531] The server selects the most suitable answer for the user and sends it to the terminal.

[0532] The terminal displays the answer to the user.

[0533] Specific examples

[0534] For example, the following shows the process when a user inputs the question "What's on the schedule tomorrow?" into the system.

[0535] 1. Data Collection

[0536] Users link their everyday smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[0537] The device obtains the user's exercise history from the smartwatch, purchase history from the electronic payment app, schedule from the calendar app, and movement history from the map app.

[0538] The secured data is periodically sent to the server.

[0539] 2. Data Analysis

[0540] The server analyzes the data from the smartwatch to identify health information such as exercise history and heart rate.

[0541] The server analyzes data from the electronic payment app to identify recent purchasing behavior.

[0542] The server parses the data from the calendar app to determine the next day's schedule.

[0543] The server analyzes data from the map app to identify movement patterns.

[0544] 3. Prompt Generation

[0545] The server generates appropriate prompts for user input based on analyzed health information, purchasing behavior, schedules, and travel patterns.

[0546] "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[0547] 4. AI Inquiry

[0548] The user types the question, "What's on the schedule tomorrow?"

[0549] The terminal sends this input to the server.

[0550] The server sends the question along with the generated prompt to multiple generating AIs.

[0551] 5. Obtaining and Presenting Answers

[0552] The server evaluates the answers received from each generation AI and selects the best answer.

[0553] The server transmits the selected response to the terminal.

[0554] The terminal displays the answer to the user, "I have a meeting tomorrow at 10 o'clock, after which I plan to travel from the office to my home."

[0555] The processing flow will be explained below.

[0556] Step 1:

[0557] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, and once the link is established, personal information from these data sources can be collected.

[0558] Step 2:

[0559] The device periodically retrieves data from linked data sources: health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and location information and movement history from a map app.

[0560] Step 3:

[0561] The device temporarily stores the collected data locally, and the stored data is sent to the server at regular intervals.

[0562] Step 4:

[0563] The server receives the data sent from the terminal and stores the received data in a database.

[0564] Step 5:

[0565] The server analyzes the information received from each data source: Analyzing data from a smartwatch to identify the user's health and exercise patterns Analyzing data from an electronic payment app to identify purchasing patterns and spending trends Analyzing data from a calendar app to identify the user's schedule Analyzing data from a map app to identify movement history and places visited

[0566] Step 6:

[0567] The server uses the analyzed data to generate personalized prompts based on the user's questions and requests, including specific information such as the user's health status, purchase history, schedule, and travel history.

[0568] Step 7:

[0569] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[0570] Step 8:

[0571] The terminal sends the user's input to the server, where it is processed.

[0572] Step 9:

[0573] The server combines the user's input with previously generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[0574] Step 10:

[0575] The server collects the answers received from the AI ​​generators. The answers from each AI generator are evaluated by the server.

[0576] Step 11:

[0577] The server evaluates the collected answers and selects the most appropriate answer, which is then stored back in the database.

[0578] Step 12:

[0579] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[0580] Example 1

[0581] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0582] Conventional systems have had difficulty efficiently collecting and analyzing users' personal data and providing personalized answers. Furthermore, mechanisms for effectively integrating data collected from multiple data sources and quickly generating optimal answers to users' questions and requests have not been fully established. Furthermore, the process of selecting the optimal answer using multiple generative AI systems is complicated, making it impossible to provide the most appropriate information to users.

[0583] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0584] In this invention, the server includes means for acquiring personal data of the user, means for periodically transmitting the acquired personal data to the server, means for analyzing the received data and identifying user behavior and patterns, means for generating personalized prompts based on the analysis results in response to the user's questions and requests, means for transmitting the generated prompts to multiple generative AIs, acquiring and evaluating answers, and means for presenting the optimal answer to the user. This makes it possible to integrate information acquired from the user's various data sources and provide personalized answers efficiently and quickly.

[0585] "User personal data" refers to data generated by a user that can identify or identify an individual, such as biometric information, purchase history, schedule, and location information.

[0586] A "server" is a centralized computer system that receives data from users' terminals or devices and analyzes and processes that data.

[0587] "Means of acquisition" refers to the organization of software and hardware for collecting data from external data sources such as smart devices and applications.

[0588] The "periodic transmission means" refers to a protocol and mechanism for transmitting acquired data to a server at regular intervals.

[0589] The "analysis means" refers to an algorithm and data processing method for identifying the user's behavioral patterns, health status, schedule details, etc. based on the received data.

[0590] A "prompt" is a specific inquiry that is generated based on a user's question or request and reflects the analysis results.

[0591] "Generative AI" is an artificial intelligence model that generates appropriate answers to prompts it receives.

[0592] "Means for obtaining and evaluating answers" refers to the algorithms and methods for evaluating the answers returned by the generative AI and selecting the most appropriate one.

[0593] "Multiple data sources" refers to various information sources that provide data, such as smart devices, electronic trading apps, schedule management apps, and location information apps.

[0594] The following detailed description is provided as a mode for implementing the invention.

[0595] System Overview

[0596] The system of the present invention obtains a user's personal data from multiple data sources, analyzes it to generate personalized prompts, and sends the prompts to a generation AI to provide the user with the most appropriate answer. The system consists of the following main components:

[0597] 1. How we collect your personal data

[0598] Smart devices (e.g. smartwatches)

[0599] E-commerce app

[0600] Schedule management apps (e.g., calendar apps)

[0601] Location-based apps (e.g., map apps)

[0602] 2. Means for periodically sending acquired personal data to a server

[0603] Data transmission protocol and dedicated software

[0604] 3. A means of analyzing the received data and identifying user behavior and patterns.

[0605] Data Analysis Algorithms

[0606] 4. A way to use the analysis results to generate personalized prompts based on the user's questions and requests.

[0607] Prompt Generation Engine

[0608] 5. A means to send the generated prompts to multiple generative AIs, obtain responses, and evaluate them.

[0609] Multiple generative AI models and an interface to control them

[0610] 6. How to present the best answer to the user

[0611] User Interface

[0612] Hardware and Software Details

[0613] Users link their everyday smartwatches and mobile applications to the system, integrating their biometric information, purchase history, schedule, location information, and more into the system. Smartwatches (e.g., health management devices) measure heart rate and exercise volume and send the data to the device.

[0614] The device uses a data transmission protocol to temporarily store this data and periodically transmit it to a server. The server receives the transmitted data and uses a data analysis algorithm to analyze the user's behavior and patterns, including health status, purchasing behavior, schedule, and movement history.

[0615] Next, the prompt generation engine uses the analysis results to generate a prompt that matches the user's question or request, such as "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[0616] The generated prompts are sent from the server to multiple generative AI models, which then return answers. The server evaluates these answers and selects the best answer, which is then presented to the user through a user interface.

[0617] Specific examples

[0618] As a concrete example, the process when the user inputs the question "What's the schedule for tomorrow?" is shown below.

[0619] 1. Data Collection

[0620] It collects data from the user's linked smartwatch, electronic payment app, calendar app, and map app.

[0621] 2. Data Analysis

[0622] The server analyzes this data to determine, for example, health information and schedules.

[0623] 3. Prompt Generation

[0624] It generates prompts such as, "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[0625] 4. AI Inquiry

[0626] This prompt is sent to a generative AI model, which chooses the best answer.

[0627] 5. Answer presentation

[0628] Ultimately, the user will see an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[0629] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0630] Step 1: Data collection

[0631] Users link their smartwatches, e-commerce apps, schedule management apps, and location apps to the system.

[0632] The device periodically collects personal data (e.g., heart rate, purchase history, schedule, movement history) from linked devices and apps.

[0633] Input: Personal data from smart devices and applications

[0634] Data processing: Using the API of each device or application, the data is formatted and temporarily stored.

[0635] Output: Formatted personal data

[0636] Step 2: Send data

[0637] The terminal transmits the temporarily stored data to the server at a fixed timing.

[0638] Input: Temporarily saved formatted personal data

[0639] Data processing: Using a data transmission protocol, the data is sent to the server.

[0640] Output: Personal data sent to the server

[0641] Step 3: Receiving data

[0642] The server receives the personal data transmitted from the terminal.

[0643] Input: Personal data sent from the device

[0644] Data processing: The received data is stored in the server's database.

[0645] Output: Personal data stored on the server

[0646] Step 4: Data analysis

[0647] The server analyzes the received data and identifies user behavior and patterns.

[0648] Input: Personal data stored on the server

[0649] Data computation: Applying data analysis algorithms to identify health conditions, purchasing behavior, schedules, and travel history.

[0650] Output: Analyzed user behavior and patterns (e.g., exercise patterns, purchase history, schedule, travel patterns)

[0651] Step 5: Prompt generation

[0652] The server uses the analysis results to generate personalized prompts that respond to the user's questions and requests.

[0653] Input: Analyzed user behavior and patterns

[0654] Data Calculation: The analysis results are converted into prompts using a prompt generation engine.

[0655] Output: Generated prompt (e.g., "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'")

[0656] Step 6: User enters question

[0657] Users enter questions and requests in natural language.

[0658] Input: A user question or request (e.g., "What's on the schedule tomorrow?")

[0659] Output: User's natural language input

[0660] Step 7: Submit your question

[0661] The terminal transmits the user's questions and requests to the server.

[0662] Input: User's natural language input

[0663] Data processing: The query or request is formatted according to the protocol and sent to the server.

[0664] Output: Formatted questions and requests to the server

[0665] Step 8: Send prompts

[0666] The server combines the generated prompts with the user's question and sends them to multiple generative AI models.

[0667] Input: User question, generated prompt

[0668] Data computation: Prompts and questions are combined and sent to each generative AI model.

[0669] Output: The prompts and questions sent to the generative AI model

[0670] Step 9: Obtaining and Evaluating Answers

[0671] The server receives the answers from the generative AI model and selects the best answer.

[0672] Input: Answer from a generative AI model

[0673] Data calculation: Evaluate each answer and select the most appropriate answer.

[0674] Output: Best answer

[0675] Step 10: Submit and present your answers

[0676] The server transmits the selected optimal answer to the terminal.

[0677] Input: Best Answer

[0678] Data processing: The responses are formatted for the user interface and sent to the device.

[0679] The terminal displays the answer to the user.

[0680] Output: The answer shown to the user (e.g., "I have a meeting tomorrow at 10:00, after which I plan to travel from my office to my home.")

[0681] (Application example 1)

[0682] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0683] Conventional personalization systems that use personal data have the problem of being unable to effectively utilize a user's diverse data sources, resulting in services and content that do not adequately address the user's interests and activities. It is also difficult to integrate data collected from different devices and applications and provide optimal content based on individual analysis results. Furthermore, there is also the issue of how to effectively utilize generative AI to provide optimal answers and recommended content to users.

[0684] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0685] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI and acquiring answers, means for presenting the acquired answers to the user, and means for recommending optimal content based on the user's interests and activities. This makes it possible to provide personalized content by integrating various data sources of the user and sending prompt sentences to the generative AI model.

[0686] "User personal data" refers to information related to an individual obtained from the digital devices and applications that a user uses on a daily basis, including data on health status, purchasing history, schedule, travel history, etc.

[0687] "Means of acquisition" refers to the functions and methods for collecting personal data from multiple data sources, such as linking a smartwatch with an electronic payment application, a calendar application, and a map application to acquire data.

[0688] "Means for analysis" refers to the functions and methods for processing collected personal data and analyzing the user's behavioral patterns and status, such as analyzing health status, purchasing patterns, schedules, and movement history.

[0689] A "personalized prompt" is a prompt that, based on the analysis results, creates a query or request that best suits the user's specific interests and needs.

[0690] "Generative AI" refers to an artificial intelligence model or system that generates answers to questions or requests in natural language based on given prompts. It may include multiple generative AIs.

[0691] "Means of obtaining answers" refers to the function or method for sending the generated prompts to the generative AI and collecting the answers obtained from them.

[0692] "Means for presenting to the user" refers to the functions and methods for displaying the obtained answers and recommended content to the user. For example, providing information to the user via a smartphone application.

[0693] "Means for recommending content" refers to functions or methods that suggest optimal content (videos, articles, music, events, etc.) based on the user's interests and activities, based on the results of an analysis of the user's personal data.

[0694] This invention relates to a system for recommending optimal content based on a user's interests and activities. The system acquires and analyzes the user's personal data, and based on the data, sends optimal prompts to a generating AI to present personalized answers and recommended content.

[0695] The system uses the following hardware and software:

[0696] Hardware: Smartphone (user device), server

[0697] Software: Smartwatch app, electronic payment app, calendar app, map app, Python, Django (cloud server side), generative AI (e.g., OpenAI GPT model)

[0698] The system works as follows:

[0699] First, the user's device links the smartwatch, electronic payment app, calendar app, and map app to collect personal data from each data source. The collected data is temporarily stored in the device's local storage and periodically sent to a cloud server. This allows data related to the user's daily activities and interests to be accumulated.

[0700] Next, the cloud server analyzes the received data. Specifically, the following analysis is performed:

[0701] Health analysis from smartwatches (e.g., steps, heart rate, etc.)

[0702] Analysis of purchasing patterns from electronic payment apps (e.g., products purchased and frequency of purchases)

[0703] Analyzing schedules from calendar apps (e.g., upcoming meetings and events)

[0704] Analysis of travel history from map apps (e.g., places visited, distance traveled, etc.)

[0705] Based on the analyzed data, the cloud server generates the optimal prompt for the user. This prompt is sent to the AI ​​generator in response to the user's specific question or request. For example, in response to the question, "What video do you want to watch now?", the following prompt sentence is generated:

[0706] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[0707] The generated prompts are sent to the generation AI to obtain the answers. The cloud server evaluates the answers received from multiple generation AIs and selects the optimal answer or recommended content. The selected answer or content is then sent to the user's device and presented to the user.

[0708] For example, if a user types the question, "What video should I watch tonight?", the cloud server generates a message that reads, "Based on your recent activity, we recommend a cooking video to help you relax," and displays it on the user's smartphone.

