system

Abstract

Provide a system. The system includes: a means for receiving input data from a user regarding the user's current situation, mental state, hobbies, and money usage; means for transmitting the input data to a server; means for formatting the input data in the server; A means for passing the formatted data to a generative AI to generate predictions and suggestions; means for transmitting the generated proposal to a user terminal; means for displaying the content of the proposal to a user; means for transmitting feedback data from the user to the server; A means for optimizing subsequent proposals based on the feedback data; A system including:

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 recent years, many people have been suffering from mental health problems and a decline in their quality of life due to busy living environments and stressful work environments. To cope with such situations, it is important to accurately understand one's own situation and emotions and receive appropriate support based on that understanding. However, conventional systems have had difficulty comprehensively assessing these factors and making suggestions suited to each individual user. They also lack mechanisms for optimizing support content based on continuous feedback. To address these challenges, there is a need for the development of a system that can comprehensively analyze an individual's situation and emotions and provide appropriate support. [Means for solving the problem]

[0005] This invention provides a means for accepting input data from a user regarding their current situation, mental state, hobbies, and financial habits, and transmitting that data to a server. The server then formats the received data and passes it to a generative AI to generate predictions and suggestions. The generated suggestions are then sent to the user's device, where they are displayed to the user. Furthermore, feedback data from the user is sent to the server, and suggestions for the next time and beyond are optimized based on the feedback. This provides a system that can comprehensively assess the user's current situation and mental state to provide appropriate support, and provide optimized support for each individual user through continuous feedback.

[0006] "User" refers to an individual who uses this system to input information about their own situation, mental state, hobbies, and financial habits, and receives appropriate support.

[0007] A "server" is a computing device that receives data sent by users, formats it, passes it to a generative AI to generate predictions and suggestions, and also stores the feedback data to optimize future suggestions.

[0008] "Device" refers to the device (e.g., smartphone, tablet, PC, etc.) on which a User enters information and displays and enters suggestions and feedback.

[0009] "Input Data" refers to information entered by a user regarding their current situation, mental state, hobbies, and financial habits.

[0010] "Formatting" refers to the server converting input data into an appropriate format, making it easier for the generative AI to process.

[0011] "Generative AI" refers to artificial intelligence that generates predictions and appropriate support content based on data received from users.

[0012] "Predictions and suggestions" refer to the support that the user should receive, generated by the generative AI after analyzing the user's data.

[0013] "Feedback data" refers to information entered by users about the effectiveness of the suggestions they implemented and their thoughts about them.

[0014] "Optimization" refers to adjusting the content of future suggestions to better suit the user based on feedback data. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and financial habits. The system continuously collects user data and analyzes it using generative AI to propose optimal support content for each individual user, further optimizing the support content through feedback.

[0037] The device displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the device to the server. The server formats the received data and passes it to the generative AI in an appropriate format to generate predictions and suggestions for assistance.

[0038] For example, if a user inputs, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server then formats the data and passes it to the generative AI. Based on the user's situation, mental state, hobbies, and financial habits, the generative AI generates specific suggestions such as "Spend about 30 minutes every day relaxing," "Make time for your hobbies between work," and "Reconsider how you spend your money."

[0039] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[0040] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate support based on the individual characteristics and behavioral patterns of each user.

[0041] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[0042] The processing flow will be explained below.

[0043] Step 1:

[0044] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[0045] Step 2:

[0046] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[0047] Step 3:

[0048] The server receives the HTTP POST request, validates the input data, formats the data appropriately, and prepares it for storage in the database.

[0049] Step 4:

[0050] The server passes the formatted data to the generative AI, which then generates predictions and suggestions based on the user's data.

[0051] Step 5:

[0052] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[0053] Step 6:

[0054] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[0055] Step 7:

[0056] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[0057] Step 8:

[0058] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[0059] Step 9:

[0060] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[0061] This series of processes provides continuous support according to the user's condition and feedback.

[0062] Example 1

[0063] 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."

[0064] Conventional systems have had difficulty in proposing appropriate support based on the user's individual circumstances and physical and mental state. Furthermore, the process of incorporating user feedback into future proposals was slow, resulting in insufficient optimization based on individual characteristics and behavioral patterns. This resulted in the issue of insufficient support being provided to continuously improve the user's quality of life.

[0065] 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.

[0066] In this invention, the server includes means for receiving input data from the user regarding their current situation, physical and mental state, preferences, and use of funds, means for transmitting the input data to a processing device, and means for formatting the input data in the processing device. This enables the server to quickly and appropriately process the input data for each user and to accurately propose support content tailored to the user's situation using a generative AI model. Furthermore, by analyzing and saving feedback data from the user and optimizing proposals for future use, the server can provide continuous and effective support.

[0067] "User" refers to an individual or organization that uses the system.

[0068] "Status" refers to the user's current state of life, work, health, etc.

[0069] "Physiological state" refers to the user's psychological and physical state.

[0070] "Preferences" refer to a user's preferred activities, hobbies, and interests.

[0071] "Use of funds" refers to information about how a user uses their funds.

[0072] "Input data" refers to all information provided by a user to a system.

[0073] "Processing device" refers to a computing device for processing data received from a user.

[0074] "Formatting" refers to the operation of converting received data into a format suitable for analysis and processing.

[0075] A "generative AI model" refers to a model that uses machine learning and artificial intelligence to analyze data and generate predictions and suggestions.

[0076] "Predictions and suggestions" refers to information generated by the generative AI model that suggests specific actions or improvements to the user.

[0077] "User device" refers to information equipment that is directly used by a user.

[0078] "Display" refers to providing information to a user in an easy-to-view format.

[0079] "Feedback data" refers to information on reactions to suggestions and implementation results that users provide to the system.

[0080] "Optimization" refers to adjusting the content of proposals from the next time onwards to make them more appropriate based on the collected data.

[0081] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, physical and mental state, preferences, and use of funds. The system continuously collects user data and analyzes it using a generative AI model to propose optimal support content for each individual user, further optimizing the support content through feedback.

[0082] Hardware and Software Configuration

[0083] A terminal is an information device that displays a form to accept input from a user. Specific examples include smartphones, tablets, and PCs. The required software is a web browser or a dedicated mobile application. For example, Google Chrome (registered trademark) or a dedicated mobile application.

[0084] The server is a processing device that receives data sent from the device, formats the data, inputs the data into the generative AI model, and generates proposals. The server is built on a cloud service (e.g., AWS (registered trademark) or Microsoft (registered trademark) Azure (registered trademark)). MySQL (registered trademark) or PostgreSQL is used as the database.

[0085] The generative AI model uses the latest natural language processing technology, such as OpenAI's GPT-4™, which generates assistance content based on user input data.

[0086] Specific examples of processing

[0087] For example, if a user enters "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them," the following processing will occur:

[0088] The terminal transmits this input data to the server.

[0089] The server formats the data and passes it to a generative AI model.

[0090] An example prompt sentence would be, "The user provided the following information: 'I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it.' Please suggest the best support for this user."

[0091] The generative AI model (GPT-4) generates specific support content based on the user's situation, physical and mental state, preferences, and intended use of funds. For example, specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work" are generated.

[0092] The server sends the generated proposal to the terminal.

[0093] The terminal displays the proposal to the user, who then acts on the proposal.

[0094] After performing the exercise, the user inputs feedback and sends a comment to the server, such as "I tried relaxation exercises, but it's difficult to do every day."

[0095] The server receives the feedback data and uses it to optimize future suggestions, such as "Consider focusing on relaxation on the weekend."

[0096] In this way, the system can provide optimal support content continuously and individually, contributing to improving the user's quality of life.

[0097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0098] Step 1:

[0099] The user enters the information.

[0100] The user enters their current situation, physical and mental state, preferences, and use of funds into a form on the device. For example, a user might use their smartphone to enter, "Recently, I've been busy with work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them." The entered information is saved in JSON format.

[0101] Step 2:

[0102] The terminal sends the input data to the server.

[0103] The terminal sends the data entered by the user to the server. The transmission method uses a REST API using the HTTPS protocol. The input (user data in JSON format) is sent from the terminal to the server, and the server receives the data.

[0104] Step 3:

[0105] The server formats the data.

[0106] The server formats the received data. Specifically, it parses the JSON format data and converts it into the required format. For example, it formats it into a format like "{"situation": "Busy at work", "mental_state": "Unstable", "hobby": "Watching movies", "monthly_spending": 5000}". This formatted data becomes the input to the generative AI model.

[0107] Step 4:

[0108] The server passes the formatted data to the generation AI.

[0109] The server passes the formatted data to the generative AI model. When passing the data, it creates a prompt and generates text in the form of, "The user provided the following information: 'Recently, I've been busy at work and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on this.' Please suggest the best support for this user." The data is then sent to the generative AI model (e.g., GPT-4) along with this prompt.

[0110] Step 5:

[0111] A generative AI model generates assistance content.

[0112] The generative AI model generates optimal support content tailored to the user's situation based on the prompt and data sent. For example, it generates specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work." The generated support content is returned to the server in text format.

[0113] Step 6:

[0114] The server sends the generated proposal to the terminal.

[0115] The server converts the proposals received from the generative AI model into JSON format and sends them to the user's device, again using a REST API over the HTTPS protocol.

[0116] Step 7:

[0117] The device displays the suggestions to the user.

[0118] The device receives suggestions from the server and displays them in a format that is easy for the user to see. Specifically, suggestions such as "Incorporate 30 minutes of relaxation every day" and "Make time for hobbies between work" are displayed in list format on the device screen.

[0119] Step 8:

[0120] The user acts on the suggestion.

[0121] The user can then take action based on the suggestions displayed, such as "try 30 minutes of relaxation every day" or "set aside time to watch a movie on the weekend."

[0122] Step 9:

[0123] The user provides feedback on the execution results.

[0124] Users input feedback about the results of taking action based on the suggestions and the effects they felt. For example, they can enter a comment such as, "I tried relaxation, but it's difficult to do every day." This feedback is saved in JSON format.

[0125] Step 10:

[0126] The terminal transmits the feedback data to the server.

[0127] The terminal sends the feedback data received from the user to the server, again using the HTTPS protocol. The server receives this data.

[0128] Step 11:

[0129] The server analyzes and stores the feedback data and reflects it in the next proposal.

[0130] The server analyzes the received feedback data and stores it in a database. It then uses this data to adjust the generative AI model to optimize future recommendations. For example, it retrains the model to suggest "focus on relaxation on the weekend."

[0131] (Application example 1)

[0132] 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."

[0133] Conventional content distribution services lacked the ability to provide suggestions based on a user's individual circumstances, mental state, and interests, making it difficult to identify the optimal content for the user. Furthermore, there was no system that could optimize future suggestions based on user feedback on the suggestions. As a result, there was a problem of a poor user experience and a loss of satisfaction with the service.

[0134] 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.

[0135] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for transmitting the input data to the server, means for formatting the input data in the server, means for passing the formatted data to a generative AI to generate predictions and suggestions, means for transmitting the generated suggestions to the user's terminal, means for displaying the suggestions to the user, means for transmitting feedback data from the user to the server, means for optimizing future suggestions based on the feedback data, and means for suggesting relaxation content and hobby-related content based on the user's hobbies and mental state. This makes it possible to suggest optimal content tailored to the user's individual situation and preferences.

[0136] "Means for accepting input data from the user regarding their current situation, mental state, hobbies, and financial usage" is a function that provides an interface for the user to input information about their individual situation, mental state, activities of interest, and usage of funds into the system.

[0137] The "means for transmitting the input data to the server" is a function for sending data input by the user to the server via a network such as the Internet.

[0138] The "means for formatting the input data in the server" is a function that performs processing in the server to analyze received user data and convert it into a required format or structure.

[0139] The "means of passing the formatted data to a generative AI and generating predictions and proposals" refers to a function that inputs formatted data into a generative AI model and generates individual support content and proposals based on that data.

[0140] "Means for sending generated proposals to the user's terminal" refers to a function for sending the proposals generated by the generative AI to the user's terminal via a network.

[0141] The "means for displaying the proposed content to the user" is a function for visually presenting the generated proposed content to the user using the display of the terminal or other display means.

[0142] The "means for transmitting feedback data from users to the server" is a function for sending opinions and evaluations regarding the suggestions provided by users to the server.

[0143] The "means for optimizing subsequent proposals based on the feedback data" is a function that analyzes the received feedback data and performs processing to make subsequent proposals more suitable for the user.

[0144] "Means for suggesting relaxation content or hobby-related content based on the user's hobbies and mental state" is a function that generates relaxing content or content related to areas of interest based on information provided by the user regarding their hobbies and mental state.

[0145] To implement the system of this invention, a combination of hardware and software is required to collect and analyze user input data. The system consists of the following main components: a user terminal, a server, and a generative AI model.

[0146] The user device will display a form for the user to enter information about their current situation, mental state, hobbies, and financial habits. For example, the user can enter information through an application on a smartphone or tablet, or through a web browser on a PC. The data collected by the user device will be sent to a server via a network such as the Internet.

[0147] The server has the ability to format the received data. Specifically, the server is developed in a programming language such as Python and is equipped with scripts for normalizing and analyzing the data. The formatted data is input into a generative AI model (such as OpenAI's GPT-4), which generates predictions and suggestions based on the user's situation, hobbies, mental state, and financial habits.

[0148] The generated suggestions are sent back to the user's device. The user's device provides an interface for displaying the received suggestions. For example, it has a function for displaying the suggestions in list format. It also has an interface for the user to check the suggestions and input the results and feedback of actually trying them out.

[0149] The user's feedback data is then sent back to the server and used to optimize the generative AI model's next suggestions. By repeating this process, highly personalized suggestions based on the user's individual characteristics and behavioral patterns can be made.

[0150] For example, if a user enters the following information:

[0151] "Recently, my work has been busy and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on that."

[0152] The system sends this information to a server, where it is formatted by a Python script via an API. The formatted data is then fed into a generative AI model, which generates suggestions such as:

[0153] "Set aside 30 minutes each day for relaxation. Make time for your hobbies between work. Reassess your financial situation."

[0154] The user's feedback, such as "Relaxation was effective, but it was difficult to find time for hobbies between work," is then sent back to the server.The next time, the server will suggest "continue relaxing while increasing the amount of time you spend watching movies on weekends."

[0155] As an example of a specific embodiment of the present invention, the prompt sentence input to the generative AI model is as follows:

[0156] User's current situation: Busy at work, mentally unstable

[0157] Mental state: Unstable

[0158] Hobbies: Watching movies

[0159] Monthly expenditure: 5,000 yen

[0160] Use this information to generate the following suggestions:

[0161] Relaxation-friendly content

[0162] Specific actions based on hobbies

[0163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0164] Step 1: Collecting User Input Data

[0165] The user uses a device (for example, a smartphone app or web browser) to input information about their current situation, mental state, hobbies, and how they spend their money. At this stage, input data such as "I've been busy at work recently and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it" is collected. The input data is sent to the server through the device's form interface.

[0166] Step 2: Sending data to the server

[0167] The user input data received from the terminal is sent to the server via the Internet. Specifically, the data is transferred using an HTTP request. The input of this step is the user data collected in step 1, and the output is the raw data received on the server side.

[0168] Step 3: Shaping the data

[0169] The server analyzes the received user data and formats it as needed. For example, Python scripts are used to normalize and categorize the data. The input is the raw data sent to the server, and the output is data formatted in a way that is suitable for the generative AI model.

[0170] Step 4: Generate proposals

[0171] The formatted data is passed to a generative AI model (e.g., OpenAI's GPT-4) to generate predictions and suggestions based on the user's situation, mental state, hobbies, and financial habits. The input is formatted user data, and the output is specific suggestions. At this stage, the following example prompt is used: "User's current situation: Busy at work, mentally unstable. Mental state: Unstable. Hobbies: Watching movies. Monthly expenditure: 5,000 yen. Based on this information, please generate the following suggestions: Content suitable for relaxation. Specific actions based on hobbies."

[0172] Step 5: Submit your proposal

[0173] The server receives the proposals output from the generative AI model and sends them back to the user's device. Data is transferred using an HTTP request. The input is the generated proposal, and the output is the proposal data sent to the user's device.

