System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting user information; generative model means for suggesting candidates based on the user information; means for providing a schedule of the suggested candidates to a user; means for receiving feedback from the user; and means for optimizing upcoming suggestions based on the feedback.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] In modern society, many people are experiencing a lack of motivation and monotony in their daily lives. Excessive stress and a lack of a reasonable work-life balance are also contributing to a decline in productivity and mental exhaustion. To solve this, a new approach is needed that allows people to improve themselves while having fun. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including a means for inputting user information, a generative model means for proposing candidates based on the user information, a means for providing the user with the proposed candidate schedule, a means for receiving feedback from the user, and a means for optimizing the next proposal based on the feedback. In this system, the user inputs their preferences, concerns, usage times, and locations, and the generative model proposes the optimal candidate schedule. Furthermore, the user provides feedback after the experience, and the system uses that data to improve future proposals, thereby improving the user's work-life balance. Furthermore, the system supports smooth implementation by linking the proposed schedule with the user's calendar app.

[0006] "User information" refers to information such as hopes, concerns, usage time, and location that users enter when using the system.

[0007] A "generative model" is an algorithm or program used to suggest optimal candidates based on user information.

[0008] A "schedule" is a proposed timetable and list of activities to mimic a day in the life of a selected candidate.

[0009] "Feedback" refers to opinions such as satisfaction and impressions of the experience provided by users after the experience.

[0010] A "calendar app" is an electronic calendar application that users use to manage their daily schedules.

[0011] "Optimization" is the process of improving future suggestions to make them more suitable for users based on data such as feedback. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The present invention provides a system that proposes a schedule that mimics the lifestyle of a specific celebrity or model in order to improve a user's daily life and work performance. Specific embodiments for implementing this system will be described below.

[0034] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The data entered by the user is sent from the terminal to the server.

[0035] The server then uses a generative model to generate a schedule for candidate celebrities or models based on the user information. The generative model considers the user's preferences, usage time, and location, randomly selects the best candidate, and proposes a schedule that mimics that candidate's day.

[0036] For example, if a user inputs information such as "I want to improve my motivation," "a day," and "my home," the generative model will create a daily schedule for a suitable celebrity based on this information. The server also obtains the current date and day of the week and constructs the schedule information.

[0037] The server then sends the generated schedule to the device, allowing the user to view the proposed schedule and spend their day based on it.

[0038] After the user experiences the designated schedule, they input feedback about their experience through their device. Specifically, they input their level of satisfaction and impressions. The feedback data is sent back to the server from the device, and the server records it in a database. This allows the system to take into account the previously collected user feedback when generating future schedules, enabling more appropriate suggestions to be made.

[0039] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[0040] In addition, the suggested schedule can be integrated with the user's calendar app, making it easier to manage schedules, allowing users to execute the suggested schedule without any hassle.

[0041] As described above, the present invention is a system that provides enjoyment and new perspectives to the user's daily life and improves work-life balance.

[0042] The processing flow will be explained below.

[0043] Step 1:

[0044] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[0045] Specific behavior:

[0046] The user enters the required information through a web form or app interface.

[0047] For example, enter "I want to improve my motivation," "1 day," and "Home."

[0048] Step 2:

[0049] The terminal collects the user's input information and sends it to the server.

[0050] Specific behavior:

[0051] The form is submitted and the user information is sent to the server in JSON format.

[0052] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[0053] Step 3:

[0054] The server uses the generative model to select candidates based on user information.

[0055] Specific behavior:

[0056] The server randomly selects candidate celebrities and models from a database based on the user's preferences, usage time, and location.

[0057] Example: "Ichiro" schedule is selected.

[0058] Step 4:

[0059] The server obtains today's date and day of the week and generates a schedule.

[0060] Specific behavior:

[0061] The server takes the current date and day of the week and combines it with the schedule of a randomly selected celebrity.

[0062] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[0063] Step 5:

[0064] The server transmits the generated schedule to the terminal and provides it to the user.

[0065] Specific behavior:

[0066] The server transmits the generated schedule to the terminal so that the user can check it.

[0067] The terminal receives the schedule and displays it on the user interface.

[0068] Step 6:

[0069] The user spends their day based on the suggested schedule.

[0070] Specific behavior:

[0071] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[0072] Step 7:

[0073] The user enters feedback after the experience.

[0074] Specific behavior:

[0075] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[0076] For example, enter "I was very satisfied."

[0077] Step 8:

[0078] The terminal collects user feedback and sends it to the server.

[0079] Specific behavior:

[0080] User feedback is sent to the server in JSON format or similar.

[0081] For example: {"Feedback": "Very satisfied"} will be sent.

[0082] Step 9:

[0083] The server records the feedback in a database and reflects it in the next schedule generation.

[0084] Specific behavior:

[0085] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[0086] Example 1

[0087] 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."

[0088] In today's busy lifestyles, many users struggle with managing their own schedules. Many also find it difficult to create specific action plans to improve their quality of life. Furthermore, there is a lack of systems that can optimize their next suggestions based on feedback. To solve these issues, there is a need for a system that can propose an optimal schedule based on the user's wishes, concerns, usage time, and location, and reflect feedback after the schedule is executed.

[0089] 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.

[0090] In this invention, the server includes a means for inputting user information, a generative AI model means for proposing candidates based on the user information, a means for sending the generated schedule to the user's device, a means for receiving feedback from the user, and a means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule based on the user's preferences, concerns, usage time, and location, and to improve the proposal content for the next time and thereafter based on feedback collected after the schedule is executed. The system can also link the generated schedule with the user's calendar app, allowing the user to easily manage and execute the proposed schedule.

[0091] "Means for inputting user information" refers to an interface that allows users to input information such as their wishes, concerns, usage time, and location through a terminal.

[0092] "Generative AI model means" is an artificial intelligence algorithm or model for suggesting appropriate candidates and generating a schedule based on user information.

[0093] A "means for providing suggested candidate schedules to a user" is a communication and display interface for providing the generated schedule to a user.

[0094] The "means for receiving feedback from users" is a function that allows users to input their satisfaction and impressions after executing a schedule and transmit them to the server.

[0095] The "means for optimizing the next proposal based on feedback" is an algorithm and database system that analyzes the received feedback and reflects it in the next schedule proposal.

[0096] The "means for transmitting the generated schedule to the user's terminal" is a communication function for transmitting the schedule generated by the server to the user's terminal.

[0097] "Means for linking the generated schedule with the user's calendar application" refers to a function for synchronizing the proposed schedule with the calendar application used by the user.

[0098] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models to improve users' daily life and work performance. The system generates an optimal schedule based on the user's wishes, concerns, usage time, and location, and then has the ability to improve the proposed schedule based on user feedback.

[0099] Hardware and software used

[0100] Device: The device used by the user to enter information, review the generated schedule, and provide feedback. This includes computers and smartphones.

[0101] Server: Receives user data, runs generative AI models, sends generated schedules, records feedback, and optimizes next proposals. Uses a cloud server (e.g., AWS, Google Cloud).

[0102] Generative AI models: Use deep learning models (e.g., GPT-4) to generate appropriate schedules based on user information.

[0103] System Operation Overview

[0104] 1. Sending user input information

[0105] The user inputs information such as their wishes, concerns, usage time, location, etc. through the device, which then formats this information and sends it to the server.

[0106] 2. Receiving data and generating schedules

[0107] The server receives user information sent from the device and passes it to a generative AI model, which then generates a schedule that mimics a day in the life of an appropriate celebrity or model based on the user's preferences, usage time, and location.

[0108] 3. Providing a schedule

[0109] The server reformats the generated schedule and sends it to the terminal, where the user can view the schedule.

[0110] 4. Submitting Feedback

[0111] After the user spends a day following the proposed schedule, they input feedback about their experience through the device, which then sends the feedback to the server.

[0112] 5. Processing feedback and optimizing next proposals

[0113] The server receives the feedback and records it in a database, which is then used to optimize future schedule suggestions.

[0114] Specific examples

[0115] Prompt Sentence Examples

[0116] "I want to improve my motivation"

[0117] "1 day"

[0118] "one's home"

[0119] Actual operation example

[0120] 1. User: The user opens the device interface and enters "I want to improve my motivation," "1 day," and "Home."

[0121] 2. Terminal: The terminal sends the entered information to the server.

[0122] 3. Server: The server sends prompts to the generative AI model to generate a schedule that mimics Celebrity A's daily routine, such as "Wake up at 6am, jog at 7am, have breakfast at 8am, read at 9am, have lunch at 12pm..."

[0123] 4. Server: The server sends the generated schedule to the terminal.

[0124] 5. Terminal: The terminal parses the schedule and displays it on the interface.

[0125] 6. User: The user checks the displayed schedule and spends their day according to it.

[0126] 7. User: After the experience is over, the user fills in the feedback form saying, "I was very satisfied with today's schedule."

[0127] 8. Device: The device sends feedback to the server.

[0128] 9. Server: The server records the feedback in a database and reflects it in future schedule suggestions. In this way, the system provides users with fun and new perspectives in their daily lives, improving their work-life balance.

[0129] The present invention enables real-time input and processing of user information, and schedule suggestions and feedback from a generative AI model, thereby effectively improving users' daily life and work performance.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] Sending user input information

[0133] User: The user opens the device interface and enters information such as their wishes, concerns, usage time, location, etc. Specifically, they enter information such as "I want to improve my motivation," "1 day," and "Home" using text boxes and dropdown boxes.

[0134] Input: User's wishes, concerns, usage time, and location (e.g., "I want to improve my motivation," "1 day," "Home")

[0135] Device: The device formats the input information and sends it to the server in the form of an API request.

[0136] Output: API request with formatted user information

[0137] Step 2:

[0138] Receiving data

[0139] Server: The server receives the API request sent from the device using an HTTP request.

[0140] Input: API request containing formatted user information

[0141] Server: The server parses the received data and extracts user information. Specifically, it parses the data using a JSON parser.

[0142] Output: Extracted user information (hopes, concerns, usage time, location)

[0143] Step 3:

[0144] Generate a schedule

[0145] Server: The server sends prompts to a generative AI model (e.g., GPT-4) to generate a schedule based on user information.

[0146] Input: Extracted user information (hopes, concerns, usage time, location)

[0147] Server: The generative AI model analyzes the prompt text and generates a schedule that mimics a day in the life of celebrity A, for example, based on "I want to improve my motivation," "1 day," and "at home."

[0148] Output: Generated schedule (e.g. "Wake up at 6, jog at 7, breakfast at 8, read at 9...")

[0149] Step 4:

[0150] Sending a Schedule

[0151] Server: The server reformats the generated schedule and sends it to the device, for example by converting the schedule to JSON format and sending it in an HTTP response.

[0152] Input: Generated schedule

[0153] Server: Sends formatted schedule data as an HTTP response.

[0154] Output: Formatted schedule data

[0155] Step 5:

[0156] Check the schedule

[0157] Terminal: The terminal analyzes the schedule data received from the server and displays it on the interface, using front-end technologies (e.g., React, Vue.js).

[0158] Input: Formatted schedule data

[0159] Terminal: Parses the data and displays it in a user interface.

[0160] User: The user sees the displayed schedule and follows it for the day.

[0161] Output: The displayed schedule

[0162] Step 6:

[0163] Send Feedback

[0164] User: After a user has spent a day following the schedule, they provide feedback on their experience, for example, their satisfaction and impressions in a dedicated feedback form.

[0165] Input: User feedback (e.g., "I was very satisfied with today's schedule")

[0166] Terminal: The terminal formats the entered feedback and sends it to the server using a POST request.

[0167] Output: Formatted feedback data

[0168] Step 7:

[0169] Processing Feedback

[0170] Server: The server receives the feedback sent from the device and records it in a database using an HTTP request.

[0171] Input: Formatted feedback data

[0172] Server: Analyzes the received feedback and stores it in a database, typically using a database management system (e.g., MySQL, PostgreSQL).

[0173] Output: Recorded feedback data

[0174] Step 8:

[0175] Next proposal optimization

[0176] Server: The server analyzes the recorded feedback and updates the algorithm to optimize future schedule suggestions.

[0177] Input: Recorded feedback data

[0178] Server: Adjusts the model parameters based on the feedback data to improve the next proposal, specifically using machine learning algorithms.

[0179] Output: An updated generative AI model

[0180] Through this process, the system provides the optimal schedule based on the user's wishes and concerns, and by incorporating feedback obtained after the schedule is implemented, it can continuously make more effective suggestions.

[0181] (Application example 1)

[0182] 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."

[0183] Conventional user schedule suggestion systems simply propose schedules based on user input, but lack dynamic navigation and feedback tailored to the actual usage environment. Therefore, there was a need for a method to further enrich users' lifestyles and in-store experiences.

[0184] 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.

[0185] In this invention, the server includes means for inputting user information, a generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for receiving feedback from the user, means for optimizing the next proposal based on the feedback, and means for navigating the user's behavior in cooperation with available installed devices, thereby enabling the user to experience the lifestyle habits of a specific celebrity and be efficiently and effectively navigated through the behavior in the store.

[0186] "User information" refers to information entered by the user, such as their wishes, concerns, usage time, and location.

[0187] The "generative model means" is a means for selecting optimal candidates based on user information and generating a schedule.

[0188] The "proposed candidate schedule" is a schedule that details the daily activities of the candidate selected by the generative model means.

[0189] "Feedback" refers to opinions such as satisfaction and impressions that users provide to the system after experiencing it.

[0190] "Means to optimize the next proposal" refers to means to better adjust the next schedule proposal based on the feedback received.

[0191] "Available installed devices" are user interface devices such as terminals and smartphones installed in physical stores.

[0192] "Means for navigating behavior" is a function for navigating the user within the store according to the proposed schedule.

[0193] A "planning management program" is an application that users use to manage their daily schedules.

[0194] A system for implementing the present invention is configured as follows.

[0195] System Overview

[0196] Users access the system using a smartphone or a dedicated terminal installed in a physical store. They enter information such as their preferences, concerns, usage times, and location. This user information is sent from the terminal to the server. The server uses a generative AI model to generate candidate schedules based on the user information and proposes these schedules to the user. The user then acts within the physical store according to the proposed schedule. Through feedback, the user's experience is reflected in the next proposal.

[0197] Hardware and Software

[0198] The system uses the following hardware and software:

[0199] Hardware: Smartphones, dedicated terminals installed in physical stores.

[0200] Software: Flask (web framework), Python (programming language), JSON (data format), server (AWS or Heroku can be used as examples).

[0201] Program Operation

[0202] 1. Entering user information: The user enters their preferences, concerns, usage time, and location using a smartphone or dedicated device. This information is sent to the server as a user template in JSON format.

[0203] 2. Schedule generation using a generative model: A generative AI model runs on the server side using Flask and Python. The generative model generates the optimal celebrity schedule based on user information.

[0204] 3. Providing the schedule: The generated schedule is sent to the user's device. It works in conjunction with each facility in the physical store to navigate the user's behavior, for example, by providing appropriate guidance on the time spent in the cafe or the time spent in the fitness area.

[0205] 4. Feedback collection: After one day of experience, users provide feedback through the system. This feedback is stored on the server and reflected in the next schedule generation.

[0206] Specific examples

[0207] For example, if a user inputs information such as "I want to relax," "a day," and "a cafe," the server will generate a daily schedule for a celebrity that is suitable for relaxation. The generated schedule may include a morning yoga session, a mid-morning massage, and time to relax at a cafe for lunch. This schedule is then displayed on the user's smartphone.

[0208] Prompt Sentence Examples

[0209] For example, a prompt that the user might enter might look like this:

[0210] "I want to relax"

[0211] "1 day"

[0212] "Cafe"

[0213] This allows the server to generate celebrity schedules tailored to the user's needs and provide them to the user.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] Entering user information

[0217] Users use their smartphones or dedicated terminals installed in stores to input information about their wishes, concerns, usage time, and location. For example, they might enter information such as "I want to relax," "1 day," or "cafe." This user information is sent from the terminal to the server as JSON-formatted data.

[0218] Step 2:

[0219] Receiving User Information

[0220] The server receives the submitted user information and stores it in a database, performing validation to ensure the information is properly formatted and to detect invalid data.

[0221] Step 3:

[0222] Schedule generation using generative models

[0223] The server runs a generative AI model based on user information. The generative AI model generates optimal candidate schedules taking into account the user's preferences, usage times, and location. For example, for a user who wants to relax, schedules such as "morning yoga session," "morning massage time," and "time to relax at a cafe during lunch" are generated. The generated schedules are stored as JSON-formatted data.

[0224] Step 4:

[0225] Schedule provision

[0226] The server sends the generated schedule to the user's device, which displays it on the user's smartphone or a dedicated device in the physical store, allowing the user to check suggested activities throughout the day.

[0227] Step 5:

[0228] Cooperation with the installed equipment

[0229] When the user moves around the store according to the proposed schedule, the server works with each device in the store (such as the cafe's ordering system or the fitness area's reservation system) to navigate the user's actions, allowing the user to smoothly carry out their activities at each location.