[0709] In this way, a system is realized that can integrate a user's various data sources and provide personalized content.

[0710] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0711] Step 1:

[0712] Data collection

[0713] Users link their smartwatches, electronic payment applications, calendar applications, and map applications to the system. The devices periodically acquire personal data (health information, purchase history, schedules, and movement history) from each linked data source. The acquired data is temporarily stored in the device's local storage and then sent to a cloud server at regular intervals.

[0714] Input: Personal data from smartwatches, electronic payment applications, calendar applications, and map applications

[0715] Output: Save data to local storage and send data to cloud server

[0716] Step 2:

[0717] Data analysis

[0718] The cloud server analyzes the data received from each data source, and in the process identifies the user's health status (number of steps, heart rate, etc.), purchasing patterns (items purchased, purchase frequency, etc.), schedule (meetings and event schedules), movement history, etc.

[0719] Input: Personal data obtained

[0720] Output: Analysis results of health status, purchasing patterns, schedules, and travel history

[0721] Step 3:

[0722] Prompt Generation

[0723] The cloud server uses the analyzed data to generate optimal prompts based on the user's questions and requests. These prompts reflect the user's health status, purchase history, schedule, and travel history. For example, the following prompts may be generated:

[0724] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[0725] Input: Analysis results

[0726] Output: personalized prompt

[0727] Step 4:

[0728] AI Inquiry

[0729] The user inputs a question or request in natural language. The device sends the user's input to a cloud server. The cloud server then constructs an optimal prompt based on the user's input and analyzed data, and sends this prompt to multiple generation AIs.

[0730] Input: User questions, requests, analysis results

[0731] Output: Prompt sent to the generation AI

[0732] Step 5:

[0733] Obtaining and evaluating answers

[0734] The cloud server receives the answers from each generation AI, evaluates each answer, and selects the most appropriate answer.

[0735] Input: Answer from the generative AI

[0736] Output: The best answer evaluated

[0737] Step 6:

[0738] Providing an answer

[0739] The cloud server then sends the selected optimal answer to the user's device, which then displays the answer to the user. For example, the user might receive a message such as, "Based on your recent activities, we recommend some cooking videos to help you relax."

[0740] Input: Best Answer

[0741] Output: Send and display the answer to the user's terminal

[0742] By repeating the above steps, a system is realized that can integrate a user's various data sources and provide personalized content.

[0743] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0744] System Overview

[0745] The system of the present invention acquires and analyzes a user's personal data and emotional data, generates personalized prompts based on that data, and sends the optimal prompts to the generation AI to obtain and present answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, electronic payment app, calendar app, and map app. Furthermore, by combining an emotion engine, it is possible to recognize the user's emotions from voice analysis, facial recognition, and text analysis. This allows the user's emotional data to be reflected in the analysis results, providing more appropriate prompts and answers.

[0746] Program processing overview

[0747] 1. Data Collection

[0748] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources.

[0749] The device periodically retrieves data from the linked data source and temporarily stores it.

[0750] The terminal periodically transmits the collected data to the server.

[0751] 2. Emotional Data Collection

[0752] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[0753] The device temporarily stores the collected emotion data and periodically transmits it to the server.

[0754] 3. Data Analysis

[0755] The server receives the personal data and emotion data transmitted from the terminal.

[0756] The server analyzes the information received from each data source: health status and exercise patterns are identified from smartwatch data, purchasing patterns from electronic payment app data, user schedules from calendar app data, and movement history from map app data.

[0757] The server also analyzes emotional data obtained through voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[0758] 4. Prompt Generation

[0759] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[0760] 5. AI Inquiry

[0761] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[0762] The terminal transmits the user's input to the server.

[0763] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[0764] 6. Obtaining and Presenting Answers

[0765] The server collects and evaluates the answers received from the generation AI.

[0766] The server selects the most appropriate answer, which is then stored in the database again.

[0767] The server sends the selected answer to the terminal.

[0768] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[0769] Specific examples

[0770] For example, if a user inputs a question into the system such as "What's on the schedule for tomorrow?", the process is as follows:

[0771] 1. Data Collection

[0772] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[0773] The device collects health data from the smartwatch, purchase history from the electronic payment app, schedules from the calendar app, and movement history from the map app, and sends this to a server.

[0774] 2. Emotional Data Collection

[0775] The device acquires emotional data through voice analysis, facial recognition, and text analysis of the user and sends it to the server.

[0776] 3. Data Analysis

[0777] The server receives and analyzes the personal data and emotional data to determine the user's health status, purchase history, schedule, and travel history, and to determine the user's current emotional state from the emotional data.

[0778] 4. Prompt Generation

[0779] The server uses the analyzed information to generate the most appropriate prompt for the user's question, which reflects the user's state and emotions.

[0780] 5. AI Inquiry

[0781] The user types, "What's on my schedule tomorrow?"

[0782] The terminal sends this input to the server.

[0783] The server combines the prompts and inputs and sends them to the generated AI.

[0784] 6. Obtaining and Presenting Answers

[0785] The server receives the answers from the generation AI, evaluates them, and selects the most appropriate answer.

[0786] The server sends the selected answer to the terminal.

[0787] The device will display an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[0788] The processing flow will be explained below.

[0789] Step 1:

[0790] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these devices and apps.

[0791] Step 2:

[0792] The device periodically retrieves data from each linked data source and temporarily stores it locally. For example, it retrieves health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and movement history from a map app.

[0793] Step 3:

[0794] The device periodically sends the collected data to the server, allowing the server to always have the most up-to-date personal data.

[0795] Step 4:

[0796] The device can analyze the user's voice, recognize their face, analyze their writing, and collect emotional data in real time. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice.

[0797] Step 5:

[0798] The device temporarily stores the collected emotion data and periodically transmits it to the server, which centrally manages the emotion data in the same way as personal data.

[0799] Step 6:

[0800] The server receives the personal data and emotion data sent from the device, which is then stored in a database for subsequent analysis.

[0801] Step 7:

[0802] The server analyzes the received personal data and emotional data. For example, it analyzes smartwatch data to identify the user's health condition and exercise patterns, analyzes electronic payment app data to identify purchasing patterns, analyzes calendar app data to identify the user's schedule, and analyzes map app data to identify movement history. Furthermore, it analyzes emotional data obtained through voice analysis, face recognition, and text analysis to identify the user's current emotional state.

[0803] Step 8:

[0804] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests. The generated prompts reflect the user's health status, purchasing history, schedule, travel history, emotional state, and other identified information.

[0805] Step 9:

[0806] A user inputs a question or request into a terminal in natural language, for example, "What's the schedule for tomorrow?"

[0807] Step 10:

[0808] The terminal sends the user's input to the server, where the user's natural language input is processed appropriately.

[0809] Step 11:

[0810] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[0811] Step 12:

[0812] The server collects and evaluates the answers received from the AI ​​generators, compares the answers from multiple AI generators, and selects the most appropriate answer.

[0813] Step 13:

[0814] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[0815] Example 2

[0816] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0817] Conventional systems have had difficulty efficiently collecting and analyzing users' personal and emotional data to generate personalized prompts. Furthermore, they lacked a mechanism for linking multiple generative AI models to select optimal answers and provide them to users. As a result, they were unable to provide appropriate services that could meet the diverse needs of users.

[0818] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring personal data of the user, means for analyzing the acquired personal data, means for collecting and analyzing emotion data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, and means for presenting the acquired answers to the user. This makes it possible to extract deep insights based on diverse user data and provide optimal prompts and answers for each individual user.

[0819] "Personal data" refers to information such as heart rate, step count, purchase history, schedule, and movement history obtained from a user's smartwatch, electronic payment app, calendar app, map app, etc.

[0820] "Emotional data" refers to data that indicates a user's emotional state obtained through voice analysis, facial recognition, and text analysis.

[0821] "Analysis" refers to the process of identifying a user's health status, purchasing patterns, schedule, travel history, and emotional state based on the acquired personal and emotional data.

[0822] "Personalized prompting" refers to the process of creating the optimal query for a user's specific question or request based on the analysis results.

[0823] "Generative AI models" refer to artificial intelligence algorithms and systems that generate appropriate answers based on user input and generated prompts.

[0824] "Presenting" refers to the act of displaying or notifying the user of the obtained answer.

[0825] "Data Source" refers to devices or applications that provide personal data and emotional data, such as smartwatches, electronic payment apps, calendar apps, and map apps.

[0826] MODE FOR CARRYING OUT THE INVENTION

[0827] The system of the present invention acquires a user's personal data and emotional data, analyzes this data to generate personalized prompts, and obtains and presents optimal answers using a generative AI model. Specific hardware and software configurations for implementing this system are described below.

[0828] Hardware and Software

[0829] 1. User device: smartphone, tablet, etc.

[0830] 2. Data Collection Devices:

[0831] Smartwatch: Collects health data such as heart rate and steps

[0832] Electronic payment app: Collecting purchase history

[0833] Calendar app: collects user schedules

[0834] Map app: Collects travel history

[0835] 3. Emotion analysis device:

[0836] Camera: Facial expression analysis through facial recognition

[0837] Microphone: Audio analysis

[0838] Text analysis software: Sentiment analysis of user-entered text

[0839] 4. Server: Receives data, analyzes it, generates prompts, and queries the AI ​​model

[0840] Data collection and analysis

[0841] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service on their devices, which allows personal data to be collected. The devices periodically retrieve data from these sources and temporarily store it in local storage. The devices then periodically transmit the data to a server. The devices also analyze the user's voice, face, and writing in real time to collect emotional data.

[0842] The server receives personal data and emotional data sent from the user's device and passes them to the analysis engine. The analysis engine identifies the user's health condition and exercise patterns from the smartwatch data, purchasing patterns from the electronic payment app data, schedules from the calendar app data, and movement history from the map app data. It also identifies the user's current emotional state based on the emotional data.

[0843] Prompt generation and AI queries

[0844] Based on the analyzed data, the server generates the most appropriate prompt based on the user's question or request. For example, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt such as, "I have a meeting tomorrow at 10 o'clock, after which I'm scheduled to travel from the office to my home."

[0845] The device then sends the user's input to the server, which uses a generative AI model to find the optimal answer. During this process, the server works with multiple generative AI models to evaluate the quality of the answers and select the most appropriate answer.

[0846] Obtaining and presenting answers

[0847] The server collects and evaluates the answers received from the generative AI model, selects the most appropriate answer, and sends it to the device, which displays it to the user.

[0848] For example, if a user types the question "What's on my schedule tomorrow?", the system will provide an answer such as "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[0849] keyword

[0850] Generative AI model, prompt sentence

[0851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] Data collection

[0854] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from those devices and apps.

[0855] Input: Data from smartwatch, electronic payment app, calendar app, map app

[0856] Output: Personal data stored on the device

[0857] The device periodically collects data from linked devices and apps and temporarily stores it: specifically, it collects heart rate and step count from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and movement history from a map app.

[0858] Specific operation: The device periodically calls the API to obtain data and saves it in local storage.

[0859] Step 2:

[0860] Emotional Data Collection

[0861] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[0862] Input: User's voice, face, and text

[0863] Output: Emotion data stored on the device

[0864] Voice analysis extracts emotions from the tone and pitch of a user's voice, facial recognition determines emotions from changes in facial expressions, and text analysis uses a dictionary of emotion words to analyze emotions such as positive, negative, and neutral.

[0865] Specific operation: The device uses a camera to analyze facial expressions, a microphone to analyze voices, and performs text analysis of sentences entered by the user.

[0866] The device transmits the collected emotional data and personal data to a server at regular intervals.

[0867] Step 3:

[0868] Data analysis

[0869] The server receives the personal data and emotion data transmitted from the terminal.

[0870] Input: Personal data and emotional data sent from the device

[0871] Output: Data analyzed by the analysis engine

[0872] The server passes the received data to the analysis engine, which performs the following specific data processing and calculations:

[0873] Health data analysis: Analyzes heart rate and step count data from smartwatches to identify the user's health status and exercise patterns.

[0874] Purchasing data analysis: Identifying user purchasing patterns and recently purchased items from data from electronic payment apps.

[0875] Schedule analysis: Analyzes the user's schedule from data from the calendar app.

[0876] Movement history analysis: Identify movement history from map app data and extract frequently visited places, etc.

[0877] Specific operation: The analysis engine in the server filters the data stored in the database and analyzes the data using various analysis algorithms.

[0878] Step 4:

[0879] Prompt Generation

[0880] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests.

[0881] Input: Analysis results (personal data and emotional data)

[0882] Output: Generated prompt

[0883] Specifically, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt saying, "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[0884] Specific operation: The server passes the parameters for prompt generation to the generative AI model and creates the optimal prompt.

[0885] Step 5:

[0886] AI Inquiry

[0887] Users input questions or requests in natural language, for example, "What's on the schedule for tomorrow?"

[0888] Input: User questions or requests

[0889] Output: User input sent to the server

[0890] The terminal transmits the user's input to the server.

[0891] The server combines the user's input with the generated prompts to pose the optimal query to the generative AI model.

[0892] Input: User input and generated prompts

[0893] Output: The query sent to the generative AI model

[0894] How it works: The server combines user input and prompts and queries the generative AI model using an API.

[0895] Step 6:

[0896] Obtaining and presenting answers

[0897] The server collects and evaluates the answers received from the generative AI models, and works with multiple generative AI models to select the optimal answer.

[0898] Input: Answer from a generative AI model

[0899] Output: The best answer selected

[0900] The server sends the best answer to the terminal.

[0901] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[0902] Specific operation: The server compares multiple answers based on the answer evaluation criteria, selects the best one, sends it to the terminal, and displays it to the user.