[0174] Step 6: View the proposal

[0175] The user device displays the received suggestions. Specifically, the suggestions are displayed in a list format using the interface of a smartphone app or the display area of ​​a web browser. The input is the suggestion data sent from the server, and the output is the suggestion content visually presented to the user.

[0176] Step 7: Provide feedback

[0177] The user checks the suggestions, tries to act on them, and then inputs feedback. For example, feedback such as "The relaxation was effective, but it was difficult to find time for hobbies between work" is collected. The input is the user's feedback information, which is sent to the server via the terminal.

[0178] Step 8: Submit your feedback

[0179] The feedback data received from the device is sent to the server via the Internet. The data is transferred using HTTP requests. The input is the feedback information from the user, and the output is the feedback data received on the server side.

[0180] Step 9: Optimize your next proposal

[0181] The server performs processing to optimize future proposals based on the received feedback data. Specifically, the feedback data is input into a generative AI model to adjust the content of the next proposal. The input for this step is the user's feedback data, and the output is adjustment information for future proposals.

[0182] 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.

[0183] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and how they spend their money. Furthermore, by combining it with an emotion engine, the system can recognize and analyze the user's emotions and generate more accurate support content.

[0184] The terminal displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the terminal to the server. The server formats the received data and passes it to the emotion engine to recognize and analyze emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's input data.

[0185] For example, if a user enters, "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server formats the data and passes it to the emotion engine. The emotion engine recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[0186] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI, which generates specific suggestions based on the user's situation, mental state, hobbies, and financial habits, such as "incorporate 30 minutes of relaxation every day," "make time for hobbies between work," and "reconsider how you spend your money."

[0187] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[0188] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate assistance based on the user's individual characteristics, behavioral patterns, and even emotions.

[0189] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[0190] The processing flow will be explained below.

[0191] Step 1:

[0192] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[0193] Step 2:

[0194] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[0195] Step 3:

[0196] The server receives the HTTP POST request, checks the input data, formats it appropriately, and prepares it for delivery to the emotion engine.

[0197] Step 4:

[0198] The server passes the formatted data to the emotion engine, which uses natural language processing technology to analyze emotions from the user's input data. For example, it can extract keywords such as "busy at work" or "mentally unstable" and recognize that the user is feeling stressed.

[0199] Step 5:

[0200] The emotion engine sends the analysis results back to the server, which receives the emotion data and prepares it for passing to the generative AI.

[0201] Step 6:

[0202] The server passes emotional data and user behavior data to the generative AI, which uses this data to generate specific support content (e.g., 30 minutes of relaxation every day, making time for hobbies between work) based on the user's situation, mental state, hobbies, and financial habits.

[0203] Step 7:

[0204] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[0205] Step 8:

[0206] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[0207] Step 9:

[0208] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[0209] Step 10:

[0210] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[0211] Step 11:

[0212] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[0213] This series of processes provides continuous support according to the user's situation, emotions, and feedback.

[0214] Example 2

[0215] 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."

[0216] Conventional support systems have difficulty making suggestions that take into account the user's current situation and mental state, making it difficult to provide optimal support to the user. Furthermore, there has been a lack of systems that can properly analyze the user's emotions and generate highly accurate support content based on the results. This has resulted in users being unable to receive effective support, making it difficult to improve their quality of life.

[0217] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for accepting input data regarding a user's current situation, emotional state, hobbies, and financial usage from the user; means for transmitting the input data from the terminal to the server; means for formatting the input data in the server; means for passing the formatted data to an emotion analysis means and recognizing and analyzing emotions; means for passing emotion data obtained from the emotion analysis means to a generative AI model and generating predictions and suggestions; means for transmitting the generated suggestions to the terminal; means for displaying the suggestions to the user; means for transmitting feedback data from the user to the server; and means for optimizing subsequent suggestions based on the feedback data. This makes it possible to provide highly accurate support content based on the user's current situation, emotional state, hobbies, and financial usage.

[0218] "User" refers to a person who utilizes the system to input data about their situation and emotional state.

[0219] "Input data" refers to information that a user inputs into a terminal regarding their current situation, emotional state, hobbies, and financial habits.

[0220] "Terminal" refers to a device through which a user enters input data and communicates with a server.

[0221] The "server" refers to the central system that receives user input data, formats it, and processes the data for sentiment analysis and generative AI models.

[0222] "Formatting" refers to the process of making the user's input data received by the server easier to process by converting its format or deleting unnecessary parts.

[0223] "Emotion analysis means" refers to a mechanism that uses natural language processing technology to recognize and analyze the emotional state of a user from input data.

[0224] "Emotion data" refers to data relating to the user's emotions and psychological state analyzed by the emotion analysis means.

[0225] A "generative AI model" refers to an artificial intelligence model that generates suggestions and support content appropriate for the user based on the user's input data and emotional data.

[0226] "Means for generating predictions and suggestions" refers to a mechanism that uses a generative AI model to create specific support content and action suggestions based on the user's situation and emotions.

[0227] "Proposal content" refers to information generated by the generative AI model regarding appropriate support and methods of action for the user.

[0228] "Feedback data" refers to information about the results and effects of actions taken by users based on the suggestions.

[0229] "Optimization" refers to the server analyzing feedback data from users and adjusting the content of suggestions from the next time onwards to make them more suitable for the user.

[0230] The system of the present invention is designed to provide appropriate support based on information entered by the user regarding their current situation, emotional state, hobbies, and financial habits. In particular, by combining it with emotion analysis means, the system aims to recognize and analyze the user's emotions and generate more accurate support content.

[0231] The terminal displays a form to accept input from the user and provides an interface for the user to send the input data. For example, if the user enters "I've been busy with work recently and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month," the terminal sends this input data to the server.

[0232] The server formats the received data (for example, by deleting unnecessary data or converting the format) and passes it to the emotion analysis means, which recognizes and analyzes emotions. The emotion analysis means uses natural language processing technology (such as Google's BERT or OpenAI's GPT) to analyze emotions from the user's input data. In this case, it recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[0233] Next, the server receives the emotion data obtained from the emotion analysis method and passes it to a generative AI model to generate appropriate suggestions. The generative AI model generates specific support content based on the user's current situation, emotional state, hobbies, and financial usage. For example, it might generate suggestions such as "Spend about 30 minutes every day relaxing," "Make time for hobbies between work," and "Reconsider how you spend your money."

[0234] The generated suggestions are sent from the server to the device, which then displays them to the user. The user checks the displayed suggestions and takes action based on them. For example, the user may take action such as "trying to relax for 30 minutes every day" or "increasing the time spent on hobbies." The user then enters feedback about the results and effects of implementing the suggestions, and sends the feedback data to the server via the device.

[0235] The server receives this feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, if a user provides feedback such as "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion and suggest "continue relaxation while increasing the amount of time you spend watching movies on weekends."

[0236] An example of a prompt is:

[0237] User input:

[0238] Situation: I've been busy at work lately and my mental health has been unstable.

[0239] Hobbies: Watching movies

[0240] How I spend money: I spend about 5,000 yen a month.

[0241] Generate appropriate suggestions.

[0242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0243] Step 1:

[0244] The user enters data into the input form. The user enters information about their current situation, emotional state, hobbies, and financial habits into the input form. For example, the user might write, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it."

[0245] Input: Information that users fill out in input forms

[0246] Output: Data entered in the input form

[0247] Step 2:

[0248] The terminal sends the input data to the server. The terminal sends the data entered by the user to the server via the interface. The terminal converts the input data into an appropriate data format and communicates according to the transmission protocol.

[0249] Input: Data entered into an input form

[0250] Output: Data sent to the server

[0251] Step 3:

[0252] The server formats the data. The server analyzes the data it receives and formats it by removing unnecessary information and converting the format. For example, it extracts necessary keywords from the text and converts them into a format suitable for analysis.

[0253] Input: Data sent to the server

[0254] Output: Formatted data

[0255] Step 4:

[0256] The server passes the formatted data to the emotion analysis means, which analyzes the emotions. The server then passes the formatted data to the emotion analysis means, which uses natural language processing technology to analyze the user's emotions. For example, it recognizes the stress level from keywords such as "busy at work" and "mentally unstable."

[0257] Input: Formatted data

[0258] Output: Emotion data

[0259] Step 5:

[0260] Emotion data is received from the emotion analysis means. The emotion analysis means analyzes the data input by the user and generates emotion data. The server receives this emotion data.

[0261] Input: Formatted data (input to sentiment analysis tool)

[0262] Output: Emotion data (returned to server)

[0263] Step 6:

[0264] The server passes the emotion data to a generative AI model that generates appropriate suggestions. The server passes the emotion data to the generative AI model and sends prompts that generate suggestions. The generative AI model generates specific assistance content based on the emotion data and user input.

[0265] Input: Emotion data, entered user information

[0266] Output: Generated proposals

[0267] Step 7:

[0268] The server sends the generated proposal to the device. The server receives the proposal from the generative AI model and sends it to the device. The sent proposal is displayed to the user.

[0269] Input: Generated proposals

[0270] Output: Suggestion sent to device

[0271] Step 8:

[0272] The terminal displays the proposal to the user. The terminal visually displays the proposal received from the server to the user. The user checks the proposal and makes an action plan based on it.

[0273] Input: Suggestion sent to device

[0274] Output: The suggestions that are displayed to the user

[0275] Step 9:

[0276] The user implements the suggestions and enters feedback. The user acts based on the suggestions and enters the results and effects as feedback into the device. For example, the user might enter feedback such as, "The relaxation was effective, but it was difficult to find time for my hobbies between work."

[0277] Input: The result of a suggestion performed by the user

[0278] Output: Feedback data

[0279] Step 10:

[0280] The device sends the feedback to the server. The device sends the feedback data collected from the user to the server. The sent data is used to optimize the next proposal.

[0281] Input: Feedback data

[0282] Output: Feedback data sent to the server

[0283] Step 11:

[0284] The server receives the feedback data and optimizes the next suggestion. The server analyzes the received feedback data and uses that data to improve the accuracy of future suggestions. For example, the server requests the generative AI model to make a specific suggestion such as "continue relaxation and increase the amount of time you spend watching movies on weekends."

[0285] Input: Feedback data

[0286] Output: Optimized next proposal

[0287] (Application example 2)

[0288] 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."

[0289] Current content delivery services lack individual optimization based on the user's mental state and emotions, making it difficult to instantly provide appropriate content that matches the user's mood and situation. Furthermore, general recommendation algorithms rely on past viewing history and ratings, making them inadequate at responding to real-time changes in the user's emotions and situation. As a result, it is difficult to provide a satisfying content viewing experience for users.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0291] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for passing the input data to an emotion engine to recognize and analyze emotions, means for passing the emotion data analyzed by the emotion engine to a generative AI to generate predictions and suggestions, and means for recommending appropriate content based on the user's emotion analysis, thereby making it possible to provide content optimized based on the user's mental state and hobbies in real time.

[0292] "Input data" refers to data provided by a user as information about their current situation, mental state, hobbies, and financial habits.

[0293] The "server" is a central processing unit that receives input data and handles the process of analyzing and passing it on to the generative AI.

[0294] An "emotion engine" is software that uses natural language processing technology to recognize and analyze user emotions from input data.

[0295] "Generative AI" is artificial intelligence that generates optimal predictions and suggestions based on the user's situation and emotional data.

[0296] A "user terminal" is a device (smartphone, tablet, etc.) operated by a user that transmits input data and displays proposals.

[0297] "Suggestions" are specific assistance and content recommendations provided based on the user's situation and emotions, generated by generative AI.

[0298] "Feedback data" refers to data that provides information about the results and effects of a user implementing a suggestion.

[0299] "Optimizing future suggestions" refers to using feedback data to adjust future suggestions to better suit the user.

[0300] "Content" refers to various information offerings that correspond to the user's mental state and hobbies, such as relaxation music, movies, books, and podcasts.

[0301] The system of this invention uses several pieces of hardware and software to recommend optimal content based on individual information such as the user's mental state and hobbies. Specific hardware includes a user device (smartphone or tablet) and a server. Software includes algorithms for accepting and transmitting input data, analyzing emotions using an emotion engine, generating suggestions using generative AI, and analyzing feedback.

[0302] The user terminal displays a form for receiving information from the user about their current situation, mental state, hobbies, and how they spend their money. The user enters the following information through this form:

[0303] Recent situation: I'm busy at work and my mental health is unstable.

[0304] Hobbies: Watching movies

[0305] How I spend money: I spend about 5,000 yen a month.

[0306] This input data is sent from the device to the server. The server formats the received data and passes it to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's emotions based on keywords such as "busy at work" and "mentally unstable." For example, it can recognize that the user is feeling stressed.

[0307] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI. The generative AI then recommends specific content that will help relieve stress based on the user's situation, mental state, hobbies, and financial habits. For example, it might recommend "relaxing music" or "comedy movies."

[0308] The generated suggestions are sent from the server to the user's device, and the device displays the suggestions to the user. The user confirms the suggestions and reflects them in their subsequent actions. For example, the suggestions may include "listened to relaxation music" or "watched a movie that interests me." The user inputs feedback about the results and effects, and the data is sent from the device to the server.

[0309] The server receives the feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, based on feedback such as "relaxation was effective, but it was difficult to find time for hobbies between work," the server can adjust the next suggestion to "continue relaxation while increasing the amount of time spent watching movies on weekends."

[0310] To give a concrete example, suppose a user enters feedback as follows:

[0311] Relaxation was helpful, but finding time for hobbies between work commitments was difficult.

[0312] Based on this feedback, the server can adjust its next suggestion, for example, suggesting "continue to relax and spend more time watching movies on weekends." This allows the system to provide specific and continuous support to improve the user's quality of life.

[0313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0314] Step 1:

[0315] The user enters information about their current situation, mental state, hobbies, and financial habits into a form provided on the terminal.

[0316] As a specific operation, the user inputs the following information:

[0317] input:

[0318] Recent situation: I'm busy at work and my mental health is unstable.

[0319] Hobbies: Watching movies

[0320] How I spend money: I spend about 5,000 yen a month.

[0321] Output: A data object that the user device takes input data from and prepares to send it to the server.

[0322] Step 2:

[0323] The terminal sends the input data to the server by an HTTP POST request.

[0324] Input: The data object obtained in step 1

[0325] Output: The input data received by the server

[0326] Step 3:

[0327] The server formats the received input data and converts it into a format that can be passed to the emotion engine.

[0328] Specifically, the input data is converted into a format that allows for emotion analysis.

[0329] Input: The input data received by the server.

[0330] Output: Formatted data to be passed to the emotion engine

[0331] Step 4:

[0332] The server sends the formatted data to the emotion engine, which analyzes the data.

[0333] The emotion engine uses natural language processing technology to extract emotions from the text.

[0334] Input: Formatted data

[0335] Output: Parsed emotion data (e.g., feeling stressed)

[0336] Step 5:

[0337] The server sends the emotion data obtained from the emotion engine to the generative AI, which then generates suggestions appropriate for the user.

[0338] As a specific example, generative AI selects content that will relieve the user's stress.

[0339] Input: Parsed emotion data

[0340] Output: Recommend optimal content to the user (e.g., relaxation music, comedy movies)

[0341] Step 6:

[0342] The generated proposals are sent from the server to the terminal and displayed to the user.

[0343] Input: Optimal content suggestions from generative AI

[0344] Output: Proposal displayed on the user's device

[0345] Step 7:

[0346] The user checks the suggested content and reflects it in their subsequent actions.

[0347] As specific actions, the user performs actions such as "listening to relaxation music" and "watching a movie as a hobby."

[0348] Input: Proposal sent from the server

[0349] Output: User execution of content

[0350] Step 8:

[0351] The user enters results and feedback on the suggested content.

[0352] As a specific action, the user inputs, "Relaxation was effective, but it was difficult to find time for hobbies between work."

[0353] Input: User input into the feedback form

[0354] Output: Feedback data

[0355] Step 9:

[0356] The terminal transmits feedback data from the user to the server.

[0357] Input: Feedback data from users

[0358] Output: Feedback data received by the server

[0359] Step 10:

[0360] The server analyzes the feedback data to optimize future proposals.

[0361] Specifically, it analyzes feedback data, learns user patterns, and adjusts the next suggestion.

[0362] Input: Received feedback data

[0363] Output: Next proposed improvement

[0364] 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.

[0365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0366] 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.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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."