[0230] Step 6:

[0231] Gathering feedback

[0232] Users experience the proposed schedule throughout the day and provide feedback after the experience. The feedback is entered via a dedicated device or smartphone and sent to the server. This feedback includes satisfaction, impressions, and further requests.

[0233] Step 7:

[0234] Processing Feedback

[0235] The server stores the received feedback in a database and performs data analysis to optimize the next schedule proposal based on the feedback, which allows the next schedule proposal to better suit the user's preferences and needs.

[0236] 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.

[0237] The present invention combines a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[0238] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The information entered by the user is sent from the terminal to the server.

[0239] The server then uses a generative model to propose optimal candidates based on user information. The generative model randomly selects an appropriate candidate, taking into account the user's preferences, usage time, and location. It then generates a schedule that mimics a day in the life of that candidate.

[0240] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions. The emotion engine analyzes the user's emotions in real time based on their voice and facial expression data, and provides this information to the generative model. Based on the results of this analysis, the generative model proposes a schedule that is more suited to the user's current state.

[0241] For example, if a user inputs information such as "I want to improve my motivation," "One day," and "Home," the server uses the generative model to generate a schedule for a suitable celebrity (such as "Ichiro"). Furthermore, if the user's voice or facial expression indicates that they are feeling stressed, the generative model will recommend a schedule that includes activities that will reduce the user's stress.

[0242] The server sends the generated schedule to the device, where the user can check the proposed schedule. This schedule also works with the user's calendar app to help ensure smooth execution.

[0243] After a user spends a day based on the specified schedule, they input feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. The feedback data is sent from the device to a server, which records it in a database. When generating future schedules, the previously collected user feedback is taken into account, allowing for more appropriate suggestions.

[0244] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[0245] As described above, the present invention is a system that combines emotion engines to provide a user with a fun and new perspective on their daily life and improve their work-life balance.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[0249] Specific behavior:

[0250] The user enters the required information through a web form or app interface.

[0251] For example, enter "I want to improve my motivation," "1 day," and "Home."

[0252] Step 2:

[0253] The terminal collects the user's input information and sends it to the server.

[0254] Specific behavior:

[0255] The form is submitted and the user information is sent to the server in JSON format.

[0256] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[0257] Step 3:

[0258] The emotion engine analyzes the user's emotions.

[0259] Specific behavior:

[0260] The user provides facial expression and voice data through the device's camera and microphone.

[0261] The emotion engine analyzes the user's emotions in real time and identifies emotional states such as stress, joy, and excitement.

[0262] Example: If the user is analyzed as "feeling stressed", that information is sent to the server.

[0263] Step 4:

[0264] The server uses a generative model to select candidates based on user information and emotion data.

[0265] Specific behavior:

[0266] The server selects candidate celebrities and models from a database based on the user's preferences, emotional state, usage time, and location.

[0267] Example: "Ichiro" schedule is selected.

[0268] Depending on the emotional data, specific activities such as "stress reduction" may be tailored to include.

[0269] Step 5:

[0270] The server obtains today's date and day of the week and generates a schedule.

[0271] Specific behavior:

[0272] The server retrieves the current date and day of the week and combines the selected celebrity's schedule with adjustments based on emotional data.

[0273] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[0274] Step 6:

[0275] The server transmits the generated schedule to the terminal and provides it to the user.

[0276] Specific behavior:

[0277] The server transmits the generated schedule to the terminal so that the user can check it.

[0278] The terminal receives the schedule and displays it on the user interface.

[0279] Step 7:

[0280] The user spends their day based on the suggested schedule.

[0281] Specific behavior:

[0282] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[0283] Step 8:

[0284] The user enters feedback after the experience.

[0285] Specific behavior:

[0286] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[0287] For example, enter "I was very satisfied."

[0288] Step 9:

[0289] The terminal collects user feedback and sends it to the server.

[0290] Specific behavior:

[0291] User feedback is sent to the server in JSON format or similar.

[0292] For example: {"Feedback": "Very satisfied"} will be sent.

[0293] Step 10:

[0294] The server records the feedback in a database and reflects it in the next schedule generation.

[0295] Specific behavior:

[0296] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[0297] This allows the system to provide a schedule that adapts to the user's emotional state and improve the user's work-life balance.

[0298] Example 2

[0299] 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."

[0300] Many people living busy lives today need effective schedules to improve their daily and work performance. However, generating an optimal schedule that reflects each user's preferences and emotional state is difficult. Furthermore, conventional scheduling systems are unable to effectively utilize user feedback, making it difficult to improve the quality of the next schedule proposal.

[0301] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, emotion engine means for analyzing the user's emotions, means for optimizing the schedule based on the analyzed emotion information, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule according to the user's wishes and emotional state.

[0302] "User information" refers to data such as hopes, concerns, usage time, and location that users enter into the system.

[0303] "Generative model means" refers to AI models or algorithms that generate appropriate schedules based on user information.

[0304] "Proposal means" refers to the interface or communication means for providing the generated schedule to the user.

[0305] "Emotion engine means" refers to technology or software for analyzing a user's voice data and facial expression data to recognize emotions.

[0306] "Emotion information" refers to data on the user's emotional state analyzed by the emotion engine means.

[0307] "Feedback" refers to data entered by users after completing a schedule, such as their impressions, satisfaction, and areas for improvement.

[0308] "Next suggestion optimization" refers to the processes or algorithms used to improve the next schedule suggestion based on user feedback.

[0309] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance, and combines an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[0310] First, the user accesses the system through their device and enters information such as their wishes, concerns, usage time, and location. Specifically, this is done using the device's web interface or mobile app UI. This entered data is then sent from the device to the server. During transmission, the data is encrypted using the secure HTTPS protocol.

[0311] The server then uses a generative AI model (such as OpenAI GPT-3) to suggest the best candidates based on the user's information. This generative model is processed using a cloud-based platform (such as Google Cloud or Amazon Web Services). The server generates a schedule for a suitable celebrity or model based on the user's preferences, usage time, and location. For example, if the user inputs information such as "I want to improve my motivation," "One day," and "At home," the server generates a schedule that mimics an athlete's daily schedule.

[0312] Furthermore, the system is equipped with an emotion engine that analyzes the user's real-time emotional information. The emotion engine uses the Microsoft Azure Emotion API and Face API. The device collects the user's voice data (collected using a microphone) and facial expression data (collected using a camera) and sends this to the server. The server analyzes this data and obtains the user's emotional information.

[0313] Based on the analysis results, the server optimizes the generated schedule to the user's current state. For example, if the user is "feeling stressed," the server will add meditation time to the schedule to reduce stress. This optimized schedule is then sent from the server to the device, where the user can view it. Furthermore, this schedule can be linked to calendar apps such as Google Calendar and Microsoft Outlook, helping users to easily manage their schedules.

[0314] After a user spends a day based on the specified schedule, they can enter feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. This feedback data is sent from the device to a server, which then stores it in a database.

[0315] The collected feedback data will be taken into account in the next schedule generation and more appropriate schedule suggestions will be made, which will increase user satisfaction. For example, if a user provides feedback such as "very satisfied," that feedback will be reflected in the next schedule suggestion, and better suggestions will be made by the generative model that has learned the user's preferences and behavioral patterns.

[0316] Specific examples

[0317] For example, suppose a user inputs the prompts "I want to improve my motivation," "One day," and "At home." In this case, the server uses a generative AI model to generate a schedule that mimics a specific athlete's day. If the server determines that the user's emotional information indicates "feeling stressed," it will add meditation time and relaxation activities to the schedule. In this way, providing an optimal schedule based on real-time emotional analysis and feedback can improve the user's daily life and work performance.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Specific processing steps of the system program

[0320] Step 1:

[0321] The user enters information such as their wishes, concerns, usage time, and location through the device. Information is entered using an input form or the application's user interface. Input data includes "desires (e.g., want to improve motivation)," "usage time (e.g., per day)," and "location (e.g., home)." This information is sent to the server in the next step.

[0322] Step 2:

[0323] The terminal sends the entered user information to the server. The data is encrypted and transmitted using the secure HTTPS protocol, ensuring that the data reaches the server safely. The input is the user information, and the output is the user information received on the server side.

[0324] Step 3:

[0325] The server generates schedule candidates using a generative AI model (e.g., OpenAI GPT-3) based on the received user information. The generative model runs on a cloud-based platform (Google Cloud or Amazon Web Services) and generates related schedule data based on the received user information. The input is user information, and the output is the generated schedule candidates.

[0326] Step 4:

[0327] The device collects the user's voice data and facial expression data and sends it to the server. Voice data is collected using the device's microphone, and facial expression data is collected using the device's camera. This data is also sent using the secure HTTPS protocol. The input is voice data and facial expression data, and the output is emotional data sent to the server.

[0328] Step 5:

[0329] The server uses the Microsoft Azure Emotion API and Face API to analyze the received voice and facial expression data and obtain the user's emotional information. The analyzed emotional information becomes data that represents the user's emotional state in real time. The input is voice data and facial expression data, and the output is emotional information.

[0330] Step 6:

[0331] The server optimizes the generated schedule based on the emotional information. For example, if the user is analyzed as "feeling stressed," it adds relaxation activities to the schedule. Specifically, optimizations are performed such as incorporating meditation time into the schedule. The input is the generated schedule candidate and emotional information, and the output is the optimized schedule.

[0332] Step 7:

[0333] The server sends the optimized schedule to the device, where the user can view it. The schedule can also be linked to calendar apps such as Google Calendar and Microsoft Outlook, making it easier for users to manage their schedules. The input is the optimized schedule, and the output is the schedule displayed on the device.

[0334] Step 8:

[0335] After a user spends a day based on a specified schedule, they input feedback through the device. The feedback includes satisfaction and impressions of the experience. The input is feedback information, and the output is feedback data stored on the device.

[0336] Step 9:

[0337] The terminal sends feedback data to the server, which stores the data in a database. The feedback data is used to generate future schedules. The input is feedback information, and the output is the feedback data stored in the database.

[0338] Step 10:

[0339] The server improves the next schedule generation based on the accumulated feedback data. It analyzes the feedback data, learns the user's preferences and behavioral patterns, and proposes a more appropriate schedule. The input is the feedback data, and the output is the improved next schedule.

[0340] (Application example 2)

[0341] 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."

[0342] Conventional schedule suggestion systems generate schedules without taking the user's emotional state into account, which means they are unable to provide suggestions that are optimal for the user's current psychological state. Furthermore, they are unable to provide appropriate suggestions for specific locations or situations, which limits the user experience. Furthermore, leisure and driving plan suggestions for autonomous vehicles are not personalized, which means user satisfaction cannot be enhanced.

[0343] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for recognizing emotions based on the user's voice and video data, means for optimizing the schedule based on the emotions, means for proposing locations and routes suitable for the user, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to generate a schedule optimized for the user's psychological state and to suggest suitable locations and routes in real time.

[0344] "User information" refers to data provided by users, such as their wishes, concerns, usage time, and location.

[0345] A "generative model means" is an algorithm or system that selects optimal candidates based on user information and generates a schedule.

[0346] The "means for providing to the user" is an interface or application for displaying the generated schedule to the user.

[0347] The "means for recognizing emotions" refers to a technology or system that analyzes a user's emotions in real time based on the user's audio and video data.

[0348] The "schedule optimization method" is a process that adjusts and optimizes the generated schedule based on the perceived user sentiment.

[0349] "Means for suggesting places and routes" refers to a system for suggesting appropriate leisure spots and driving routes based on the user's current emotional state and location.

[0350] A "feedback channel" is an interface that collects data about the user's experience and satisfaction.

[0351] "Next proposal optimization" is the process of improving future schedule proposals based on collected feedback.

[0352] The present invention provides a system for analyzing the emotional state of a user in an autonomous vehicle and proposing leisure plans or driving plans based on the analyzed emotional state. The system includes a means for inputting user information, a generative model means, an emotion recognition means, a means for optimizing a schedule, a means for proposing places and routes, a means for receiving feedback, and a means for optimizing the next proposal.

[0353] System Programming and Processing

[0354] Program processing

[0355] 1. Enter your user information:

[0356] The server receives information provided by the user, such as their wishes, concerns, usage time, and location, via the terminal and stores it in a database.

[0357] 2. Candidate Proposal:

[0358] The server generates optimal schedule candidates based on the user information using a generative model means, which generates randomly selected candidates taking into account the user information.

[0359] 3. Emotion recognition:

[0360] The user's voice and video data are captured in real time through microphones and cameras installed in the vehicle, and the server analyzes the data using emotion recognition means, such as EmotionRecognizer.

[0361] 4. Schedule optimization:

[0362] The server optimizes the generated schedule based on the emotion recognition results, for example, suggesting a visit to a relaxation facility if the user is feeling stressed.

[0363] 5. Location and route suggestions:

[0364] Based on the optimized schedule, the server suggests suitable locations and routes for the user. The optimal route to the suggested locations is calculated using Google Maps APIs, etc.

[0365] 6. Gathering Feedback:

[0366] After spending a day based on the specified schedule, the user inputs feedback about the experience through the device, and the level of satisfaction and impressions about the experience are sent to the server as feedback data.

[0367] 7. Optimize your next proposal:

[0368] The server records the collected feedback data in a database and reflects the feedback in future schedule generation using the generative modeling means, thereby enabling suggestions that are more suited to the user's preferences and emotional state.

[0369] Hardware and software used

[0370] Hardware: In-car camera, microphone, GPS module, display, audio system

[0371] Software: Python program, emotion recognition model (e.g., EmotionRecognizer), route planning API (e.g., Google Maps API), database management system

[0372] Specific examples

[0373] For example, if the driver's emotions are detected as "stressed" through video and audio analysis from the in-car camera, the system will search for nearby spas and relaxation facilities and suggest routes based on those. This information will be displayed on the vehicle's display, and the navigation system will begin driving automatically.

[0374] Prompt Sentence Examples

[0375] If the user is feeling stressed, suggest suitable relaxation spots to relieve stress. Also, show the best route to those places. The user's current location is XXX, time is YYY. For example, nearby spa facilities, parks, and relaxation rooms could be suggested.

[0376] The present invention can improve user satisfaction by providing a personalized driving plan based on the user's psychological state.

[0377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0378] Step 1:

[0379] Entering user information

[0380] The server collects information provided by the user via the device, such as their wishes, concerns, usage time, location, etc. The data entered by the user into the device is sent to the server and stored in a database, providing basic information about the user that will be used in the next step.

[0381] Input: Hopes, worries, usage time, location

[0382] Output: User information stored on the server

[0383] Step 2:

[0384] Candidate suggestions

[0385] The server uses a generative model means to generate optimal schedule candidates based on the user information stored in step 1. The generative model means uses a machine learning algorithm to analyze the user information and randomly generate an appropriate schedule.

[0386] Input: User information

[0387] Output: Candidate schedule

[0388] Step 3:

[0389] emotion recognition

[0390] The server receives the user's voice and video data acquired through the microphone and camera installed in the vehicle, analyzes the user's emotions in real time using emotion recognition means (e.g., EmotionRecognizer), and sends the analysis results to the server.

[0391] Input: Audio data, video data

[0392] Output: User's emotional state

[0393] Step 4:

[0394] Schedule optimization

[0395] The server optimizes the schedule based on the candidate schedule generated in step 2 and the emotion recognition results obtained in step 3. For example, if the user is feeling stressed, the server adjusts the schedule to include a visit to a relaxation facility.

[0396] Input: candidate schedule, emotional state

[0397] Output: Optimized schedule

[0398] Step 5:

[0399] Location and route suggestions

[0400] The server then suggests locations and routes suitable for the user based on the optimized schedule, using Google Maps APIs and other tools to search for suitable facilities and routes and generate detailed navigation information.

[0401] Input: Optimized schedule

[0402] Output: Suggested locations, route information

[0403] Step 6:

[0404] Providing suggestions

[0405] The server then displays the generated location and route information on the vehicle's display, notifies the user of the suggestions via the voice assistant, and initiates autonomous driving in conjunction with the navigation system.

[0406] Input: location, route information

[0407] Output: Proposals displayed on the in-vehicle display

[0408] Step 7:

[0409] Gathering feedback

[0410] After spending a day based on the proposed schedule, the user enters feedback about their experience through the device, and their satisfaction and impressions of the experience are sent from the device to the server and stored in a database.

[0411] Input: User feedback

[0412] Output: Feedback stored on the server

[0413] Step 8:

[0414] Optimize your next proposal

[0415] The server uses the collected feedback data to improve future schedule suggestions by means of a generative model, and uses a machine learning algorithm to retrain the model to generate schedules that better suit the user's preferences and emotional state.

[0416] Input: Feedback data

[0417] Output: Optimized next schedule proposal

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Second embodiment]

[0422] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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."

[0434] The present invention provides a system that proposes a schedule that mimics the lifestyle of a specific celebrity or model in order to improve a user's daily life and work performance. Specific embodiments for implementing this system will be described below.

[0435] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The data entered by the user is sent from the terminal to the server.

[0436] The server then uses a generative model to generate a schedule for candidate celebrities or models based on the user information. The generative model considers the user's preferences, usage time, and location, randomly selects the best candidate, and proposes a schedule that mimics that candidate's day.