[0903] (Application example 2)

[0904] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0905] Conventional virtual store systems have difficulty in making product recommendations that reflect the user's individual needs and emotional state. Furthermore, a lack of personalization has led to a poor user experience. Therefore, there is a need for a system that can utilize the user's personal data and emotional data to make highly accurate personalized product recommendations.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0907] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, means for presenting the acquired answers to the user, means for acquiring personal data from multiple data sources such as a smartwatch, an electronic payment app, a calendar app, and a map app, means for collecting emotional data through voice analysis, face recognition, and text analysis, means for generating prompt sentences based on the analyzed personal data and emotional data, means for selecting an optimal answer in cooperation with multiple generative AI models, and means for presenting the selected answer to the user as a personalized product proposal. This enables highly accurate personalized product proposals that reflect the user's individual needs and emotional state.

[0908] "User's personal data" refers to information about the user, such as health status, purchase history, schedule, and travel history.

[0909] "Means of acquisition" refers to the means of collecting data from smartwatches, electronic payment apps, calendar apps, map apps, etc.

[0910] "Means of analysis" refers to the means for analyzing the acquired personal data and understanding the user's behavior and status.

[0911] "Personalized prompts" refer to questions or requests that are tailored to a user's individual characteristics and needs, based on analyzed personal and emotional data.

[0912] "Generative AI model" refers to an artificial intelligence model that generates appropriate answers to input prompts.

[0913] "Means for obtaining answers" refers to the means for collecting the answers generated from the generative AI model.

[0914] The "means for presenting to the user" refers to a means for visually or audibly presenting the obtained answer to the user.

[0915] "Voice analysis" refers to the technology of analyzing a user's emotions and state from voice data.

[0916] "Facial recognition" refers to the technology of recognizing a user's face from image data and analyzing their facial expressions.

[0917] "Text analysis" refers to the technology of analyzing a user's emotions and intentions from text data.

[0918] "Personalized product suggestions" refer to suggestions that recommend the most suitable products and services to a user based on the user's personal data and emotional data.

[0919] The system of the present invention collects and analyzes a user's personal data and emotional data, generates prompts based on the collected data, and sends the optimal prompts to a generative AI model to obtain and present answers. This system acquires personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. It can also collect emotional data through voice analysis, face recognition, and text analysis. Specific embodiments are described below.

[0920] 1. Data collection method

[0921] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources. The device periodically retrieves data from the linked data sources and temporarily stores it. The device periodically transmits the collected data to the server.

[0922] 2. Emotion data collection method

[0923] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis, and the collected emotional data is temporarily stored and periodically sent to a server.

[0924] 3. Data Analysis

[0925] The server receives and analyzes the personal and emotional data sent from the device. It identifies health conditions and exercise patterns from smartwatch data, purchasing patterns from electronic payment app data, the user's schedule from calendar app data, and movement history from map app data. Furthermore, it analyzes the emotional data based on voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[0926] 4. Prompt Generation

[0927] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[0928] 5. AI Inquiry

[0929] A user inputs a question or request in natural language. For example, they might input a question like, "What product best suits my mood right now?" The device sends this input to a server, which combines the user's input with generated prompts to form an optimal query and send it to multiple generative AI models.

[0930] 6. Obtaining and Presenting Answers

[0931] The server collects and evaluates the answers received from the generative AI model to select the most appropriate answer. The selected answer is then saved in the database again. The server then sends the selected answer to the device, which then presents the answer to the user. For example, the answer displayed might be, "The relaxing product that best suits your current mood is an aroma candle. It costs 1,500 yen, and more details are available here."

[0932] Specific examples

[0933] For example, if User A inputs the question "What product best suits my mood right now?" into the system, the following prompt sentence will be generated:

[0934] Based on User A's health data, purchase history, schedule, and movement history, please suggest products that suit their current emotional state (relaxation). Based on their recent purchase history, please make a list of recommended products, including new products that will help them relax.

[0935] These prompts are input into a generative AI model, and the answers obtained provide optimal product suggestions to the user.

[0936] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0937] Step 1:

[0938] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows for the collection of personal data from the user. The input is the user-provided credentials for each application, and the output is the readiness to collect personal data from linked data sources.

[0939] Step 2:

[0940] The device periodically retrieves and temporarily stores data from linked data sources, including health data from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and travel history from a map app. The input is data retrieved from each application, and the output is temporarily stored personal data.

[0941] Step 3:

[0942] The device periodically transmits the collected data to the server. The input is the temporarily stored personal data, and the output is the personal data transmitted to the server. This allows the data to be analyzed on the server.

[0943] Step 4:

[0944] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis. The input is the user's voice, facial image, and text data (chat, email, etc.), and the output is analyzed emotional data.

[0945] Step 5:

[0946] The device temporarily stores the collected emotion data and periodically transmits it to the server. The input is the analyzed emotion data, and the output is the emotion data transmitted to the server.

[0947] Step 6:

[0948] The server receives and analyzes the personal data and emotional data sent from the device. The input is the user's personal data and emotional data, and the output is the analysis results. Based on this data, the server identifies the user's health condition, purchasing patterns, schedule, movement history, and emotional state.

[0949] Step 7:

[0950] The server generates personalized prompts based on the analysis results. The input is the analysis results, and the output is the optimal prompt text. The prompt text reflects the user's state and emotions.

[0951] Step 8:

[0952] The user inputs a question or request in natural language. The input is the user's question or request (e.g., "What product would suit my mood right now?"), and the output is receiving and displaying the user's question or request.

[0953] Step 9:

[0954] The terminal sends the user's input to the server. The input is the user's question or request, and the output is the user's question or request sent to the server.

[0955] Step 10:

[0956] The server combines the user input and generated prompts to form an optimal query, where the input is the user input and prompt, and the output is the completed query, which is then sent to multiple generative AI models.

[0957] Step 11:

[0958] The server collects and evaluates the answers received from the generative AI model. The input is the answer of the generative AI model, and the output is the most appropriate answer. The server selects the best answer and stores it in the database again.

[0959] Step 12:

[0960] The server sends the selected answer to the terminal, and the terminal presents the answer to the user. The input is the selected answer, and the output is the answer presented to the user. For example, an answer such as "The relaxing product that is perfect for your current mood is an aroma candle. It costs 1,500 yen, and details are here" is displayed.

[0961] 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.

[0962] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0963] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0964] [Third embodiment]

[0965] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0966] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0967] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0968] 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.

[0969] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0970] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0971] 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.

[0972] 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.

[0973] 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 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.

[0974] 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.

[0975] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0976] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0977] System Overview

[0978] The system of the present invention acquires and analyzes a user's personal data, sends optimal prompts to a generation AI based on that data, and obtains and presents personalized answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. Next, it analyzes that data and generates optimal prompts for the user. Finally, it sends the generated prompts to the generation AI, which then presents the answers to the user.

[0979] Program processing overview

[0980] 1. Data Collection

[0981] Users link their smartwatch, electronic payment app, calendar app, and map app to the system.

[0982] The terminal periodically retrieves data from the linked data sources.

[0983] The terminal temporarily stores the acquired data and transmits it to the server at a fixed timing.

[0984] 2. Data Analysis

[0985] The server analyzes the data received from each data source.

[0986] The server analyzes the user's health condition and exercise patterns based on data from the smartwatch.

[0987] The server analyzes users' purchasing patterns based on data from the electronic payment app.

[0988] The server analyzes the user's schedule based on data from the calendar app.

[0989] The server analyzes the user's movement history based on data from the map app.

[0990] 3. Prompt Generation

[0991] The server generates the most appropriate prompt based on the analyzed data in response to the user's question or request.

[0992] The prompt reflects the user's health condition, purchase history, schedule, travel history, etc.

[0993] 4. AI Inquiry

[0994] Users enter questions and requests in natural language.

[0995] The terminal transmits the user's input to the server.

[0996] The server combines the user's input with the parsed data to construct the optimal prompt.

[0997] The server sends this constructed prompt to multiple generating AIs.

[0998] The server receives and evaluates the answers from each generated AI.

[0999] 5. Obtaining and Presenting Answers

[1000] The server evaluates the received answers and selects the most appropriate answer.

[1001] The server selects the most suitable answer for the user and sends it to the terminal.

[1002] The terminal displays the answer to the user.

[1003] Specific examples

[1004] For example, the following shows the process when a user inputs the question "What's on the schedule tomorrow?" into the system.

[1005] 1. Data Collection

[1006] Users link their everyday smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[1007] The device obtains the user's exercise history from the smartwatch, purchase history from the electronic payment app, schedule from the calendar app, and movement history from the map app.

[1008] The secured data is periodically sent to the server.

[1009] 2. Data Analysis

[1010] The server analyzes the data from the smartwatch to identify health information such as exercise history and heart rate.

[1011] The server analyzes data from the electronic payment app to identify recent purchasing behavior.

[1012] The server parses the data from the calendar app to determine the next day's schedule.

[1013] The server analyzes data from the map app to identify movement patterns.

[1014] 3. Prompt Generation

[1015] The server generates appropriate prompts for user input based on analyzed health information, purchasing behavior, schedules, and travel patterns.

[1016] "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[1017] 4. AI Inquiry

[1018] The user types the question, "What's on the schedule tomorrow?"

[1019] The terminal sends this input to the server.

[1020] The server sends the question along with the generated prompt to multiple generating AIs.

[1021] 5. Obtaining and Presenting Answers

[1022] The server evaluates the answers received from each generation AI and selects the best answer.

[1023] The server transmits the selected response to the terminal.

[1024] The terminal displays the answer to the user, "I have a meeting tomorrow at 10 o'clock, after which I plan to travel from the office to my home."

[1025] The processing flow will be explained below.

[1026] Step 1:

[1027] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, and once the link is established, personal information from these data sources can be collected.

[1028] Step 2:

[1029] The device periodically retrieves data from linked data sources: health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and location information and movement history from a map app.

[1030] Step 3:

[1031] The device temporarily stores the collected data locally, and the stored data is sent to the server at regular intervals.

[1032] Step 4:

[1033] The server receives the data sent from the terminal and stores the received data in a database.

[1034] Step 5:

[1035] The server analyzes the information received from each data source: Analyzing data from a smartwatch to identify the user's health and exercise patterns Analyzing data from an electronic payment app to identify purchasing patterns and spending trends Analyzing data from a calendar app to identify the user's schedule Analyzing data from a map app to identify movement history and places visited

[1036] Step 6:

[1037] The server uses the analyzed data to generate personalized prompts based on the user's questions and requests, including specific information such as the user's health status, purchase history, schedule, and travel history.

[1038] Step 7:

[1039] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[1040] Step 8:

[1041] The terminal sends the user's input to the server, where it is processed.

[1042] Step 9:

[1043] The server combines the user's input with previously generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[1044] Step 10:

[1045] The server collects the answers received from the AI ​​generators. The answers from each AI generator are evaluated by the server.

[1046] Step 11:

[1047] The server evaluates the collected answers and selects the most appropriate answer, which is then stored back in the database.

[1048] Step 12:

[1049] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[1050] Example 1

[1051] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1052] Conventional systems have had difficulty efficiently collecting and analyzing users' personal data and providing personalized answers. Furthermore, mechanisms for effectively integrating data collected from multiple data sources and quickly generating optimal answers to users' questions and requests have not been fully established. Furthermore, the process of selecting the optimal answer using multiple generative AI systems is complicated, making it impossible to provide the most appropriate information to users.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1054] In this invention, the server includes means for acquiring personal data of the user, means for periodically transmitting the acquired personal data to the server, means for analyzing the received data and identifying user behavior and patterns, means for generating personalized prompts based on the analysis results in response to the user's questions and requests, means for transmitting the generated prompts to multiple generative AIs, acquiring and evaluating answers, and means for presenting the optimal answer to the user. This makes it possible to integrate information acquired from the user's various data sources and provide personalized answers efficiently and quickly.

[1055] "User personal data" refers to data generated by a user that can identify or identify an individual, such as biometric information, purchase history, schedule, and location information.

[1056] A "server" is a centralized computer system that receives data from users' terminals or devices and analyzes and processes that data.

[1057] "Means of acquisition" refers to the organization of software and hardware for collecting data from external data sources such as smart devices and applications.

[1058] The "periodic transmission means" refers to a protocol and mechanism for transmitting acquired data to a server at regular intervals.

[1059] The "analysis means" refers to an algorithm and data processing method for identifying the user's behavioral patterns, health status, schedule details, etc. based on the received data.

[1060] A "prompt" is a specific inquiry that is generated based on a user's question or request and reflects the analysis results.

[1061] "Generative AI" is an artificial intelligence model that generates appropriate answers to prompts it receives.

[1062] "Means for obtaining and evaluating answers" refers to the algorithms and methods for evaluating the answers returned by the generative AI and selecting the most appropriate one.

[1063] "Multiple data sources" refers to various information sources that provide data, such as smart devices, electronic trading apps, schedule management apps, and location information apps.

[1064] The following detailed description is provided as a mode for implementing the invention.

[1065] System Overview

[1066] The system of the present invention obtains a user's personal data from multiple data sources, analyzes it to generate personalized prompts, and sends the prompts to a generation AI to provide the user with the most appropriate answer. The system consists of the following main components:

[1067] 1. How we collect your personal data

[1068] Smart devices (e.g. smartwatches)

[1069] E-commerce app

[1070] Schedule management apps (e.g., calendar apps)

[1071] Location-based apps (e.g., map apps)

[1072] 2. Means for periodically sending acquired personal data to a server

[1073] Data transmission protocol and dedicated software

[1074] 3. A means of analyzing the received data and identifying user behavior and patterns.

[1075] Data Analysis Algorithms

[1076] 4. A way to use the analysis results to generate personalized prompts based on the user's questions and requests.

[1077] Prompt Generation Engine

[1078] 5. A means to send the generated prompts to multiple generative AIs, obtain responses, and evaluate them.

[1079] Multiple generative AI models and an interface to control them

[1080] 6. How to present the best answer to the user

[1081] User Interface

[1082] Hardware and Software Details

[1083] Users link their everyday smartwatches and mobile applications to the system, integrating their biometric information, purchase history, schedule, location information, and more into the system. Smartwatches (e.g., health management devices) measure heart rate and exercise volume and send the data to the device.