[0380] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and financial habits. The system continuously collects user data and analyzes it using generative AI to propose optimal support content for each individual user, further optimizing the support content through feedback.

[0381] The device displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the device to the server. The server formats the received data and passes it to the generative AI in an appropriate format to generate predictions and suggestions for assistance.

[0382] For example, if a user inputs, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server then formats the data and passes it to the generative AI. Based on the user's situation, mental state, hobbies, and financial habits, the generative AI generates specific suggestions such as "Spend about 30 minutes every day relaxing," "Make time for your hobbies between work," and "Reconsider how you spend your money."

[0383] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[0384] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate support based on the individual characteristics and behavioral patterns of each user.

[0385] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[0386] The processing flow will be explained below.

[0387] Step 1:

[0388] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[0389] Step 2:

[0390] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[0391] Step 3:

[0392] The server receives the HTTP POST request, validates the input data, formats the data appropriately, and prepares it for storage in the database.

[0393] Step 4:

[0394] The server passes the formatted data to the generative AI, which then generates predictions and suggestions based on the user's data.

[0395] Step 5:

[0396] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[0397] Step 6:

[0398] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[0399] Step 7:

[0400] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[0401] Step 8:

[0402] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[0403] Step 9:

[0404] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[0405] This series of processes provides continuous support according to the user's condition and feedback.

[0406] Example 1

[0407] 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."

[0408] Conventional systems have had difficulty in proposing appropriate support based on the user's individual circumstances and physical and mental state. Furthermore, the process of incorporating user feedback into future proposals was slow, resulting in insufficient optimization based on individual characteristics and behavioral patterns. This resulted in the issue of insufficient support being provided to continuously improve the user's quality of life.

[0409] 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.

[0410] In this invention, the server includes means for receiving input data from the user regarding their current situation, physical and mental state, preferences, and use of funds, means for transmitting the input data to a processing device, and means for formatting the input data in the processing device. This enables the server to quickly and appropriately process the input data for each user and to accurately propose support content tailored to the user's situation using a generative AI model. Furthermore, by analyzing and saving feedback data from the user and optimizing proposals for future use, the server can provide continuous and effective support.

[0411] "User" refers to an individual or organization that uses the system.

[0412] "Status" refers to the user's current state of life, work, health, etc.

[0413] "Physiological state" refers to the user's psychological and physical state.

[0414] "Preferences" refer to a user's preferred activities, hobbies, and interests.

[0415] "Use of funds" refers to information about how a user uses their funds.

[0416] "Input data" refers to all information provided by a user to a system.

[0417] "Processing device" refers to a computing device for processing data received from a user.

[0418] "Formatting" refers to the operation of converting received data into a format suitable for analysis and processing.

[0419] A "generative AI model" refers to a model that uses machine learning and artificial intelligence to analyze data and generate predictions and suggestions.

[0420] "Predictions and suggestions" refers to information generated by the generative AI model that suggests specific actions or improvements to the user.

[0421] "User device" refers to information equipment that is directly used by a user.

[0422] "Display" refers to providing information to a user in an easy-to-view format.

[0423] "Feedback data" refers to information on reactions to suggestions and implementation results that users provide to the system.

[0424] "Optimization" refers to adjusting the content of proposals from the next time onwards to make them more appropriate based on the collected data.

[0425] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, physical and mental state, preferences, and use of funds. The system continuously collects user data and analyzes it using a generative AI model to propose optimal support content for each individual user, further optimizing the support content through feedback.

[0426] Hardware and Software Configuration

[0427] A device is an information device that displays a form to accept input from a user. Examples include smartphones, tablets, and PCs. The required software is a web browser or a dedicated mobile application. Examples include Google Chrome or a dedicated mobile application.

[0428] The server is a processing device that receives data sent from the device, formats the data, inputs it into the generative AI model, and generates proposals. The server is built on a cloud service (e.g., AWS or Microsoft Azure), and uses MySQL or PostgreSQL as the database.

[0429] The generative AI model uses the latest natural language processing technology, such as OpenAI's GPT-4, to generate assistance content based on user input data.

[0430] Specific examples of processing

[0431] For example, if a user enters "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them," the following processing will occur:

[0432] The terminal transmits this input data to the server.

[0433] The server formats the data and passes it to a generative AI model.

[0434] An example prompt sentence would be, "The user provided the following information: 'I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it.' Please suggest the best support for this user."

[0435] The generative AI model (GPT-4) generates specific support content based on the user's situation, physical and mental state, preferences, and intended use of funds. For example, specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work" are generated.

[0436] The server sends the generated proposal to the terminal.

[0437] The terminal displays the proposal to the user, who then acts on the proposal.

[0438] After performing the exercise, the user inputs feedback and sends a comment to the server, such as "I tried relaxation exercises, but it's difficult to do every day."

[0439] The server receives the feedback data and uses it to optimize future suggestions, such as "Consider focusing on relaxation on the weekend."

[0440] In this way, the system can provide optimal support content continuously and individually, contributing to improving the user's quality of life.

[0441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] The user enters the information.

[0444] The user enters their current situation, physical and mental state, preferences, and use of funds into a form on the device. For example, a user might use their smartphone to enter, "Recently, I've been busy with work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them." The entered information is saved in JSON format.

[0445] Step 2:

[0446] The terminal sends the input data to the server.

[0447] The terminal sends the data entered by the user to the server. The transmission method uses a REST API using the HTTPS protocol. The input (user data in JSON format) is sent from the terminal to the server, and the server receives the data.

[0448] Step 3:

[0449] The server formats the data.

[0450] The server formats the received data. Specifically, it parses the JSON format data and converts it into the required format. For example, it formats it into a format like "{"situation": "Busy at work", "mental_state": "Unstable", "hobby": "Watching movies", "monthly_spending": 5000}". This formatted data becomes the input to the generative AI model.

[0451] Step 4:

[0452] The server passes the formatted data to the generation AI.

[0453] The server passes the formatted data to the generative AI model. When passing the data, it creates a prompt and generates text in the form of, "The user provided the following information: 'Recently, I've been busy at work and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on this.' Please suggest the best support for this user." The data is then sent to the generative AI model (e.g., GPT-4) along with this prompt.

[0454] Step 5:

[0455] A generative AI model generates assistance content.

[0456] The generative AI model generates optimal support content tailored to the user's situation based on the prompt and data sent. For example, it generates specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work." The generated support content is returned to the server in text format.

[0457] Step 6:

[0458] The server sends the generated proposal to the terminal.

[0459] The server converts the proposals received from the generative AI model into JSON format and sends them to the user's device, again using a REST API over the HTTPS protocol.

[0460] Step 7:

[0461] The device displays the suggestions to the user.

[0462] The device receives suggestions from the server and displays them in a format that is easy for the user to see. Specifically, suggestions such as "Incorporate 30 minutes of relaxation every day" and "Make time for hobbies between work" are displayed in list format on the device screen.

[0463] Step 8:

[0464] The user acts on the suggestion.

[0465] The user can then take action based on the suggestions displayed, such as "try 30 minutes of relaxation every day" or "set aside time to watch a movie on the weekend."

[0466] Step 9:

[0467] The user provides feedback on the execution results.

[0468] Users input feedback about the results of taking action based on the suggestions and the effects they felt. For example, they can enter a comment such as, "I tried relaxation, but it's difficult to do every day." This feedback is saved in JSON format.

[0469] Step 10:

[0470] The terminal transmits the feedback data to the server.

[0471] The terminal sends the feedback data received from the user to the server, again using the HTTPS protocol. The server receives this data.

[0472] Step 11:

[0473] The server analyzes and stores the feedback data and reflects it in the next proposal.

[0474] The server analyzes the received feedback data and stores it in a database. It then uses this data to adjust the generative AI model to optimize future recommendations. For example, it retrains the model to suggest "focus on relaxation on the weekend."

[0475] (Application example 1)

[0476] 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."

[0477] Conventional content distribution services lacked the ability to provide suggestions based on a user's individual circumstances, mental state, and interests, making it difficult to identify the optimal content for the user. Furthermore, there was no system that could optimize future suggestions based on user feedback on the suggestions. As a result, there was a problem of a poor user experience and a loss of satisfaction with the service.

[0478] 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.

[0479] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for transmitting the input data to the server, means for formatting the input data in the server, means for passing the formatted data to a generative AI to generate predictions and suggestions, means for transmitting the generated suggestions to the user's terminal, means for displaying the suggestions to the user, means for transmitting feedback data from the user to the server, means for optimizing future suggestions based on the feedback data, and means for suggesting relaxation content and hobby-related content based on the user's hobbies and mental state. This makes it possible to suggest optimal content tailored to the user's individual situation and preferences.

[0480] "Means for accepting input data from the user regarding their current situation, mental state, hobbies, and financial usage" is a function that provides an interface for the user to input information about their individual situation, mental state, activities of interest, and usage of funds into the system.

[0481] The "means for transmitting the input data to the server" is a function for sending data input by the user to the server via a network such as the Internet.

[0482] The "means for formatting the input data in the server" is a function that performs processing in the server to analyze received user data and convert it into a required format or structure.

[0483] The "means of passing the formatted data to a generative AI and generating predictions and proposals" refers to a function that inputs formatted data into a generative AI model and generates individual support content and proposals based on that data.

[0484] "Means for sending generated proposals to the user's terminal" refers to a function for sending the proposals generated by the generative AI to the user's terminal via a network.

[0485] The "means for displaying the proposed content to the user" is a function for visually presenting the generated proposed content to the user using the display of the terminal or other display means.

[0486] The "means for transmitting feedback data from users to the server" is a function for sending opinions and evaluations regarding the suggestions provided by users to the server.

[0487] The "means for optimizing subsequent proposals based on the feedback data" is a function that analyzes the received feedback data and performs processing to make subsequent proposals more suitable for the user.

[0488] "Means for suggesting relaxation content or hobby-related content based on the user's hobbies and mental state" is a function that generates relaxing content or content related to areas of interest based on information provided by the user regarding their hobbies and mental state.

[0489] To implement the system of this invention, a combination of hardware and software is required to collect and analyze user input data. The system consists of the following main components: a user terminal, a server, and a generative AI model.

[0490] The user device will display a form for the user to enter information about their current situation, mental state, hobbies, and financial habits. For example, the user can enter information through an application on a smartphone or tablet, or through a web browser on a PC. The data collected by the user device will be sent to a server via a network such as the Internet.

[0491] The server has the ability to format the received data. Specifically, the server is developed in a programming language such as Python and is equipped with scripts for normalizing and analyzing the data. The formatted data is input into a generative AI model (such as OpenAI's GPT-4), which generates predictions and suggestions based on the user's situation, hobbies, mental state, and financial habits.

[0492] The generated suggestions are sent back to the user's device. The user's device provides an interface for displaying the received suggestions. For example, it has a function for displaying the suggestions in list format. It also has an interface for the user to check the suggestions and input the results and feedback of actually trying them out.

[0493] The user's feedback data is then sent back to the server and used to optimize the generative AI model's next suggestions. By repeating this process, highly personalized suggestions based on the user's individual characteristics and behavioral patterns can be made.

[0494] For example, if a user enters the following information:

[0495] "Recently, my work has been busy and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on that."

[0496] The system sends this information to a server, where it is formatted by a Python script via an API. The formatted data is then fed into a generative AI model, which generates suggestions such as:

[0497] "Set aside 30 minutes each day for relaxation. Make time for your hobbies between work. Reassess your financial situation."

[0498] The user's feedback, such as "Relaxation was effective, but it was difficult to find time for hobbies between work," is then sent back to the server.The next time, the server will suggest "continue relaxing while increasing the amount of time you spend watching movies on weekends."

[0499] As an example of a specific embodiment of the present invention, the prompt sentence input to the generative AI model is as follows:

[0500] User's current situation: Busy at work, mentally unstable

[0501] Mental state: Unstable

[0502] Hobbies: Watching movies

[0503] Monthly expenditure: 5,000 yen

[0504] Use this information to generate the following suggestions:

[0505] Relaxation-friendly content

[0506] Specific actions based on hobbies

[0507] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0508] Step 1: Collecting User Input Data

[0509] The user uses a device (for example, a smartphone app or web browser) to input information about their current situation, mental state, hobbies, and how they spend their money. At this stage, input data such as "I've been busy at work recently and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it" is collected. The input data is sent to the server through the device's form interface.

[0510] Step 2: Sending data to the server

[0511] The user input data received from the terminal is sent to the server via the Internet. Specifically, the data is transferred using an HTTP request. The input of this step is the user data collected in step 1, and the output is the raw data received on the server side.

[0512] Step 3: Shaping the data

[0513] The server analyzes the received user data and formats it as needed. For example, Python scripts are used to normalize and categorize the data. The input is the raw data sent to the server, and the output is data formatted in a way that is suitable for the generative AI model.

[0514] Step 4: Generate proposals

[0515] The formatted data is passed to a generative AI model (e.g., OpenAI's GPT-4) to generate predictions and suggestions based on the user's situation, mental state, hobbies, and financial habits. The input is formatted user data, and the output is specific suggestions. At this stage, the following example prompt is used: "User's current situation: Busy at work, mentally unstable. Mental state: Unstable. Hobbies: Watching movies. Monthly expenditure: 5,000 yen. Based on this information, please generate the following suggestions: Content suitable for relaxation. Specific actions based on hobbies."

[0516] Step 5: Submit your proposal

[0517] The server receives the proposals output from the generative AI model and sends them back to the user's device. Data is transferred using an HTTP request. The input is the generated proposal, and the output is the proposal data sent to the user's device.

[0518] Step 6: View the proposal

[0519] The user device displays the received suggestions. Specifically, the suggestions are displayed in a list format using the interface of a smartphone app or the display area of ​​a web browser. The input is the suggestion data sent from the server, and the output is the suggestion content visually presented to the user.

[0520] Step 7: Provide feedback

[0521] The user checks the suggestions, tries to act on them, and then inputs feedback. For example, feedback such as "The relaxation was effective, but it was difficult to find time for hobbies between work" is collected. The input is the user's feedback information, which is sent to the server via the terminal.

[0522] Step 8: Submit your feedback

[0523] The feedback data received from the device is sent to the server via the Internet. The data is transferred using HTTP requests. The input is the feedback information from the user, and the output is the feedback data received on the server side.

[0524] Step 9: Optimize your next proposal

[0525] The server performs processing to optimize future proposals based on the received feedback data. Specifically, the feedback data is input into a generative AI model to adjust the content of the next proposal. The input for this step is the user's feedback data, and the output is adjustment information for future proposals.

[0526] 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.

[0527] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and how they spend their money. Furthermore, by combining it with an emotion engine, the system can recognize and analyze the user's emotions and generate more accurate support content.

[0528] The terminal displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the terminal to the server. The server formats the received data and passes it to the emotion engine to recognize and analyze emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's input data.

[0529] For example, if a user enters, "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server formats the data and passes it to the emotion engine. The emotion engine recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[0530] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI, which generates specific suggestions based on the user's situation, mental state, hobbies, and financial habits, such as "incorporate 30 minutes of relaxation every day," "make time for hobbies between work," and "reconsider how you spend your money."

[0531] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[0532] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate assistance based on the user's individual characteristics, behavioral patterns, and even emotions.

[0533] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[0537] Step 2:

[0538] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[0539] Step 3:

[0540] The server receives the HTTP POST request, checks the input data, formats it appropriately, and prepares it for delivery to the emotion engine.

[0541] Step 4:

[0542] The server passes the formatted data to the emotion engine, which uses natural language processing technology to analyze emotions from the user's input data. For example, it can extract keywords such as "busy at work" or "mentally unstable" and recognize that the user is feeling stressed.

[0543] Step 5:

[0544] The emotion engine sends the analysis results back to the server, which receives the emotion data and prepares it for passing to the generative AI.

[0545] Step 6:

[0546] The server passes emotional data and user behavior data to the generative AI, which uses this data to generate specific support content (e.g., 30 minutes of relaxation every day, making time for hobbies between work) based on the user's situation, mental state, hobbies, and financial habits.

[0547] Step 7:

[0548] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[0549] Step 8:

[0550] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[0551] Step 9:

[0552] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[0553] Step 10:

[0554] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[0555] Step 11:

[0556] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[0557] This series of processes provides continuous support according to the user's situation, emotions, and feedback.