[0437] For example, if a user inputs information such as "I want to improve my motivation," "a day," and "my home," the generative model will create a daily schedule for a suitable celebrity based on this information. The server also obtains the current date and day of the week and constructs the schedule information.

[0438] The server then sends the generated schedule to the device, allowing the user to view the proposed schedule and spend their day based on it.

[0439] After the user experiences the designated schedule, they input feedback about their experience through their device. Specifically, they input their level of satisfaction and impressions. The feedback data is sent back to the server from the device, and the server records it in a database. This allows the system to take into account the previously collected user feedback when generating future schedules, enabling more appropriate suggestions to be made.

[0440] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[0441] In addition, the suggested schedule can be integrated with the user's calendar app, making it easier to manage schedules, allowing users to execute the suggested schedule without any hassle.

[0442] As described above, the present invention is a system that provides enjoyment and new perspectives to the user's daily life and improves work-life balance.

[0443] The processing flow will be explained below.

[0444] Step 1:

[0445] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[0446] Specific behavior:

[0447] The user enters the required information through a web form or app interface.

[0448] For example, enter "I want to improve my motivation," "1 day," and "Home."

[0449] Step 2:

[0450] The terminal collects the user's input information and sends it to the server.

[0451] Specific behavior:

[0452] The form is submitted and the user information is sent to the server in JSON format.

[0453] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[0454] Step 3:

[0455] The server uses the generative model to select candidates based on user information.

[0456] Specific behavior:

[0457] The server randomly selects candidate celebrities and models from a database based on the user's preferences, usage time, and location.

[0458] Example: "Ichiro" schedule is selected.

[0459] Step 4:

[0460] The server obtains today's date and day of the week and generates a schedule.

[0461] Specific behavior:

[0462] The server takes the current date and day of the week and combines it with the schedule of a randomly selected celebrity.

[0463] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[0464] Step 5:

[0465] The server transmits the generated schedule to the terminal and provides it to the user.

[0466] Specific behavior:

[0467] The server transmits the generated schedule to the terminal so that the user can check it.

[0468] The terminal receives the schedule and displays it on the user interface.

[0469] Step 6:

[0470] The user spends their day based on the suggested schedule.

[0471] Specific behavior:

[0472] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[0473] Step 7:

[0474] The user enters feedback after the experience.

[0475] Specific behavior:

[0476] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[0477] For example, enter "I was very satisfied."

[0478] Step 8:

[0479] The terminal collects user feedback and sends it to the server.

[0480] Specific behavior:

[0481] User feedback is sent to the server in JSON format or similar.

[0482] For example: {"Feedback": "Very satisfied"} will be sent.

[0483] Step 9:

[0484] The server records the feedback in a database and reflects it in the next schedule generation.

[0485] Specific behavior:

[0486] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[0487] Example 1

[0488] 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."

[0489] In today's busy lifestyles, many users struggle with managing their own schedules. Many also find it difficult to create specific action plans to improve their quality of life. Furthermore, there is a lack of systems that can optimize their next suggestions based on feedback. To solve these issues, there is a need for a system that can propose an optimal schedule based on the user's wishes, concerns, usage time, and location, and reflect feedback after the schedule is executed.

[0490] 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.

[0491] In this invention, the server includes a means for inputting user information, a generative AI model means for proposing candidates based on the user information, a means for sending the generated schedule to the user's device, a means for receiving feedback from the user, and a means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule based on the user's preferences, concerns, usage time, and location, and to improve the proposal content for the next time and thereafter based on feedback collected after the schedule is executed. The system can also link the generated schedule with the user's calendar app, allowing the user to easily manage and execute the proposed schedule.

[0492] "Means for inputting user information" refers to an interface that allows users to input information such as their wishes, concerns, usage time, and location through a terminal.

[0493] "Generative AI model means" is an artificial intelligence algorithm or model for suggesting appropriate candidates and generating a schedule based on user information.

[0494] A "means for providing suggested candidate schedules to a user" is a communication and display interface for providing the generated schedule to a user.

[0495] The "means for receiving feedback from users" is a function that allows users to input their satisfaction and impressions after executing a schedule and transmit them to the server.

[0496] The "means for optimizing the next proposal based on feedback" is an algorithm and database system that analyzes the received feedback and reflects it in the next schedule proposal.

[0497] The "means for transmitting the generated schedule to the user's terminal" is a communication function for transmitting the schedule generated by the server to the user's terminal.

[0498] "Means for linking the generated schedule with the user's calendar application" refers to a function for synchronizing the proposed schedule with the calendar application used by the user.

[0499] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models to improve users' daily life and work performance. The system generates an optimal schedule based on the user's wishes, concerns, usage time, and location, and then has the ability to improve the proposed schedule based on user feedback.

[0500] Hardware and software used

[0501] Device: The device used by the user to enter information, review the generated schedule, and provide feedback. This includes computers and smartphones.

[0502] Server: Receives user data, runs generative AI models, sends generated schedules, records feedback, and optimizes next proposals. Uses a cloud server (e.g., AWS, Google Cloud).

[0503] Generative AI models: Use deep learning models (e.g., GPT-4) to generate appropriate schedules based on user information.

[0504] System Operation Overview

[0505] 1. Sending user input information

[0506] The user inputs information such as their wishes, concerns, usage time, location, etc. through the device, which then formats this information and sends it to the server.

[0507] 2. Receiving data and generating schedules

[0508] The server receives user information sent from the device and passes it to a generative AI model, which then generates a schedule that mimics a day in the life of an appropriate celebrity or model based on the user's preferences, usage time, and location.

[0509] 3. Providing a schedule

[0510] The server reformats the generated schedule and sends it to the terminal, where the user can view the schedule.

[0511] 4. Submitting Feedback

[0512] After the user spends a day following the proposed schedule, they input feedback about their experience through the device, which then sends the feedback to the server.

[0513] 5. Processing feedback and optimizing next proposals

[0514] The server receives the feedback and records it in a database, which is then used to optimize future schedule suggestions.

[0515] Specific examples

[0516] Prompt Sentence Examples

[0517] "I want to improve my motivation"

[0518] "1 day"

[0519] "one's home"

[0520] Actual operation example

[0521] 1. User: The user opens the device interface and enters "I want to improve my motivation," "1 day," and "Home."

[0522] 2. Terminal: The terminal sends the entered information to the server.

[0523] 3. Server: The server sends prompts to the generative AI model to generate a schedule that mimics Celebrity A's daily routine, such as "Wake up at 6am, jog at 7am, have breakfast at 8am, read at 9am, have lunch at 12pm..."

[0524] 4. Server: The server sends the generated schedule to the terminal.

[0525] 5. Terminal: The terminal parses the schedule and displays it on the interface.

[0526] 6. User: The user checks the displayed schedule and spends their day according to it.

[0527] 7. User: After the experience is over, the user fills in the feedback form saying, "I was very satisfied with today's schedule."

[0528] 8. Device: The device sends feedback to the server.

[0529] 9. Server: The server records the feedback in a database and reflects it in future schedule suggestions. In this way, the system provides users with fun and new perspectives in their daily lives, improving their work-life balance.

[0530] The present invention enables real-time input and processing of user information, and schedule suggestions and feedback from a generative AI model, thereby effectively improving users' daily life and work performance.

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] Sending user input information

[0534] User: The user opens the device interface and enters information such as their wishes, concerns, usage time, location, etc. Specifically, they enter information such as "I want to improve my motivation," "1 day," and "Home" using text boxes and dropdown boxes.

[0535] Input: User's wishes, concerns, usage time, and location (e.g., "I want to improve my motivation," "1 day," "Home")

[0536] Device: The device formats the input information and sends it to the server in the form of an API request.

[0537] Output: API request with formatted user information

[0538] Step 2:

[0539] Receiving data

[0540] Server: The server receives the API request sent from the device using an HTTP request.

[0541] Input: API request containing formatted user information

[0542] Server: The server parses the received data and extracts user information. Specifically, it parses the data using a JSON parser.

[0543] Output: Extracted user information (hopes, concerns, usage time, location)

[0544] Step 3:

[0545] Generate a schedule

[0546] Server: The server sends prompts to a generative AI model (e.g., GPT-4) to generate a schedule based on user information.

[0547] Input: Extracted user information (hopes, concerns, usage time, location)

[0548] Server: The generative AI model analyzes the prompt text and generates a schedule that mimics a day in the life of celebrity A, for example, based on "I want to improve my motivation," "1 day," and "at home."

[0549] Output: Generated schedule (e.g. "Wake up at 6, jog at 7, breakfast at 8, read at 9...")

[0550] Step 4:

[0551] Sending a Schedule

[0552] Server: The server reformats the generated schedule and sends it to the device, for example by converting the schedule to JSON format and sending it in an HTTP response.

[0553] Input: Generated schedule

[0554] Server: Sends formatted schedule data as an HTTP response.

[0555] Output: Formatted schedule data

[0556] Step 5:

[0557] Check the schedule

[0558] Terminal: The terminal analyzes the schedule data received from the server and displays it on the interface, using front-end technologies (e.g., React, Vue.js).

[0559] Input: Formatted schedule data

[0560] Terminal: Parses the data and displays it in a user interface.

[0561] User: The user sees the displayed schedule and follows it for the day.

[0562] Output: The displayed schedule

[0563] Step 6:

[0564] Send Feedback

[0565] User: After a user has spent a day following the schedule, they provide feedback on their experience, for example, their satisfaction and impressions in a dedicated feedback form.

[0566] Input: User feedback (e.g., "I was very satisfied with today's schedule")

[0567] Terminal: The terminal formats the entered feedback and sends it to the server using a POST request.

[0568] Output: Formatted feedback data

[0569] Step 7:

[0570] Processing Feedback

[0571] Server: The server receives the feedback sent from the device and records it in a database using an HTTP request.

[0572] Input: Formatted feedback data

[0573] Server: Analyzes the received feedback and stores it in a database, typically using a database management system (e.g., MySQL, PostgreSQL).

[0574] Output: Recorded feedback data

[0575] Step 8:

[0576] Next proposal optimization

[0577] Server: The server analyzes the recorded feedback and updates the algorithm to optimize future schedule suggestions.

[0578] Input: Recorded feedback data

[0579] Server: Adjusts the model parameters based on the feedback data to improve the next proposal, specifically using machine learning algorithms.

[0580] Output: An updated generative AI model

[0581] Through this process, the system provides the optimal schedule based on the user's wishes and concerns, and by incorporating feedback obtained after the schedule is implemented, it can continuously make more effective suggestions.

[0582] (Application example 1)

[0583] 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."

[0584] Conventional user schedule suggestion systems simply propose schedules based on user input, but lack dynamic navigation and feedback tailored to the actual usage environment. Therefore, there was a need for a method to further enrich users' lifestyles and in-store experiences.

[0585] 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.

[0586] In this invention, the server includes means for inputting user information, a generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for receiving feedback from the user, means for optimizing the next proposal based on the feedback, and means for navigating the user's behavior in cooperation with available installed devices, thereby enabling the user to experience the lifestyle habits of a specific celebrity and be efficiently and effectively navigated through the behavior in the store.

[0587] "User information" refers to information entered by the user, such as their wishes, concerns, usage time, and location.

[0588] The "generative model means" is a means for selecting optimal candidates based on user information and generating a schedule.

[0589] The "proposed candidate schedule" is a schedule that details the daily activities of the candidate selected by the generative model means.

[0590] "Feedback" refers to opinions such as satisfaction and impressions that users provide to the system after experiencing it.

[0591] "Means to optimize the next proposal" refers to means to better adjust the next schedule proposal based on the feedback received.

[0592] "Available installed devices" are user interface devices such as terminals and smartphones installed in physical stores.

[0593] "Means for navigating behavior" is a function for navigating the user within the store according to the proposed schedule.

[0594] A "planning management program" is an application that users use to manage their daily schedules.

[0595] A system for implementing the present invention is configured as follows.

[0596] System Overview

[0597] Users access the system using a smartphone or a dedicated terminal installed in a physical store. They enter information such as their preferences, concerns, usage times, and location. This user information is sent from the terminal to the server. The server uses a generative AI model to generate candidate schedules based on the user information and proposes these schedules to the user. The user then acts within the physical store according to the proposed schedule. Through feedback, the user's experience is reflected in the next proposal.

[0598] Hardware and Software

[0599] The system uses the following hardware and software:

[0600] Hardware: Smartphones, dedicated terminals installed in physical stores.

[0601] Software: Flask (web framework), Python (programming language), JSON (data format), server (AWS or Heroku can be used as examples).

[0602] Program Operation

[0603] 1. Entering user information: The user enters their preferences, concerns, usage time, and location using a smartphone or dedicated device. This information is sent to the server as a user template in JSON format.

[0604] 2. Schedule generation using a generative model: A generative AI model runs on the server side using Flask and Python. The generative model generates the optimal celebrity schedule based on user information.

[0605] 3. Providing the schedule: The generated schedule is sent to the user's device. It works in conjunction with each facility in the physical store to navigate the user's behavior, for example, by providing appropriate guidance on the time spent in the cafe or the time spent in the fitness area.

[0606] 4. Feedback collection: After one day of experience, users provide feedback through the system. This feedback is stored on the server and reflected in the next schedule generation.

[0607] Specific examples

[0608] For example, if a user inputs information such as "I want to relax," "a day," and "a cafe," the server will generate a daily schedule for a celebrity that is suitable for relaxation. The generated schedule may include a morning yoga session, a mid-morning massage, and time to relax at a cafe for lunch. This schedule is then displayed on the user's smartphone.

[0609] Prompt Sentence Examples

[0610] For example, a prompt that the user might enter might look like this:

[0611] "I want to relax"

[0612] "1 day"

[0613] "Cafe"

[0614] This allows the server to generate celebrity schedules tailored to the user's needs and provide them to the user.

[0615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0616] Step 1:

[0617] Entering user information

[0618] Users use their smartphones or dedicated terminals installed in stores to input information about their wishes, concerns, usage time, and location. For example, they might enter information such as "I want to relax," "1 day," or "cafe." This user information is sent from the terminal to the server as JSON-formatted data.

[0619] Step 2:

[0620] Receiving User Information

[0621] The server receives the submitted user information and stores it in a database, performing validation to ensure the information is properly formatted and to detect invalid data.

[0622] Step 3:

[0623] Schedule generation using generative models

[0624] The server runs a generative AI model based on user information. The generative AI model generates optimal candidate schedules taking into account the user's preferences, usage times, and location. For example, for a user who wants to relax, schedules such as "morning yoga session," "morning massage time," and "time to relax at a cafe during lunch" are generated. The generated schedules are stored as JSON-formatted data.

[0625] Step 4:

[0626] Schedule provision

[0627] The server sends the generated schedule to the user's device, which displays it on the user's smartphone or a dedicated device in the physical store, allowing the user to check suggested activities throughout the day.

[0628] Step 5:

[0629] Cooperation with the installed equipment

[0630] When the user moves around the store according to the proposed schedule, the server works with each device in the store (such as the cafe's ordering system or the fitness area's reservation system) to navigate the user's actions, allowing the user to smoothly carry out their activities at each location.

[0631] Step 6:

[0632] Gathering feedback

[0633] Users experience the proposed schedule throughout the day and provide feedback after the experience. The feedback is entered via a dedicated device or smartphone and sent to the server. This feedback includes satisfaction, impressions, and further requests.

[0634] Step 7:

[0635] Processing Feedback

[0636] The server stores the received feedback in a database and performs data analysis to optimize the next schedule proposal based on the feedback, which allows the next schedule proposal to better suit the user's preferences and needs.

[0637] 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.

[0638] The present invention combines a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[0639] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The information entered by the user is sent from the terminal to the server.

[0640] The server then uses a generative model to propose optimal candidates based on user information. The generative model randomly selects an appropriate candidate, taking into account the user's preferences, usage time, and location. It then generates a schedule that mimics a day in the life of that candidate.

[0641] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions. The emotion engine analyzes the user's emotions in real time based on their voice and facial expression data, and provides this information to the generative model. Based on the results of this analysis, the generative model proposes a schedule that is more suited to the user's current state.

[0642] For example, if a user inputs information such as "I want to improve my motivation," "One day," and "Home," the server uses the generative model to generate a schedule for a suitable celebrity (such as "Ichiro"). Furthermore, if the user's voice or facial expression indicates that they are feeling stressed, the generative model will recommend a schedule that includes activities that will reduce the user's stress.

[0643] The server sends the generated schedule to the device, where the user can check the proposed schedule. This schedule also works with the user's calendar app to help ensure smooth execution.

[0644] After a user spends a day based on the specified schedule, they input feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. The feedback data is sent from the device to a server, which records it in a database. When generating future schedules, the previously collected user feedback is taken into account, allowing for more appropriate suggestions.

[0645] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[0646] As described above, the present invention is a system that combines emotion engines to provide a user with a fun and new perspective on their daily life and improve their work-life balance.

[0647] The processing flow will be explained below.

[0648] Step 1:

[0649] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[0650] Specific behavior:

[0651] The user enters the required information through a web form or app interface.

[0652] For example, enter "I want to improve my motivation," "1 day," and "Home."