[1084] The device uses a data transmission protocol to temporarily store this data and periodically transmit it to a server. The server receives the transmitted data and uses a data analysis algorithm to analyze the user's behavior and patterns, including health status, purchasing behavior, schedule, and movement history.

[1085] Next, the prompt generation engine uses the analysis results to generate a prompt that matches the user's question or request, such as "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[1086] The generated prompts are sent from the server to multiple generative AI models, which then return answers. The server evaluates these answers and selects the best answer, which is then presented to the user through a user interface.

[1087] Specific examples

[1088] As a concrete example, the process when the user inputs the question "What's the schedule for tomorrow?" is shown below.

[1089] 1. Data Collection

[1090] It collects data from the user's linked smartwatch, electronic payment app, calendar app, and map app.

[1091] 2. Data Analysis

[1092] The server analyzes this data to determine, for example, health information and schedules.

[1093] 3. Prompt Generation

[1094] It generates prompts such as, "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[1095] 4. AI Inquiry

[1096] This prompt is sent to a generative AI model, which chooses the best answer.

[1097] 5. Answer presentation

[1098] Ultimately, the user will see an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[1099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1100] Step 1: Data collection

[1101] Users link their smartwatches, e-commerce apps, schedule management apps, and location apps to the system.

[1102] The device periodically collects personal data (e.g., heart rate, purchase history, schedule, movement history) from linked devices and apps.

[1103] Input: Personal data from smart devices and applications

[1104] Data processing: Using the API of each device or application, the data is formatted and temporarily stored.

[1105] Output: Formatted personal data

[1106] Step 2: Send data

[1107] The terminal transmits the temporarily stored data to the server at a fixed timing.

[1108] Input: Temporarily saved formatted personal data

[1109] Data processing: Using a data transmission protocol, the data is sent to the server.

[1110] Output: Personal data sent to the server

[1111] Step 3: Receiving data

[1112] The server receives the personal data transmitted from the terminal.

[1113] Input: Personal data sent from the device

[1114] Data processing: The received data is stored in the server's database.

[1115] Output: Personal data stored on the server

[1116] Step 4: Data analysis

[1117] The server analyzes the received data and identifies user behavior and patterns.

[1118] Input: Personal data stored on the server

[1119] Data computation: Applying data analysis algorithms to identify health conditions, purchasing behavior, schedules, and travel history.

[1120] Output: Analyzed user behavior and patterns (e.g., exercise patterns, purchase history, schedule, travel patterns)

[1121] Step 5: Prompt generation

[1122] The server uses the analysis results to generate personalized prompts that respond to the user's questions and requests.

[1123] Input: Analyzed user behavior and patterns

[1124] Data Calculation: The analysis results are converted into prompts using a prompt generation engine.

[1125] Output: Generated prompt (e.g., "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'")

[1126] Step 6: User enters question

[1127] Users enter questions and requests in natural language.

[1128] Input: A user question or request (e.g., "What's on the schedule tomorrow?")

[1129] Output: User's natural language input

[1130] Step 7: Submit your question

[1131] The terminal transmits the user's questions and requests to the server.

[1132] Input: User's natural language input

[1133] Data processing: The query or request is formatted according to the protocol and sent to the server.

[1134] Output: Formatted questions and requests to the server

[1135] Step 8: Send prompts

[1136] The server combines the generated prompts with the user's question and sends them to multiple generative AI models.

[1137] Input: User question, generated prompt

[1138] Data computation: Prompts and questions are combined and sent to each generative AI model.

[1139] Output: The prompts and questions sent to the generative AI model

[1140] Step 9: Obtaining and Evaluating Answers

[1141] The server receives the answers from the generative AI model and selects the best answer.

[1142] Input: Answer from a generative AI model

[1143] Data calculation: Evaluate each answer and select the most appropriate answer.

[1144] Output: Best answer

[1145] Step 10: Submit and present your answers

[1146] The server transmits the selected optimal answer to the terminal.

[1147] Input: Best Answer

[1148] Data processing: The responses are formatted for the user interface and sent to the device.

[1149] The terminal displays the answer to the user.

[1150] Output: The answer shown to the user (e.g., "I have a meeting tomorrow at 10:00, after which I plan to travel from my office to my home.")

[1151] (Application example 1)

[1152] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1153] Conventional personalization systems that use personal data have the problem of being unable to effectively utilize a user's diverse data sources, resulting in services and content that do not adequately address the user's interests and activities. It is also difficult to integrate data collected from different devices and applications and provide optimal content based on individual analysis results. Furthermore, there is also the issue of how to effectively utilize generative AI to provide optimal answers and recommended content to users.

[1154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1155] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI and acquiring answers, means for presenting the acquired answers to the user, and means for recommending optimal content based on the user's interests and activities. This makes it possible to provide personalized content by integrating various data sources of the user and sending prompt sentences to the generative AI model.

[1156] "User personal data" refers to information related to an individual obtained from the digital devices and applications that a user uses on a daily basis, including data on health status, purchasing history, schedule, travel history, etc.

[1157] "Means of acquisition" refers to the functions and methods for collecting personal data from multiple data sources, such as linking a smartwatch with an electronic payment application, a calendar application, and a map application to acquire data.

[1158] "Means for analysis" refers to the functions and methods for processing collected personal data and analyzing the user's behavioral patterns and status, such as analyzing health status, purchasing patterns, schedules, and movement history.

[1159] A "personalized prompt" is a prompt that, based on the analysis results, creates a query or request that best suits the user's specific interests and needs.

[1160] "Generative AI" refers to an artificial intelligence model or system that generates answers to questions or requests in natural language based on given prompts. It may include multiple generative AIs.

[1161] "Means of obtaining answers" refers to the function or method for sending the generated prompts to the generative AI and collecting the answers obtained from them.

[1162] "Means for presenting to the user" refers to the functions and methods for displaying the obtained answers and recommended content to the user. For example, providing information to the user via a smartphone application.

[1163] "Means for recommending content" refers to functions or methods that suggest optimal content (videos, articles, music, events, etc.) based on the user's interests and activities, based on the results of an analysis of the user's personal data.

[1164] This invention relates to a system for recommending optimal content based on a user's interests and activities. The system acquires and analyzes the user's personal data, and based on the data, sends optimal prompts to a generating AI to present personalized answers and recommended content.

[1165] The system uses the following hardware and software:

[1166] Hardware: Smartphone (user device), server

[1167] Software: Smartwatch app, electronic payment app, calendar app, map app, Python, Django (cloud server side), generative AI (e.g., OpenAI GPT model)

[1168] The system works as follows:

[1169] First, the user's device links the smartwatch, electronic payment app, calendar app, and map app to collect personal data from each data source. The collected data is temporarily stored in the device's local storage and periodically sent to a cloud server. This allows data related to the user's daily activities and interests to be accumulated.

[1170] Next, the cloud server analyzes the received data. Specifically, the following analysis is performed:

[1171] Health analysis from smartwatches (e.g., steps, heart rate, etc.)

[1172] Analysis of purchasing patterns from electronic payment apps (e.g., products purchased and frequency of purchases)

[1173] Analyzing schedules from calendar apps (e.g., upcoming meetings and events)

[1174] Analysis of travel history from map apps (e.g., places visited, distance traveled, etc.)

[1175] Based on the analyzed data, the cloud server generates the optimal prompt for the user. This prompt is sent to the AI ​​generator in response to the user's specific question or request. For example, in response to the question, "What video do you want to watch now?", the following prompt sentence is generated:

[1176] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[1177] The generated prompts are sent to the generation AI to obtain the answers. The cloud server evaluates the answers received from multiple generation AIs and selects the optimal answer or recommended content. The selected answer or content is then sent to the user's device and presented to the user.

[1178] For example, if a user types the question, "What video should I watch tonight?", the cloud server generates a message that reads, "Based on your recent activity, we recommend a cooking video to help you relax," and displays it on the user's smartphone.

[1179] In this way, a system is realized that can integrate a user's various data sources and provide personalized content.

[1180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1181] Step 1:

[1182] Data collection

[1183] Users link their smartwatches, electronic payment applications, calendar applications, and map applications to the system. The devices periodically acquire personal data (health information, purchase history, schedules, and movement history) from each linked data source. The acquired data is temporarily stored in the device's local storage and then sent to a cloud server at regular intervals.

[1184] Input: Personal data from smartwatches, electronic payment applications, calendar applications, and map applications

[1185] Output: Save data to local storage and send data to cloud server

[1186] Step 2:

[1187] Data analysis

[1188] The cloud server analyzes the data received from each data source, and in the process identifies the user's health status (number of steps, heart rate, etc.), purchasing patterns (items purchased, purchase frequency, etc.), schedule (meetings and event schedules), movement history, etc.

[1189] Input: Personal data obtained

[1190] Output: Analysis results of health status, purchasing patterns, schedules, and travel history

[1191] Step 3:

[1192] Prompt Generation

[1193] The cloud server uses the analyzed data to generate optimal prompts based on the user's questions and requests. These prompts reflect the user's health status, purchase history, schedule, and travel history. For example, the following prompts may be generated:

[1194] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[1195] Input: Analysis results

[1196] Output: personalized prompt

[1197] Step 4:

[1198] AI Inquiry

[1199] The user inputs a question or request in natural language. The device sends the user's input to a cloud server. The cloud server then constructs an optimal prompt based on the user's input and analyzed data, and sends this prompt to multiple generation AIs.

[1200] Input: User questions, requests, analysis results

[1201] Output: Prompt sent to the generation AI

[1202] Step 5:

[1203] Obtaining and evaluating answers

[1204] The cloud server receives the answers from each generation AI, evaluates each answer, and selects the most appropriate answer.

[1205] Input: Answer from the generative AI

[1206] Output: The best answer evaluated

[1207] Step 6:

[1208] Providing an answer

[1209] The cloud server then sends the selected optimal answer to the user's device, which then displays the answer to the user. For example, the user might receive a message such as, "Based on your recent activities, we recommend some cooking videos to help you relax."

[1210] Input: Best Answer

[1211] Output: Send and display the answer to the user's terminal

[1212] By repeating the above steps, a system is realized that can integrate a user's various data sources and provide personalized content.

[1213] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1214] System Overview

[1215] The system of the present invention acquires and analyzes a user's personal data and emotional data, generates personalized prompts based on that data, and sends the optimal prompts to the generation AI to obtain and present answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, electronic payment app, calendar app, and map app. Furthermore, by combining an emotion engine, it is possible to recognize the user's emotions from voice analysis, facial recognition, and text analysis. This allows the user's emotional data to be reflected in the analysis results, providing more appropriate prompts and answers.

[1216] Program processing overview

[1217] 1. Data Collection

[1218] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources.

[1219] The device periodically retrieves data from the linked data source and temporarily stores it.

[1220] The terminal periodically transmits the collected data to the server.

[1221] 2. Emotional Data Collection

[1222] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[1223] The device temporarily stores the collected emotion data and periodically transmits it to the server.

[1224] 3. Data Analysis

[1225] The server receives the personal data and emotion data transmitted from the terminal.

[1226] The server analyzes the information received from each data source: health status and exercise patterns are identified from smartwatch data, purchasing patterns from electronic payment app data, user schedules from calendar app data, and movement history from map app data.

[1227] The server also analyzes emotional data obtained through voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[1228] 4. Prompt Generation

[1229] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[1230] 5. AI Inquiry

[1231] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[1232] The terminal transmits the user's input to the server.

[1233] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[1234] 6. Obtaining and Presenting Answers

[1235] The server collects and evaluates the answers received from the generation AI.

[1236] The server selects the most appropriate answer, which is then stored in the database again.

[1237] The server sends the selected answer to the terminal.

[1238] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[1239] Specific examples

[1240] For example, if a user inputs a question into the system such as "What's on the schedule for tomorrow?", the process is as follows:

[1241] 1. Data Collection

[1242] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[1243] The device collects health data from the smartwatch, purchase history from the electronic payment app, schedules from the calendar app, and movement history from the map app, and sends this to a server.

[1244] 2. Emotional Data Collection

[1245] The device acquires emotional data through voice analysis, facial recognition, and text analysis of the user and sends it to the server.

[1246] 3. Data Analysis

[1247] The server receives and analyzes the personal data and emotional data to determine the user's health status, purchase history, schedule, and travel history, and to determine the user's current emotional state from the emotional data.

[1248] 4. Prompt Generation

[1249] The server uses the analyzed information to generate the most appropriate prompt for the user's question, which reflects the user's state and emotions.

[1250] 5. AI Inquiry

[1251] The user types, "What's on my schedule tomorrow?"

[1252] The terminal sends this input to the server.

[1253] The server combines the prompts and inputs and sends them to the generated AI.

[1254] 6. Obtaining and Presenting Answers

[1255] The server receives the answers from the generation AI, evaluates them, and selects the most appropriate answer.

[1256] The server sends the selected answer to the terminal.

[1257] The device will display an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these devices and apps.

[1261] Step 2:

[1262] The device periodically retrieves data from each linked data source and temporarily stores it locally. For example, it retrieves health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and movement history from a map app.

[1263] Step 3:

[1264] The device periodically sends the collected data to the server, allowing the server to always have the most up-to-date personal data.

[1265] Step 4:

[1266] The device can analyze the user's voice, recognize their face, analyze their writing, and collect emotional data in real time. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice.

[1267] Step 5:

[1268] The device temporarily stores the collected emotion data and periodically transmits it to the server, which centrally manages the emotion data in the same way as personal data.

[1269] Step 6:

[1270] The server receives the personal data and emotion data sent from the device, which is then stored in a database for subsequent analysis.

[1271] Step 7:

[1272] The server analyzes the received personal data and emotional data. For example, it analyzes smartwatch data to identify the user's health condition and exercise patterns, analyzes electronic payment app data to identify purchasing patterns, analyzes calendar app data to identify the user's schedule, and analyzes map app data to identify movement history. Furthermore, it analyzes emotional data obtained through voice analysis, face recognition, and text analysis to identify the user's current emotional state.