[0558] Example 2

[0559] 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."

[0560] Conventional support systems have difficulty making suggestions that take into account the user's current situation and mental state, making it difficult to provide optimal support to the user. Furthermore, there has been a lack of systems that can properly analyze the user's emotions and generate highly accurate support content based on the results. This has resulted in users being unable to receive effective support, making it difficult to improve their quality of life.

[0561] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for accepting input data regarding a user's current situation, emotional state, hobbies, and financial usage from the user; means for transmitting the input data from the terminal to the server; means for formatting the input data in the server; means for passing the formatted data to an emotion analysis means and recognizing and analyzing emotions; means for passing emotion data obtained from the emotion analysis means to a generative AI model and generating predictions and suggestions; means for transmitting the generated suggestions to the terminal; means for displaying the suggestions to the user; means for transmitting feedback data from the user to the server; and means for optimizing subsequent suggestions based on the feedback data. This makes it possible to provide highly accurate support content based on the user's current situation, emotional state, hobbies, and financial usage.

[0562] "User" refers to a person who utilizes the system to input data about their situation and emotional state.

[0563] "Input data" refers to information that a user inputs into a terminal regarding their current situation, emotional state, hobbies, and financial habits.

[0564] "Terminal" refers to a device through which a user enters input data and communicates with a server.

[0565] The "server" refers to the central system that receives user input data, formats it, and processes the data for sentiment analysis and generative AI models.

[0566] "Formatting" refers to the process of making the user's input data received by the server easier to process by converting its format or deleting unnecessary parts.

[0567] "Emotion analysis means" refers to a mechanism that uses natural language processing technology to recognize and analyze the emotional state of a user from input data.

[0568] "Emotion data" refers to data relating to the user's emotions and psychological state analyzed by the emotion analysis means.

[0569] A "generative AI model" refers to an artificial intelligence model that generates suggestions and support content appropriate for the user based on the user's input data and emotional data.

[0570] "Means for generating predictions and suggestions" refers to a mechanism that uses a generative AI model to create specific support content and action suggestions based on the user's situation and emotions.

[0571] "Proposal content" refers to information generated by the generative AI model regarding appropriate support and methods of action for the user.

[0572] "Feedback data" refers to information about the results and effects of actions taken by users based on the suggestions.

[0573] "Optimization" refers to the server analyzing feedback data from users and adjusting the content of suggestions from the next time onwards to make them more suitable for the user.

[0574] The system of the present invention is designed to provide appropriate support based on information entered by the user regarding their current situation, emotional state, hobbies, and financial habits. In particular, by combining it with emotion analysis means, the system aims to recognize and analyze the user's emotions and generate more accurate support content.

[0575] The terminal displays a form to accept input from the user and provides an interface for the user to send the input data. For example, if the user enters "I've been busy with work recently and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month," the terminal sends this input data to the server.

[0576] The server formats the received data (for example, by deleting unnecessary data or converting the format) and passes it to the emotion analysis means to recognize and analyze emotions. The emotion analysis means uses natural language processing technology (such as Google's BERT or OpenAI's GPT) to analyze emotions from the user's input data. In this case, it recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[0577] Next, the server receives the emotion data obtained from the emotion analysis method and passes it to a generative AI model to generate appropriate suggestions. The generative AI model generates specific support content based on the user's current situation, emotional state, hobbies, and financial usage. For example, it might generate suggestions such as "Spend about 30 minutes every day relaxing," "Make time for hobbies between work," and "Reconsider how you spend your money."

[0578] The generated suggestions are sent from the server to the device, which then displays them to the user. The user checks the displayed suggestions and takes action based on them. For example, the user may take action such as "trying to relax for 30 minutes every day" or "increasing the time spent on hobbies." The user then enters feedback about the results and effects of implementing the suggestions, and sends the feedback data to the server via the device.

[0579] The server receives this feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, if a user provides feedback such as "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion and suggest "continue relaxation while increasing the amount of time you spend watching movies on weekends."

[0580] An example of a prompt is:

[0581] User input:

[0582] Situation: I've been busy at work lately and my mental health has been unstable.

[0583] Hobbies: Watching movies

[0584] How I spend money: I spend about 5,000 yen a month.

[0585] Generate appropriate suggestions.

[0586] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0587] Step 1:

[0588] The user enters data into the input form. The user enters information about their current situation, emotional state, hobbies, and financial habits into the input form. For example, the user might write, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it."

[0589] Input: Information that users fill out in input forms

[0590] Output: Data entered in the input form

[0591] Step 2:

[0592] The terminal sends the input data to the server. The terminal sends the data entered by the user to the server via the interface. The terminal converts the input data into an appropriate data format and communicates according to the transmission protocol.

[0593] Input: Data entered into an input form

[0594] Output: Data sent to the server

[0595] Step 3:

[0596] The server formats the data. The server analyzes the data it receives and formats it by removing unnecessary information and converting the format. For example, it extracts necessary keywords from the text and converts them into a format suitable for analysis.

[0597] Input: Data sent to the server

[0598] Output: Formatted data

[0599] Step 4:

[0600] The server passes the formatted data to the emotion analysis means, which analyzes the emotions. The server then passes the formatted data to the emotion analysis means, which uses natural language processing technology to analyze the user's emotions. For example, it recognizes the stress level from keywords such as "busy at work" and "mentally unstable."

[0601] Input: Formatted data

[0602] Output: Emotion data

[0603] Step 5:

[0604] Emotion data is received from the emotion analysis means. The emotion analysis means analyzes the data input by the user and generates emotion data. The server receives this emotion data.

[0605] Input: Formatted data (input to sentiment analysis tool)

[0606] Output: Emotion data (returned to server)

[0607] Step 6:

[0608] The server passes the emotion data to a generative AI model that generates appropriate suggestions. The server passes the emotion data to the generative AI model and sends prompts that generate suggestions. The generative AI model generates specific assistance content based on the emotion data and user input.

[0609] Input: Emotion data, entered user information

[0610] Output: Generated proposals

[0611] Step 7:

[0612] The server sends the generated proposal to the device. The server receives the proposal from the generative AI model and sends it to the device. The sent proposal is displayed to the user.

[0613] Input: Generated proposals

[0614] Output: Suggestion sent to device

[0615] Step 8:

[0616] The terminal displays the proposal to the user. The terminal visually displays the proposal received from the server to the user. The user checks the proposal and makes an action plan based on it.

[0617] Input: Suggestion sent to device

[0618] Output: The suggestions that are displayed to the user

[0619] Step 9:

[0620] The user implements the suggestions and enters feedback. The user acts based on the suggestions and enters the results and effects as feedback into the device. For example, the user might enter feedback such as, "The relaxation was effective, but it was difficult to find time for my hobbies between work."

[0621] Input: The result of a suggestion performed by the user

[0622] Output: Feedback data

[0623] Step 10:

[0624] The device sends the feedback to the server. The device sends the feedback data collected from the user to the server. The sent data is used to optimize the next proposal.

[0625] Input: Feedback data

[0626] Output: Feedback data sent to the server

[0627] Step 11:

[0628] The server receives the feedback data and optimizes the next suggestion. The server analyzes the received feedback data and uses that data to improve the accuracy of future suggestions. For example, the server requests the generative AI model to make a specific suggestion such as "continue relaxation and increase the amount of time you spend watching movies on weekends."

[0629] Input: Feedback data

[0630] Output: Optimized next proposal

[0631] (Application example 2)

[0632] 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."

[0633] Current content delivery services lack individual optimization based on the user's mental state and emotions, making it difficult to instantly provide appropriate content that matches the user's mood and situation. Furthermore, general recommendation algorithms rely on past viewing history and ratings, making them inadequate at responding to real-time changes in the user's emotions and situation. As a result, it is difficult to provide a satisfying content viewing experience for users.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0635] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for passing the input data to an emotion engine to recognize and analyze emotions, means for passing the emotion data analyzed by the emotion engine to a generative AI to generate predictions and suggestions, and means for recommending appropriate content based on the user's emotion analysis, thereby making it possible to provide content optimized based on the user's mental state and hobbies in real time.

[0636] "Input data" refers to data provided by a user as information about their current situation, mental state, hobbies, and financial habits.

[0637] The "server" is a central processing unit that receives input data and handles the process of analyzing and passing it on to the generative AI.

[0638] An "emotion engine" is software that uses natural language processing technology to recognize and analyze user emotions from input data.

[0639] "Generative AI" is artificial intelligence that generates optimal predictions and suggestions based on the user's situation and emotional data.

[0640] A "user terminal" is a device (smartphone, tablet, etc.) operated by a user that transmits input data and displays proposals.

[0641] "Suggestions" are specific assistance and content recommendations provided based on the user's situation and emotions, generated by generative AI.

[0642] "Feedback data" refers to data that provides information about the results and effects of a user implementing a suggestion.

[0643] "Optimizing future suggestions" refers to using feedback data to adjust future suggestions to better suit the user.

[0644] "Content" refers to various information offerings that correspond to the user's mental state and hobbies, such as relaxation music, movies, books, and podcasts.

[0645] The system of this invention uses several pieces of hardware and software to recommend optimal content based on individual information such as the user's mental state and hobbies. Specific hardware includes a user device (smartphone or tablet) and a server. Software includes algorithms for accepting and transmitting input data, analyzing emotions using an emotion engine, generating suggestions using generative AI, and analyzing feedback.

[0646] The user terminal displays a form for receiving information from the user about their current situation, mental state, hobbies, and how they spend their money. The user enters the following information through this form:

[0647] Recent situation: I'm busy at work and my mental health is unstable.

[0648] Hobbies: Watching movies

[0649] How I spend money: I spend about 5,000 yen a month.

[0650] This input data is sent from the device to the server. The server formats the received data and passes it to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's emotions based on keywords such as "busy at work" and "mentally unstable." For example, it can recognize that the user is feeling stressed.

[0651] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI. The generative AI then recommends specific content that will help relieve stress based on the user's situation, mental state, hobbies, and financial habits. For example, it might recommend "relaxing music" or "comedy movies."

[0652] The generated suggestions are sent from the server to the user's device, and the device displays the suggestions to the user. The user confirms the suggestions and reflects them in their subsequent actions. For example, the suggestions may include "listened to relaxation music" or "watched a movie that interests me." The user inputs feedback about the results and effects, and the data is sent from the device to the server.

[0653] The server receives the feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, based on feedback such as "relaxation was effective, but it was difficult to find time for hobbies between work," the server can adjust the next suggestion to "continue relaxation while increasing the amount of time spent watching movies on weekends."

[0654] To give a concrete example, suppose a user enters feedback as follows:

[0655] Relaxation was helpful, but finding time for hobbies between work commitments was difficult.

[0656] Based on this feedback, the server can adjust its next suggestion, for example, suggesting "continue to relax and spend more time watching movies on weekends." This allows the system to provide specific and continuous support to improve the user's quality of life.

[0657] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0658] Step 1:

[0659] The user enters information about their current situation, mental state, hobbies, and financial habits into a form provided on the terminal.

[0660] As a specific operation, the user inputs the following information:

[0661] input:

[0662] Recent situation: I'm busy at work and my mental health is unstable.

[0663] Hobbies: Watching movies

[0664] How I spend money: I spend about 5,000 yen a month.

[0665] Output: A data object that the user device takes input data from and prepares to send it to the server.

[0666] Step 2:

[0667] The terminal sends the input data to the server by an HTTP POST request.

[0668] Input: The data object obtained in step 1

[0669] Output: The input data received by the server

[0670] Step 3:

[0671] The server formats the received input data and converts it into a format that can be passed to the emotion engine.

[0672] Specifically, the input data is converted into a format that allows for emotion analysis.

[0673] Input: The input data received by the server.

[0674] Output: Formatted data to be passed to the emotion engine

[0675] Step 4:

[0676] The server sends the formatted data to the emotion engine, which analyzes the data.

[0677] The emotion engine uses natural language processing technology to extract emotions from the text.

[0678] Input: Formatted data

[0679] Output: Parsed emotion data (e.g., feeling stressed)

[0680] Step 5:

[0681] The server sends the emotion data obtained from the emotion engine to the generative AI, which then generates suggestions appropriate for the user.

[0682] As a specific example, generative AI selects content that will relieve the user's stress.

[0683] Input: Parsed emotion data

[0684] Output: Recommend optimal content to the user (e.g., relaxation music, comedy movies)

[0685] Step 6:

[0686] The generated proposals are sent from the server to the terminal and displayed to the user.

[0687] Input: Optimal content suggestions from generative AI

[0688] Output: Proposal displayed on the user's device

[0689] Step 7:

[0690] The user checks the suggested content and reflects it in their subsequent actions.

[0691] As specific actions, the user performs actions such as "listening to relaxation music" and "watching a movie as a hobby."

[0692] Input: Proposal sent from the server

[0693] Output: User execution of content

[0694] Step 8:

[0695] The user enters results and feedback on the suggested content.

[0696] As a specific action, the user inputs, "Relaxation was effective, but it was difficult to find time for hobbies between work."

[0697] Input: User input into the feedback form

[0698] Output: Feedback data

[0699] Step 9:

[0700] The terminal transmits feedback data from the user to the server.

[0701] Input: Feedback data from users

[0702] Output: Feedback data received by the server

[0703] Step 10:

[0704] The server analyzes the feedback data to optimize future proposals.

[0705] Specifically, it analyzes feedback data, learns user patterns, and adjusts the next suggestion.

[0706] Input: Received feedback data

[0707] Output: Next proposed improvement

[0708] 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.

[0709] 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.

[0710] 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.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 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.

[0714] 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).

[0715] 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.

[0716] 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.

[0717] 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).

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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."

[0724] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and financial habits. The system continuously collects user data and analyzes it using generative AI to propose optimal support content for each individual user, further optimizing the support content through feedback.

[0725] The device displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the device to the server. The server formats the received data and passes it to the generative AI in an appropriate format to generate predictions and suggestions for assistance.

[0726] For example, if a user inputs, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server then formats the data and passes it to the generative AI. Based on the user's situation, mental state, hobbies, and financial habits, the generative AI generates specific suggestions such as "Spend about 30 minutes every day relaxing," "Make time for your hobbies between work," and "Reconsider how you spend your money."

[0727] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[0728] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate support based on the individual characteristics and behavioral patterns of each user.

[0729] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[0733] Step 2:

[0734] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[0735] Step 3:

[0736] The server receives the HTTP POST request, validates the input data, formats the data appropriately, and prepares it for storage in the database.

[0737] Step 4:

[0738] The server passes the formatted data to the generative AI, which then generates predictions and suggestions based on the user's data.

[0739] Step 5:

[0740] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[0741] Step 6:

[0742] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[0743] Step 7:

[0744] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[0745] Step 8:

[0746] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[0747] Step 9:

[0748] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[0749] This series of processes provides continuous support according to the user's condition and feedback.

[0750] Example 1

[0751] 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."

[0752] Conventional systems have had difficulty in proposing appropriate support based on the user's individual circumstances and physical and mental state. Furthermore, the process of incorporating user feedback into future proposals was slow, resulting in insufficient optimization based on individual characteristics and behavioral patterns. This resulted in the issue of insufficient support being provided to continuously improve the user's quality of life.

[0753] 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.

[0754] In this invention, the server includes means for receiving input data from the user regarding their current situation, physical and mental state, preferences, and use of funds, means for transmitting the input data to a processing device, and means for formatting the input data in the processing device. This enables the server to quickly and appropriately process the input data for each user and to accurately propose support content tailored to the user's situation using a generative AI model. Furthermore, by analyzing and saving feedback data from the user and optimizing proposals for future use, the server can provide continuous and effective support.

[0755] "User" refers to an individual or organization that uses the system.

[0756] "Status" refers to the user's current state of life, work, health, etc.

[0757] "Physiological state" refers to the user's psychological and physical state.

[0758] "Preferences" refer to a user's preferred activities, hobbies, and interests.

[0759] "Use of funds" refers to information about how a user uses their funds.

[0760] "Input data" refers to all information provided by a user to a system.

[0761] "Processing device" refers to a computing device for processing data received from a user.

[0762] "Formatting" refers to the operation of converting received data into a format suitable for analysis and processing.

[0763] A "generative AI model" refers to a model that uses machine learning and artificial intelligence to analyze data and generate predictions and suggestions.