[0653] Step 2:

[0654] The terminal collects the user's input information and sends it to the server.

[0655] Specific behavior:

[0656] The form is submitted and the user information is sent to the server in JSON format.

[0657] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[0658] Step 3:

[0659] The emotion engine analyzes the user's emotions.

[0660] Specific behavior:

[0661] The user provides facial expression and voice data through the device's camera and microphone.

[0662] The emotion engine analyzes the user's emotions in real time and identifies emotional states such as stress, joy, and excitement.

[0663] Example: If the user is analyzed as "feeling stressed", that information is sent to the server.

[0664] Step 4:

[0665] The server uses a generative model to select candidates based on user information and emotion data.

[0666] Specific behavior:

[0667] The server selects candidate celebrities and models from a database based on the user's preferences, emotional state, usage time, and location.

[0668] Example: "Ichiro" schedule is selected.

[0669] Depending on the emotional data, specific activities such as "stress reduction" may be tailored to include.

[0670] Step 5:

[0671] The server obtains today's date and day of the week and generates a schedule.

[0672] Specific behavior:

[0673] The server retrieves the current date and day of the week and combines the selected celebrity's schedule with adjustments based on emotional data.

[0674] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[0675] Step 6:

[0676] The server transmits the generated schedule to the terminal and provides it to the user.

[0677] Specific behavior:

[0678] The server transmits the generated schedule to the terminal so that the user can check it.

[0679] The terminal receives the schedule and displays it on the user interface.

[0680] Step 7:

[0681] The user spends their day based on the suggested schedule.

[0682] Specific behavior:

[0683] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[0684] Step 8:

[0685] The user enters feedback after the experience.

[0686] Specific behavior:

[0687] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[0688] For example, enter "I was very satisfied."

[0689] Step 9:

[0690] The terminal collects user feedback and sends it to the server.

[0691] Specific behavior:

[0692] User feedback is sent to the server in JSON format or similar.

[0693] For example: {"Feedback": "Very satisfied"} will be sent.

[0694] Step 10:

[0695] The server records the feedback in a database and reflects it in the next schedule generation.

[0696] Specific behavior:

[0697] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[0698] This allows the system to provide a schedule that adapts to the user's emotional state and improve the user's work-life balance.

[0699] Example 2

[0700] 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."

[0701] Many people living busy lives today need effective schedules to improve their daily and work performance. However, generating an optimal schedule that reflects each user's preferences and emotional state is difficult. Furthermore, conventional scheduling systems are unable to effectively utilize user feedback, making it difficult to improve the quality of the next schedule proposal.

[0702] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, emotion engine means for analyzing the user's emotions, means for optimizing the schedule based on the analyzed emotion information, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule according to the user's wishes and emotional state.

[0703] "User information" refers to data such as hopes, concerns, usage time, and location that users enter into the system.

[0704] "Generative model means" refers to AI models or algorithms that generate appropriate schedules based on user information.

[0705] "Proposal means" refers to the interface or communication means for providing the generated schedule to the user.

[0706] "Emotion engine means" refers to technology or software for analyzing a user's voice data and facial expression data to recognize emotions.

[0707] "Emotion information" refers to data on the user's emotional state analyzed by the emotion engine means.

[0708] "Feedback" refers to data entered by users after completing a schedule, such as their impressions, satisfaction, and areas for improvement.

[0709] "Next suggestion optimization" refers to the processes or algorithms used to improve the next schedule suggestion based on user feedback.

[0710] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance, and combines an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[0711] First, the user accesses the system through their device and enters information such as their wishes, concerns, usage time, and location. Specifically, this is done using the device's web interface or mobile app UI. This entered data is then sent from the device to the server. During transmission, the data is encrypted using the secure HTTPS protocol.

[0712] The server then uses a generative AI model (such as OpenAI GPT-3) to suggest the best candidates based on the user's information. This generative model is processed using a cloud-based platform (such as Google Cloud or Amazon Web Services). The server generates a schedule for a suitable celebrity or model based on the user's preferences, usage time, and location. For example, if the user inputs information such as "I want to improve my motivation," "One day," and "At home," the server generates a schedule that mimics an athlete's daily schedule.

[0713] Furthermore, the system is equipped with an emotion engine that analyzes the user's real-time emotional information. The emotion engine uses the Microsoft Azure Emotion API and Face API. The device collects the user's voice data (collected using a microphone) and facial expression data (collected using a camera) and sends this to the server. The server analyzes this data and obtains the user's emotional information.

[0714] Based on the analysis results, the server optimizes the generated schedule to the user's current state. For example, if the user is "feeling stressed," the server will add meditation time to the schedule to reduce stress. This optimized schedule is then sent from the server to the device, where the user can view it. Furthermore, this schedule can be linked to calendar apps such as Google Calendar and Microsoft Outlook, helping users to easily manage their schedules.

[0715] After a user spends a day based on the specified schedule, they can enter feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. This feedback data is sent from the device to a server, which then stores it in a database.

[0716] The collected feedback data will be taken into account in the next schedule generation and more appropriate schedule suggestions will be made, which will increase user satisfaction. For example, if a user provides feedback such as "very satisfied," that feedback will be reflected in the next schedule suggestion, and better suggestions will be made by the generative model that has learned the user's preferences and behavioral patterns.

[0717] Specific examples

[0718] For example, suppose a user inputs the prompts "I want to improve my motivation," "One day," and "At home." In this case, the server uses a generative AI model to generate a schedule that mimics a specific athlete's day. If the server determines that the user's emotional information indicates "feeling stressed," it will add meditation time and relaxation activities to the schedule. In this way, providing an optimal schedule based on real-time emotional analysis and feedback can improve the user's daily life and work performance.

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] Specific processing steps of the system program

[0721] Step 1:

[0722] The user enters information such as their wishes, concerns, usage time, and location through the device. Information is entered using an input form or the application's user interface. Input data includes "desires (e.g., want to improve motivation)," "usage time (e.g., per day)," and "location (e.g., home)." This information is sent to the server in the next step.

[0723] Step 2:

[0724] The terminal sends the entered user information to the server. The data is encrypted and transmitted using the secure HTTPS protocol, ensuring that the data reaches the server safely. The input is the user information, and the output is the user information received on the server side.

[0725] Step 3:

[0726] The server generates schedule candidates using a generative AI model (e.g., OpenAI GPT-3) based on the received user information. The generative model runs on a cloud-based platform (Google Cloud or Amazon Web Services) and generates related schedule data based on the received user information. The input is user information, and the output is the generated schedule candidates.

[0727] Step 4:

[0728] The device collects the user's voice data and facial expression data and sends it to the server. Voice data is collected using the device's microphone, and facial expression data is collected using the device's camera. This data is also sent using the secure HTTPS protocol. The input is voice data and facial expression data, and the output is emotional data sent to the server.

[0729] Step 5:

[0730] The server uses the Microsoft Azure Emotion API and Face API to analyze the received voice and facial expression data and obtain the user's emotional information. The analyzed emotional information becomes data that represents the user's emotional state in real time. The input is voice data and facial expression data, and the output is emotional information.

[0731] Step 6:

[0732] The server optimizes the generated schedule based on the emotional information. For example, if the user is analyzed as "feeling stressed," it adds relaxation activities to the schedule. Specifically, optimizations are performed such as incorporating meditation time into the schedule. The input is the generated schedule candidate and emotional information, and the output is the optimized schedule.

[0733] Step 7:

[0734] The server sends the optimized schedule to the device, where the user can view it. The schedule can also be linked to calendar apps such as Google Calendar and Microsoft Outlook, making it easier for users to manage their schedules. The input is the optimized schedule, and the output is the schedule displayed on the device.

[0735] Step 8:

[0736] After a user spends a day based on a specified schedule, they input feedback through the device. The feedback includes satisfaction and impressions of the experience. The input is feedback information, and the output is feedback data stored on the device.

[0737] Step 9:

[0738] The terminal sends feedback data to the server, which stores the data in a database. The feedback data is used to generate future schedules. The input is feedback information, and the output is the feedback data stored in the database.

[0739] Step 10:

[0740] The server improves the next schedule generation based on the accumulated feedback data. It analyzes the feedback data, learns the user's preferences and behavioral patterns, and proposes a more appropriate schedule. The input is the feedback data, and the output is the improved next schedule.

[0741] (Application example 2)

[0742] 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."

[0743] Conventional schedule suggestion systems generate schedules without taking the user's emotional state into account, which means they are unable to provide suggestions that are optimal for the user's current psychological state. Furthermore, they are unable to provide appropriate suggestions for specific locations or situations, which limits the user experience. Furthermore, leisure and driving plan suggestions for autonomous vehicles are not personalized, which means user satisfaction cannot be enhanced.

[0744] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for recognizing emotions based on the user's voice and video data, means for optimizing the schedule based on the emotions, means for proposing locations and routes suitable for the user, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to generate a schedule optimized for the user's psychological state and to suggest suitable locations and routes in real time.

[0745] "User information" refers to data provided by users, such as their wishes, concerns, usage time, and location.

[0746] A "generative model means" is an algorithm or system that selects optimal candidates based on user information and generates a schedule.

[0747] The "means for providing to the user" is an interface or application for displaying the generated schedule to the user.

[0748] The "means for recognizing emotions" refers to a technology or system that analyzes a user's emotions in real time based on the user's audio and video data.

[0749] The "schedule optimization method" is a process that adjusts and optimizes the generated schedule based on the perceived user sentiment.

[0750] "Means for suggesting places and routes" refers to a system for suggesting appropriate leisure spots and driving routes based on the user's current emotional state and location.

[0751] A "feedback channel" is an interface that collects data about the user's experience and satisfaction.

[0752] "Next proposal optimization" is the process of improving future schedule proposals based on collected feedback.

[0753] The present invention provides a system for analyzing the emotional state of a user in an autonomous vehicle and proposing leisure plans or driving plans based on the analyzed emotional state. The system includes a means for inputting user information, a generative model means, an emotion recognition means, a means for optimizing a schedule, a means for proposing places and routes, a means for receiving feedback, and a means for optimizing the next proposal.

[0754] System Programming and Processing

[0755] Program processing

[0756] 1. Enter your user information:

[0757] The server receives information provided by the user, such as their wishes, concerns, usage time, and location, via the terminal and stores it in a database.

[0758] 2. Candidate Proposal:

[0759] The server generates optimal schedule candidates based on the user information using a generative model means, which generates randomly selected candidates taking into account the user information.

[0760] 3. Emotion recognition:

[0761] The user's voice and video data are captured in real time through microphones and cameras installed in the vehicle, and the server analyzes the data using emotion recognition means, such as EmotionRecognizer.

[0762] 4. Schedule optimization:

[0763] The server optimizes the generated schedule based on the emotion recognition results, for example, suggesting a visit to a relaxation facility if the user is feeling stressed.

[0764] 5. Location and route suggestions:

[0765] Based on the optimized schedule, the server suggests suitable locations and routes for the user. The optimal route to the suggested locations is calculated using Google Maps APIs, etc.

[0766] 6. Gathering Feedback:

[0767] After spending a day based on the specified schedule, the user inputs feedback about the experience through the device, and the level of satisfaction and impressions about the experience are sent to the server as feedback data.

[0768] 7. Optimize your next proposal:

[0769] The server records the collected feedback data in a database and reflects the feedback in future schedule generation using the generative modeling means, thereby enabling suggestions that are more suited to the user's preferences and emotional state.

[0770] Hardware and software used

[0771] Hardware: In-car camera, microphone, GPS module, display, audio system

[0772] Software: Python program, emotion recognition model (e.g., EmotionRecognizer), route planning API (e.g., Google Maps API), database management system

[0773] Specific examples

[0774] For example, if the driver's emotions are detected as "stressed" through video and audio analysis from the in-car camera, the system will search for nearby spas and relaxation facilities and suggest routes based on those. This information will be displayed on the vehicle's display, and the navigation system will begin driving automatically.

[0775] Prompt Sentence Examples

[0776] If the user is feeling stressed, suggest suitable relaxation spots to relieve stress. Also, show the best route to those places. The user's current location is XXX, time is YYY. For example, nearby spa facilities, parks, and relaxation rooms could be suggested.

[0777] The present invention can improve user satisfaction by providing a personalized driving plan based on the user's psychological state.

[0778] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0779] Step 1:

[0780] Entering user information

[0781] The server collects information provided by the user via the device, such as their wishes, concerns, usage time, location, etc. The data entered by the user into the device is sent to the server and stored in a database, providing basic information about the user that will be used in the next step.

[0782] Input: Hopes, worries, usage time, location

[0783] Output: User information stored on the server

[0784] Step 2:

[0785] Candidate suggestions

[0786] The server uses a generative model means to generate optimal schedule candidates based on the user information stored in step 1. The generative model means uses a machine learning algorithm to analyze the user information and randomly generate an appropriate schedule.

[0787] Input: User information

[0788] Output: Candidate schedule

[0789] Step 3:

[0790] emotion recognition

[0791] The server receives the user's voice and video data acquired through the microphone and camera installed in the vehicle, analyzes the user's emotions in real time using emotion recognition means (e.g., EmotionRecognizer), and sends the analysis results to the server.

[0792] Input: Audio data, video data

[0793] Output: User's emotional state

[0794] Step 4:

[0795] Schedule optimization

[0796] The server optimizes the schedule based on the candidate schedule generated in step 2 and the emotion recognition results obtained in step 3. For example, if the user is feeling stressed, the server adjusts the schedule to include a visit to a relaxation facility.

[0797] Input: candidate schedule, emotional state

[0798] Output: Optimized schedule

[0799] Step 5:

[0800] Location and route suggestions

[0801] The server then suggests locations and routes suitable for the user based on the optimized schedule, using Google Maps APIs and other tools to search for suitable facilities and routes and generate detailed navigation information.

[0802] Input: Optimized schedule

[0803] Output: Suggested locations, route information

[0804] Step 6:

[0805] Providing suggestions

[0806] The server then displays the generated location and route information on the vehicle's display, notifies the user of the suggestions via the voice assistant, and initiates autonomous driving in conjunction with the navigation system.

[0807] Input: location, route information

[0808] Output: Proposals displayed on the in-vehicle display

[0809] Step 7:

[0810] Gathering feedback

[0811] After spending a day based on the proposed schedule, the user enters feedback about their experience through the device, and their satisfaction and impressions of the experience are sent from the device to the server and stored in a database.

[0812] Input: User feedback

[0813] Output: Feedback stored on the server

[0814] Step 8:

[0815] Optimize your next proposal

[0816] The server uses the collected feedback data to improve future schedule suggestions by means of a generative model, and uses a machine learning algorithm to retrain the model to generate schedules that better suit the user's preferences and emotional state.

[0817] Input: Feedback data

[0818] Output: Optimized next schedule proposal

[0819] 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.

[0820] 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.

[0821] 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.

[0822] [Third embodiment]

[0823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0825] 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).

[0826] 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.

[0827] 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.

[0828] 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).

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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."

[0835] The present invention provides a system that proposes a schedule that mimics the lifestyle of a specific celebrity or model in order to improve a user's daily life and work performance. Specific embodiments for implementing this system will be described below.

[0836] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The data entered by the user is sent from the terminal to the server.

[0837] The server then uses a generative model to generate a schedule for candidate celebrities or models based on the user information. The generative model considers the user's preferences, usage time, and location, randomly selects the best candidate, and proposes a schedule that mimics that candidate's day.

[0838] For example, if a user inputs information such as "I want to improve my motivation," "a day," and "my home," the generative model will create a daily schedule for a suitable celebrity based on this information. The server also obtains the current date and day of the week and constructs the schedule information.

[0839] The server then sends the generated schedule to the device, allowing the user to view the proposed schedule and spend their day based on it.

[0840] After the user experiences the designated schedule, they input feedback about their experience through their device. Specifically, they input their level of satisfaction and impressions. The feedback data is sent back to the server from the device, and the server records it in a database. This allows the system to take into account the previously collected user feedback when generating future schedules, enabling more appropriate suggestions to be made.

[0841] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[0842] In addition, the suggested schedule can be integrated with the user's calendar app, making it easier to manage schedules, allowing users to execute the suggested schedule without any hassle.

[0843] As described above, the present invention is a system that provides enjoyment and new perspectives to the user's daily life and improves work-life balance.

[0844] The processing flow will be explained below.

[0845] Step 1:

[0846] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[0847] Specific behavior:

[0848] The user enters the required information through a web form or app interface.

[0849] For example, enter "I want to improve my motivation," "1 day," and "Home."

[0850] Step 2:

[0851] The terminal collects the user's input information and sends it to the server.

[0852] Specific behavior:

[0853] The form is submitted and the user information is sent to the server in JSON format.

[0854] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[0855] Step 3:

[0856] The server uses the generative model to select candidates based on user information.

[0857] Specific behavior:

[0858] The server randomly selects candidate celebrities and models from a database based on the user's preferences, usage time, and location.

[0859] Example: "Ichiro" schedule is selected.

[0860] Step 4:

[0861] The server obtains today's date and day of the week and generates a schedule.

[0862] Specific behavior:

[0863] The server takes the current date and day of the week and combines it with the schedule of a randomly selected celebrity.

