system

Abstract

We provide the system. [Solution] A means of providing an interface for receiving user input data, A method that uses a generative model to analyze received input data and evaluate the user's psychological state, Based on the evaluation results, a means of selecting and providing a mental training program suitable for the user, A means to support the user in executing the provided program and to record its progress, A system that includes means for collecting user feedback and evaluating and adjusting the effectiveness of a program.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there is a problem that there is a lack of means to quickly and individually provide appropriate support for reducing the stress and psychological burden borne by many people and maintaining mental health.

Means for Solving the Problems

[0005] The present invention provides a system that receives input data from a user and evaluates a mental state using a generation model. As a result, it becomes possible to automatically select and provide a mental training program suitable for each user. Furthermore, by collecting feedback after program execution, evaluating the effect, and adjusting the program, mental support optimized for individual needs is realized.

[0006] "User input data" refers to information provided by users regarding their psychological state and stress factors.

[0007] An "interface" refers to the means by which a user and a system exchange information with each other.

[0008] "Generative models" refer to machine learning techniques used to analyze user input data and evaluate their psychological state.

[0009] A "mental training program" refers to a set of activities and instructions designed to promote a user's mental health.

[0010] "Recording progress" refers to retaining data on the progress and performance of programs executed by the user.

[0011] "Feedback" refers to information about the user's impressions and the effects of the program after they have completed it. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] In order to implement this invention, a server, a terminal, and a user must work together in coordination. This system aims to provide psychological support to the user in their daily life.

[0034] The server collects input data through user interaction and feeds it into a generative model. The generative model analyzes this data to assess the psychological challenges the user faces. Based on the assessment, the server creates a mental training program tailored to the user. This program provides specific activities to promote the user's mental well-being.

[0035] The terminal displays a mental training program received from the server to the user. The user can execute the program through the terminal. The progress during execution is recorded in real time from the terminal to the server. User feedback regarding the program's effectiveness is also collected via the terminal.

[0036] As a concrete example of implementation, let's consider a case where the user aims to reduce stress. First, the user inputs information into the platform, such as "I've been feeling stressed lately because I have a lot of work." Based on this information, the server uses a generative model to analyze it and proposes a mental training program focused on "stress management." This program includes daily mindfulness meditation and relaxation exercises. The user performs these activities on their device and leaves feedback such as their impressions afterward.

[0037] In this way, the system of the present invention makes it possible to continuously provide personalized support to support the mental health of users.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users access the platform using their devices and provide input data about their current mental health status and the problems they are facing.

[0041] Step 2:

[0042] The terminal transmits input data received from the user to the server in real time. During this process, the data is transferred quickly while maintaining its accuracy.

[0043] Step 3:

[0044] The server receives the transferred data and inputs it into a generative model for analysis. The generative model uses machine learning algorithms to evaluate the user's psychological state and stress factors.

[0045] Step 4:

[0046] Based on the evaluation results, the server creates a mental training program best suited to each user. This program includes exercises and activities that support psychological well-being.

[0047] Step 5:

[0048] A mental training program created on the server is sent to the terminal, and the terminal displays its contents to the user. The user can then perform the training on a daily basis according to the program.

[0049] Step 6:

[0050] Users input their perceived effects and feedback into their device after running the program. This includes a record of the activity performed and their impressions.

[0051] Step 7:

[0052] The terminal then sends the collected feedback and progress updates back to the server. The server uses this information to evaluate the program's effectiveness and make adjustments as needed.

[0053] Through this series of processes, the system provides personalized support to maintain and improve the user's mental health.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern society, many people face various psychological challenges, but opportunities to receive individualized support are limited. In particular, there is a problem in that it is difficult for users to accurately understand their own mental state and receive appropriate psychological training based on that understanding. A system is needed to efficiently address this challenge.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for providing a device for receiving information from users, means for using a generation system that processes the received information and evaluates the user's mental state, and means for creating and providing a psychological training plan tailored to the user based on the evaluation results. This makes it possible to provide individualized psychological support to each user and promote their mental health.

[0059] A "device for receiving information from users" is a technical means that provides an interface for users to input their psychological state and related information into the system, and for accurately collecting that information.

[0060] A "generation system" refers to a mechanism used to analyze received information, evaluate the user's mental state, and perform individual data processing based on that evaluation.

[0061] A "psychological training plan" is a collective term for a series of activities and exercises designed to promote the mental health of users, and is specifically tailored to the individual needs of each user.