[0764] "Predictions and suggestions" refers to information generated by the generative AI model that suggests specific actions or improvements to the user.

[0765] "User device" refers to information equipment that is directly used by a user.

[0766] "Display" refers to providing information to a user in an easy-to-view format.

[0767] "Feedback data" refers to information on reactions to suggestions and implementation results that users provide to the system.

[0768] "Optimization" refers to adjusting the content of proposals from the next time onwards to make them more appropriate based on the collected data.

[0769] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, physical and mental state, preferences, and use of funds. The system continuously collects user data and analyzes it using a generative AI model to propose optimal support content for each individual user, further optimizing the support content through feedback.

[0770] Hardware and Software Configuration

[0771] A device is an information device that displays a form to accept input from a user. Examples include smartphones, tablets, and PCs. The required software is a web browser or a dedicated mobile application. Examples include Google Chrome or a dedicated mobile application.

[0772] The server is a processing device that receives data sent from the device, formats the data, inputs it into the generative AI model, and generates proposals. The server is built on a cloud service (e.g., AWS or Microsoft Azure), and uses MySQL or PostgreSQL as the database.

[0773] The generative AI model uses the latest natural language processing technology, such as OpenAI's GPT-4, to generate assistance content based on user input data.

[0774] Specific examples of processing

[0775] For example, if a user enters "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them," the following processing will occur:

[0776] The terminal transmits this input data to the server.

[0777] The server formats the data and passes it to a generative AI model.

[0778] An example prompt sentence would be, "The user provided the following information: 'I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it.' Please suggest the best support for this user."

[0779] The generative AI model (GPT-4) generates specific support content based on the user's situation, physical and mental state, preferences, and intended use of funds. For example, specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work" are generated.

[0780] The server sends the generated proposal to the terminal.

[0781] The terminal displays the proposal to the user, who then acts on the proposal.

[0782] After performing the exercise, the user inputs feedback and sends a comment to the server, such as "I tried relaxation exercises, but it's difficult to do every day."

[0783] The server receives the feedback data and uses it to optimize future suggestions, such as "Consider focusing on relaxation on the weekend."

[0784] In this way, the system can provide optimal support content continuously and individually, contributing to improving the user's quality of life.

[0785] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] The user enters the information.

[0788] The user enters their current situation, physical and mental state, preferences, and use of funds into a form on the device. For example, a user might use their smartphone to enter, "Recently, I've been busy with work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them." The entered information is saved in JSON format.

[0789] Step 2:

[0790] The terminal sends the input data to the server.

[0791] The terminal sends the data entered by the user to the server. The transmission method uses a REST API using the HTTPS protocol. The input (user data in JSON format) is sent from the terminal to the server, and the server receives the data.

[0792] Step 3:

[0793] The server formats the data.

[0794] The server formats the received data. Specifically, it parses the JSON format data and converts it into the required format. For example, it formats it into a format like "{"situation": "Busy at work", "mental_state": "Unstable", "hobby": "Watching movies", "monthly_spending": 5000}". This formatted data becomes the input to the generative AI model.

[0795] Step 4:

[0796] The server passes the formatted data to the generation AI.

[0797] The server passes the formatted data to the generative AI model. When passing the data, it creates a prompt and generates text in the form of, "The user provided the following information: 'Recently, I've been busy at work and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on this.' Please suggest the best support for this user." The data is then sent to the generative AI model (e.g., GPT-4) along with this prompt.

[0798] Step 5:

[0799] A generative AI model generates assistance content.

[0800] The generative AI model generates optimal support content tailored to the user's situation based on the prompt and data sent. For example, it generates specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work." The generated support content is returned to the server in text format.

[0801] Step 6:

[0802] The server sends the generated proposal to the terminal.

[0803] The server converts the proposals received from the generative AI model into JSON format and sends them to the user's device, again using a REST API over the HTTPS protocol.

[0804] Step 7:

[0805] The device displays the suggestions to the user.

[0806] The device receives suggestions from the server and displays them in a format that is easy for the user to see. Specifically, suggestions such as "Incorporate 30 minutes of relaxation every day" and "Make time for hobbies between work" are displayed in list format on the device screen.

[0807] Step 8:

[0808] The user acts on the suggestion.

[0809] The user can then take action based on the suggestions displayed, such as "try 30 minutes of relaxation every day" or "set aside time to watch a movie on the weekend."

[0810] Step 9:

[0811] The user provides feedback on the execution results.

[0812] Users input feedback about the results of taking action based on the suggestions and the effects they felt. For example, they can enter a comment such as, "I tried relaxation, but it's difficult to do every day." This feedback is saved in JSON format.

[0813] Step 10:

[0814] The terminal transmits the feedback data to the server.

[0815] The terminal sends the feedback data received from the user to the server, again using the HTTPS protocol. The server receives this data.

[0816] Step 11:

[0817] The server analyzes and stores the feedback data and reflects it in the next proposal.

[0818] The server analyzes the received feedback data and stores it in a database. It then uses this data to adjust the generative AI model to optimize future recommendations. For example, it retrains the model to suggest "focus on relaxation on the weekend."

[0819] (Application example 1)

[0820] 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."

[0821] Conventional content distribution services lacked the ability to provide suggestions based on a user's individual circumstances, mental state, and interests, making it difficult to identify the optimal content for the user. Furthermore, there was no system that could optimize future suggestions based on user feedback on the suggestions. As a result, there was a problem of a poor user experience and a loss of satisfaction with the service.

[0822] 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.

[0823] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for transmitting the input data to the server, means for formatting the input data in the server, means for passing the formatted data to a generative AI to generate predictions and suggestions, means for transmitting the generated suggestions to the user's terminal, means for displaying the suggestions to the user, means for transmitting feedback data from the user to the server, means for optimizing future suggestions based on the feedback data, and means for suggesting relaxation content and hobby-related content based on the user's hobbies and mental state. This makes it possible to suggest optimal content tailored to the user's individual situation and preferences.

[0824] "Means for accepting input data from the user regarding their current situation, mental state, hobbies, and financial usage" is a function that provides an interface for the user to input information about their individual situation, mental state, activities of interest, and usage of funds into the system.

[0825] The "means for transmitting the input data to the server" is a function for sending data input by the user to the server via a network such as the Internet.

[0826] The "means for formatting the input data in the server" is a function that performs processing in the server to analyze received user data and convert it into a required format or structure.

[0827] The "means of passing the formatted data to a generative AI and generating predictions and proposals" refers to a function that inputs formatted data into a generative AI model and generates individual support content and proposals based on that data.

[0828] "Means for sending generated proposals to the user's terminal" refers to a function for sending the proposals generated by the generative AI to the user's terminal via a network.

[0829] The "means for displaying the proposed content to the user" is a function for visually presenting the generated proposed content to the user using the display of the terminal or other display means.

[0830] The "means for transmitting feedback data from users to the server" is a function for sending opinions and evaluations regarding the suggestions provided by users to the server.

[0831] The "means for optimizing subsequent proposals based on the feedback data" is a function that analyzes the received feedback data and performs processing to make subsequent proposals more suitable for the user.

[0832] "Means for suggesting relaxation content or hobby-related content based on the user's hobbies and mental state" is a function that generates relaxing content or content related to areas of interest based on information provided by the user regarding their hobbies and mental state.

[0833] To implement the system of this invention, a combination of hardware and software is required to collect and analyze user input data. The system consists of the following main components: a user terminal, a server, and a generative AI model.

[0834] The user device will display a form for the user to enter information about their current situation, mental state, hobbies, and financial habits. For example, the user can enter information through an application on a smartphone or tablet, or through a web browser on a PC. The data collected by the user device will be sent to a server via a network such as the Internet.

[0835] The server has the ability to format the received data. Specifically, the server is developed in a programming language such as Python and is equipped with scripts for normalizing and analyzing the data. The formatted data is input into a generative AI model (such as OpenAI's GPT-4), which generates predictions and suggestions based on the user's situation, hobbies, mental state, and financial habits.

[0836] The generated suggestions are sent back to the user's device. The user's device provides an interface for displaying the received suggestions. For example, it has a function for displaying the suggestions in list format. It also has an interface for the user to check the suggestions and input the results and feedback of actually trying them out.

[0837] The user's feedback data is then sent back to the server and used to optimize the generative AI model's next suggestions. By repeating this process, highly personalized suggestions based on the user's individual characteristics and behavioral patterns can be made.

[0838] For example, if a user enters the following information:

[0839] "Recently, my work has been busy and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on that."

[0840] The system sends this information to a server, where it is formatted by a Python script via an API. The formatted data is then fed into a generative AI model, which generates suggestions such as:

[0841] "Set aside 30 minutes each day for relaxation. Make time for your hobbies between work. Reassess your financial situation."

[0842] The user's feedback, such as "Relaxation was effective, but it was difficult to find time for hobbies between work," is then sent back to the server.The next time, the server will suggest "continue relaxing while increasing the amount of time you spend watching movies on weekends."

[0843] As an example of a specific embodiment of the present invention, the prompt sentence input to the generative AI model is as follows:

[0844] User's current situation: Busy at work, mentally unstable

[0845] Mental state: Unstable

[0846] Hobbies: Watching movies

[0847] Monthly expenditure: 5,000 yen

[0848] Use this information to generate the following suggestions:

[0849] Relaxation-friendly content

[0850] Specific actions based on hobbies

[0851] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0852] Step 1: Collecting User Input Data

[0853] The user uses a device (for example, a smartphone app or web browser) to input information about their current situation, mental state, hobbies, and how they spend their money. At this stage, input data such as "I've been busy at work recently and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it" is collected. The input data is sent to the server through the device's form interface.

[0854] Step 2: Sending data to the server

[0855] The user input data received from the terminal is sent to the server via the Internet. Specifically, the data is transferred using an HTTP request. The input of this step is the user data collected in step 1, and the output is the raw data received on the server side.

[0856] Step 3: Shaping the data

[0857] The server analyzes the received user data and formats it as needed. For example, Python scripts are used to normalize and categorize the data. The input is the raw data sent to the server, and the output is data formatted in a way that is suitable for the generative AI model.

[0858] Step 4: Generate proposals

[0859] The formatted data is passed to a generative AI model (e.g., OpenAI's GPT-4) to generate predictions and suggestions based on the user's situation, mental state, hobbies, and financial habits. The input is formatted user data, and the output is specific suggestions. At this stage, the following example prompt is used: "User's current situation: Busy at work, mentally unstable. Mental state: Unstable. Hobbies: Watching movies. Monthly expenditure: 5,000 yen. Based on this information, please generate the following suggestions: Content suitable for relaxation. Specific actions based on hobbies."

[0860] Step 5: Submit your proposal

[0861] The server receives the proposals output from the generative AI model and sends them back to the user's device. Data is transferred using an HTTP request. The input is the generated proposal, and the output is the proposal data sent to the user's device.

[0862] Step 6: View the proposal

[0863] The user device displays the received suggestions. Specifically, the suggestions are displayed in a list format using the interface of a smartphone app or the display area of ​​a web browser. The input is the suggestion data sent from the server, and the output is the suggestion content visually presented to the user.

[0864] Step 7: Provide feedback

[0865] The user checks the suggestions, tries to act on them, and then inputs feedback. For example, feedback such as "The relaxation was effective, but it was difficult to find time for hobbies between work" is collected. The input is the user's feedback information, which is sent to the server via the terminal.

[0866] Step 8: Submit your feedback

[0867] The feedback data received from the device is sent to the server via the Internet. The data is transferred using HTTP requests. The input is the feedback information from the user, and the output is the feedback data received on the server side.

[0868] Step 9: Optimize your next proposal

[0869] The server performs processing to optimize future proposals based on the received feedback data. Specifically, the feedback data is input into a generative AI model to adjust the content of the next proposal. The input for this step is the user's feedback data, and the output is adjustment information for future proposals.

[0870] 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.

[0871] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and how they spend their money. Furthermore, by combining it with an emotion engine, the system can recognize and analyze the user's emotions and generate more accurate support content.

[0872] The terminal displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the terminal to the server. The server formats the received data and passes it to the emotion engine to recognize and analyze emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's input data.

[0873] For example, if a user enters, "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server formats the data and passes it to the emotion engine. The emotion engine recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[0874] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI, which generates specific suggestions based on the user's situation, mental state, hobbies, and financial habits, such as "incorporate 30 minutes of relaxation every day," "make time for hobbies between work," and "reconsider how you spend your money."

[0875] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[0876] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate assistance based on the user's individual characteristics, behavioral patterns, and even emotions.

[0877] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[0878] The processing flow will be explained below.

[0879] Step 1:

[0880] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[0881] Step 2:

[0882] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[0883] Step 3:

[0884] The server receives the HTTP POST request, checks the input data, formats it appropriately, and prepares it for delivery to the emotion engine.

[0885] Step 4:

[0886] The server passes the formatted data to the emotion engine, which uses natural language processing technology to analyze emotions from the user's input data. For example, it can extract keywords such as "busy at work" or "mentally unstable" and recognize that the user is feeling stressed.

[0887] Step 5:

[0888] The emotion engine sends the analysis results back to the server, which receives the emotion data and prepares it for passing to the generative AI.

[0889] Step 6:

[0890] The server passes emotional data and user behavior data to the generative AI, which uses this data to generate specific support content (e.g., 30 minutes of relaxation every day, making time for hobbies between work) based on the user's situation, mental state, hobbies, and financial habits.

[0891] Step 7:

[0892] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[0893] Step 8:

[0894] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[0895] Step 9:

[0896] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[0897] Step 10:

[0898] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[0899] Step 11:

[0900] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[0901] This series of processes provides continuous support according to the user's situation, emotions, and feedback.

[0902] Example 2

[0903] 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."

[0904] Conventional support systems have difficulty making suggestions that take into account the user's current situation and mental state, making it difficult to provide optimal support to the user. Furthermore, there has been a lack of systems that can properly analyze the user's emotions and generate highly accurate support content based on the results. This has resulted in users being unable to receive effective support, making it difficult to improve their quality of life.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for accepting input data regarding a user's current situation, emotional state, hobbies, and financial usage from the user; means for transmitting the input data from the terminal to the server; means for formatting the input data in the server; means for passing the formatted data to an emotion analysis means and recognizing and analyzing emotions; means for passing emotion data obtained from the emotion analysis means to a generative AI model and generating predictions and suggestions; means for transmitting the generated suggestions to the terminal; means for displaying the suggestions to the user; means for transmitting feedback data from the user to the server; and means for optimizing subsequent suggestions based on the feedback data. This makes it possible to provide highly accurate support content based on the user's current situation, emotional state, hobbies, and financial usage.

[0906] "User" refers to a person who utilizes the system to input data about their situation and emotional state.

[0907] "Input data" refers to information that a user inputs into a terminal regarding their current situation, emotional state, hobbies, and financial habits.

[0908] "Terminal" refers to a device through which a user enters input data and communicates with a server.

[0909] The "server" refers to the central system that receives user input data, formats it, and processes the data for sentiment analysis and generative AI models.

[0910] "Formatting" refers to the process of making the user's input data received by the server easier to process by converting its format or deleting unnecessary parts.

[0911] "Emotion analysis means" refers to a mechanism that uses natural language processing technology to recognize and analyze the emotional state of a user from input data.

[0912] "Emotion data" refers to data relating to the user's emotions and psychological state analyzed by the emotion analysis means.

[0913] A "generative AI model" refers to an artificial intelligence model that generates suggestions and support content appropriate for the user based on the user's input data and emotional data.

[0914] "Means for generating predictions and suggestions" refers to a mechanism that uses a generative AI model to create specific support content and action suggestions based on the user's situation and emotions.

[0915] "Proposal content" refers to information generated by the generative AI model regarding appropriate support and methods of action for the user.

[0916] "Feedback data" refers to information about the results and effects of actions taken by users based on the suggestions.

[0917] "Optimization" refers to the server analyzing feedback data from users and adjusting the content of suggestions from the next time onwards to make them more suitable for the user.

[0918] The system of the present invention is designed to provide appropriate support based on information entered by the user regarding their current situation, emotional state, hobbies, and financial habits. In particular, by combining it with emotion analysis means, the system aims to recognize and analyze the user's emotions and generate more accurate support content.