[0864] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[0865] Step 5:

[0866] The server transmits the generated schedule to the terminal and provides it to the user.

[0867] Specific behavior:

[0868] The server transmits the generated schedule to the terminal so that the user can check it.

[0869] The terminal receives the schedule and displays it on the user interface.

[0870] Step 6:

[0871] The user spends their day based on the suggested schedule.

[0872] Specific behavior:

[0873] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[0874] Step 7:

[0875] The user enters feedback after the experience.

[0876] Specific behavior:

[0877] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[0878] For example, enter "I was very satisfied."

[0879] Step 8:

[0880] The terminal collects user feedback and sends it to the server.

[0881] Specific behavior:

[0882] User feedback is sent to the server in JSON format or similar.

[0883] For example: {"Feedback": "Very satisfied"} will be sent.

[0884] Step 9:

[0885] The server records the feedback in a database and reflects it in the next schedule generation.

[0886] Specific behavior:

[0887] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[0888] Example 1

[0889] 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."

[0890] In today's busy lifestyles, many users struggle with managing their own schedules. Many also find it difficult to create specific action plans to improve their quality of life. Furthermore, there is a lack of systems that can optimize their next suggestions based on feedback. To solve these issues, there is a need for a system that can propose an optimal schedule based on the user's wishes, concerns, usage time, and location, and reflect feedback after the schedule is executed.

[0891] 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.

[0892] In this invention, the server includes a means for inputting user information, a generative AI model means for proposing candidates based on the user information, a means for sending the generated schedule to the user's device, a means for receiving feedback from the user, and a means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule based on the user's preferences, concerns, usage time, and location, and to improve the proposal content for the next time and thereafter based on feedback collected after the schedule is executed. The system can also link the generated schedule with the user's calendar app, allowing the user to easily manage and execute the proposed schedule.

[0893] "Means for inputting user information" refers to an interface that allows users to input information such as their wishes, concerns, usage time, and location through a terminal.

[0894] "Generative AI model means" is an artificial intelligence algorithm or model for suggesting appropriate candidates and generating a schedule based on user information.

[0895] A "means for providing suggested candidate schedules to a user" is a communication and display interface for providing the generated schedule to a user.

[0896] The "means for receiving feedback from users" is a function that allows users to input their satisfaction and impressions after executing a schedule and transmit them to the server.

[0897] The "means for optimizing the next proposal based on feedback" is an algorithm and database system that analyzes the received feedback and reflects it in the next schedule proposal.

[0898] The "means for transmitting the generated schedule to the user's terminal" is a communication function for transmitting the schedule generated by the server to the user's terminal.

[0899] "Means for linking the generated schedule with the user's calendar application" refers to a function for synchronizing the proposed schedule with the calendar application used by the user.

[0900] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models to improve users' daily life and work performance. The system generates an optimal schedule based on the user's wishes, concerns, usage time, and location, and then has the ability to improve the proposed schedule based on user feedback.

[0901] Hardware and software used

[0902] Device: The device used by the user to enter information, review the generated schedule, and provide feedback. This includes computers and smartphones.

[0903] Server: Receives user data, runs generative AI models, sends generated schedules, records feedback, and optimizes next proposals. Uses a cloud server (e.g., AWS, Google Cloud).

[0904] Generative AI models: Use deep learning models (e.g., GPT-4) to generate appropriate schedules based on user information.

[0905] System Operation Overview

[0906] 1. Sending user input information

[0907] The user inputs information such as their wishes, concerns, usage time, location, etc. through the device, which then formats this information and sends it to the server.

[0908] 2. Receiving data and generating schedules

[0909] The server receives user information sent from the device and passes it to a generative AI model, which then generates a schedule that mimics a day in the life of an appropriate celebrity or model based on the user's preferences, usage time, and location.

[0910] 3. Providing a schedule

[0911] The server reformats the generated schedule and sends it to the terminal, where the user can view the schedule.

[0912] 4. Submitting Feedback

[0913] After the user spends a day following the proposed schedule, they input feedback about their experience through the device, which then sends the feedback to the server.

[0914] 5. Processing feedback and optimizing next proposals

[0915] The server receives the feedback and records it in a database, which is then used to optimize future schedule suggestions.

[0916] Specific examples

[0917] Prompt Sentence Examples

[0918] "I want to improve my motivation"

[0919] "1 day"

[0920] "one's home"

[0921] Actual operation example

[0922] 1. User: The user opens the device interface and enters "I want to improve my motivation," "1 day," and "Home."

[0923] 2. Terminal: The terminal sends the entered information to the server.

[0924] 3. Server: The server sends prompts to the generative AI model to generate a schedule that mimics Celebrity A's daily routine, such as "Wake up at 6am, jog at 7am, have breakfast at 8am, read at 9am, have lunch at 12pm..."

[0925] 4. Server: The server sends the generated schedule to the terminal.

[0926] 5. Terminal: The terminal parses the schedule and displays it on the interface.

[0927] 6. User: The user checks the displayed schedule and spends their day according to it.

[0928] 7. User: After the experience is over, the user fills in the feedback form saying, "I was very satisfied with today's schedule."

[0929] 8. Device: The device sends feedback to the server.

[0930] 9. Server: The server records the feedback in a database and reflects it in future schedule suggestions. In this way, the system provides users with fun and new perspectives in their daily lives, improving their work-life balance.

[0931] The present invention enables real-time input and processing of user information, and schedule suggestions and feedback from a generative AI model, thereby effectively improving users' daily life and work performance.

[0932] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0933] Step 1:

[0934] Sending user input information

[0935] User: The user opens the device interface and enters information such as their wishes, concerns, usage time, location, etc. Specifically, they enter information such as "I want to improve my motivation," "1 day," and "Home" using text boxes and dropdown boxes.

[0936] Input: User's wishes, concerns, usage time, and location (e.g., "I want to improve my motivation," "1 day," "Home")

[0937] Device: The device formats the input information and sends it to the server in the form of an API request.

[0938] Output: API request with formatted user information

[0939] Step 2:

[0940] Receiving data

[0941] Server: The server receives the API request sent from the device using an HTTP request.

[0942] Input: API request containing formatted user information

[0943] Server: The server parses the received data and extracts user information. Specifically, it parses the data using a JSON parser.

[0944] Output: Extracted user information (hopes, concerns, usage time, location)

[0945] Step 3:

[0946] Generate a schedule

[0947] Server: The server sends prompts to a generative AI model (e.g., GPT-4) to generate a schedule based on user information.

[0948] Input: Extracted user information (hopes, concerns, usage time, location)

[0949] Server: The generative AI model analyzes the prompt text and generates a schedule that mimics a day in the life of celebrity A, for example, based on "I want to improve my motivation," "1 day," and "at home."

[0950] Output: Generated schedule (e.g. "Wake up at 6, jog at 7, breakfast at 8, read at 9...")

[0951] Step 4:

[0952] Sending a Schedule

[0953] Server: The server reformats the generated schedule and sends it to the device, for example by converting the schedule to JSON format and sending it in an HTTP response.

[0954] Input: Generated schedule

[0955] Server: Sends formatted schedule data as an HTTP response.

[0956] Output: Formatted schedule data

[0957] Step 5:

[0958] Check the schedule

[0959] Terminal: The terminal analyzes the schedule data received from the server and displays it on the interface, using front-end technologies (e.g., React, Vue.js).

[0960] Input: Formatted schedule data

[0961] Terminal: Parses the data and displays it in a user interface.

[0962] User: The user sees the displayed schedule and follows it for the day.

[0963] Output: The displayed schedule

[0964] Step 6:

[0965] Send Feedback

[0966] User: After a user has spent a day following the schedule, they provide feedback on their experience, for example, their satisfaction and impressions in a dedicated feedback form.

[0967] Input: User feedback (e.g., "I was very satisfied with today's schedule")

[0968] Terminal: The terminal formats the entered feedback and sends it to the server using a POST request.

[0969] Output: Formatted feedback data

[0970] Step 7:

[0971] Processing Feedback

[0972] Server: The server receives the feedback sent from the device and records it in a database using an HTTP request.

[0973] Input: Formatted feedback data

[0974] Server: Analyzes the received feedback and stores it in a database, typically using a database management system (e.g., MySQL, PostgreSQL).

[0975] Output: Recorded feedback data

[0976] Step 8:

[0977] Next proposal optimization

[0978] Server: The server analyzes the recorded feedback and updates the algorithm to optimize future schedule suggestions.

[0979] Input: Recorded feedback data

[0980] Server: Adjusts the model parameters based on the feedback data to improve the next proposal, specifically using machine learning algorithms.

[0981] Output: An updated generative AI model

[0982] Through this process, the system provides the optimal schedule based on the user's wishes and concerns, and by incorporating feedback obtained after the schedule is implemented, it can continuously make more effective suggestions.

[0983] (Application example 1)

[0984] 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."

[0985] Conventional user schedule suggestion systems simply propose schedules based on user input, but lack dynamic navigation and feedback tailored to the actual usage environment. Therefore, there was a need for a method to further enrich users' lifestyles and in-store experiences.

[0986] 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.

[0987] In this invention, the server includes means for inputting user information, a generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for receiving feedback from the user, means for optimizing the next proposal based on the feedback, and means for navigating the user's behavior in cooperation with available installed devices, thereby enabling the user to experience the lifestyle habits of a specific celebrity and be efficiently and effectively navigated through the behavior in the store.

[0988] "User information" refers to information entered by the user, such as their wishes, concerns, usage time, and location.

[0989] The "generative model means" is a means for selecting optimal candidates based on user information and generating a schedule.

[0990] The "proposed candidate schedule" is a schedule that details the daily activities of the candidate selected by the generative model means.

[0991] "Feedback" refers to opinions such as satisfaction and impressions that users provide to the system after experiencing it.

[0992] "Means to optimize the next proposal" refers to means to better adjust the next schedule proposal based on the feedback received.

[0993] "Available installed devices" are user interface devices such as terminals and smartphones installed in physical stores.

[0994] "Means for navigating behavior" is a function for navigating the user within the store according to the proposed schedule.

[0995] A "planning management program" is an application that users use to manage their daily schedules.

[0996] A system for implementing the present invention is configured as follows.

[0997] System Overview

[0998] Users access the system using a smartphone or a dedicated terminal installed in a physical store. They enter information such as their preferences, concerns, usage times, and location. This user information is sent from the terminal to the server. The server uses a generative AI model to generate candidate schedules based on the user information and proposes these schedules to the user. The user then acts within the physical store according to the proposed schedule. Through feedback, the user's experience is reflected in the next proposal.

[0999] Hardware and Software

[1000] The system uses the following hardware and software:

[1001] Hardware: Smartphones, dedicated terminals installed in physical stores.

[1002] Software: Flask (web framework), Python (programming language), JSON (data format), server (AWS or Heroku can be used as examples).

[1003] Program Operation

[1004] 1. Entering user information: The user enters their preferences, concerns, usage time, and location using a smartphone or dedicated device. This information is sent to the server as a user template in JSON format.

[1005] 2. Schedule generation using a generative model: A generative AI model runs on the server side using Flask and Python. The generative model generates the optimal celebrity schedule based on user information.

[1006] 3. Providing the schedule: The generated schedule is sent to the user's device. It works in conjunction with each facility in the physical store to navigate the user's behavior, for example, by providing appropriate guidance on the time spent in the cafe or the time spent in the fitness area.

[1007] 4. Feedback collection: After one day of experience, users provide feedback through the system. This feedback is stored on the server and reflected in the next schedule generation.

[1008] Specific examples

[1009] For example, if a user inputs information such as "I want to relax," "a day," and "a cafe," the server will generate a daily schedule for a celebrity that is suitable for relaxation. The generated schedule may include a morning yoga session, a mid-morning massage, and time to relax at a cafe for lunch. This schedule is then displayed on the user's smartphone.

[1010] Prompt Sentence Examples

[1011] For example, a prompt that the user might enter might look like this:

[1012] "I want to relax"

[1013] "1 day"

[1014] "Cafe"

[1015] This allows the server to generate celebrity schedules tailored to the user's needs and provide them to the user.

[1016] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1017] Step 1:

[1018] Entering user information

[1019] Users use their smartphones or dedicated terminals installed in stores to input information about their wishes, concerns, usage time, and location. For example, they might enter information such as "I want to relax," "1 day," or "cafe." This user information is sent from the terminal to the server as JSON-formatted data.

[1020] Step 2:

[1021] Receiving User Information

[1022] The server receives the submitted user information and stores it in a database, performing validation to ensure the information is properly formatted and to detect invalid data.

[1023] Step 3:

[1024] Schedule generation using generative models

[1025] The server runs a generative AI model based on user information. The generative AI model generates optimal candidate schedules taking into account the user's preferences, usage times, and location. For example, for a user who wants to relax, schedules such as "morning yoga session," "morning massage time," and "time to relax at a cafe during lunch" are generated. The generated schedules are stored as JSON-formatted data.

[1026] Step 4:

[1027] Schedule provision

[1028] The server sends the generated schedule to the user's device, which displays it on the user's smartphone or a dedicated device in the physical store, allowing the user to check suggested activities throughout the day.

[1029] Step 5:

[1030] Cooperation with the installed equipment

[1031] When the user moves around the store according to the proposed schedule, the server works with each device in the store (such as the cafe's ordering system or the fitness area's reservation system) to navigate the user's actions, allowing the user to smoothly carry out their activities at each location.

[1032] Step 6:

[1033] Gathering feedback

[1034] Users experience the proposed schedule throughout the day and provide feedback after the experience. The feedback is entered via a dedicated device or smartphone and sent to the server. This feedback includes satisfaction, impressions, and further requests.

[1035] Step 7:

[1036] Processing Feedback

[1037] The server stores the received feedback in a database and performs data analysis to optimize the next schedule proposal based on the feedback, which allows the next schedule proposal to better suit the user's preferences and needs.

[1038] 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.

[1039] The present invention combines a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[1040] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The information entered by the user is sent from the terminal to the server.

[1041] The server then uses a generative model to propose optimal candidates based on user information. The generative model randomly selects an appropriate candidate, taking into account the user's preferences, usage time, and location. It then generates a schedule that mimics a day in the life of that candidate.

[1042] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions. The emotion engine analyzes the user's emotions in real time based on their voice and facial expression data, and provides this information to the generative model. Based on the results of this analysis, the generative model proposes a schedule that is more suited to the user's current state.

[1043] For example, if a user inputs information such as "I want to improve my motivation," "One day," and "Home," the server uses the generative model to generate a schedule for a suitable celebrity (such as "Ichiro"). Furthermore, if the user's voice or facial expression indicates that they are feeling stressed, the generative model will recommend a schedule that includes activities that will reduce the user's stress.

[1044] The server sends the generated schedule to the device, where the user can check the proposed schedule. This schedule also works with the user's calendar app to help ensure smooth execution.

[1045] After a user spends a day based on the specified schedule, they input feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. The feedback data is sent from the device to a server, which records it in a database. When generating future schedules, the previously collected user feedback is taken into account, allowing for more appropriate suggestions.

[1046] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[1047] As described above, the present invention is a system that combines emotion engines to provide a user with a fun and new perspective on their daily life and improve their work-life balance.

[1048] The processing flow will be explained below.

[1049] Step 1:

[1050] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[1051] Specific behavior:

[1052] The user enters the required information through a web form or app interface.

[1053] For example, enter "I want to improve my motivation," "1 day," and "Home."

[1054] Step 2:

[1055] The terminal collects the user's input information and sends it to the server.

[1056] Specific behavior:

[1057] The form is submitted and the user information is sent to the server in JSON format.

[1058] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[1059] Step 3:

[1060] The emotion engine analyzes the user's emotions.

[1061] Specific behavior:

[1062] The user provides facial expression and voice data through the device's camera and microphone.

[1063] The emotion engine analyzes the user's emotions in real time and identifies emotional states such as stress, joy, and excitement.

[1064] Example: If the user is analyzed as "feeling stressed", that information is sent to the server.

[1065] Step 4:

[1066] The server uses a generative model to select candidates based on user information and emotion data.

[1067] Specific behavior:

[1068] The server selects candidate celebrities and models from a database based on the user's preferences, emotional state, usage time, and location.

[1069] Example: "Ichiro" schedule is selected.

[1070] Depending on the emotional data, specific activities such as "stress reduction" may be tailored to include.

[1071] Step 5:

[1072] The server obtains today's date and day of the week and generates a schedule.

[1073] Specific behavior:

[1074] The server retrieves the current date and day of the week and combines the selected celebrity's schedule with adjustments based on emotional data.

[1075] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[1076] Step 6:

[1077] The server transmits the generated schedule to the terminal and provides it to the user.

[1078] Specific behavior:

[1079] The server transmits the generated schedule to the terminal so that the user can check it.

[1080] The terminal receives the schedule and displays it on the user interface.

[1081] Step 7:

[1082] The user spends their day based on the suggested schedule.

[1083] Specific behavior:

[1084] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[1085] Step 8:

[1086] The user enters feedback after the experience.

[1087] Specific behavior:

[1088] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[1089] For example, enter "I was very satisfied."