[1273] Step 8:

[1274] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests. The generated prompts reflect the user's health status, purchasing history, schedule, travel history, emotional state, and other identified information.

[1275] Step 9:

[1276] A user inputs a question or request into a terminal in natural language, for example, "What's the schedule for tomorrow?"

[1277] Step 10:

[1278] The terminal sends the user's input to the server, where the user's natural language input is processed appropriately.

[1279] Step 11:

[1280] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[1281] Step 12:

[1282] The server collects and evaluates the answers received from the AI ​​generators, compares the answers from multiple AI generators, and selects the most appropriate answer.

[1283] Step 13:

[1284] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[1285] Example 2

[1286] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1287] Conventional systems have had difficulty efficiently collecting and analyzing users' personal and emotional data to generate personalized prompts. Furthermore, they lacked a mechanism for linking multiple generative AI models to select optimal answers and provide them to users. As a result, they were unable to provide appropriate services that could meet the diverse needs of users.

[1288] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring personal data of the user, means for analyzing the acquired personal data, means for collecting and analyzing emotion data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, and means for presenting the acquired answers to the user. This makes it possible to extract deep insights based on diverse user data and provide optimal prompts and answers for each individual user.

[1289] "Personal data" refers to information such as heart rate, step count, purchase history, schedule, and movement history obtained from a user's smartwatch, electronic payment app, calendar app, map app, etc.

[1290] "Emotional data" refers to data that indicates a user's emotional state obtained through voice analysis, facial recognition, and text analysis.

[1291] "Analysis" refers to the process of identifying a user's health status, purchasing patterns, schedule, travel history, and emotional state based on the acquired personal and emotional data.

[1292] "Personalized prompting" refers to the process of creating the optimal query for a user's specific question or request based on the analysis results.

[1293] "Generative AI models" refer to artificial intelligence algorithms and systems that generate appropriate answers based on user input and generated prompts.

[1294] "Presenting" refers to the act of displaying or notifying the user of the obtained answer.

[1295] "Data Source" refers to devices or applications that provide personal data and emotional data, such as smartwatches, electronic payment apps, calendar apps, and map apps.

[1296] MODE FOR CARRYING OUT THE INVENTION

[1297] The system of the present invention acquires a user's personal data and emotional data, analyzes this data to generate personalized prompts, and obtains and presents optimal answers using a generative AI model. Specific hardware and software configurations for implementing this system are described below.

[1298] Hardware and Software

[1299] 1. User device: smartphone, tablet, etc.

[1300] 2. Data Collection Devices:

[1301] Smartwatch: Collects health data such as heart rate and steps

[1302] Electronic payment app: Collecting purchase history

[1303] Calendar app: collects user schedules

[1304] Map app: Collects travel history

[1305] 3. Emotion analysis device:

[1306] Camera: Facial expression analysis through facial recognition

[1307] Microphone: Audio analysis

[1308] Text analysis software: Sentiment analysis of user-entered text

[1309] 4. Server: Receives data, analyzes it, generates prompts, and queries the AI ​​model

[1310] Data collection and analysis

[1311] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service on their devices, which allows personal data to be collected. The devices periodically retrieve data from these sources and temporarily store it in local storage. The devices then periodically transmit the data to a server. The devices also analyze the user's voice, face, and writing in real time to collect emotional data.

[1312] The server receives personal data and emotional data sent from the user's device and passes them to the analysis engine. The analysis engine identifies the user's health condition and exercise patterns from the smartwatch data, purchasing patterns from the electronic payment app data, schedules from the calendar app data, and movement history from the map app data. It also identifies the user's current emotional state based on the emotional data.

[1313] Prompt generation and AI queries

[1314] Based on the analyzed data, the server generates the most appropriate prompt based on the user's question or request. For example, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt such as, "I have a meeting tomorrow at 10 o'clock, after which I'm scheduled to travel from the office to my home."

[1315] The device then sends the user's input to the server, which uses a generative AI model to find the optimal answer. During this process, the server works with multiple generative AI models to evaluate the quality of the answers and select the most appropriate answer.

[1316] Obtaining and presenting answers

[1317] The server collects and evaluates the answers received from the generative AI model, selects the most appropriate answer, and sends it to the device, which displays it to the user.

[1318] For example, if a user types the question "What's on my schedule tomorrow?", the system will provide an answer such as "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[1319] keyword

[1320] Generative AI model, prompt sentence

[1321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1322] Step 1:

[1323] Data collection

[1324] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from those devices and apps.

[1325] Input: Data from smartwatch, electronic payment app, calendar app, map app

[1326] Output: Personal data stored on the device

[1327] The device periodically collects data from linked devices and apps and temporarily stores it: specifically, it collects heart rate and step count from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and movement history from a map app.

[1328] Specific operation: The device periodically calls the API to obtain data and saves it in local storage.

[1329] Step 2:

[1330] Emotional Data Collection

[1331] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[1332] Input: User's voice, face, and text

[1333] Output: Emotion data stored on the device

[1334] Voice analysis extracts emotions from the tone and pitch of a user's voice, facial recognition determines emotions from changes in facial expressions, and text analysis uses a dictionary of emotion words to analyze emotions such as positive, negative, and neutral.

[1335] Specific operation: The device uses a camera to analyze facial expressions, a microphone to analyze voices, and performs text analysis of sentences entered by the user.

[1336] The device transmits the collected emotional data and personal data to a server at regular intervals.

[1337] Step 3:

[1338] Data analysis

[1339] The server receives the personal data and emotion data transmitted from the terminal.

[1340] Input: Personal data and emotional data sent from the device

[1341] Output: Data analyzed by the analysis engine

[1342] The server passes the received data to the analysis engine, which performs the following specific data processing and calculations:

[1343] Health data analysis: Analyzes heart rate and step count data from smartwatches to identify the user's health status and exercise patterns.

[1344] Purchasing data analysis: Identifying user purchasing patterns and recently purchased items from data from electronic payment apps.

[1345] Schedule analysis: Analyzes the user's schedule from data from the calendar app.

[1346] Movement history analysis: Identify movement history from map app data and extract frequently visited places, etc.

[1347] Specific operation: The analysis engine in the server filters the data stored in the database and analyzes the data using various analysis algorithms.

[1348] Step 4:

[1349] Prompt Generation

[1350] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests.

[1351] Input: Analysis results (personal data and emotional data)

[1352] Output: Generated prompt

[1353] Specifically, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt saying, "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[1354] Specific operation: The server passes the parameters for prompt generation to the generative AI model and creates the optimal prompt.

[1355] Step 5:

[1356] AI Inquiry

[1357] Users input questions or requests in natural language, for example, "What's on the schedule for tomorrow?"

[1358] Input: User questions or requests

[1359] Output: User input sent to the server

[1360] The terminal transmits the user's input to the server.

[1361] The server combines the user's input with the generated prompts to pose the optimal query to the generative AI model.

[1362] Input: User input and generated prompts

[1363] Output: The query sent to the generative AI model

[1364] How it works: The server combines user input and prompts and queries the generative AI model using an API.

[1365] Step 6:

[1366] Obtaining and presenting answers

[1367] The server collects and evaluates the answers received from the generative AI models, and works with multiple generative AI models to select the optimal answer.

[1368] Input: Answer from a generative AI model

[1369] Output: The best answer selected

[1370] The server sends the best answer to the terminal.

[1371] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[1372] Specific operation: The server compares multiple answers based on the answer evaluation criteria, selects the best one, sends it to the terminal, and displays it to the user.

[1373] (Application example 2)

[1374] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1375] Conventional virtual store systems have difficulty in making product recommendations that reflect the user's individual needs and emotional state. Furthermore, a lack of personalization has led to a poor user experience. Therefore, there is a need for a system that can utilize the user's personal data and emotional data to make highly accurate personalized product recommendations.

[1376] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1377] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, means for presenting the acquired answers to the user, means for acquiring personal data from multiple data sources such as a smartwatch, an electronic payment app, a calendar app, and a map app, means for collecting emotional data through voice analysis, face recognition, and text analysis, means for generating prompt sentences based on the analyzed personal data and emotional data, means for selecting an optimal answer in cooperation with multiple generative AI models, and means for presenting the selected answer to the user as a personalized product proposal. This enables highly accurate personalized product proposals that reflect the user's individual needs and emotional state.

[1378] "User's personal data" refers to information about the user, such as health status, purchase history, schedule, and travel history.

[1379] "Means of acquisition" refers to the means of collecting data from smartwatches, electronic payment apps, calendar apps, map apps, etc.

[1380] "Means of analysis" refers to the means for analyzing the acquired personal data and understanding the user's behavior and status.

[1381] "Personalized prompts" refer to questions or requests that are tailored to a user's individual characteristics and needs, based on analyzed personal and emotional data.

[1382] "Generative AI model" refers to an artificial intelligence model that generates appropriate answers to input prompts.

[1383] "Means for obtaining answers" refers to the means for collecting the answers generated from the generative AI model.

[1384] The "means for presenting to the user" refers to a means for visually or audibly presenting the obtained answer to the user.

[1385] "Voice analysis" refers to the technology of analyzing a user's emotions and state from voice data.

[1386] "Facial recognition" refers to the technology of recognizing a user's face from image data and analyzing their facial expressions.

[1387] "Text analysis" refers to the technology of analyzing a user's emotions and intentions from text data.

[1388] "Personalized product suggestions" refer to suggestions that recommend the most suitable products and services to a user based on the user's personal data and emotional data.

[1389] The system of the present invention collects and analyzes a user's personal data and emotional data, generates prompts based on the collected data, and sends the optimal prompts to a generative AI model to obtain and present answers. This system acquires personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. It can also collect emotional data through voice analysis, face recognition, and text analysis. Specific embodiments are described below.

[1390] 1. Data collection method

[1391] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources. The device periodically retrieves data from the linked data sources and temporarily stores it. The device periodically transmits the collected data to the server.

[1392] 2. Emotion data collection method

[1393] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis, and the collected emotional data is temporarily stored and periodically sent to a server.

[1394] 3. Data Analysis

[1395] The server receives and analyzes the personal and emotional data sent from the device. It identifies health conditions and exercise patterns from smartwatch data, purchasing patterns from electronic payment app data, the user's schedule from calendar app data, and movement history from map app data. Furthermore, it analyzes the emotional data based on voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[1396] 4. Prompt Generation

[1397] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[1398] 5. AI Inquiry

[1399] A user inputs a question or request in natural language. For example, they might input a question like, "What product best suits my mood right now?" The device sends this input to a server, which combines the user's input with generated prompts to form an optimal query and send it to multiple generative AI models.

[1400] 6. Obtaining and Presenting Answers

[1401] The server collects and evaluates the answers received from the generative AI model to select the most appropriate answer. The selected answer is then saved in the database again. The server then sends the selected answer to the device, which then presents the answer to the user. For example, the answer displayed might be, "The relaxing product that best suits your current mood is an aroma candle. It costs 1,500 yen, and more details are available here."

[1402] Specific examples

[1403] For example, if User A inputs the question "What product best suits my mood right now?" into the system, the following prompt sentence will be generated:

[1404] Based on User A's health data, purchase history, schedule, and movement history, please suggest products that suit their current emotional state (relaxation). Based on their recent purchase history, please make a list of recommended products, including new products that will help them relax.

[1405] These prompts are input into a generative AI model, and the answers obtained provide optimal product suggestions to the user.

[1406] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1407] Step 1:

[1408] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows for the collection of personal data from the user. The input is the user-provided credentials for each application, and the output is the readiness to collect personal data from linked data sources.

[1409] Step 2:

[1410] The device periodically retrieves and temporarily stores data from linked data sources, including health data from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and travel history from a map app. The input is data retrieved from each application, and the output is temporarily stored personal data.

[1411] Step 3:

[1412] The device periodically transmits the collected data to the server. The input is the temporarily stored personal data, and the output is the personal data transmitted to the server. This allows the data to be analyzed on the server.

[1413] Step 4:

[1414] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis. The input is the user's voice, facial image, and text data (chat, email, etc.), and the output is analyzed emotional data.

[1415] Step 5:

[1416] The device temporarily stores the collected emotion data and periodically transmits it to the server. The input is the analyzed emotion data, and the output is the emotion data transmitted to the server.

[1417] Step 6:

[1418] The server receives and analyzes the personal data and emotional data sent from the device. The input is the user's personal data and emotional data, and the output is the analysis results. Based on this data, the server identifies the user's health condition, purchasing patterns, schedule, movement history, and emotional state.

[1419] Step 7:

[1420] The server generates personalized prompts based on the analysis results. The input is the analysis results, and the output is the optimal prompt text. The prompt text reflects the user's state and emotions.

[1421] Step 8:

[1422] The user inputs a question or request in natural language. The input is the user's question or request (e.g., "What product would suit my mood right now?"), and the output is receiving and displaying the user's question or request.

[1423] Step 9:

[1424] The terminal sends the user's input to the server. The input is the user's question or request, and the output is the user's question or request sent to the server.

[1425] Step 10:

[1426] The server combines the user input and generated prompts to form an optimal query, where the input is the user input and prompt, and the output is the completed query, which is then sent to multiple generative AI models.

[1427] Step 11:

[1428] The server collects and evaluates the answers received from the generative AI model. The input is the answer of the generative AI model, and the output is the most appropriate answer. The server selects the best answer and stores it in the database again.

[1429] Step 12:

[1430] The server sends the selected answer to the terminal, and the terminal presents the answer to the user. The input is the selected answer, and the output is the answer presented to the user. For example, an answer such as "The relaxing product that is perfect for your current mood is an aroma candle. It costs 1,500 yen, and details are here" is displayed.

[1431] 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.

[1432] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1433] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1434] [Fourth embodiment]

[1435] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1436] 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.

[1437] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1438] 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.

[1439] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1440] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1441] 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.

[1442] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1443] 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.

[1444] 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 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.

[1445] 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.