[0919] The terminal displays a form to accept input from the user and provides an interface for the user to send the input data. For example, if the user enters "I've been busy with work recently and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month," the terminal sends this input data to the server.

[0920] The server formats the received data (for example, by deleting unnecessary data or converting the format) and passes it to the emotion analysis means to recognize and analyze emotions. The emotion analysis means uses natural language processing technology (such as Google's BERT or OpenAI's GPT) to analyze emotions from the user's input data. In this case, it recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[0921] Next, the server receives the emotion data obtained from the emotion analysis method and passes it to a generative AI model to generate appropriate suggestions. The generative AI model generates specific support content based on the user's current situation, emotional state, hobbies, and financial usage. For example, it might generate suggestions such as "Spend about 30 minutes every day relaxing," "Make time for hobbies between work," and "Reconsider how you spend your money."

[0922] The generated suggestions are sent from the server to the device, which then displays them to the user. The user checks the displayed suggestions and takes action based on them. For example, the user may take action such as "trying to relax for 30 minutes every day" or "increasing the time spent on hobbies." The user then enters feedback about the results and effects of implementing the suggestions, and sends the feedback data to the server via the device.

[0923] The server receives this feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, if a user provides feedback such as "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion and suggest "continue relaxation while increasing the amount of time you spend watching movies on weekends."

[0924] An example of a prompt is:

[0925] User input:

[0926] Situation: I've been busy at work lately and my mental health has been unstable.

[0927] Hobbies: Watching movies

[0928] How I spend money: I spend about 5,000 yen a month.

[0929] Generate appropriate suggestions.

[0930] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] The user enters data into the input form. The user enters information about their current situation, emotional state, hobbies, and financial habits into the input form. For example, the user might write, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it."

[0933] Input: Information that users fill out in input forms

[0934] Output: Data entered in the input form

[0935] Step 2:

[0936] The terminal sends the input data to the server. The terminal sends the data entered by the user to the server via the interface. The terminal converts the input data into an appropriate data format and communicates according to the transmission protocol.

[0937] Input: Data entered into an input form

[0938] Output: Data sent to the server

[0939] Step 3:

[0940] The server formats the data. The server analyzes the data it receives and formats it by removing unnecessary information and converting the format. For example, it extracts necessary keywords from the text and converts them into a format suitable for analysis.

[0941] Input: Data sent to the server

[0942] Output: Formatted data

[0943] Step 4:

[0944] The server passes the formatted data to the emotion analysis means, which analyzes the emotions. The server then passes the formatted data to the emotion analysis means, which uses natural language processing technology to analyze the user's emotions. For example, it recognizes the stress level from keywords such as "busy at work" and "mentally unstable."

[0945] Input: Formatted data

[0946] Output: Emotion data

[0947] Step 5:

[0948] Emotion data is received from the emotion analysis means. The emotion analysis means analyzes the data input by the user and generates emotion data. The server receives this emotion data.

[0949] Input: Formatted data (input to sentiment analysis tool)

[0950] Output: Emotion data (returned to server)

[0951] Step 6:

[0952] The server passes the emotion data to a generative AI model that generates appropriate suggestions. The server passes the emotion data to the generative AI model and sends prompts that generate suggestions. The generative AI model generates specific assistance content based on the emotion data and user input.

[0953] Input: Emotion data, entered user information

[0954] Output: Generated proposals

[0955] Step 7:

[0956] The server sends the generated proposal to the device. The server receives the proposal from the generative AI model and sends it to the device. The sent proposal is displayed to the user.

[0957] Input: Generated proposals

[0958] Output: Suggestion sent to device

[0959] Step 8:

[0960] The terminal displays the proposal to the user. The terminal visually displays the proposal received from the server to the user. The user checks the proposal and makes an action plan based on it.

[0961] Input: Suggestion sent to device

[0962] Output: The suggestions that are displayed to the user

[0963] Step 9:

[0964] The user implements the suggestions and enters feedback. The user acts based on the suggestions and enters the results and effects as feedback into the device. For example, the user might enter feedback such as, "The relaxation was effective, but it was difficult to find time for my hobbies between work."

[0965] Input: The result of a suggestion performed by the user

[0966] Output: Feedback data

[0967] Step 10:

[0968] The device sends the feedback to the server. The device sends the feedback data collected from the user to the server. The sent data is used to optimize the next proposal.

[0969] Input: Feedback data

[0970] Output: Feedback data sent to the server

[0971] Step 11:

[0972] The server receives the feedback data and optimizes the next suggestion. The server analyzes the received feedback data and uses that data to improve the accuracy of future suggestions. For example, the server requests the generative AI model to make a specific suggestion such as "continue relaxation and increase the amount of time you spend watching movies on weekends."

[0973] Input: Feedback data

[0974] Output: Optimized next proposal

[0975] (Application example 2)

[0976] 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."

[0977] Current content delivery services lack individual optimization based on the user's mental state and emotions, making it difficult to instantly provide appropriate content that matches the user's mood and situation. Furthermore, general recommendation algorithms rely on past viewing history and ratings, making them inadequate at responding to real-time changes in the user's emotions and situation. As a result, it is difficult to provide a satisfying content viewing experience for users.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0979] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for passing the input data to an emotion engine to recognize and analyze emotions, means for passing the emotion data analyzed by the emotion engine to a generative AI to generate predictions and suggestions, and means for recommending appropriate content based on the user's emotion analysis, thereby making it possible to provide content optimized based on the user's mental state and hobbies in real time.

[0980] "Input data" refers to data provided by a user as information about their current situation, mental state, hobbies, and financial habits.

[0981] The "server" is a central processing unit that receives input data and handles the process of analyzing and passing it on to the generative AI.

[0982] An "emotion engine" is software that uses natural language processing technology to recognize and analyze user emotions from input data.

[0983] "Generative AI" is artificial intelligence that generates optimal predictions and suggestions based on the user's situation and emotional data.

[0984] A "user terminal" is a device (smartphone, tablet, etc.) operated by a user that transmits input data and displays proposals.

[0985] "Suggestions" are specific assistance and content recommendations provided based on the user's situation and emotions, generated by generative AI.

[0986] "Feedback data" refers to data that provides information about the results and effects of a user implementing a suggestion.

[0987] "Optimizing future suggestions" refers to using feedback data to adjust future suggestions to better suit the user.

[0988] "Content" refers to various information offerings that correspond to the user's mental state and hobbies, such as relaxation music, movies, books, and podcasts.

[0989] The system of this invention uses several pieces of hardware and software to recommend optimal content based on individual information such as the user's mental state and hobbies. Specific hardware includes a user device (smartphone or tablet) and a server. Software includes algorithms for accepting and transmitting input data, analyzing emotions using an emotion engine, generating suggestions using generative AI, and analyzing feedback.

[0990] The user terminal displays a form for receiving information from the user about their current situation, mental state, hobbies, and how they spend their money. The user enters the following information through this form:

[0991] Recent situation: I'm busy at work and my mental health is unstable.

[0992] Hobbies: Watching movies

[0993] How I spend money: I spend about 5,000 yen a month.

[0994] This input data is sent from the device to the server. The server formats the received data and passes it to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's emotions based on keywords such as "busy at work" and "mentally unstable." For example, it can recognize that the user is feeling stressed.

[0995] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI. The generative AI then recommends specific content that will help relieve stress based on the user's situation, mental state, hobbies, and financial habits. For example, it might recommend "relaxing music" or "comedy movies."

[0996] The generated suggestions are sent from the server to the user's device, and the device displays the suggestions to the user. The user confirms the suggestions and reflects them in their subsequent actions. For example, the suggestions may include "listened to relaxation music" or "watched a movie that interests me." The user inputs feedback about the results and effects, and the data is sent from the device to the server.

[0997] The server receives the feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, based on feedback such as "relaxation was effective, but it was difficult to find time for hobbies between work," the server can adjust the next suggestion to "continue relaxation while increasing the amount of time spent watching movies on weekends."

[0998] To give a concrete example, suppose a user enters feedback as follows:

[0999] Relaxation was helpful, but finding time for hobbies between work commitments was difficult.

[1000] Based on this feedback, the server can adjust its next suggestion, for example, suggesting "continue to relax and spend more time watching movies on weekends." This allows the system to provide specific and continuous support to improve the user's quality of life.

[1001] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1002] Step 1:

[1003] The user enters information about their current situation, mental state, hobbies, and financial habits into a form provided on the terminal.

[1004] As a specific operation, the user inputs the following information:

[1005] input:

[1006] Recent situation: I'm busy at work and my mental health is unstable.

[1007] Hobbies: Watching movies

[1008] How I spend money: I spend about 5,000 yen a month.

[1009] Output: A data object that the user device takes input data from and prepares to send it to the server.

[1010] Step 2:

[1011] The terminal sends the input data to the server by an HTTP POST request.

[1012] Input: The data object obtained in step 1

[1013] Output: The input data received by the server

[1014] Step 3:

[1015] The server formats the received input data and converts it into a format that can be passed to the emotion engine.

[1016] Specifically, the input data is converted into a format that allows for emotion analysis.

[1017] Input: The input data received by the server.

[1018] Output: Formatted data to be passed to the emotion engine

[1019] Step 4:

[1020] The server sends the formatted data to the emotion engine, which analyzes the data.

[1021] The emotion engine uses natural language processing technology to extract emotions from the text.

[1022] Input: Formatted data

[1023] Output: Parsed emotion data (e.g., feeling stressed)

[1024] Step 5:

[1025] The server sends the emotion data obtained from the emotion engine to the generative AI, which then generates suggestions appropriate for the user.

[1026] As a specific example, generative AI selects content that will relieve the user's stress.

[1027] Input: Parsed emotion data

[1028] Output: Recommend optimal content to the user (e.g., relaxation music, comedy movies)

[1029] Step 6:

[1030] The generated proposals are sent from the server to the terminal and displayed to the user.

[1031] Input: Optimal content suggestions from generative AI

[1032] Output: Proposal displayed on the user's device

[1033] Step 7:

[1034] The user checks the suggested content and reflects it in their subsequent actions.

[1035] As specific actions, the user performs actions such as "listening to relaxation music" and "watching a movie as a hobby."

[1036] Input: Proposal sent from the server

[1037] Output: User execution of content

[1038] Step 8:

[1039] The user enters results and feedback on the suggested content.

[1040] As a specific action, the user inputs, "Relaxation was effective, but it was difficult to find time for hobbies between work."

[1041] Input: User input into the feedback form

[1042] Output: Feedback data

[1043] Step 9:

[1044] The terminal transmits feedback data from the user to the server.

[1045] Input: Feedback data from users

[1046] Output: Feedback data received by the server

[1047] Step 10:

[1048] The server analyzes the feedback data to optimize future proposals.

[1049] Specifically, it analyzes feedback data, learns user patterns, and adjusts the next suggestion.

[1050] Input: Received feedback data

[1051] Output: Next proposed improvement

[1052] 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.

[1053] 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.

[1054] 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.

[1055] [Fourth embodiment]

[1056] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1057] 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.

[1058] 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).

[1059] 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.

[1060] 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.

[1061] 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).

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] 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.

[1067] 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.

[1068] 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."

[1069] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and financial habits. The system continuously collects user data and analyzes it using generative AI to propose optimal support content for each individual user, further optimizing the support content through feedback.

[1070] The device displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the device to the server. The server formats the received data and passes it to the generative AI in an appropriate format to generate predictions and suggestions for assistance.

[1071] For example, if a user inputs, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server then formats the data and passes it to the generative AI. Based on the user's situation, mental state, hobbies, and financial habits, the generative AI generates specific suggestions such as "Spend about 30 minutes every day relaxing," "Make time for your hobbies between work," and "Reconsider how you spend your money."

[1072] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[1073] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate support based on the individual characteristics and behavioral patterns of each user.

[1074] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[1075] The processing flow will be explained below.

[1076] Step 1:

[1077] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[1078] Step 2:

[1079] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[1080] Step 3:

[1081] The server receives the HTTP POST request, validates the input data, formats the data appropriately, and prepares it for storage in the database.

[1082] Step 4:

[1083] The server passes the formatted data to the generative AI, which then generates predictions and suggestions based on the user's data.

[1084] Step 5:

[1085] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[1086] Step 6:

[1087] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[1088] Step 7:

[1089] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[1090] Step 8:

[1091] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[1092] Step 9:

[1093] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[1094] This series of processes provides continuous support according to the user's condition and feedback.

[1095] Example 1

[1096] 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."

[1097] Conventional systems have had difficulty in proposing appropriate support based on the user's individual circumstances and physical and mental state. Furthermore, the process of incorporating user feedback into future proposals was slow, resulting in insufficient optimization based on individual characteristics and behavioral patterns. This resulted in the issue of insufficient support being provided to continuously improve the user's quality of life.

[1098] 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.

[1099] In this invention, the server includes means for receiving input data from the user regarding their current situation, physical and mental state, preferences, and use of funds, means for transmitting the input data to a processing device, and means for formatting the input data in the processing device. This enables the server to quickly and appropriately process the input data for each user and to accurately propose support content tailored to the user's situation using a generative AI model. Furthermore, by analyzing and saving feedback data from the user and optimizing proposals for future use, the server can provide continuous and effective support.

[1100] "User" refers to an individual or organization that uses the system.

[1101] "Status" refers to the user's current state of life, work, health, etc.

[1102] "Physiological state" refers to the user's psychological and physical state.

[1103] "Preferences" refer to a user's preferred activities, hobbies, and interests.

[1104] "Use of funds" refers to information about how a user uses their funds.

[1105] "Input data" refers to all information provided by a user to a system.

[1106] "Processing device" refers to a computing device for processing data received from a user.

[1107] "Formatting" refers to the operation of converting received data into a format suitable for analysis and processing.

[1108] A "generative AI model" refers to a model that uses machine learning and artificial intelligence to analyze data and generate predictions and suggestions.

[1109] "Predictions and suggestions" refers to information generated by the generative AI model that suggests specific actions or improvements to the user.

[1110] "User device" refers to information equipment that is directly used by a user.

[1111] "Display" refers to providing information to a user in an easy-to-view format.

[1112] "Feedback data" refers to information on reactions to suggestions and implementation results that users provide to the system.

[1113] "Optimization" refers to adjusting the content of proposals from the next time onwards to make them more appropriate based on the collected data.

[1114] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, physical and mental state, preferences, and use of funds. The system continuously collects user data and analyzes it using a generative AI model to propose optimal support content for each individual user, further optimizing the support content through feedback.

[1115] Hardware and Software Configuration

[1116] A device is an information device that displays a form to accept input from a user. Examples include smartphones, tablets, and PCs. The required software is a web browser or a dedicated mobile application. Examples include Google Chrome or a dedicated mobile application.

[1117] The server is a processing device that receives data sent from the device, formats the data, inputs it into the generative AI model, and generates proposals. The server is built on a cloud service (e.g., AWS or Microsoft Azure), and uses MySQL or PostgreSQL as the database.

[1118] The generative AI model uses the latest natural language processing technology, such as OpenAI's GPT-4, to generate assistance content based on user input data.

[1119] Specific examples of processing

[1120] For example, if a user enters "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them," the following processing will occur:

[1121] The terminal transmits this input data to the server.

[1122] The server formats the data and passes it to a generative AI model.

[1123] An example prompt sentence would be, "The user provided the following information: 'I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it.' Please suggest the best support for this user."

[1124] The generative AI model (GPT-4) generates specific support content based on the user's situation, physical and mental state, preferences, and intended use of funds. For example, specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work" are generated.

[1125] The server sends the generated proposal to the terminal.

[1126] The terminal displays the proposal to the user, who then acts on the proposal.

[1127] After performing the exercise, the user inputs feedback and sends a comment to the server, such as "I tried relaxation exercises, but it's difficult to do every day."

[1128] The server receives the feedback data and uses it to optimize future suggestions, such as "Consider focusing on relaxation on the weekend."

[1129] In this way, the system can provide optimal support content continuously and individually, contributing to improving the user's quality of life.

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] The user enters the information.

[1133] The user enters their current situation, physical and mental state, preferences, and use of funds into a form on the device. For example, a user might use their smartphone to enter, "Recently, I've been busy with work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on them." The entered information is saved in JSON format.

[1134] Step 2:

[1135] The terminal sends the input data to the server.