[1090] Step 9:

[1091] The terminal collects user feedback and sends it to the server.

[1092] Specific behavior:

[1093] User feedback is sent to the server in JSON format or similar.

[1094] For example: {"Feedback": "Very satisfied"} will be sent.

[1095] Step 10:

[1096] The server records the feedback in a database and reflects it in the next schedule generation.

[1097] Specific behavior:

[1098] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[1099] This allows the system to provide a schedule that adapts to the user's emotional state and improve the user's work-life balance.

[1100] Example 2

[1101] 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."

[1102] Many people living busy lives today need effective schedules to improve their daily and work performance. However, generating an optimal schedule that reflects each user's preferences and emotional state is difficult. Furthermore, conventional scheduling systems are unable to effectively utilize user feedback, making it difficult to improve the quality of the next schedule proposal.

[1103] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, emotion engine means for analyzing the user's emotions, means for optimizing the schedule based on the analyzed emotion information, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule according to the user's wishes and emotional state.

[1104] "User information" refers to data such as hopes, concerns, usage time, and location that users enter into the system.

[1105] "Generative model means" refers to AI models or algorithms that generate appropriate schedules based on user information.

[1106] "Proposal means" refers to the interface or communication means for providing the generated schedule to the user.

[1107] "Emotion engine means" refers to technology or software for analyzing a user's voice data and facial expression data to recognize emotions.

[1108] "Emotion information" refers to data on the user's emotional state analyzed by the emotion engine means.

[1109] "Feedback" refers to data entered by users after completing a schedule, such as their impressions, satisfaction, and areas for improvement.

[1110] "Next suggestion optimization" refers to the processes or algorithms used to improve the next schedule suggestion based on user feedback.

[1111] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance, and combines an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[1112] First, the user accesses the system through their device and enters information such as their wishes, concerns, usage time, and location. Specifically, this is done using the device's web interface or mobile app UI. This entered data is then sent from the device to the server. During transmission, the data is encrypted using the secure HTTPS protocol.

[1113] The server then uses a generative AI model (such as OpenAI GPT-3) to suggest the best candidates based on the user's information. This generative model is processed using a cloud-based platform (such as Google Cloud or Amazon Web Services). The server generates a schedule for a suitable celebrity or model based on the user's preferences, usage time, and location. For example, if the user inputs information such as "I want to improve my motivation," "One day," and "At home," the server generates a schedule that mimics an athlete's daily schedule.

[1114] Furthermore, the system is equipped with an emotion engine that analyzes the user's real-time emotional information. The emotion engine uses the Microsoft Azure Emotion API and Face API. The device collects the user's voice data (collected using a microphone) and facial expression data (collected using a camera) and sends this to the server. The server analyzes this data and obtains the user's emotional information.

[1115] Based on the analysis results, the server optimizes the generated schedule to the user's current state. For example, if the user is "feeling stressed," the server will add meditation time to the schedule to reduce stress. This optimized schedule is then sent from the server to the device, where the user can view it. Furthermore, this schedule can be linked to calendar apps such as Google Calendar and Microsoft Outlook, helping users to easily manage their schedules.

[1116] After a user spends a day based on the specified schedule, they can enter feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. This feedback data is sent from the device to a server, which then stores it in a database.

[1117] The collected feedback data will be taken into account in the next schedule generation and more appropriate schedule suggestions will be made, which will increase user satisfaction. For example, if a user provides feedback such as "very satisfied," that feedback will be reflected in the next schedule suggestion, and better suggestions will be made by the generative model that has learned the user's preferences and behavioral patterns.

[1118] Specific examples

[1119] For example, suppose a user inputs the prompts "I want to improve my motivation," "One day," and "At home." In this case, the server uses a generative AI model to generate a schedule that mimics a specific athlete's day. If the server determines that the user's emotional information indicates "feeling stressed," it will add meditation time and relaxation activities to the schedule. In this way, providing an optimal schedule based on real-time emotional analysis and feedback can improve the user's daily life and work performance.

[1120] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1121] Specific processing steps of the system program

[1122] Step 1:

[1123] The user enters information such as their wishes, concerns, usage time, and location through the device. Information is entered using an input form or the application's user interface. Input data includes "desires (e.g., want to improve motivation)," "usage time (e.g., per day)," and "location (e.g., home)." This information is sent to the server in the next step.

[1124] Step 2:

[1125] The terminal sends the entered user information to the server. The data is encrypted and transmitted using the secure HTTPS protocol, ensuring that the data reaches the server safely. The input is the user information, and the output is the user information received on the server side.

[1126] Step 3:

[1127] The server generates schedule candidates using a generative AI model (e.g., OpenAI GPT-3) based on the received user information. The generative model runs on a cloud-based platform (Google Cloud or Amazon Web Services) and generates related schedule data based on the received user information. The input is user information, and the output is the generated schedule candidates.

[1128] Step 4:

[1129] The device collects the user's voice data and facial expression data and sends it to the server. Voice data is collected using the device's microphone, and facial expression data is collected using the device's camera. This data is also sent using the secure HTTPS protocol. The input is voice data and facial expression data, and the output is emotional data sent to the server.

[1130] Step 5:

[1131] The server uses the Microsoft Azure Emotion API and Face API to analyze the received voice and facial expression data and obtain the user's emotional information. The analyzed emotional information becomes data that represents the user's emotional state in real time. The input is voice data and facial expression data, and the output is emotional information.

[1132] Step 6:

[1133] The server optimizes the generated schedule based on the emotional information. For example, if the user is analyzed as "feeling stressed," it adds relaxation activities to the schedule. Specifically, optimizations are performed such as incorporating meditation time into the schedule. The input is the generated schedule candidate and emotional information, and the output is the optimized schedule.

[1134] Step 7:

[1135] The server sends the optimized schedule to the device, where the user can view it. The schedule can also be linked to calendar apps such as Google Calendar and Microsoft Outlook, making it easier for users to manage their schedules. The input is the optimized schedule, and the output is the schedule displayed on the device.

[1136] Step 8:

[1137] After a user spends a day based on a specified schedule, they input feedback through the device. The feedback includes satisfaction and impressions of the experience. The input is feedback information, and the output is feedback data stored on the device.

[1138] Step 9:

[1139] The terminal sends feedback data to the server, which stores the data in a database. The feedback data is used to generate future schedules. The input is feedback information, and the output is the feedback data stored in the database.

[1140] Step 10:

[1141] The server improves the next schedule generation based on the accumulated feedback data. It analyzes the feedback data, learns the user's preferences and behavioral patterns, and proposes a more appropriate schedule. The input is the feedback data, and the output is the improved next schedule.

[1142] (Application example 2)

[1143] 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."

[1144] Conventional schedule suggestion systems generate schedules without taking the user's emotional state into account, which means they are unable to provide suggestions that are optimal for the user's current psychological state. Furthermore, they are unable to provide appropriate suggestions for specific locations or situations, which limits the user experience. Furthermore, leisure and driving plan suggestions for autonomous vehicles are not personalized, which means user satisfaction cannot be enhanced.

[1145] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for recognizing emotions based on the user's voice and video data, means for optimizing the schedule based on the emotions, means for proposing locations and routes suitable for the user, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to generate a schedule optimized for the user's psychological state and to suggest suitable locations and routes in real time.

[1146] "User information" refers to data provided by users, such as their wishes, concerns, usage time, and location.

[1147] A "generative model means" is an algorithm or system that selects optimal candidates based on user information and generates a schedule.

[1148] The "means for providing to the user" is an interface or application for displaying the generated schedule to the user.

[1149] The "means for recognizing emotions" refers to a technology or system that analyzes a user's emotions in real time based on the user's audio and video data.

[1150] The "schedule optimization method" is a process that adjusts and optimizes the generated schedule based on the perceived user sentiment.

[1151] "Means for suggesting places and routes" refers to a system for suggesting appropriate leisure spots and driving routes based on the user's current emotional state and location.

[1152] A "feedback channel" is an interface that collects data about the user's experience and satisfaction.

[1153] "Next proposal optimization" is the process of improving future schedule proposals based on collected feedback.

[1154] The present invention provides a system for analyzing the emotional state of a user in an autonomous vehicle and proposing leisure plans or driving plans based on the analyzed emotional state. The system includes a means for inputting user information, a generative model means, an emotion recognition means, a means for optimizing a schedule, a means for proposing places and routes, a means for receiving feedback, and a means for optimizing the next proposal.

[1155] System Programming and Processing

[1156] Program processing

[1157] 1. Enter your user information:

[1158] The server receives information provided by the user, such as their wishes, concerns, usage time, and location, via the terminal and stores it in a database.

[1159] 2. Candidate Proposal:

[1160] The server generates optimal schedule candidates based on the user information using a generative model means, which generates randomly selected candidates taking into account the user information.

[1161] 3. Emotion recognition:

[1162] The user's voice and video data are captured in real time through microphones and cameras installed in the vehicle, and the server analyzes the data using emotion recognition means, such as EmotionRecognizer.

[1163] 4. Schedule optimization:

[1164] The server optimizes the generated schedule based on the emotion recognition results, for example, suggesting a visit to a relaxation facility if the user is feeling stressed.

[1165] 5. Location and route suggestions:

[1166] Based on the optimized schedule, the server suggests suitable locations and routes for the user. The optimal route to the suggested locations is calculated using Google Maps APIs, etc.

[1167] 6. Gathering Feedback:

[1168] After spending a day based on the specified schedule, the user inputs feedback about the experience through the device, and the level of satisfaction and impressions about the experience are sent to the server as feedback data.

[1169] 7. Optimize your next proposal:

[1170] The server records the collected feedback data in a database and reflects the feedback in future schedule generation using the generative modeling means, thereby enabling suggestions that are more suited to the user's preferences and emotional state.

[1171] Hardware and software used

[1172] Hardware: In-car camera, microphone, GPS module, display, audio system

[1173] Software: Python program, emotion recognition model (e.g., EmotionRecognizer), route planning API (e.g., Google Maps API), database management system

[1174] Specific examples

[1175] For example, if the driver's emotions are detected as "stressed" through video and audio analysis from the in-car camera, the system will search for nearby spas and relaxation facilities and suggest routes based on those. This information will be displayed on the vehicle's display, and the navigation system will begin driving automatically.

[1176] Prompt Sentence Examples

[1177] If the user is feeling stressed, suggest suitable relaxation spots to relieve stress. Also, show the best route to those places. The user's current location is XXX, time is YYY. For example, nearby spa facilities, parks, and relaxation rooms could be suggested.

[1178] The present invention can improve user satisfaction by providing a personalized driving plan based on the user's psychological state.

[1179] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1180] Step 1:

[1181] Entering user information

[1182] The server collects information provided by the user via the device, such as their wishes, concerns, usage time, location, etc. The data entered by the user into the device is sent to the server and stored in a database, providing basic information about the user that will be used in the next step.

[1183] Input: Hopes, worries, usage time, location

[1184] Output: User information stored on the server

[1185] Step 2:

[1186] Candidate suggestions

[1187] The server uses a generative model means to generate optimal schedule candidates based on the user information stored in step 1. The generative model means uses a machine learning algorithm to analyze the user information and randomly generate an appropriate schedule.

[1188] Input: User information

[1189] Output: Candidate schedule

[1190] Step 3:

[1191] emotion recognition

[1192] The server receives the user's voice and video data acquired through the microphone and camera installed in the vehicle, analyzes the user's emotions in real time using emotion recognition means (e.g., EmotionRecognizer), and sends the analysis results to the server.

[1193] Input: Audio data, video data

[1194] Output: User's emotional state

[1195] Step 4:

[1196] Schedule optimization

[1197] The server optimizes the schedule based on the candidate schedule generated in step 2 and the emotion recognition results obtained in step 3. For example, if the user is feeling stressed, the server adjusts the schedule to include a visit to a relaxation facility.

[1198] Input: candidate schedule, emotional state

[1199] Output: Optimized schedule

[1200] Step 5:

[1201] Location and route suggestions

[1202] The server then suggests locations and routes suitable for the user based on the optimized schedule, using Google Maps APIs and other tools to search for suitable facilities and routes and generate detailed navigation information.

[1203] Input: Optimized schedule

[1204] Output: Suggested locations, route information

[1205] Step 6:

[1206] Providing suggestions

[1207] The server then displays the generated location and route information on the vehicle's display, notifies the user of the suggestions via the voice assistant, and initiates autonomous driving in conjunction with the navigation system.

[1208] Input: location, route information

[1209] Output: Proposals displayed on the in-vehicle display

[1210] Step 7:

[1211] Gathering feedback

[1212] After spending a day based on the proposed schedule, the user enters feedback about their experience through the device, and their satisfaction and impressions of the experience are sent from the device to the server and stored in a database.

[1213] Input: User feedback

[1214] Output: Feedback stored on the server

[1215] Step 8:

[1216] Optimize your next proposal

[1217] The server uses the collected feedback data to improve future schedule suggestions by means of a generative model, and uses a machine learning algorithm to retrain the model to generate schedules that better suit the user's preferences and emotional state.

[1218] Input: Feedback data

[1219] Output: Optimized next schedule proposal

[1220] 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.

[1221] 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.

[1222] 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.

[1223] [Fourth embodiment]

[1224] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1225] 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.

[1226] 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).

[1227] 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.

[1228] 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.

[1229] 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).

[1230] 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.

[1231] 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.

[1232] 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.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] 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."

[1237] The present invention provides a system that proposes a schedule that mimics the lifestyle of a specific celebrity or model in order to improve a user's daily life and work performance. Specific embodiments for implementing this system will be described below.

[1238] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The data entered by the user is sent from the terminal to the server.

[1239] The server then uses a generative model to generate a schedule for candidate celebrities or models based on the user information. The generative model considers the user's preferences, usage time, and location, randomly selects the best candidate, and proposes a schedule that mimics that candidate's day.

[1240] For example, if a user inputs information such as "I want to improve my motivation," "a day," and "my home," the generative model will create a daily schedule for a suitable celebrity based on this information. The server also obtains the current date and day of the week and constructs the schedule information.

[1241] The server then sends the generated schedule to the device, allowing the user to view the proposed schedule and spend their day based on it.

[1242] After the user experiences the designated schedule, they input feedback about their experience through their device. Specifically, they input their level of satisfaction and impressions. The feedback data is sent back to the server from the device, and the server records it in a database. This allows the system to take into account the previously collected user feedback when generating future schedules, enabling more appropriate suggestions to be made.

[1243] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[1244] In addition, the suggested schedule can be integrated with the user's calendar app, making it easier to manage schedules, allowing users to execute the suggested schedule without any hassle.

[1245] As described above, the present invention is a system that provides enjoyment and new perspectives to the user's daily life and improves work-life balance.

[1246] The processing flow will be explained below.

[1247] Step 1:

[1248] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[1249] Specific behavior:

[1250] The user enters the required information through a web form or app interface.

[1251] For example, enter "I want to improve my motivation," "1 day," and "Home."

[1252] Step 2:

[1253] The terminal collects the user's input information and sends it to the server.

[1254] Specific behavior:

[1255] The form is submitted and the user information is sent to the server in JSON format.

[1256] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[1257] Step 3:

[1258] The server uses the generative model to select candidates based on user information.

[1259] Specific behavior:

[1260] The server randomly selects candidate celebrities and models from a database based on the user's preferences, usage time, and location.

[1261] Example: "Ichiro" schedule is selected.

[1262] Step 4:

[1263] The server obtains today's date and day of the week and generates a schedule.

[1264] Specific behavior:

[1265] The server takes the current date and day of the week and combines it with the schedule of a randomly selected celebrity.

[1266] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[1267] Step 5:

[1268] The server transmits the generated schedule to the terminal and provides it to the user.

[1269] Specific behavior:

[1270] The server transmits the generated schedule to the terminal so that the user can check it.

[1271] The terminal receives the schedule and displays it on the user interface.

[1272] Step 6:

[1273] The user spends their day based on the suggested schedule.

[1274] Specific behavior:

[1275] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[1276] Step 7:

[1277] The user enters feedback after the experience.

[1278] Specific behavior:

[1279] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[1280] For example, enter "I was very satisfied."

[1281] Step 8:

[1282] The terminal collects user feedback and sends it to the server.

[1283] Specific behavior:

[1284] User feedback is sent to the server in JSON format or similar.

[1285] For example: {"Feedback": "Very satisfied"} will be sent.

[1286] Step 9:

[1287] The server records the feedback in a database and reflects it in the next schedule generation.

[1288] Specific behavior:

[1289] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[1290] Example 1

[1291] 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."

[1292] In today's busy lifestyles, many users struggle with managing their own schedules. Many also find it difficult to create specific action plans to improve their quality of life. Furthermore, there is a lack of systems that can optimize their next suggestions based on feedback. To solve these issues, there is a need for a system that can propose an optimal schedule based on the user's wishes, concerns, usage time, and location, and reflect feedback after the schedule is executed.

[1293] 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.