[1446] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1447] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1448] System Overview

[1449] The system of the present invention acquires and analyzes a user's personal data, sends optimal prompts to a generation AI based on that data, and obtains and presents personalized answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. Next, it analyzes that data and generates optimal prompts for the user. Finally, it sends the generated prompts to the generation AI, which then presents the answers to the user.

[1450] Program processing overview

[1451] 1. Data Collection

[1452] Users link their smartwatch, electronic payment app, calendar app, and map app to the system.

[1453] The terminal periodically retrieves data from the linked data sources.

[1454] The terminal temporarily stores the acquired data and transmits it to the server at a fixed timing.

[1455] 2. Data Analysis

[1456] The server analyzes the data received from each data source.

[1457] The server analyzes the user's health condition and exercise patterns based on data from the smartwatch.

[1458] The server analyzes users' purchasing patterns based on data from the electronic payment app.

[1459] The server analyzes the user's schedule based on data from the calendar app.

[1460] The server analyzes the user's movement history based on data from the map app.

[1461] 3. Prompt Generation

[1462] The server generates the most appropriate prompt based on the analyzed data in response to the user's question or request.

[1463] The prompt reflects the user's health condition, purchase history, schedule, travel history, etc.

[1464] 4. AI Inquiry

[1465] Users enter questions and requests in natural language.

[1466] The terminal transmits the user's input to the server.

[1467] The server combines the user's input with the parsed data to construct the optimal prompt.

[1468] The server sends this constructed prompt to multiple generating AIs.

[1469] The server receives and evaluates the answers from each generated AI.

[1470] 5. Obtaining and Presenting Answers

[1471] The server evaluates the received answers and selects the most appropriate answer.

[1472] The server selects the most suitable answer for the user and sends it to the terminal.

[1473] The terminal displays the answer to the user.

[1474] Specific examples

[1475] For example, the following shows the process when a user inputs the question "What's on the schedule tomorrow?" into the system.

[1476] 1. Data Collection

[1477] Users link their everyday smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[1478] The device obtains the user's exercise history from the smartwatch, purchase history from the electronic payment app, schedule from the calendar app, and movement history from the map app.

[1479] The secured data is periodically sent to the server.

[1480] 2. Data Analysis

[1481] The server analyzes the data from the smartwatch to identify health information such as exercise history and heart rate.

[1482] The server analyzes data from the electronic payment app to identify recent purchasing behavior.

[1483] The server parses the data from the calendar app to determine the next day's schedule.

[1484] The server analyzes data from the map app to identify movement patterns.

[1485] 3. Prompt Generation

[1486] The server generates appropriate prompts for user input based on analyzed health information, purchasing behavior, schedules, and travel patterns.

[1487] "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[1488] 4. AI Inquiry

[1489] The user types the question, "What's on the schedule tomorrow?"

[1490] The terminal sends this input to the server.

[1491] The server sends the question along with the generated prompt to multiple generating AIs.

[1492] 5. Obtaining and Presenting Answers

[1493] The server evaluates the answers received from each generation AI and selects the best answer.

[1494] The server transmits the selected response to the terminal.

[1495] The terminal displays the answer to the user, "I have a meeting tomorrow at 10 o'clock, after which I plan to travel from the office to my home."

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, and once the link is established, personal information from these data sources can be collected.

[1499] Step 2:

[1500] The device periodically retrieves data from linked data sources: health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and location information and movement history from a map app.

[1501] Step 3:

[1502] The device temporarily stores the collected data locally, and the stored data is sent to the server at regular intervals.

[1503] Step 4:

[1504] The server receives the data sent from the terminal and stores the received data in a database.

[1505] Step 5:

[1506] The server analyzes the information received from each data source: Analyzing data from a smartwatch to identify the user's health and exercise patterns Analyzing data from an electronic payment app to identify purchasing patterns and spending trends Analyzing data from a calendar app to identify the user's schedule Analyzing data from a map app to identify movement history and places visited

[1507] Step 6:

[1508] The server uses the analyzed data to generate personalized prompts based on the user's questions and requests, including specific information such as the user's health status, purchase history, schedule, and travel history.

[1509] Step 7:

[1510] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[1511] Step 8:

[1512] The terminal sends the user's input to the server, where it is processed.

[1513] Step 9:

[1514] The server combines the user's input with previously generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[1515] Step 10:

[1516] The server collects the answers received from the AI ​​generators. The answers from each AI generator are evaluated by the server.

[1517] Step 11:

[1518] The server evaluates the collected answers and selects the most appropriate answer, which is then stored back in the database.

[1519] Step 12:

[1520] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[1521] Example 1

[1522] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1523] Conventional systems have had difficulty efficiently collecting and analyzing users' personal data and providing personalized answers. Furthermore, mechanisms for effectively integrating data collected from multiple data sources and quickly generating optimal answers to users' questions and requests have not been fully established. Furthermore, the process of selecting the optimal answer using multiple generative AI systems is complicated, making it impossible to provide the most appropriate information to users.

[1524] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1525] In this invention, the server includes means for acquiring personal data of the user, means for periodically transmitting the acquired personal data to the server, means for analyzing the received data and identifying user behavior and patterns, means for generating personalized prompts based on the analysis results in response to the user's questions and requests, means for transmitting the generated prompts to multiple generative AIs, acquiring and evaluating answers, and means for presenting the optimal answer to the user. This makes it possible to integrate information acquired from the user's various data sources and provide personalized answers efficiently and quickly.

[1526] "User personal data" refers to data generated by a user that can identify or identify an individual, such as biometric information, purchase history, schedule, and location information.

[1527] A "server" is a centralized computer system that receives data from users' terminals or devices and analyzes and processes that data.

[1528] "Means of acquisition" refers to the organization of software and hardware for collecting data from external data sources such as smart devices and applications.

[1529] The "periodic transmission means" refers to a protocol and mechanism for transmitting acquired data to a server at regular intervals.

[1530] The "analysis means" refers to an algorithm and data processing method for identifying the user's behavioral patterns, health status, schedule details, etc. based on the received data.

[1531] A "prompt" is a specific inquiry that is generated based on a user's question or request and reflects the analysis results.

[1532] "Generative AI" is an artificial intelligence model that generates appropriate answers to prompts it receives.

[1533] "Means for obtaining and evaluating answers" refers to the algorithms and methods for evaluating the answers returned by the generative AI and selecting the most appropriate one.

[1534] "Multiple data sources" refers to various information sources that provide data, such as smart devices, electronic trading apps, schedule management apps, and location information apps.

[1535] The following detailed description is provided as a mode for implementing the invention.

[1536] System Overview

[1537] The system of the present invention obtains a user's personal data from multiple data sources, analyzes it to generate personalized prompts, and sends the prompts to a generation AI to provide the user with the most appropriate answer. The system consists of the following main components:

[1538] 1. How we collect your personal data

[1539] Smart devices (e.g. smartwatches)

[1540] E-commerce app

[1541] Schedule management apps (e.g., calendar apps)

[1542] Location-based apps (e.g., map apps)

[1543] 2. Means for periodically sending acquired personal data to a server

[1544] Data transmission protocol and dedicated software

[1545] 3. A means of analyzing the received data and identifying user behavior and patterns.

[1546] Data Analysis Algorithms

[1547] 4. A way to use the analysis results to generate personalized prompts based on the user's questions and requests.

[1548] Prompt Generation Engine

[1549] 5. A means to send the generated prompts to multiple generative AIs, obtain responses, and evaluate them.

[1550] Multiple generative AI models and an interface to control them

[1551] 6. How to present the best answer to the user

[1552] User Interface

[1553] Hardware and Software Details

[1554] Users link their everyday smartwatches and mobile applications to the system, integrating their biometric information, purchase history, schedule, location information, and more into the system. Smartwatches (e.g., health management devices) measure heart rate and exercise volume and send the data to the device.

[1555] The device uses a data transmission protocol to temporarily store this data and periodically transmit it to a server. The server receives the transmitted data and uses a data analysis algorithm to analyze the user's behavior and patterns, including health status, purchasing behavior, schedule, and movement history.

[1556] Next, the prompt generation engine uses the analysis results to generate a prompt that matches the user's question or request, such as "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[1557] The generated prompts are sent from the server to multiple generative AI models, which then return answers. The server evaluates these answers and selects the best answer, which is then presented to the user through a user interface.

[1558] Specific examples

[1559] As a concrete example, the process when the user inputs the question "What's the schedule for tomorrow?" is shown below.

[1560] 1. Data Collection

[1561] It collects data from the user's linked smartwatch, electronic payment app, calendar app, and map app.

[1562] 2. Data Analysis

[1563] The server analyzes this data to determine, for example, health information and schedules.

[1564] 3. Prompt Generation

[1565] It generates prompts such as, "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'"

[1566] 4. AI Inquiry

[1567] This prompt is sent to a generative AI model, which chooses the best answer.

[1568] 5. Answer presentation

[1569] Ultimately, the user will see an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[1570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1571] Step 1: Data collection

[1572] Users link their smartwatches, e-commerce apps, schedule management apps, and location apps to the system.

[1573] The device periodically collects personal data (e.g., heart rate, purchase history, schedule, movement history) from linked devices and apps.

[1574] Input: Personal data from smart devices and applications

[1575] Data processing: Using the API of each device or application, the data is formatted and temporarily stored.

[1576] Output: Formatted personal data

[1577] Step 2: Send data

[1578] The terminal transmits the temporarily stored data to the server at a fixed timing.

[1579] Input: Temporarily saved formatted personal data

[1580] Data processing: Using a data transmission protocol, the data is sent to the server.

[1581] Output: Personal data sent to the server

[1582] Step 3: Receiving data

[1583] The server receives the personal data transmitted from the terminal.

[1584] Input: Personal data sent from the device

[1585] Data processing: The received data is stored in the server's database.

[1586] Output: Personal data stored on the server

[1587] Step 4: Data analysis

[1588] The server analyzes the received data and identifies user behavior and patterns.

[1589] Input: Personal data stored on the server

[1590] Data computation: Applying data analysis algorithms to identify health conditions, purchasing behavior, schedules, and travel history.

[1591] Output: Analyzed user behavior and patterns (e.g., exercise patterns, purchase history, schedule, travel patterns)

[1592] Step 5: Prompt generation

[1593] The server uses the analysis results to generate personalized prompts that respond to the user's questions and requests.

[1594] Input: Analyzed user behavior and patterns

[1595] Data Calculation: The analysis results are converted into prompts using a prompt generation engine.

[1596] Output: Generated prompt (e.g., "Based on your recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is your personalized answer for 'What is my schedule like tomorrow?'")

[1597] Step 6: User enters question

[1598] Users enter questions and requests in natural language.

[1599] Input: A user question or request (e.g., "What's on the schedule tomorrow?")

[1600] Output: User's natural language input

[1601] Step 7: Submit your question

[1602] The terminal transmits the user's questions and requests to the server.

[1603] Input: User's natural language input

[1604] Data processing: The query or request is formatted according to the protocol and sent to the server.

[1605] Output: Formatted questions and requests to the server

[1606] Step 8: Send prompts

[1607] The server combines the generated prompts with the user's question and sends them to multiple generative AI models.

[1608] Input: User question, generated prompt

[1609] Data computation: Prompts and questions are combined and sent to each generative AI model.

[1610] Output: The prompts and questions sent to the generative AI model

[1611] Step 9: Obtaining and Evaluating Answers

[1612] The server receives the answers from the generative AI model and selects the best answer.

[1613] Input: Answer from a generative AI model

[1614] Data calculation: Evaluate each answer and select the most appropriate answer.

[1615] Output: Best answer

[1616] Step 10: Submit and present your answers

[1617] The server transmits the selected optimal answer to the terminal.

[1618] Input: Best Answer

[1619] Data processing: The responses are formatted for the user interface and sent to the device.

[1620] The terminal displays the answer to the user.

[1621] Output: The answer shown to the user (e.g., "I have a meeting tomorrow at 10:00, after which I plan to travel from my office to my home.")

[1622] (Application example 1)

[1623] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1624] Conventional personalization systems that use personal data have the problem of being unable to effectively utilize a user's diverse data sources, resulting in services and content that do not adequately address the user's interests and activities. It is also difficult to integrate data collected from different devices and applications and provide optimal content based on individual analysis results. Furthermore, there is also the issue of how to effectively utilize generative AI to provide optimal answers and recommended content to users.

[1625] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1626] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI and acquiring answers, means for presenting the acquired answers to the user, and means for recommending optimal content based on the user's interests and activities. This makes it possible to provide personalized content by integrating various data sources of the user and sending prompt sentences to the generative AI model.

[1627] "User personal data" refers to information related to an individual obtained from the digital devices and applications that a user uses on a daily basis, including data on health status, purchasing history, schedule, travel history, etc.

[1628] "Means of acquisition" refers to the functions and methods for collecting personal data from multiple data sources, such as linking a smartwatch with an electronic payment application, a calendar application, and a map application to acquire data.

[1629] "Means for analysis" refers to the functions and methods for processing collected personal data and analyzing the user's behavioral patterns and status, such as analyzing health status, purchasing patterns, schedules, and movement history.

[1630] A "personalized prompt" is a prompt that, based on the analysis results, creates a query or request that best suits the user's specific interests and needs.

[1631] "Generative AI" refers to an artificial intelligence model or system that generates answers to questions or requests in natural language based on given prompts. It may include multiple generative AIs.

[1632] "Means of obtaining answers" refers to the function or method for sending the generated prompts to the generative AI and collecting the answers obtained from them.

[1633] "Means for presenting to the user" refers to the functions and methods for displaying the obtained answers and recommended content to the user. For example, providing information to the user via a smartphone application.

[1634] "Means for recommending content" refers to functions or methods that suggest optimal content (videos, articles, music, events, etc.) based on the user's interests and activities, based on the results of an analysis of the user's personal data.

[1635] This invention relates to a system for recommending optimal content based on a user's interests and activities. The system acquires and analyzes the user's personal data, and based on the data, sends optimal prompts to a generating AI to present personalized answers and recommended content.