[1136] The terminal sends the data entered by the user to the server. The transmission method uses a REST API using the HTTPS protocol. The input (user data in JSON format) is sent from the terminal to the server, and the server receives the data.

[1137] Step 3:

[1138] The server formats the data.

[1139] The server formats the received data. Specifically, it parses the JSON format data and converts it into the required format. For example, it formats it into a format like "{"situation": "Busy at work", "mental_state": "Unstable", "hobby": "Watching movies", "monthly_spending": 5000}". This formatted data becomes the input to the generative AI model.

[1140] Step 4:

[1141] The server passes the formatted data to the generation AI.

[1142] The server passes the formatted data to the generative AI model. When passing the data, it creates a prompt and generates text in the form of, "The user provided the following information: 'Recently, I've been busy at work and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on this.' Please suggest the best support for this user." The data is then sent to the generative AI model (e.g., GPT-4) along with this prompt.

[1143] Step 5:

[1144] A generative AI model generates assistance content.

[1145] The generative AI model generates optimal support content tailored to the user's situation based on the prompt and data sent. For example, it generates specific suggestions such as "incorporate 30 minutes of relaxation every day" or "make time for hobbies between work." The generated support content is returned to the server in text format.

[1146] Step 6:

[1147] The server sends the generated proposal to the terminal.

[1148] The server converts the proposals received from the generative AI model into JSON format and sends them to the user's device, again using a REST API over the HTTPS protocol.

[1149] Step 7:

[1150] The device displays the suggestions to the user.

[1151] The device receives suggestions from the server and displays them in a format that is easy for the user to see. Specifically, suggestions such as "Incorporate 30 minutes of relaxation every day" and "Make time for hobbies between work" are displayed in list format on the device screen.

[1152] Step 8:

[1153] The user acts on the suggestion.

[1154] The user can then take action based on the suggestions displayed, such as "try 30 minutes of relaxation every day" or "set aside time to watch a movie on the weekend."

[1155] Step 9:

[1156] The user provides feedback on the execution results.

[1157] Users input feedback about the results of taking action based on the suggestions and the effects they felt. For example, they can enter a comment such as, "I tried relaxation, but it's difficult to do every day." This feedback is saved in JSON format.

[1158] Step 10:

[1159] The terminal transmits the feedback data to the server.

[1160] The terminal sends the feedback data received from the user to the server, again using the HTTPS protocol. The server receives this data.

[1161] Step 11:

[1162] The server analyzes and stores the feedback data and reflects it in the next proposal.

[1163] The server analyzes the received feedback data and stores it in a database. It then uses this data to adjust the generative AI model to optimize future recommendations. For example, it retrains the model to suggest "focus on relaxation on the weekend."

[1164] (Application example 1)

[1165] 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."

[1166] Conventional content distribution services lacked the ability to provide suggestions based on a user's individual circumstances, mental state, and interests, making it difficult to identify the optimal content for the user. Furthermore, there was no system that could optimize future suggestions based on user feedback on the suggestions. As a result, there was a problem of a poor user experience and a loss of satisfaction with the service.

[1167] 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.

[1168] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for transmitting the input data to the server, means for formatting the input data in the server, means for passing the formatted data to a generative AI to generate predictions and suggestions, means for transmitting the generated suggestions to the user's terminal, means for displaying the suggestions to the user, means for transmitting feedback data from the user to the server, means for optimizing future suggestions based on the feedback data, and means for suggesting relaxation content and hobby-related content based on the user's hobbies and mental state. This makes it possible to suggest optimal content tailored to the user's individual situation and preferences.

[1169] "Means for accepting input data from the user regarding their current situation, mental state, hobbies, and financial usage" is a function that provides an interface for the user to input information about their individual situation, mental state, activities of interest, and usage of funds into the system.

[1170] The "means for transmitting the input data to the server" is a function for sending data input by the user to the server via a network such as the Internet.

[1171] The "means for formatting the input data in the server" is a function that performs processing in the server to analyze received user data and convert it into a required format or structure.

[1172] The "means of passing the formatted data to a generative AI and generating predictions and proposals" refers to a function that inputs formatted data into a generative AI model and generates individual support content and proposals based on that data.

[1173] "Means for sending generated proposals to the user's terminal" refers to a function for sending the proposals generated by the generative AI to the user's terminal via a network.

[1174] The "means for displaying the proposed content to the user" is a function for visually presenting the generated proposed content to the user using the display of the terminal or other display means.

[1175] The "means for transmitting feedback data from users to the server" is a function for sending opinions and evaluations regarding the suggestions provided by users to the server.

[1176] The "means for optimizing subsequent proposals based on the feedback data" is a function that analyzes the received feedback data and performs processing to make subsequent proposals more suitable for the user.

[1177] "Means for suggesting relaxation content or hobby-related content based on the user's hobbies and mental state" is a function that generates relaxing content or content related to areas of interest based on information provided by the user regarding their hobbies and mental state.

[1178] To implement the system of this invention, a combination of hardware and software is required to collect and analyze user input data. The system consists of the following main components: a user terminal, a server, and a generative AI model.

[1179] The user device will display a form for the user to enter information about their current situation, mental state, hobbies, and financial habits. For example, the user can enter information through an application on a smartphone or tablet, or through a web browser on a PC. The data collected by the user device will be sent to a server via a network such as the Internet.

[1180] The server has the ability to format the received data. Specifically, the server is developed in a programming language such as Python and is equipped with scripts for normalizing and analyzing the data. The formatted data is input into a generative AI model (such as OpenAI's GPT-4), which generates predictions and suggestions based on the user's situation, hobbies, mental state, and financial habits.

[1181] The generated suggestions are sent back to the user's device. The user's device provides an interface for displaying the received suggestions. For example, it has a function for displaying the suggestions in list format. It also has an interface for the user to check the suggestions and input the results and feedback of actually trying them out.

[1182] The user's feedback data is then sent back to the server and used to optimize the generative AI model's next suggestions. By repeating this process, highly personalized suggestions based on the user's individual characteristics and behavioral patterns can be made.

[1183] For example, if a user enters the following information:

[1184] "Recently, my work has been busy and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on that."

[1185] The system sends this information to a server, where it is formatted by a Python script via an API. The formatted data is then fed into a generative AI model, which generates suggestions such as:

[1186] "Set aside 30 minutes each day for relaxation. Make time for your hobbies between work. Reassess your financial situation."

[1187] The user's feedback, such as "Relaxation was effective, but it was difficult to find time for hobbies between work," is then sent back to the server.The next time, the server will suggest "continue relaxing while increasing the amount of time you spend watching movies on weekends."

[1188] As an example of a specific embodiment of the present invention, the prompt sentence input to the generative AI model is as follows:

[1189] User's current situation: Busy at work, mentally unstable

[1190] Mental state: Unstable

[1191] Hobbies: Watching movies

[1192] Monthly expenditure: 5,000 yen

[1193] Use this information to generate the following suggestions:

[1194] Relaxation-friendly content

[1195] Specific actions based on hobbies

[1196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1197] Step 1: Collecting User Input Data

[1198] The user uses a device (for example, a smartphone app or web browser) to input information about their current situation, mental state, hobbies, and how they spend their money. At this stage, input data such as "I've been busy at work recently and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it" is collected. The input data is sent to the server through the device's form interface.

[1199] Step 2: Sending data to the server

[1200] The user input data received from the terminal is sent to the server via the Internet. Specifically, the data is transferred using an HTTP request. The input of this step is the user data collected in step 1, and the output is the raw data received on the server side.

[1201] Step 3: Shaping the data

[1202] The server analyzes the received user data and formats it as needed. For example, Python scripts are used to normalize and categorize the data. The input is the raw data sent to the server, and the output is data formatted in a way that is suitable for the generative AI model.

[1203] Step 4: Generate proposals

[1204] The formatted data is passed to a generative AI model (e.g., OpenAI's GPT-4) to generate predictions and suggestions based on the user's situation, mental state, hobbies, and financial habits. The input is formatted user data, and the output is specific suggestions. At this stage, the following example prompt is used: "User's current situation: Busy at work, mentally unstable. Mental state: Unstable. Hobbies: Watching movies. Monthly expenditure: 5,000 yen. Based on this information, please generate the following suggestions: Content suitable for relaxation. Specific actions based on hobbies."

[1205] Step 5: Submit your proposal

[1206] The server receives the proposals output from the generative AI model and sends them back to the user's device. Data is transferred using an HTTP request. The input is the generated proposal, and the output is the proposal data sent to the user's device.

[1207] Step 6: View the proposal

[1208] The user device displays the received suggestions. Specifically, the suggestions are displayed in a list format using the interface of a smartphone app or the display area of ​​a web browser. The input is the suggestion data sent from the server, and the output is the suggestion content visually presented to the user.

[1209] Step 7: Provide feedback

[1210] The user checks the suggestions, tries to act on them, and then inputs feedback. For example, feedback such as "The relaxation was effective, but it was difficult to find time for hobbies between work" is collected. The input is the user's feedback information, which is sent to the server via the terminal.

[1211] Step 8: Submit your feedback

[1212] The feedback data received from the device is sent to the server via the Internet. The data is transferred using HTTP requests. The input is the feedback information from the user, and the output is the feedback data received on the server side.

[1213] Step 9: Optimize your next proposal

[1214] The server performs processing to optimize future proposals based on the received feedback data. Specifically, the feedback data is input into a generative AI model to adjust the content of the next proposal. The input for this step is the user's feedback data, and the output is adjustment information for future proposals.

[1215] 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.

[1216] The system of the present invention provides appropriate support based on information entered by the user regarding their current situation, mental state, hobbies, and how they spend their money. Furthermore, by combining it with an emotion engine, the system can recognize and analyze the user's emotions and generate more accurate support content.

[1217] The terminal displays a form to accept input from the user and provides an interface for the user to submit input data. The data entered by the user is sent from the terminal to the server. The server formats the received data and passes it to the emotion engine to recognize and analyze emotions. The emotion engine uses natural language processing technology to analyze emotions from the user's input data.

[1218] For example, if a user enters, "I've been busy at work lately and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it," the device sends this input data to the server. The server formats the data and passes it to the emotion engine. The emotion engine recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[1219] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI, which generates specific suggestions based on the user's situation, mental state, hobbies, and financial habits, such as "incorporate 30 minutes of relaxation every day," "make time for hobbies between work," and "reconsider how you spend your money."

[1220] The generated suggestions are sent from the server to the device, and the device displays the suggestions to the user. The user checks the suggestions and takes action based on them. For example, the suggestions could be "I tried relaxation" or "I increased the time I spent on my hobbies." The user enters feedback about the results and effects, and the data is sent from the device to the server.

[1221] The server receives the feedback data and uses it to optimize future suggestions, enabling it to provide more accurate assistance based on the user's individual characteristics, behavioral patterns, and even emotions.

[1222] For example, if a user provides monthly feedback stating, "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion, for example, to suggest, "Continue relaxation while increasing the amount of time spent watching movies on weekends." In this way, the system provides specific and ongoing support to improve the user's quality of life.

[1223] The processing flow will be explained below.

[1224] Step 1:

[1225] The terminal displays a form to the user asking, "Please enter your current situation." The user enters their situation in the text box and clicks the submit button.

[1226] Step 2:

[1227] The device converts the user's input data into JSON format and sends it to the server as an HTTP POST request.

[1228] Step 3:

[1229] The server receives the HTTP POST request, checks the input data, formats it appropriately, and prepares it for delivery to the emotion engine.

[1230] Step 4:

[1231] The server passes the formatted data to the emotion engine, which uses natural language processing technology to analyze emotions from the user's input data. For example, it can extract keywords such as "busy at work" or "mentally unstable" and recognize that the user is feeling stressed.

[1232] Step 5:

[1233] The emotion engine sends the analysis results back to the server, which receives the emotion data and prepares it for passing to the generative AI.

[1234] Step 6:

[1235] The server passes emotional data and user behavior data to the generative AI, which uses this data to generate specific support content (e.g., 30 minutes of relaxation every day, making time for hobbies between work) based on the user's situation, mental state, hobbies, and financial habits.

[1236] Step 7:

[1237] The server receives the proposals generated by the generative AI, formats them appropriately, converts them into JSON format, and sends them to the device as an HTTP response.

[1238] Step 8:

[1239] The device processes the JSON data received from the server and displays the suggestions in an easy-to-understand format for the user. The user confirms the suggestions.

[1240] Step 9:

[1241] The device displays a feedback form asking, "To what extent were you able to implement the suggestions?" The user enters what they did and its effect, and clicks the submit button.

[1242] Step 10:

[1243] The device converts the user's feedback data into JSON format and sends it to the server as an HTTP POST request.

[1244] Step 11:

[1245] The server receives the feedback data, stores it in a database, and uses it to optimize future suggestions.

[1246] This series of processes provides continuous support according to the user's situation, emotions, and feedback.

[1247] Example 2

[1248] 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."

[1249] Conventional support systems have difficulty making suggestions that take into account the user's current situation and mental state, making it difficult to provide optimal support to the user. Furthermore, there has been a lack of systems that can properly analyze the user's emotions and generate highly accurate support content based on the results. This has resulted in users being unable to receive effective support, making it difficult to improve their quality of life.

[1250] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for accepting input data regarding a user's current situation, emotional state, hobbies, and financial usage from the user; means for transmitting the input data from the terminal to the server; means for formatting the input data in the server; means for passing the formatted data to an emotion analysis means and recognizing and analyzing emotions; means for passing emotion data obtained from the emotion analysis means to a generative AI model and generating predictions and suggestions; means for transmitting the generated suggestions to the terminal; means for displaying the suggestions to the user; means for transmitting feedback data from the user to the server; and means for optimizing subsequent suggestions based on the feedback data. This makes it possible to provide highly accurate support content based on the user's current situation, emotional state, hobbies, and financial usage.

[1251] "User" refers to a person who utilizes the system to input data about their situation and emotional state.

[1252] "Input data" refers to information that a user inputs into a terminal regarding their current situation, emotional state, hobbies, and financial habits.

[1253] "Terminal" refers to a device through which a user enters input data and communicates with a server.

[1254] The "server" refers to the central system that receives user input data, formats it, and processes the data for sentiment analysis and generative AI models.

[1255] "Formatting" refers to the process of making the user's input data received by the server easier to process by converting its format or deleting unnecessary parts.

[1256] "Emotion analysis means" refers to a mechanism that uses natural language processing technology to recognize and analyze the emotional state of a user from input data.

[1257] "Emotion data" refers to data relating to the user's emotions and psychological state analyzed by the emotion analysis means.

[1258] A "generative AI model" refers to an artificial intelligence model that generates suggestions and support content appropriate for the user based on the user's input data and emotional data.

[1259] "Means for generating predictions and suggestions" refers to a mechanism that uses a generative AI model to create specific support content and action suggestions based on the user's situation and emotions.

[1260] "Proposal content" refers to information generated by the generative AI model regarding appropriate support and methods of action for the user.

[1261] "Feedback data" refers to information about the results and effects of actions taken by users based on the suggestions.

[1262] "Optimization" refers to the server analyzing feedback data from users and adjusting the content of suggestions from the next time onwards to make them more suitable for the user.

[1263] The system of the present invention is designed to provide appropriate support based on information entered by the user regarding their current situation, emotional state, hobbies, and financial habits. In particular, by combining it with emotion analysis means, the system aims to recognize and analyze the user's emotions and generate more accurate support content.

[1264] The terminal displays a form to accept input from the user and provides an interface for the user to send the input data. For example, if the user enters "I've been busy with work recently and my mental health is unstable. My hobby is watching movies, and I spend about 5,000 yen a month," the terminal sends this input data to the server.

[1265] The server formats the received data (for example, by deleting unnecessary data or converting the format) and passes it to the emotion analysis means to recognize and analyze emotions. The emotion analysis means uses natural language processing technology (such as Google's BERT or OpenAI's GPT) to analyze emotions from the user's input data. In this case, it recognizes that the user is feeling stressed from the keywords "busy at work" and "mentally unstable."

[1266] Next, the server receives the emotion data obtained from the emotion analysis method and passes it to a generative AI model to generate appropriate suggestions. The generative AI model generates specific support content based on the user's current situation, emotional state, hobbies, and financial usage. For example, it might generate suggestions such as "Spend about 30 minutes every day relaxing," "Make time for hobbies between work," and "Reconsider how you spend your money."