[1294] In this invention, the server includes a means for inputting user information, a generative AI model means for proposing candidates based on the user information, a means for sending the generated schedule to the user's device, a means for receiving feedback from the user, and a means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule based on the user's preferences, concerns, usage time, and location, and to improve the proposal content for the next time and thereafter based on feedback collected after the schedule is executed. The system can also link the generated schedule with the user's calendar app, allowing the user to easily manage and execute the proposed schedule.

[1295] "Means for inputting user information" refers to an interface that allows users to input information such as their wishes, concerns, usage time, and location through a terminal.

[1296] "Generative AI model means" is an artificial intelligence algorithm or model for suggesting appropriate candidates and generating a schedule based on user information.

[1297] A "means for providing suggested candidate schedules to a user" is a communication and display interface for providing the generated schedule to a user.

[1298] The "means for receiving feedback from users" is a function that allows users to input their satisfaction and impressions after executing a schedule and transmit them to the server.

[1299] The "means for optimizing the next proposal based on feedback" is an algorithm and database system that analyzes the received feedback and reflects it in the next schedule proposal.

[1300] The "means for transmitting the generated schedule to the user's terminal" is a communication function for transmitting the schedule generated by the server to the user's terminal.

[1301] "Means for linking the generated schedule with the user's calendar application" refers to a function for synchronizing the proposed schedule with the calendar application used by the user.

[1302] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models to improve users' daily life and work performance. The system generates an optimal schedule based on the user's wishes, concerns, usage time, and location, and then has the ability to improve the proposed schedule based on user feedback.

[1303] Hardware and software used

[1304] Device: The device used by the user to enter information, review the generated schedule, and provide feedback. This includes computers and smartphones.

[1305] Server: Receives user data, runs generative AI models, sends generated schedules, records feedback, and optimizes next proposals. Uses a cloud server (e.g., AWS, Google Cloud).

[1306] Generative AI models: Use deep learning models (e.g., GPT-4) to generate appropriate schedules based on user information.

[1307] System Operation Overview

[1308] 1. Sending user input information

[1309] The user inputs information such as their wishes, concerns, usage time, location, etc. through the device, which then formats this information and sends it to the server.

[1310] 2. Receiving data and generating schedules

[1311] The server receives user information sent from the device and passes it to a generative AI model, which then generates a schedule that mimics a day in the life of an appropriate celebrity or model based on the user's preferences, usage time, and location.

[1312] 3. Providing a schedule

[1313] The server reformats the generated schedule and sends it to the terminal, where the user can view the schedule.

[1314] 4. Submitting Feedback

[1315] After the user spends a day following the proposed schedule, they input feedback about their experience through the device, which then sends the feedback to the server.

[1316] 5. Processing feedback and optimizing next proposals

[1317] The server receives the feedback and records it in a database, which is then used to optimize future schedule suggestions.

[1318] Specific examples

[1319] Prompt Sentence Examples

[1320] "I want to improve my motivation"

[1321] "1 day"

[1322] "one's home"

[1323] Actual operation example

[1324] 1. User: The user opens the device interface and enters "I want to improve my motivation," "1 day," and "Home."

[1325] 2. Terminal: The terminal sends the entered information to the server.

[1326] 3. Server: The server sends prompts to the generative AI model to generate a schedule that mimics Celebrity A's daily routine, such as "Wake up at 6am, jog at 7am, have breakfast at 8am, read at 9am, have lunch at 12pm..."

[1327] 4. Server: The server sends the generated schedule to the terminal.

[1328] 5. Terminal: The terminal parses the schedule and displays it on the interface.

[1329] 6. User: The user checks the displayed schedule and spends their day according to it.

[1330] 7. User: After the experience is over, the user fills in the feedback form saying, "I was very satisfied with today's schedule."

[1331] 8. Device: The device sends feedback to the server.

[1332] 9. Server: The server records the feedback in a database and reflects it in future schedule suggestions. In this way, the system provides users with fun and new perspectives in their daily lives, improving their work-life balance.

[1333] The present invention enables real-time input and processing of user information, and schedule suggestions and feedback from a generative AI model, thereby effectively improving users' daily life and work performance.

[1334] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] Sending user input information

[1337] User: The user opens the device interface and enters information such as their wishes, concerns, usage time, location, etc. Specifically, they enter information such as "I want to improve my motivation," "1 day," and "Home" using text boxes and dropdown boxes.

[1338] Input: User's wishes, concerns, usage time, and location (e.g., "I want to improve my motivation," "1 day," "Home")

[1339] Device: The device formats the input information and sends it to the server in the form of an API request.

[1340] Output: API request with formatted user information

[1341] Step 2:

[1342] Receiving data

[1343] Server: The server receives the API request sent from the device using an HTTP request.

[1344] Input: API request containing formatted user information

[1345] Server: The server parses the received data and extracts user information. Specifically, it parses the data using a JSON parser.

[1346] Output: Extracted user information (hopes, concerns, usage time, location)

[1347] Step 3:

[1348] Generate a schedule

[1349] Server: The server sends prompts to a generative AI model (e.g., GPT-4) to generate a schedule based on user information.

[1350] Input: Extracted user information (hopes, concerns, usage time, location)

[1351] Server: The generative AI model analyzes the prompt text and generates a schedule that mimics a day in the life of celebrity A, for example, based on "I want to improve my motivation," "1 day," and "at home."

[1352] Output: Generated schedule (e.g. "Wake up at 6, jog at 7, breakfast at 8, read at 9...")

[1353] Step 4:

[1354] Sending a Schedule

[1355] Server: The server reformats the generated schedule and sends it to the device, for example by converting the schedule to JSON format and sending it in an HTTP response.

[1356] Input: Generated schedule

[1357] Server: Sends formatted schedule data as an HTTP response.

[1358] Output: Formatted schedule data

[1359] Step 5:

[1360] Check the schedule

[1361] Terminal: The terminal analyzes the schedule data received from the server and displays it on the interface, using front-end technologies (e.g., React, Vue.js).

[1362] Input: Formatted schedule data

[1363] Terminal: Parses the data and displays it in a user interface.

[1364] User: The user sees the displayed schedule and follows it for the day.

[1365] Output: The displayed schedule

[1366] Step 6:

[1367] Send Feedback

[1368] User: After a user has spent a day following the schedule, they provide feedback on their experience, for example, their satisfaction and impressions in a dedicated feedback form.

[1369] Input: User feedback (e.g., "I was very satisfied with today's schedule")

[1370] Terminal: The terminal formats the entered feedback and sends it to the server using a POST request.

[1371] Output: Formatted feedback data

[1372] Step 7:

[1373] Processing Feedback

[1374] Server: The server receives the feedback sent from the device and records it in a database using an HTTP request.

[1375] Input: Formatted feedback data

[1376] Server: Analyzes the received feedback and stores it in a database, typically using a database management system (e.g., MySQL, PostgreSQL).

[1377] Output: Recorded feedback data

[1378] Step 8:

[1379] Next proposal optimization

[1380] Server: The server analyzes the recorded feedback and updates the algorithm to optimize future schedule suggestions.

[1381] Input: Recorded feedback data

[1382] Server: Adjusts the model parameters based on the feedback data to improve the next proposal, specifically using machine learning algorithms.

[1383] Output: An updated generative AI model

[1384] Through this process, the system provides the optimal schedule based on the user's wishes and concerns, and by incorporating feedback obtained after the schedule is implemented, it can continuously make more effective suggestions.

[1385] (Application example 1)

[1386] 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."

[1387] Conventional user schedule suggestion systems simply propose schedules based on user input, but lack dynamic navigation and feedback tailored to the actual usage environment. Therefore, there was a need for a method to further enrich users' lifestyles and in-store experiences.

[1388] 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.

[1389] In this invention, the server includes means for inputting user information, a generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for receiving feedback from the user, means for optimizing the next proposal based on the feedback, and means for navigating the user's behavior in cooperation with available installed devices, thereby enabling the user to experience the lifestyle habits of a specific celebrity and be efficiently and effectively navigated through the behavior in the store.

[1390] "User information" refers to information entered by the user, such as their wishes, concerns, usage time, and location.

[1391] The "generative model means" is a means for selecting optimal candidates based on user information and generating a schedule.

[1392] The "proposed candidate schedule" is a schedule that details the daily activities of the candidate selected by the generative model means.

[1393] "Feedback" refers to opinions such as satisfaction and impressions that users provide to the system after experiencing it.

[1394] "Means to optimize the next proposal" refers to means to better adjust the next schedule proposal based on the feedback received.

[1395] "Available installed devices" are user interface devices such as terminals and smartphones installed in physical stores.

[1396] "Means for navigating behavior" is a function for navigating the user within the store according to the proposed schedule.

[1397] A "planning management program" is an application that users use to manage their daily schedules.

[1398] A system for implementing the present invention is configured as follows.

[1399] System Overview

[1400] Users access the system using a smartphone or a dedicated terminal installed in a physical store. They enter information such as their preferences, concerns, usage times, and location. This user information is sent from the terminal to the server. The server uses a generative AI model to generate candidate schedules based on the user information and proposes these schedules to the user. The user then acts within the physical store according to the proposed schedule. Through feedback, the user's experience is reflected in the next proposal.

[1401] Hardware and Software

[1402] The system uses the following hardware and software:

[1403] Hardware: Smartphones, dedicated terminals installed in physical stores.

[1404] Software: Flask (web framework), Python (programming language), JSON (data format), server (AWS or Heroku can be used as examples).

[1405] Program Operation

[1406] 1. Entering user information: The user enters their preferences, concerns, usage time, and location using a smartphone or dedicated device. This information is sent to the server as a user template in JSON format.

[1407] 2. Schedule generation using a generative model: A generative AI model runs on the server side using Flask and Python. The generative model generates the optimal celebrity schedule based on user information.

[1408] 3. Providing the schedule: The generated schedule is sent to the user's device. It works in conjunction with each facility in the physical store to navigate the user's behavior, for example, by providing appropriate guidance on the time spent in the cafe or the time spent in the fitness area.

[1409] 4. Feedback collection: After one day of experience, users provide feedback through the system. This feedback is stored on the server and reflected in the next schedule generation.

[1410] Specific examples

[1411] For example, if a user inputs information such as "I want to relax," "a day," and "a cafe," the server will generate a daily schedule for a celebrity that is suitable for relaxation. The generated schedule may include a morning yoga session, a mid-morning massage, and time to relax at a cafe for lunch. This schedule is then displayed on the user's smartphone.

[1412] Prompt Sentence Examples

[1413] For example, a prompt that the user might enter might look like this:

[1414] "I want to relax"

[1415] "1 day"

[1416] "Cafe"

[1417] This allows the server to generate celebrity schedules tailored to the user's needs and provide them to the user.

[1418] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1419] Step 1:

[1420] Entering user information

[1421] Users use their smartphones or dedicated terminals installed in stores to input information about their wishes, concerns, usage time, and location. For example, they might enter information such as "I want to relax," "1 day," or "cafe." This user information is sent from the terminal to the server as JSON-formatted data.

[1422] Step 2:

[1423] Receiving User Information

[1424] The server receives the submitted user information and stores it in a database, performing validation to ensure the information is properly formatted and to detect invalid data.

[1425] Step 3:

[1426] Schedule generation using generative models

[1427] The server runs a generative AI model based on user information. The generative AI model generates optimal candidate schedules taking into account the user's preferences, usage times, and location. For example, for a user who wants to relax, schedules such as "morning yoga session," "morning massage time," and "time to relax at a cafe during lunch" are generated. The generated schedules are stored as JSON-formatted data.

[1428] Step 4:

[1429] Schedule provision

[1430] The server sends the generated schedule to the user's device, which displays it on the user's smartphone or a dedicated device in the physical store, allowing the user to check suggested activities throughout the day.

[1431] Step 5:

[1432] Cooperation with the installed equipment

[1433] When the user moves around the store according to the proposed schedule, the server works with each device in the store (such as the cafe's ordering system or the fitness area's reservation system) to navigate the user's actions, allowing the user to smoothly carry out their activities at each location.

[1434] Step 6:

[1435] Gathering feedback

[1436] Users experience the proposed schedule throughout the day and provide feedback after the experience. The feedback is entered via a dedicated device or smartphone and sent to the server. This feedback includes satisfaction, impressions, and further requests.

[1437] Step 7:

[1438] Processing Feedback

[1439] The server stores the received feedback in a database and performs data analysis to optimize the next schedule proposal based on the feedback, which allows the next schedule proposal to better suit the user's preferences and needs.

[1440] 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.

[1441] The present invention combines a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[1442] First, the user accesses the system through a terminal and enters information such as their wishes, concerns, usage time, location, etc. The information entered by the user is sent from the terminal to the server.

[1443] The server then uses a generative model to propose optimal candidates based on user information. The generative model randomly selects an appropriate candidate, taking into account the user's preferences, usage time, and location. It then generates a schedule that mimics a day in the life of that candidate.

[1444] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions. The emotion engine analyzes the user's emotions in real time based on their voice and facial expression data, and provides this information to the generative model. Based on the results of this analysis, the generative model proposes a schedule that is more suited to the user's current state.

[1445] For example, if a user inputs information such as "I want to improve my motivation," "One day," and "Home," the server uses the generative model to generate a schedule for a suitable celebrity (such as "Ichiro"). Furthermore, if the user's voice or facial expression indicates that they are feeling stressed, the generative model will recommend a schedule that includes activities that will reduce the user's stress.

[1446] The server sends the generated schedule to the device, where the user can check the proposed schedule. This schedule also works with the user's calendar app to help ensure smooth execution.

[1447] After a user spends a day based on the specified schedule, they input feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. The feedback data is sent from the device to a server, which records it in a database. When generating future schedules, the previously collected user feedback is taken into account, allowing for more appropriate suggestions.

[1448] For example, if a user provides feedback such as "I was very satisfied with today's schedule," that feedback will be reflected in the next schedule proposal. Based on this feedback, the server will gain a more detailed understanding of the user's preferences and improve the schedule for the next time.

[1449] As described above, the present invention is a system that combines emotion engines to provide a user with a fun and new perspective on their daily life and improve their work-life balance.

[1450] The processing flow will be explained below.

[1451] Step 1:

[1452] Users access the system through a terminal and enter information such as their wishes, concerns, usage time, and location.

[1453] Specific behavior:

[1454] The user enters the required information through a web form or app interface.

[1455] For example, enter "I want to improve my motivation," "1 day," and "Home."

[1456] Step 2:

[1457] The terminal collects the user's input information and sends it to the server.

[1458] Specific behavior:

[1459] The form is submitted and the user information is sent to the server in JSON format.

[1460] For example, {"Want": "I want to improve my motivation", "Usage time": "1 day", "Location": "Home"} will be sent.

[1461] Step 3:

[1462] The emotion engine analyzes the user's emotions.

[1463] Specific behavior:

[1464] The user provides facial expression and voice data through the device's camera and microphone.

[1465] The emotion engine analyzes the user's emotions in real time and identifies emotional states such as stress, joy, and excitement.

[1466] Example: If the user is analyzed as "feeling stressed", that information is sent to the server.

[1467] Step 4:

[1468] The server uses a generative model to select candidates based on user information and emotion data.

[1469] Specific behavior:

[1470] The server selects candidate celebrities and models from a database based on the user's preferences, emotional state, usage time, and location.

[1471] Example: "Ichiro" schedule is selected.

[1472] Depending on the emotional data, specific activities such as "stress reduction" may be tailored to include.

[1473] Step 5:

[1474] The server obtains today's date and day of the week and generates a schedule.

[1475] Specific behavior:

[1476] The server retrieves the current date and day of the week and combines the selected celebrity's schedule with adjustments based on emotional data.

[1477] Example: {"Date": "2023-10-05", "Day": "Thursday", "Celebrity": "Ichiro", "Schedule": ["Training", "Batting Practice", "Meeting", "Recovery"]}

[1478] Step 6:

[1479] The server transmits the generated schedule to the terminal and provides it to the user.

[1480] Specific behavior:

[1481] The server transmits the generated schedule to the terminal so that the user can check it.

[1482] The terminal receives the schedule and displays it on the user interface.

[1483] Step 7:

[1484] The user spends their day based on the suggested schedule.

[1485] Specific behavior:

[1486] The user follows the schedule displayed on the device and spends their day imitating a particular celebrity's day.

[1487] Step 8:

[1488] The user enters feedback after the experience.

[1489] Specific behavior:

[1490] The user inputs feedback such as satisfaction and impressions after the experience through the device.

[1491] For example, enter "I was very satisfied."

[1492] Step 9:

[1493] The terminal collects user feedback and sends it to the server.

[1494] Specific behavior:

[1495] User feedback is sent to the server in JSON format or similar.

[1496] For example: {"Feedback": "Very satisfied"} will be sent.

[1497] Step 10:

[1498] The server records the feedback in a database and reflects it in the next schedule generation.

[1499] Specific behavior:

[1500] The server records the feedback data in a database, and the generative model refers to this to improve the accuracy of future schedule suggestions.

[1501] This allows the system to provide a schedule that adapts to the user's emotional state and improve the user's work-life balance.

[1502] Example 2

[1503] 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."

[1504] Many people living busy lives today need effective schedules to improve their daily and work performance. However, generating an optimal schedule that reflects each user's preferences and emotional state is difficult. Furthermore, conventional scheduling systems are unable to effectively utilize user feedback, making it difficult to improve the quality of the next schedule proposal.