[1636] The system uses the following hardware and software:

[1637] Hardware: Smartphone (user device), server

[1638] Software: Smartwatch app, electronic payment app, calendar app, map app, Python, Django (cloud server side), generative AI (e.g., OpenAI GPT model)

[1639] The system works as follows:

[1640] First, the user's device links the smartwatch, electronic payment app, calendar app, and map app to collect personal data from each data source. The collected data is temporarily stored in the device's local storage and periodically sent to a cloud server. This allows data related to the user's daily activities and interests to be accumulated.

[1641] Next, the cloud server analyzes the received data. Specifically, the following analysis is performed:

[1642] Health analysis from smartwatches (e.g., steps, heart rate, etc.)

[1643] Analysis of purchasing patterns from electronic payment apps (e.g., products purchased and frequency of purchases)

[1644] Analyzing schedules from calendar apps (e.g., upcoming meetings and events)

[1645] Analysis of travel history from map apps (e.g., places visited, distance traveled, etc.)

[1646] Based on the analyzed data, the cloud server generates the optimal prompt for the user. This prompt is sent to the AI ​​generator in response to the user's specific question or request. For example, in response to the question, "What video do you want to watch now?", the following prompt sentence is generated:

[1647] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[1648] The generated prompts are sent to the generation AI to obtain the answers. The cloud server evaluates the answers received from multiple generation AIs and selects the optimal answer or recommended content. The selected answer or content is then sent to the user's device and presented to the user.

[1649] For example, if a user types the question, "What video should I watch tonight?", the cloud server generates a message that reads, "Based on your recent activity, we recommend a cooking video to help you relax," and displays it on the user's smartphone.

[1650] In this way, a system is realized that can integrate a user's various data sources and provide personalized content.

[1651] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1652] Step 1:

[1653] Data collection

[1654] Users link their smartwatches, electronic payment applications, calendar applications, and map applications to the system. The devices periodically acquire personal data (health information, purchase history, schedules, and movement history) from each linked data source. The acquired data is temporarily stored in the device's local storage and then sent to a cloud server at regular intervals.

[1655] Input: Personal data from smartwatches, electronic payment applications, calendar applications, and map applications

[1656] Output: Save data to local storage and send data to cloud server

[1657] Step 2:

[1658] Data analysis

[1659] The cloud server analyzes the data received from each data source, and in the process identifies the user's health status (number of steps, heart rate, etc.), purchasing patterns (items purchased, purchase frequency, etc.), schedule (meetings and event schedules), movement history, etc.

[1660] Input: Personal data obtained

[1661] Output: Analysis results of health status, purchasing patterns, schedules, and travel history

[1662] Step 3:

[1663] Prompt Generation

[1664] The cloud server uses the analyzed data to generate optimal prompts based on the user's questions and requests. These prompts reflect the user's health status, purchase history, schedule, and travel history. For example, the following prompts may be generated:

[1665] "Based on the user's recent activities: Exercise: walking 5000 steps daily, Purchases: groceries, Schedule: meeting at 10 AM, Movement: home to office, Here is a personalized recommendation for 'What video should I watch this evening?'"

[1666] Input: Analysis results

[1667] Output: personalized prompt

[1668] Step 4:

[1669] AI Inquiry

[1670] The user inputs a question or request in natural language. The device sends the user's input to a cloud server. The cloud server then constructs an optimal prompt based on the user's input and analyzed data, and sends this prompt to multiple generation AIs.

[1671] Input: User questions, requests, analysis results

[1672] Output: Prompt sent to the generation AI

[1673] Step 5:

[1674] Obtaining and evaluating answers

[1675] The cloud server receives the answers from each generation AI, evaluates each answer, and selects the most appropriate answer.

[1676] Input: Answer from the generative AI

[1677] Output: The best answer evaluated

[1678] Step 6:

[1679] Providing an answer

[1680] The cloud server then sends the selected optimal answer to the user's device, which then displays the answer to the user. For example, the user might receive a message such as, "Based on your recent activities, we recommend some cooking videos to help you relax."

[1681] Input: Best Answer

[1682] Output: Send and display the answer to the user's terminal

[1683] By repeating the above steps, a system is realized that can integrate a user's various data sources and provide personalized content.

[1684] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1685] System Overview

[1686] The system of the present invention acquires and analyzes a user's personal data and emotional data, generates personalized prompts based on that data, and sends the optimal prompts to the generation AI to obtain and present answers. This system acquires the user's personal data from multiple data sources, such as a smartwatch, electronic payment app, calendar app, and map app. Furthermore, by combining an emotion engine, it is possible to recognize the user's emotions from voice analysis, facial recognition, and text analysis. This allows the user's emotional data to be reflected in the analysis results, providing more appropriate prompts and answers.

[1687] Program processing overview

[1688] 1. Data Collection

[1689] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources.

[1690] The device periodically retrieves data from the linked data source and temporarily stores it.

[1691] The terminal periodically transmits the collected data to the server.

[1692] 2. Emotional Data Collection

[1693] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[1694] The device temporarily stores the collected emotion data and periodically transmits it to the server.

[1695] 3. Data Analysis

[1696] The server receives the personal data and emotion data transmitted from the terminal.

[1697] The server analyzes the information received from each data source: health status and exercise patterns are identified from smartwatch data, purchasing patterns from electronic payment app data, user schedules from calendar app data, and movement history from map app data.

[1698] The server also analyzes emotional data obtained through voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[1699] 4. Prompt Generation

[1700] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[1701] 5. AI Inquiry

[1702] A user enters a question or request in natural language, for example, "What's on the schedule for tomorrow?"

[1703] The terminal transmits the user's input to the server.

[1704] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[1705] 6. Obtaining and Presenting Answers

[1706] The server collects and evaluates the answers received from the generation AI.

[1707] The server selects the most appropriate answer, which is then stored in the database again.

[1708] The server sends the selected answer to the terminal.

[1709] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[1710] Specific examples

[1711] For example, if a user inputs a question into the system such as "What's on the schedule for tomorrow?", the process is as follows:

[1712] 1. Data Collection

[1713] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the system.

[1714] The device collects health data from the smartwatch, purchase history from the electronic payment app, schedules from the calendar app, and movement history from the map app, and sends this to a server.

[1715] 2. Emotional Data Collection

[1716] The device acquires emotional data through voice analysis, facial recognition, and text analysis of the user and sends it to the server.

[1717] 3. Data Analysis

[1718] The server receives and analyzes the personal data and emotional data to determine the user's health status, purchase history, schedule, and travel history, and to determine the user's current emotional state from the emotional data.

[1719] 4. Prompt Generation

[1720] The server uses the analyzed information to generate the most appropriate prompt for the user's question, which reflects the user's state and emotions.

[1721] 5. AI Inquiry

[1722] The user types, "What's on my schedule tomorrow?"

[1723] The terminal sends this input to the server.

[1724] The server combines the prompts and inputs and sends them to the generated AI.

[1725] 6. Obtaining and Presenting Answers

[1726] The server receives the answers from the generation AI, evaluates them, and selects the most appropriate answer.

[1727] The server sends the selected answer to the terminal.

[1728] The device will display an answer such as, "I have a meeting tomorrow at 10:00, after which I will be traveling from the office to my home."

[1729] The processing flow will be explained below.

[1730] Step 1:

[1731] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these devices and apps.

[1732] Step 2:

[1733] The device periodically retrieves data from each linked data source and temporarily stores it locally. For example, it retrieves health data such as heart rate, step count, and exercise history from a smartwatch, purchase history from an electronic payment app, appointment and event information from a calendar app, and movement history from a map app.

[1734] Step 3:

[1735] The device periodically sends the collected data to the server, allowing the server to always have the most up-to-date personal data.

[1736] Step 4:

[1737] The device can analyze the user's voice, recognize their face, analyze their writing, and collect emotional data in real time. For example, it can use a camera and microphone to analyze the user's facial expressions and tone of voice.

[1738] Step 5:

[1739] The device temporarily stores the collected emotion data and periodically transmits it to the server, which centrally manages the emotion data in the same way as personal data.

[1740] Step 6:

[1741] The server receives the personal data and emotion data sent from the device, which is then stored in a database for subsequent analysis.

[1742] Step 7:

[1743] The server analyzes the received personal data and emotional data. For example, it analyzes smartwatch data to identify the user's health condition and exercise patterns, analyzes electronic payment app data to identify purchasing patterns, analyzes calendar app data to identify the user's schedule, and analyzes map app data to identify movement history. Furthermore, it analyzes emotional data obtained through voice analysis, face recognition, and text analysis to identify the user's current emotional state.

[1744] Step 8:

[1745] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests. The generated prompts reflect the user's health status, purchasing history, schedule, travel history, emotional state, and other identified information.

[1746] Step 9:

[1747] A user inputs a question or request into a terminal in natural language, for example, "What's the schedule for tomorrow?"

[1748] Step 10:

[1749] The terminal sends the user's input to the server, where the user's natural language input is processed appropriately.

[1750] Step 11:

[1751] The server combines the user's input with the generated prompts to form an optimal query, which is then sent to multiple generation AIs.

[1752] Step 12:

[1753] The server collects and evaluates the answers received from the AI ​​generators, compares the answers from multiple AI generators, and selects the most appropriate answer.

[1754] Step 13:

[1755] The server sends the selected answer to the terminal, which then displays the selected answer to the user. For example, the answer displayed may be something like, "There will be a meeting tomorrow at 10:00, after which you will be scheduled to travel from the office to your home."

[1756] Example 2

[1757] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1758] Conventional systems have had difficulty efficiently collecting and analyzing users' personal and emotional data to generate personalized prompts. Furthermore, they lacked a mechanism for linking multiple generative AI models to select optimal answers and provide them to users. As a result, they were unable to provide appropriate services that could meet the diverse needs of users.

[1759] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring personal data of the user, means for analyzing the acquired personal data, means for collecting and analyzing emotion data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, and means for presenting the acquired answers to the user. This makes it possible to extract deep insights based on diverse user data and provide optimal prompts and answers for each individual user.

[1760] "Personal data" refers to information such as heart rate, step count, purchase history, schedule, and movement history obtained from a user's smartwatch, electronic payment app, calendar app, map app, etc.

[1761] "Emotional data" refers to data that indicates a user's emotional state obtained through voice analysis, facial recognition, and text analysis.

[1762] "Analysis" refers to the process of identifying a user's health status, purchasing patterns, schedule, travel history, and emotional state based on the acquired personal and emotional data.

[1763] "Personalized prompting" refers to the process of creating the optimal query for a user's specific question or request based on the analysis results.

[1764] "Generative AI models" refer to artificial intelligence algorithms and systems that generate appropriate answers based on user input and generated prompts.

[1765] "Presenting" refers to the act of displaying or notifying the user of the obtained answer.

[1766] "Data Source" refers to devices or applications that provide personal data and emotional data, such as smartwatches, electronic payment apps, calendar apps, and map apps.

[1767] MODE FOR CARRYING OUT THE INVENTION

[1768] The system of the present invention acquires a user's personal data and emotional data, analyzes this data to generate personalized prompts, and obtains and presents optimal answers using a generative AI model. Specific hardware and software configurations for implementing this system are described below.

[1769] Hardware and Software

[1770] 1. User device: smartphone, tablet, etc.

[1771] 2. Data Collection Devices:

[1772] Smartwatch: Collects health data such as heart rate and steps

[1773] Electronic payment app: Collecting purchase history

[1774] Calendar app: collects user schedules

[1775] Map app: Collects travel history

[1776] 3. Emotion analysis device:

[1777] Camera: Facial expression analysis through facial recognition

[1778] Microphone: Audio analysis

[1779] Text analysis software: Sentiment analysis of user-entered text

[1780] 4. Server: Receives data, analyzes it, generates prompts, and queries the AI ​​model

[1781] Data collection and analysis

[1782] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service on their devices, which allows personal data to be collected. The devices periodically retrieve data from these sources and temporarily store it in local storage. The devices then periodically transmit the data to a server. The devices also analyze the user's voice, face, and writing in real time to collect emotional data.

[1783] The server receives personal data and emotional data sent from the user's device and passes them to the analysis engine. The analysis engine identifies the user's health condition and exercise patterns from the smartwatch data, purchasing patterns from the electronic payment app data, schedules from the calendar app data, and movement history from the map app data. It also identifies the user's current emotional state based on the emotional data.

[1784] Prompt generation and AI queries

[1785] Based on the analyzed data, the server generates the most appropriate prompt based on the user's question or request. For example, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt such as, "I have a meeting tomorrow at 10 o'clock, after which I'm scheduled to travel from the office to my home."

[1786] The device then sends the user's input to the server, which uses a generative AI model to find the optimal answer. During this process, the server works with multiple generative AI models to evaluate the quality of the answers and select the most appropriate answer.

[1787] Obtaining and presenting answers

[1788] The server collects and evaluates the answers received from the generative AI model, selects the most appropriate answer, and sends it to the device, which displays it to the user.

[1789] For example, if a user types the question "What's on my schedule tomorrow?", the system will provide an answer such as "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[1790] keyword

[1791] Generative AI model, prompt sentence

[1792] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1793] Step 1:

[1794] Data collection

[1795] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from those devices and apps.

[1796] Input: Data from smartwatch, electronic payment app, calendar app, map app

[1797] Output: Personal data stored on the device

[1798] The device periodically collects data from linked devices and apps and temporarily stores it: specifically, it collects heart rate and step count from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and movement history from a map app.

[1799] Specific operation: The device periodically calls the API to obtain data and saves it in local storage.

[1800] Step 2:

[1801] Emotional Data Collection

[1802] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis.

[1803] Input: User's voice, face, and text

[1804] Output: Emotion data stored on the device

[1805] Voice analysis extracts emotions from the tone and pitch of a user's voice, facial recognition determines emotions from changes in facial expressions, and text analysis uses a dictionary of emotion words to analyze emotions such as positive, negative, and neutral.

[1806] Specific operation: The device uses a camera to analyze facial expressions, a microphone to analyze voices, and performs text analysis of sentences entered by the user.