[1267] The generated suggestions are sent from the server to the device, which then displays them to the user. The user checks the displayed suggestions and takes action based on them. For example, the user may take action such as "trying to relax for 30 minutes every day" or "increasing the time spent on hobbies." The user then enters feedback about the results and effects of implementing the suggestions, and sends the feedback data to the server via the device.

[1268] The server receives this feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, if a user provides feedback such as "Relaxation was effective, but it was difficult to find time for hobbies between work," the server will adjust the next suggestion and suggest "continue relaxation while increasing the amount of time you spend watching movies on weekends."

[1269] An example of a prompt is:

[1270] User input:

[1271] Situation: I've been busy at work lately and my mental health has been unstable.

[1272] Hobbies: Watching movies

[1273] How I spend money: I spend about 5,000 yen a month.

[1274] Generate appropriate suggestions.

[1275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1276] Step 1:

[1277] The user enters data into the input form. The user enters information about their current situation, emotional state, hobbies, and financial habits into the input form. For example, the user might write, "Recently, I've been busy at work and my mental health has been unstable. My hobby is watching movies, and I spend about 5,000 yen a month on it."

[1278] Input: Information that users fill out in input forms

[1279] Output: Data entered in the input form

[1280] Step 2:

[1281] The terminal sends the input data to the server. The terminal sends the data entered by the user to the server via the interface. The terminal converts the input data into an appropriate data format and communicates according to the transmission protocol.

[1282] Input: Data entered into an input form

[1283] Output: Data sent to the server

[1284] Step 3:

[1285] The server formats the data. The server analyzes the data it receives and formats it by removing unnecessary information and converting the format. For example, it extracts necessary keywords from the text and converts them into a format suitable for analysis.

[1286] Input: Data sent to the server

[1287] Output: Formatted data

[1288] Step 4:

[1289] The server passes the formatted data to the emotion analysis means, which analyzes the emotions. The server then passes the formatted data to the emotion analysis means, which uses natural language processing technology to analyze the user's emotions. For example, it recognizes the stress level from keywords such as "busy at work" and "mentally unstable."

[1290] Input: Formatted data

[1291] Output: Emotion data

[1292] Step 5:

[1293] Emotion data is received from the emotion analysis means. The emotion analysis means analyzes the data input by the user and generates emotion data. The server receives this emotion data.

[1294] Input: Formatted data (input to sentiment analysis tool)

[1295] Output: Emotion data (returned to server)

[1296] Step 6:

[1297] The server passes the emotion data to a generative AI model that generates appropriate suggestions. The server passes the emotion data to the generative AI model and sends prompts that generate suggestions. The generative AI model generates specific assistance content based on the emotion data and user input.

[1298] Input: Emotion data, entered user information

[1299] Output: Generated proposals

[1300] Step 7:

[1301] The server sends the generated proposal to the device. The server receives the proposal from the generative AI model and sends it to the device. The sent proposal is displayed to the user.

[1302] Input: Generated proposals

[1303] Output: Suggestion sent to device

[1304] Step 8:

[1305] The terminal displays the proposal to the user. The terminal visually displays the proposal received from the server to the user. The user checks the proposal and makes an action plan based on it.

[1306] Input: Suggestion sent to device

[1307] Output: The suggestions that are displayed to the user

[1308] Step 9:

[1309] The user implements the suggestions and enters feedback. The user acts based on the suggestions and enters the results and effects as feedback into the device. For example, the user might enter feedback such as, "The relaxation was effective, but it was difficult to find time for my hobbies between work."

[1310] Input: The result of a suggestion performed by the user

[1311] Output: Feedback data

[1312] Step 10:

[1313] The device sends the feedback to the server. The device sends the feedback data collected from the user to the server. The sent data is used to optimize the next proposal.

[1314] Input: Feedback data

[1315] Output: Feedback data sent to the server

[1316] Step 11:

[1317] The server receives the feedback data and optimizes the next suggestion. The server analyzes the received feedback data and uses that data to improve the accuracy of future suggestions. For example, the server requests the generative AI model to make a specific suggestion such as "continue relaxation and increase the amount of time you spend watching movies on weekends."

[1318] Input: Feedback data

[1319] Output: Optimized next proposal

[1320] (Application example 2)

[1321] 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."

[1322] Current content delivery services lack individual optimization based on the user's mental state and emotions, making it difficult to instantly provide appropriate content that matches the user's mood and situation. Furthermore, general recommendation algorithms rely on past viewing history and ratings, making them inadequate at responding to real-time changes in the user's emotions and situation. As a result, it is difficult to provide a satisfying content viewing experience for users.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1324] In this invention, the server includes means for receiving input data from a user regarding their current situation, mental state, hobbies, and financial habits, means for passing the input data to an emotion engine to recognize and analyze emotions, means for passing the emotion data analyzed by the emotion engine to a generative AI to generate predictions and suggestions, and means for recommending appropriate content based on the user's emotion analysis, thereby making it possible to provide content optimized based on the user's mental state and hobbies in real time.

[1325] "Input data" refers to data provided by a user as information about their current situation, mental state, hobbies, and financial habits.

[1326] The "server" is a central processing unit that receives input data and handles the process of analyzing and passing it on to the generative AI.

[1327] An "emotion engine" is software that uses natural language processing technology to recognize and analyze user emotions from input data.

[1328] "Generative AI" is artificial intelligence that generates optimal predictions and suggestions based on the user's situation and emotional data.

[1329] A "user terminal" is a device (smartphone, tablet, etc.) operated by a user that transmits input data and displays proposals.

[1330] "Suggestions" are specific assistance and content recommendations provided based on the user's situation and emotions, generated by generative AI.

[1331] "Feedback data" refers to data that provides information about the results and effects of a user implementing a suggestion.

[1332] "Optimizing future suggestions" refers to using feedback data to adjust future suggestions to better suit the user.

[1333] "Content" refers to various information offerings that correspond to the user's mental state and hobbies, such as relaxation music, movies, books, and podcasts.

[1334] The system of this invention uses several pieces of hardware and software to recommend optimal content based on individual information such as the user's mental state and hobbies. Specific hardware includes a user device (smartphone or tablet) and a server. Software includes algorithms for accepting and transmitting input data, analyzing emotions using an emotion engine, generating suggestions using generative AI, and analyzing feedback.

[1335] The user terminal displays a form for receiving information from the user about their current situation, mental state, hobbies, and how they spend their money. The user enters the following information through this form:

[1336] Recent situation: I'm busy at work and my mental health is unstable.

[1337] Hobbies: Watching movies

[1338] How I spend money: I spend about 5,000 yen a month.

[1339] This input data is sent from the device to the server. The server formats the received data and passes it to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's emotions based on keywords such as "busy at work" and "mentally unstable." For example, it can recognize that the user is feeling stressed.

[1340] The server receives the emotional data obtained from the emotion engine and passes it to the generative AI. The generative AI then recommends specific content that will help relieve stress based on the user's situation, mental state, hobbies, and financial habits. For example, it might recommend "relaxing music" or "comedy movies."

[1341] The generated suggestions are sent from the server to the user's device, and the device displays the suggestions to the user. The user confirms the suggestions and reflects them in their subsequent actions. For example, the suggestions may include "listened to relaxation music" or "watched a movie that interests me." The user inputs feedback about the results and effects, and the data is sent from the device to the server.

[1342] The server receives the feedback data and uses it to optimize future suggestions. This makes it possible to provide more accurate support based on the user's individual characteristics, behavioral patterns, and even emotions. For example, based on feedback such as "relaxation was effective, but it was difficult to find time for hobbies between work," the server can adjust the next suggestion to "continue relaxation while increasing the amount of time spent watching movies on weekends."

[1343] To give a concrete example, suppose a user enters feedback as follows:

[1344] Relaxation was helpful, but finding time for hobbies between work commitments was difficult.

[1345] Based on this feedback, the server can adjust its next suggestion, for example, suggesting "continue to relax and spend more time watching movies on weekends." This allows the system to provide specific and continuous support to improve the user's quality of life.

[1346] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1347] Step 1:

[1348] The user enters information about their current situation, mental state, hobbies, and financial habits into a form provided on the terminal.

[1349] As a specific operation, the user inputs the following information:

[1350] input:

[1351] Recent situation: I'm busy at work and my mental health is unstable.

[1352] Hobbies: Watching movies

[1353] How I spend money: I spend about 5,000 yen a month.

[1354] Output: A data object that the user device takes input data from and prepares to send it to the server.

[1355] Step 2:

[1356] The terminal sends the input data to the server by an HTTP POST request.

[1357] Input: The data object obtained in step 1

[1358] Output: The input data received by the server

[1359] Step 3:

[1360] The server formats the received input data and converts it into a format that can be passed to the emotion engine.

[1361] Specifically, the input data is converted into a format that allows for emotion analysis.

[1362] Input: The input data received by the server.

[1363] Output: Formatted data to be passed to the emotion engine

[1364] Step 4:

[1365] The server sends the formatted data to the emotion engine, which analyzes the data.

[1366] The emotion engine uses natural language processing technology to extract emotions from the text.

[1367] Input: Formatted data

[1368] Output: Parsed emotion data (e.g., feeling stressed)

[1369] Step 5:

[1370] The server sends the emotion data obtained from the emotion engine to the generative AI, which then generates suggestions appropriate for the user.

[1371] As a specific example, generative AI selects content that will relieve the user's stress.

[1372] Input: Parsed emotion data

[1373] Output: Recommend optimal content to the user (e.g., relaxation music, comedy movies)

[1374] Step 6:

[1375] The generated proposals are sent from the server to the terminal and displayed to the user.

[1376] Input: Optimal content suggestions from generative AI

[1377] Output: Proposal displayed on the user's device

[1378] Step 7:

[1379] The user checks the suggested content and reflects it in their subsequent actions.

[1380] As specific actions, the user performs actions such as "listening to relaxation music" and "watching a movie as a hobby."

[1381] Input: Proposal sent from the server

[1382] Output: User execution of content

[1383] Step 8:

[1384] The user enters results and feedback on the suggested content.

[1385] As a specific action, the user inputs, "Relaxation was effective, but it was difficult to find time for hobbies between work."

[1386] Input: User input into the feedback form

[1387] Output: Feedback data

[1388] Step 9:

[1389] The terminal transmits feedback data from the user to the server.

[1390] Input: Feedback data from users

[1391] Output: Feedback data received by the server

[1392] Step 10:

[1393] The server analyzes the feedback data to optimize future proposals.

[1394] Specifically, it analyzes feedback data, learns user patterns, and adjusts the next suggestion.

[1395] Input: Received feedback data

[1396] Output: Next proposed improvement

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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).

[1404] 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.

[1405] 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."

[1406] 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.

[1407] 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).

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] The following is further disclosed regarding the above embodiment.

[1419] (Claim 1)

[1420] means for accepting input data from a user regarding their current situation, mental state, hobbies, and financial spending habits;

[1421] means for transmitting the input data to a server;

[1422] means for formatting the input data in the server;

[1423] A means for passing the formatted data to a generative AI to generate predictions and suggestions;

[1424] means for transmitting the generated proposal to a user terminal;

[1425] means for displaying the content of the proposal to a user;

[1426] means for transmitting feedback data from the user to the server;

[1427] A means for optimizing subsequent proposals based on the feedback data;

[1428] A system including:

[1429] (Claim 2)

[1430] 2. The system of claim 1, wherein the generative AI includes means for generating specific support content based on the user's situation, mental state, hobbies, and financial habits.

[1431] (Claim 3)

[1432] 2. The system according to claim 1, further comprising means for displaying the proposed contents to the user in a list format.

[1433] "Example 1"

[1434] (Claim 1)

[1435] means for accepting input data from a user regarding current circumstances, physical and mental conditions, preferences, and use of funds;

[1436] means for transmitting said input data to a processing device;

[1437] means for formatting the input data in the processing device;

[1438] means for passing the formatted data to a generative AI model to generate predictions and recommendations;

[1439] means for transmitting the generated proposal to a user device;

[1440] means for displaying the content of the proposal to a user;

[1441] means for transmitting feedback data from the user to the processing device;

[1442] A means for optimizing subsequent proposals based on the feedback data;

[1443] A system including:

[1444] (Claim 2)

[1445] The system of claim 1, wherein the generative AI model includes means for generating specific support content based on the user's situation, physical and mental state, preferences, and use of funds.

[1446] (Claim 3)

[1447] 2. The system according to claim 1, further comprising means for displaying the proposed contents to the user in a list format.

[1448] "Application Example 1"

[1449] (Claim 1)

[1450] means for accepting input data from a user regarding their current situation, mental state, hobbies, and financial spending habits;

[1451] means for transmitting the input data to a server;

[1452] means for formatting the input data in the server;

[1453] A means for passing the formatted data to a generative AI to generate predictions and suggestions;

[1454] means for transmitting the generated proposal to a user terminal;

[1455] means for displaying the content of the proposal to a user;

[1456] means for transmitting feedback data from the user to the server;

[1457] A means for optimizing subsequent proposals based on the feedback data;

[1458] A means for suggesting relaxation content or hobby-related content based on the user's hobby or mental state;

[1459] A system including:

[1460] (Claim 2)

[1461] 2. The system of claim 1, wherein the generative AI includes means for generating specific support content based on the user's situation, mental state, hobbies, and financial habits.

[1462] (Claim 3)

[1463] 2. The system according to claim 1, further comprising means for displaying the proposed contents to the user in a list format.

[1464] "Example 2: Combining Emotion Engines"

[1465] (Claim 1)

[1466] means for accepting input data from a user regarding their current situation, emotional state, hobbies, and financial habits;

[1467] means for transmitting the input data from the terminal to a server;

[1468] means for formatting the input data in the server;

[1469] means for passing the formatted data to an emotion analysis means for recognizing and analyzing emotions;

[1470] means for passing emotion data obtained from the emotion analysis means to a generative AI model to generate predictions and suggestions;

[1471] means for transmitting the generated proposal to a terminal;

[1472] means for displaying the content of the proposal to a user;

[1473] means for transmitting feedback data from the user to the server;

[1474] A means for optimizing subsequent proposals based on the feedback data;

[1475] A system including:

[1476] (Claim 2)

[1477] 2. The system of claim 1, wherein the generative AI model includes means for generating specific assistance content based on the user's situation, emotional state, hobbies, and financial habits.

[1478] (Claim 3)

[1479] 2. The system according to claim 1, further comprising means for displaying the proposed contents to the user in a list format.

[1480] "Application example 2 when combining emotion engines"

[1481] (Claim 1)

[1482] means for accepting input data from a user regarding their current situation, mental state, hobbies, and financial spending habits;

[1483] means for transmitting the input data to a server;

[1484] means for formatting the input data in the server;

[1485] means for passing the formatted data to an emotion engine to recognize and analyze emotions;

[1486] means for passing the emotion data analyzed by the emotion engine to a generative AI to generate predictions and suggestions;

[1487] means for transmitting the generated proposal to a user terminal;

[1488] means for displaying the content of the proposal to a user;

[1489] means for transmitting feedback data from the user to the server;

[1490] A means for optimizing subsequent proposals based on the feedback data;

[1491] A system including a means for recommending appropriate content based on user sentiment analysis.

[1492] (Claim 2)

[1493] 2. The system of claim 1, wherein the generative AI includes means for generating specific support content based on the user's situation, mental state, hobbies, and financial habits.

[1494] (Claim 3)

[1495] 2. The system according to claim 1, further comprising means for displaying the proposed contents to the user in a list format. [Explanation of symbols]

[1496] 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. means for accepting input data from a user regarding their current situation, mental state, hobbies, and financial spending habits; means for transmitting the input data to a server; means for formatting the input data in the server; A means for passing the formatted data to a generative AI to generate predictions and suggestions; means for transmitting the generated proposal to a user terminal; means for displaying the content of the proposal to a user; means for transmitting feedback data from the user to the server; A means for optimizing subsequent proposals based on the feedback data; A system including:

2. The system according to claim 1 , wherein the generative AI includes means for generating specific support content based on the user's situation, mental state, hobbies, and financial habits.

3. 2. The system according to claim 1, further comprising means for displaying said proposal contents to the user in a list format.

Images