[1505] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, emotion engine means for analyzing the user's emotions, means for optimizing the schedule based on the analyzed emotion information, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to propose an optimal schedule according to the user's wishes and emotional state.

[1506] "User information" refers to data such as hopes, concerns, usage time, and location that users enter into the system.

[1507] "Generative model means" refers to AI models or algorithms that generate appropriate schedules based on user information.

[1508] "Proposal means" refers to the interface or communication means for providing the generated schedule to the user.

[1509] "Emotion engine means" refers to technology or software for analyzing a user's voice data and facial expression data to recognize emotions.

[1510] "Emotion information" refers to data on the user's emotional state analyzed by the emotion engine means.

[1511] "Feedback" refers to data entered by users after completing a schedule, such as their impressions, satisfaction, and areas for improvement.

[1512] "Next suggestion optimization" refers to the processes or algorithms used to improve the next schedule suggestion based on user feedback.

[1513] The present invention is a system that proposes schedules that mimic the lifestyles of specific celebrities or models in order to improve a user's daily life and work performance, and combines an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system will be described below.

[1514] First, the user accesses the system through their device and enters information such as their wishes, concerns, usage time, and location. Specifically, this is done using the device's web interface or mobile app UI. This entered data is then sent from the device to the server. During transmission, the data is encrypted using the secure HTTPS protocol.

[1515] The server then uses a generative AI model (such as OpenAI GPT-3) to suggest the best candidates based on the user's information. This generative model is processed using a cloud-based platform (such as Google Cloud or Amazon Web Services). The server generates a schedule for a suitable celebrity or model based on the user's preferences, usage time, and location. For example, if the user inputs information such as "I want to improve my motivation," "One day," and "At home," the server generates a schedule that mimics an athlete's daily schedule.

[1516] Furthermore, the system is equipped with an emotion engine that analyzes the user's real-time emotional information. The emotion engine uses the Microsoft Azure Emotion API and Face API. The device collects the user's voice data (collected using a microphone) and facial expression data (collected using a camera) and sends this to the server. The server analyzes this data and obtains the user's emotional information.

[1517] Based on the analysis results, the server optimizes the generated schedule to the user's current state. For example, if the user is "feeling stressed," the server will add meditation time to the schedule to reduce stress. This optimized schedule is then sent from the server to the device, where the user can view it. Furthermore, this schedule can be linked to calendar apps such as Google Calendar and Microsoft Outlook, helping users to easily manage their schedules.

[1518] After a user spends a day based on the specified schedule, they can enter feedback about their experience through their device. The feedback includes their level of satisfaction and thoughts about the experience. This feedback data is sent from the device to a server, which then stores it in a database.

[1519] The collected feedback data will be taken into account in the next schedule generation and more appropriate schedule suggestions will be made, which will increase user satisfaction. For example, if a user provides feedback such as "very satisfied," that feedback will be reflected in the next schedule suggestion, and better suggestions will be made by the generative model that has learned the user's preferences and behavioral patterns.

[1520] Specific examples

[1521] For example, suppose a user inputs the prompts "I want to improve my motivation," "One day," and "At home." In this case, the server uses a generative AI model to generate a schedule that mimics a specific athlete's day. If the server determines that the user's emotional information indicates "feeling stressed," it will add meditation time and relaxation activities to the schedule. In this way, providing an optimal schedule based on real-time emotional analysis and feedback can improve the user's daily life and work performance.

[1522] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1523] Specific processing steps of the system program

[1524] Step 1:

[1525] The user enters information such as their wishes, concerns, usage time, and location through the device. Information is entered using an input form or the application's user interface. Input data includes "desires (e.g., want to improve motivation)," "usage time (e.g., per day)," and "location (e.g., home)." This information is sent to the server in the next step.

[1526] Step 2:

[1527] The terminal sends the entered user information to the server. The data is encrypted and transmitted using the secure HTTPS protocol, ensuring that the data reaches the server safely. The input is the user information, and the output is the user information received on the server side.

[1528] Step 3:

[1529] The server generates schedule candidates using a generative AI model (e.g., OpenAI GPT-3) based on the received user information. The generative model runs on a cloud-based platform (Google Cloud or Amazon Web Services) and generates related schedule data based on the received user information. The input is user information, and the output is the generated schedule candidates.

[1530] Step 4:

[1531] The device collects the user's voice data and facial expression data and sends it to the server. Voice data is collected using the device's microphone, and facial expression data is collected using the device's camera. This data is also sent using the secure HTTPS protocol. The input is voice data and facial expression data, and the output is emotional data sent to the server.

[1532] Step 5:

[1533] The server uses the Microsoft Azure Emotion API and Face API to analyze the received voice and facial expression data and obtain the user's emotional information. The analyzed emotional information becomes data that represents the user's emotional state in real time. The input is voice data and facial expression data, and the output is emotional information.

[1534] Step 6:

[1535] The server optimizes the generated schedule based on the emotional information. For example, if the user is analyzed as "feeling stressed," it adds relaxation activities to the schedule. Specifically, optimizations are performed such as incorporating meditation time into the schedule. The input is the generated schedule candidate and emotional information, and the output is the optimized schedule.

[1536] Step 7:

[1537] The server sends the optimized schedule to the device, where the user can view it. The schedule can also be linked to calendar apps such as Google Calendar and Microsoft Outlook, making it easier for users to manage their schedules. The input is the optimized schedule, and the output is the schedule displayed on the device.

[1538] Step 8:

[1539] After a user spends a day based on a specified schedule, they input feedback through the device. The feedback includes satisfaction and impressions of the experience. The input is feedback information, and the output is feedback data stored on the device.

[1540] Step 9:

[1541] The terminal sends feedback data to the server, which stores the data in a database. The feedback data is used to generate future schedules. The input is feedback information, and the output is the feedback data stored in the database.

[1542] Step 10:

[1543] The server improves the next schedule generation based on the accumulated feedback data. It analyzes the feedback data, learns the user's preferences and behavioral patterns, and proposes a more appropriate schedule. The input is the feedback data, and the output is the improved next schedule.

[1544] (Application example 2)

[1545] 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."

[1546] Conventional schedule suggestion systems generate schedules without taking the user's emotional state into account, which means they are unable to provide suggestions that are optimal for the user's current psychological state. Furthermore, they are unable to provide appropriate suggestions for specific locations or situations, which limits the user experience. Furthermore, leisure and driving plan suggestions for autonomous vehicles are not personalized, which means user satisfaction cannot be enhanced.

[1547] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user information, generative model means for proposing candidates based on the user information, means for providing the user with a schedule of the proposed candidates, means for recognizing emotions based on the user's voice and video data, means for optimizing the schedule based on the emotions, means for proposing locations and routes suitable for the user, means for receiving feedback from the user, and means for optimizing the next proposal based on the feedback. This makes it possible to generate a schedule optimized for the user's psychological state and to suggest suitable locations and routes in real time.

[1548] "User information" refers to data provided by users, such as their wishes, concerns, usage time, and location.

[1549] A "generative model means" is an algorithm or system that selects optimal candidates based on user information and generates a schedule.

[1550] The "means for providing to the user" is an interface or application for displaying the generated schedule to the user.

[1551] The "means for recognizing emotions" refers to a technology or system that analyzes a user's emotions in real time based on the user's audio and video data.

[1552] The "schedule optimization method" is a process that adjusts and optimizes the generated schedule based on the perceived user sentiment.

[1553] "Means for suggesting places and routes" refers to a system for suggesting appropriate leisure spots and driving routes based on the user's current emotional state and location.

[1554] A "feedback channel" is an interface that collects data about the user's experience and satisfaction.

[1555] "Next proposal optimization" is the process of improving future schedule proposals based on collected feedback.

[1556] The present invention provides a system for analyzing the emotional state of a user in an autonomous vehicle and proposing leisure plans or driving plans based on the analyzed emotional state. The system includes a means for inputting user information, a generative model means, an emotion recognition means, a means for optimizing a schedule, a means for proposing places and routes, a means for receiving feedback, and a means for optimizing the next proposal.

[1557] System Programming and Processing

[1558] Program processing

[1559] 1. Enter your user information:

[1560] The server receives information provided by the user, such as their wishes, concerns, usage time, and location, via the terminal and stores it in a database.

[1561] 2. Candidate Proposal:

[1562] The server generates optimal schedule candidates based on the user information using a generative model means, which generates randomly selected candidates taking into account the user information.

[1563] 3. Emotion recognition:

[1564] The user's voice and video data are captured in real time through microphones and cameras installed in the vehicle, and the server analyzes the data using emotion recognition means, such as EmotionRecognizer.

[1565] 4. Schedule optimization:

[1566] The server optimizes the generated schedule based on the emotion recognition results, for example, suggesting a visit to a relaxation facility if the user is feeling stressed.

[1567] 5. Location and route suggestions:

[1568] Based on the optimized schedule, the server suggests suitable locations and routes for the user. The optimal route to the suggested locations is calculated using Google Maps APIs, etc.

[1569] 6. Gathering Feedback:

[1570] After spending a day based on the specified schedule, the user inputs feedback about the experience through the device, and the level of satisfaction and impressions about the experience are sent to the server as feedback data.

[1571] 7. Optimize your next proposal:

[1572] The server records the collected feedback data in a database and reflects the feedback in future schedule generation using the generative modeling means, thereby enabling suggestions that are more suited to the user's preferences and emotional state.

[1573] Hardware and software used

[1574] Hardware: In-car camera, microphone, GPS module, display, audio system

[1575] Software: Python program, emotion recognition model (e.g., EmotionRecognizer), route planning API (e.g., Google Maps API), database management system

[1576] Specific examples

[1577] For example, if the driver's emotions are detected as "stressed" through video and audio analysis from the in-car camera, the system will search for nearby spas and relaxation facilities and suggest routes based on those. This information will be displayed on the vehicle's display, and the navigation system will begin driving automatically.

[1578] Prompt Sentence Examples

[1579] If the user is feeling stressed, suggest suitable relaxation spots to relieve stress. Also, show the best route to those places. The user's current location is XXX, time is YYY. For example, nearby spa facilities, parks, and relaxation rooms could be suggested.

[1580] The present invention can improve user satisfaction by providing a personalized driving plan based on the user's psychological state.

[1581] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1582] Step 1:

[1583] Entering user information

[1584] The server collects information provided by the user via the device, such as their wishes, concerns, usage time, location, etc. The data entered by the user into the device is sent to the server and stored in a database, providing basic information about the user that will be used in the next step.

[1585] Input: Hopes, worries, usage time, location

[1586] Output: User information stored on the server

[1587] Step 2:

[1588] Candidate suggestions

[1589] The server uses a generative model means to generate optimal schedule candidates based on the user information stored in step 1. The generative model means uses a machine learning algorithm to analyze the user information and randomly generate an appropriate schedule.

[1590] Input: User information

[1591] Output: Candidate schedule

[1592] Step 3:

[1593] emotion recognition

[1594] The server receives the user's voice and video data acquired through the microphone and camera installed in the vehicle, analyzes the user's emotions in real time using emotion recognition means (e.g., EmotionRecognizer), and sends the analysis results to the server.

[1595] Input: Audio data, video data

[1596] Output: User's emotional state

[1597] Step 4:

[1598] Schedule optimization

[1599] The server optimizes the schedule based on the candidate schedule generated in step 2 and the emotion recognition results obtained in step 3. For example, if the user is feeling stressed, the server adjusts the schedule to include a visit to a relaxation facility.

[1600] Input: candidate schedule, emotional state

[1601] Output: Optimized schedule

[1602] Step 5:

[1603] Location and route suggestions

[1604] The server then suggests locations and routes suitable for the user based on the optimized schedule, using Google Maps APIs and other tools to search for suitable facilities and routes and generate detailed navigation information.

[1605] Input: Optimized schedule

[1606] Output: Suggested locations, route information

[1607] Step 6:

[1608] Providing suggestions

[1609] The server then displays the generated location and route information on the vehicle's display, notifies the user of the suggestions via the voice assistant, and initiates autonomous driving in conjunction with the navigation system.

[1610] Input: location, route information

[1611] Output: Proposals displayed on the in-vehicle display

[1612] Step 7:

[1613] Gathering feedback

[1614] After spending a day based on the proposed schedule, the user enters feedback about their experience through the device, and their satisfaction and impressions of the experience are sent from the device to the server and stored in a database.

[1615] Input: User feedback

[1616] Output: Feedback stored on the server

[1617] Step 8:

[1618] Optimize your next proposal

[1619] The server uses the collected feedback data to improve future schedule suggestions by means of a generative model, and uses a machine learning algorithm to retrain the model to generate schedules that better suit the user's preferences and emotional state.

[1620] Input: Feedback data

[1621] Output: Optimized next schedule proposal

[1622] 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.

[1623] 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.

[1624] 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.

[1625] 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.

[1626] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion 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.

[1627] 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.

[1628] 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).

[1629] 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.

[1630] 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."

[1631] 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.

[1632] 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).

[1633] 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.

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] The following is further disclosed regarding the above embodiment.

[1644] (Claim 1)

[1645] a means for inputting user information;

[1646] a generative model means for suggesting candidates based on user information;

[1647] a means for providing a user with a schedule of suggested candidates;

[1648] A means of receiving feedback from users; and

[1649] A way to optimize your next proposal based on feedback, and

[1650] A system including:

[1651] (Claim 2)

[1652] The system according to claim 1, wherein the generative model means selects the most suitable candidate based on the user's wishes, concerns, usage time, and location.

[1653] (Claim 3)

[1654] The system of claim 1 , further comprising means for integrating the suggested schedule with a user's calendar application.

[1655] "Example 1"

[1656] (Claim 1)

[1657] a means for inputting user information;

[1658] A generative AI model means for suggesting candidates based on user information;

[1659] a means for providing a user with a schedule of suggested candidates;

[1660] A means of receiving feedback from users; and

[1661] A way to optimize your next proposal based on feedback, and

[1662] A means for transmitting the generated schedule to a user's terminal;

[1663] A way to link the generated schedule with the user's calendar app,

[1664] A system including:

[1665] (Claim 2)

[1666] The system of claim 1, wherein the generating AI model means selects the optimal candidate based on the user's wishes, concerns, usage time, and location.

[1667] (Claim 3)

[1668] 10. The system of claim 1, further comprising: means for transmitting the proposed schedule to a user terminal.

[1669] "Application Example 1"

[1670] (Claim 1)

[1671] a means for inputting user information;

[1672] a generative model means for suggesting candidates based on user information;

[1673] a means for providing a user with a schedule of suggested candidates;

[1674] A means of receiving feedback from users; and

[1675] A way to optimize your next proposal based on feedback, and

[1676] a means for navigating user actions in conjunction with available installation devices;

[1677] A system including:

[1678] (Claim 2)

[1679] The system according to claim 1, wherein the generative model means selects the most suitable candidate based on the user's wishes, concerns, usage time, and location.

[1680] (Claim 3)

[1681] 10. The system of claim 1, further comprising means for integrating the proposed schedule with a user's plan management program.

[1682] "Example 2: Combining Emotion Engines"

[1683] (Claim 1)

[1684] a means for inputting user information;

[1685] a generative model means for suggesting candidates based on user information;

[1686] a means for providing a user with a schedule of suggested candidates;

[1687] an emotion engine means for analyzing the emotion of a user;

[1688] A means for optimizing a schedule based on the analyzed emotion information;

[1689] A means of receiving feedback from users; and

[1690] A way to optimize your next proposal based on feedback, and

[1691] A system including:

[1692] (Claim 2)

[1693] The system according to claim 1, wherein the generative model means selects the most suitable candidate based on the user's wishes, concerns, usage time, and location.

[1694] (Claim 3)

[1695] The system of claim 1 , further comprising means for integrating the suggested schedule with a user's calendar application.

[1696] "Application example 2 when combining emotion engines"

[1697] (Claim 1)

[1698] a means for inputting user information;

[1699] a generative model means for suggesting candidates based on user information;

[1700] a means for providing a user with a schedule of suggested candidates;

[1701] means for recognizing emotions based on audio and video data of a user;

[1702] A means of optimizing schedules based on emotions;

[1703] A way to suggest suitable locations and routes for users,

[1704] A means of receiving feedback from users; and

[1705] A way to optimize your next proposal based on feedback, and

[1706] A system including:

[1707] (Claim 2)

[1708] The system of claim 1, wherein the generative model means selects optimal candidates based on the user's wishes, concerns, usage time, location, and emotions.

[1709] (Claim 3)

[1710] The system of claim 1 , further comprising means for integrating the suggested schedule with a user's calendar application. [Explanation of symbols]

[1711] 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 inputting user information; a generative model means for suggesting candidates based on user information; a means for providing a user with a schedule of suggested candidates; A means of receiving feedback from users; and A way to optimize your next proposal based on feedback, and A system including:

2. The system according to claim 1 , wherein the generative model means selects the most suitable candidate based on the user's wishes, concerns, usage time, and location.

3. The system of claim 1 , further comprising means for integrating the suggested schedule with a user's calendar application.

Images