[1807] The device transmits the collected emotional data and personal data to a server at regular intervals.

[1808] Step 3:

[1809] Data analysis

[1810] The server receives the personal data and emotion data transmitted from the terminal.

[1811] Input: Personal data and emotional data sent from the device

[1812] Output: Data analyzed by the analysis engine

[1813] The server passes the received data to the analysis engine, which performs the following specific data processing and calculations:

[1814] Health data analysis: Analyzes heart rate and step count data from smartwatches to identify the user's health status and exercise patterns.

[1815] Purchasing data analysis: Identifying user purchasing patterns and recently purchased items from data from electronic payment apps.

[1816] Schedule analysis: Analyzes the user's schedule from data from the calendar app.

[1817] Movement history analysis: Identify movement history from map app data and extract frequently visited places, etc.

[1818] Specific operation: The analysis engine in the server filters the data stored in the database and analyzes the data using various analysis algorithms.

[1819] Step 4:

[1820] Prompt Generation

[1821] The server generates optimal prompts based on the analyzed personal and emotional data in response to the user's questions and requests.

[1822] Input: Analysis results (personal data and emotional data)

[1823] Output: Generated prompt

[1824] Specifically, if a user asks, "What's on my schedule tomorrow?", the server generates a prompt saying, "I have a meeting tomorrow at 10:00, after which I'll be traveling from the office to my home."

[1825] Specific operation: The server passes the parameters for prompt generation to the generative AI model and creates the optimal prompt.

[1826] Step 5:

[1827] AI Inquiry

[1828] Users input questions or requests in natural language, for example, "What's on the schedule for tomorrow?"

[1829] Input: User questions or requests

[1830] Output: User input sent to the server

[1831] The terminal transmits the user's input to the server.

[1832] The server combines the user's input with the generated prompts to pose the optimal query to the generative AI model.

[1833] Input: User input and generated prompts

[1834] Output: The query sent to the generative AI model

[1835] How it works: The server combines user input and prompts and queries the generative AI model using an API.

[1836] Step 6:

[1837] Obtaining and presenting answers

[1838] The server collects and evaluates the answers received from the generative AI models, and works with multiple generative AI models to select the optimal answer.

[1839] Input: Answer from a generative AI model

[1840] Output: The best answer selected

[1841] The server sends the best answer to the terminal.

[1842] The device displays the answer to the user, for example, "There will be a meeting tomorrow at 10 o'clock, after which you will be scheduled to travel from the office to your home."

[1843] Specific operation: The server compares multiple answers based on the answer evaluation criteria, selects the best one, sends it to the terminal, and displays it to the user.

[1844] (Application example 2)

[1845] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1846] Conventional virtual store systems have difficulty in making product recommendations that reflect the user's individual needs and emotional state. Furthermore, a lack of personalization has led to a poor user experience. Therefore, there is a need for a system that can utilize the user's personal data and emotional data to make highly accurate personalized product recommendations.

[1847] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1848] In this invention, the server includes means for acquiring personal data of a user, means for analyzing the acquired personal data, means for generating personalized prompts based on the analysis results, means for sending the generated prompts to a generative AI model and acquiring answers, means for presenting the acquired answers to the user, means for acquiring personal data from multiple data sources such as a smartwatch, an electronic payment app, a calendar app, and a map app, means for collecting emotional data through voice analysis, face recognition, and text analysis, means for generating prompt sentences based on the analyzed personal data and emotional data, means for selecting an optimal answer in cooperation with multiple generative AI models, and means for presenting the selected answer to the user as a personalized product proposal. This enables highly accurate personalized product proposals that reflect the user's individual needs and emotional state.

[1849] "User's personal data" refers to information about the user, such as health status, purchase history, schedule, and travel history.

[1850] "Means of acquisition" refers to the means of collecting data from smartwatches, electronic payment apps, calendar apps, map apps, etc.

[1851] "Means of analysis" refers to the means for analyzing the acquired personal data and understanding the user's behavior and status.

[1852] "Personalized prompts" refer to questions or requests that are tailored to a user's individual characteristics and needs, based on analyzed personal and emotional data.

[1853] "Generative AI model" refers to an artificial intelligence model that generates appropriate answers to input prompts.

[1854] "Means for obtaining answers" refers to the means for collecting the answers generated from the generative AI model.

[1855] The "means for presenting to the user" refers to a means for visually or audibly presenting the obtained answer to the user.

[1856] "Voice analysis" refers to the technology of analyzing a user's emotions and state from voice data.

[1857] "Facial recognition" refers to the technology of recognizing a user's face from image data and analyzing their facial expressions.

[1858] "Text analysis" refers to the technology of analyzing a user's emotions and intentions from text data.

[1859] "Personalized product suggestions" refer to suggestions that recommend the most suitable products and services to a user based on the user's personal data and emotional data.

[1860] The system of the present invention collects and analyzes a user's personal data and emotional data, generates prompts based on the collected data, and sends the optimal prompts to a generative AI model to obtain and present answers. This system acquires personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app. It can also collect emotional data through voice analysis, face recognition, and text analysis. Specific embodiments are described below.

[1861] 1. Data collection method

[1862] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows personal data to be collected from these data sources. The device periodically retrieves data from the linked data sources and temporarily stores it. The device periodically transmits the collected data to the server.

[1863] 2. Emotion data collection method

[1864] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis, and the collected emotional data is temporarily stored and periodically sent to a server.

[1865] 3. Data Analysis

[1866] The server receives and analyzes the personal and emotional data sent from the device. It identifies health conditions and exercise patterns from smartwatch data, purchasing patterns from electronic payment app data, the user's schedule from calendar app data, and movement history from map app data. Furthermore, it analyzes the emotional data based on voice analysis, facial recognition, and text analysis to determine the user's current emotional state.

[1867] 4. Prompt Generation

[1868] The server generates optimal prompts based on the analyzed personal and emotional data, reflecting the user's health status, purchasing history, schedule, travel history, emotional state, and other factors.

[1869] 5. AI Inquiry

[1870] A user inputs a question or request in natural language. For example, they might input a question like, "What product best suits my mood right now?" The device sends this input to a server, which combines the user's input with generated prompts to form an optimal query and send it to multiple generative AI models.

[1871] 6. Obtaining and Presenting Answers

[1872] The server collects and evaluates the answers received from the generative AI model to select the most appropriate answer. The selected answer is then saved in the database again. The server then sends the selected answer to the device, which then presents the answer to the user. For example, the answer displayed might be, "The relaxing product that best suits your current mood is an aroma candle. It costs 1,500 yen, and more details are available here."

[1873] Specific examples

[1874] For example, if User A inputs the question "What product best suits my mood right now?" into the system, the following prompt sentence will be generated:

[1875] Based on User A's health data, purchase history, schedule, and movement history, please suggest products that suit their current emotional state (relaxation). Based on their recent purchase history, please make a list of recommended products, including new products that will help them relax.

[1876] These prompts are input into a generative AI model, and the answers obtained provide optimal product suggestions to the user.

[1877] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1878] Step 1:

[1879] Users link their smartwatches, electronic payment apps, calendar apps, and map apps to the service, which allows for the collection of personal data from the user. The input is the user-provided credentials for each application, and the output is the readiness to collect personal data from linked data sources.

[1880] Step 2:

[1881] The device periodically retrieves and temporarily stores data from linked data sources, including health data from a smartwatch, purchase history from an electronic payment app, appointments from a calendar app, and travel history from a map app. The input is data retrieved from each application, and the output is temporarily stored personal data.

[1882] Step 3:

[1883] The device periodically transmits the collected data to the server. The input is the temporarily stored personal data, and the output is the personal data transmitted to the server. This allows the data to be analyzed on the server.

[1884] Step 4:

[1885] The device collects user emotional data in real time through voice analysis, facial recognition, and text analysis. The input is the user's voice, facial image, and text data (chat, email, etc.), and the output is analyzed emotional data.

[1886] Step 5:

[1887] The device temporarily stores the collected emotion data and periodically transmits it to the server. The input is the analyzed emotion data, and the output is the emotion data transmitted to the server.

[1888] Step 6:

[1889] The server receives and analyzes the personal data and emotional data sent from the device. The input is the user's personal data and emotional data, and the output is the analysis results. Based on this data, the server identifies the user's health condition, purchasing patterns, schedule, movement history, and emotional state.

[1890] Step 7:

[1891] The server generates personalized prompts based on the analysis results. The input is the analysis results, and the output is the optimal prompt text. The prompt text reflects the user's state and emotions.

[1892] Step 8:

[1893] The user inputs a question or request in natural language. The input is the user's question or request (e.g., "What product would suit my mood right now?"), and the output is receiving and displaying the user's question or request.

[1894] Step 9:

[1895] The terminal sends the user's input to the server. The input is the user's question or request, and the output is the user's question or request sent to the server.

[1896] Step 10:

[1897] The server combines the user input and generated prompts to form an optimal query, where the input is the user input and prompt, and the output is the completed query, which is then sent to multiple generative AI models.

[1898] Step 11:

[1899] The server collects and evaluates the answers received from the generative AI model. The input is the answer of the generative AI model, and the output is the most appropriate answer. The server selects the best answer and stores it in the database again.

[1900] Step 12:

[1901] The server sends the selected answer to the terminal, and the terminal presents the answer to the user. The input is the selected answer, and the output is the answer presented to the user. For example, an answer such as "The relaxing product that is perfect for your current mood is an aroma candle. It costs 1,500 yen, and details are here" is displayed.

[1902] 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.

[1903] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1904] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1905] 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.

[1906] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1907] 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.

[1908] 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).

[1909] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1910] 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."

[1911] 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.

[1912] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1913] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1914] 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.

[1915] 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.

[1916] 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.

[1917] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1918] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] The following is further disclosed regarding the above embodiment.

[1924] (Claim 1)

[1925] a means for obtaining personal data of the user;

[1926] The means for analyzing the acquired personal data;

[1927] a means for generating personalized prompts based on the analysis results;

[1928] A means for sending the generated prompts to the generative AI and obtaining answers; and

[1929] means for presenting the obtained answers to the user;

[1930] A system including:

[1931] (Claim 2)

[1932] 10. The system of claim 1, further comprising means for acquiring personal data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app.

[1933] (Claim 3)

[1934] The system of claim 1, comprising means for working with multiple generative AIs to select the optimal answer.

[1935] "Example 1"

[1936] (Claim 1)

[1937] a means for obtaining personal data of the user;

[1938] A means for periodically transmitting the acquired personal data to a server;

[1939] means for analyzing the received data to identify user behavior and patterns;

[1940] a means for generating personalized prompts based on the analysis results in response to a user's question or request;

[1941] A means for sending the generated prompts to multiple generative AIs to obtain and evaluate responses; and

[1942] a means for presenting the best answer to the user;

[1943] A system including:

[1944] (Claim 2)

[1945] The system of claim 1, further comprising means for acquiring personal data from a plurality of data sources, such as a smart device, an electronic trading app, a schedule management app, and a location information app.

[1946] (Claim 3)

[1947] The system of claim 1, further comprising means for evaluating answers received from a plurality of generative AIs and selecting an optimal answer.

[1948] "Application Example 1"

[1949] (Claim 1)

[1950] a means for obtaining personal data of the user;

[1951] The means for analyzing the acquired personal data;

[1952] a means for generating personalized prompts based on the analysis results;

[1953] A means for sending the generated prompts to the generative AI and obtaining answers; and

[1954] means for presenting the obtained answers to the user;

[1955] A means of recommending optimal content based on a user's interests and activities;

[1956] A system including:

[1957] (Claim 2)

[1958] 10. The system of claim 1, comprising means for obtaining personal data from multiple data sources, such as a smartwatch, an electronic payment application, a calendar application, and a map application.

[1959] (Claim 3)

[1960] The system of claim 1, comprising means for working with multiple generative AIs to select optimal answers and recommended content.

[1961] "Example 2: Combining Emotion Engines"

[1962] (Claim 1)

[1963] a means for obtaining personal data of the user;

[1964] The means for analyzing the acquired personal data;

[1965] a means for collecting and analyzing emotion data;

[1966] a means for generating personalized prompts based on the analysis results;

[1967] a means for sending the generated prompts to a generative AI model and obtaining answers;

[1968] means for presenting the obtained answers to the user;

[1969] A system including:

[1970] (Claim 2)

[1971] 10. The system of claim 1, further comprising means for acquiring personal data and emotion data from multiple data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app.

[1972] (Claim 3)

[1973] 10. The system of claim 1, further comprising means for working with multiple generative AI models to select an optimal answer.

[1974] "Application example 2 when combining emotion engines"

[1975] (Claim 1)

[1976] a means for obtaining personal data of the user;

[1977] The means for analyzing the acquired personal data;

[1978] a means for generating personalized prompts based on the analysis results;

[1979] a means for sending the generated prompts to a generative AI model and obtaining answers;

[1980] means for presenting the obtained answers to the user;

[1981] A system including:

[1982] (Claim 2)

[1983] A means for acquiring personal data from multiple data sources such as smartwatches, electronic payment apps, calendar apps, and map apps;

[1984] A means of collecting emotional data through voice analysis, facial recognition, and text analysis;

[1985] means for generating prompt sentences based on the analyzed personal data and emotion data;

[1986] 10. The system of claim 1, comprising:

[1987] (Claim 3)

[1988] A means of linking multiple generative AI models to select the optimal answer,

[1989] means for presenting the selected answers to the user as personalized product suggestions;

[1990] 10. The system of claim 1, comprising: [Explanation of symbols]

[1991] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for obtaining personal data of the user; The means for analyzing the acquired personal data; a means for generating personalized prompts based on the analysis results; A means for sending the generated prompts to the generative AI and obtaining answers; and means for presenting the obtained answers to the user; A system including:

2. The system of claim 1 , further comprising means for acquiring personal data from a plurality of data sources, such as a smartwatch, an electronic payment app, a calendar app, and a map app.

3. The system according to claim 1, further comprising a means for selecting the optimal answer in cooperation with a plurality of generative AIs.

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