[0062] "Data processing techniques" refer to algorithms and models used to quickly and efficiently analyze large amounts of data and derive useful conclusions from it.

[0063] "Thought regulation techniques" refer to a type of psychotherapy technique used to guide users' perceptions and behaviors in a more appropriate direction, and specifically those that include elements of cognitive behavioral therapy.

[0064] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together. The server first provides a device to receive information from the user, allowing the user to input information about their psychological state. For example, the user inputs a situation such as, "I've been feeling stressed lately because I have a lot of work."

[0065] Subsequently, the terminal sends the collected user information to the server. The server uses a generation system to process the received information and evaluate the user's mental state. This generation system is built using machine learning libraries such as TENSORFLOW® and performs analysis using sophisticated data processing techniques.

[0066] Based on the evaluation results obtained from the generation system, the server automatically creates a psychological training plan tailored to the user. This psychological training plan might include, for example, training on the theme of "stress management," which could include mindfulness meditation and relaxation exercises.

[0067] The terminal displays a psychological training plan provided by the server to the user and supports its implementation. The user can carry out the plan and record their progress through the terminal. After implementation, the user is asked to input feedback on their impressions and the effects into the terminal. This feedback is collected by the server, and the effectiveness of the program is continuously evaluated, with adjustments made as needed.

[0068] A concrete example of a prompt message would be, "I'm feeling stressed, what are some ways to cope?" In this way, the invention represents a form for implementing a system that can provide personalized psychological support to each user and promote mental health.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] Users input their psychological state and related information into the system via a terminal. This information includes specific details about their emotions and the stress they are experiencing. The input data is collected by the terminal in text format.

[0072] Step 2:

[0073] The terminal sends user input data to the server. The server receives this data and performs preprocessing for text analysis. This preprocessing involves standardizing the data and cleaning it using natural language processing techniques as needed. The output is preprocessed text data.

[0074] Step 3:

[0075] The server inputs pre-processed text data into a generative AI model and begins the analysis. The generative AI model uses machine learning algorithms to numerically or categorically evaluate the user's mental state. The output of this analysis is the evaluation of the user's psychological state.

[0076] Step 4:

[0077] The server creates an optimal psychological training plan for the user based on evaluation results obtained from the generated AI model. This plan is created by selecting an appropriate template from predefined training templates and customizing it to the user's specific needs. The output is an individualized psychological training plan.

[0078] Step 5:

[0079] The server sends the generated psychological training plan to the terminal. The terminal displays the received training plan to the user, allowing the user to perform each step of the plan. The display uses text, images, audio guides, etc., to aid the user's understanding.

[0080] Step 6:

[0081] The user implements a psychological training plan via a terminal and records their progress throughout the process. The terminal transmits the user's operation logs and progress data to a server in real time for storage.

[0082] Step 7:

[0083] After completing the psychological training plan, users input feedback on their impressions and perceived effects into a terminal. The terminal sends this feedback to a server, which evaluates the effectiveness of the training plan based on the collected data and modifies the plan as needed. The output is the improved training plan and analysis results based on the feedback.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] In modern society, the psychological burden and stress that individuals face in their daily lives and commercial spaces are increasing. In particular, in commercial environments, customers' psychological states significantly influence their experiences and consumption behavior, making effective psychological support essential. However, existing systems struggle to provide real-time psychological support tailored to individual circumstances. To address this challenge, there is a need to develop a system that provides personalized psychological support activities in real time, based on the user's current location and psychological state.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means for providing communication means to receive input information from the user, means for using a generation algorithm to analyze the received input information and evaluate the user's psychological state, and means for providing personalized psychological support activities to the user in real time using location information. This enables effective psychological support in a commercial environment.

[0089] "User" refers to an individual who uses the system and is a subject who requires emotional support.

[0090] "Input information" refers to data that the user provides to the system, including psychological state and location information.

[0091] "Communication means" refers to the function for exchanging information between a user and a server, and the technology for sending and receiving data via a network connection.

[0092] A "generative algorithm" is a computer program that analyzes user input information to evaluate their psychological state.

[0093] A "mental training program" refers to a series of activities and exercises designed to improve the user's mental health.

[0094] "Location information" refers to data indicating the user's current geographical location, and is used to provide support in real time.

[0095] "Psychological support activities" refer to specific actions and advice provided to reduce the user's stress and improve their psychological state.

[0096] The system necessary to implement this invention mainly consists of a server, a terminal, and a user. The server has a communication means to receive input information provided by the user and uses a generation algorithm to analyze the information. The generation algorithm can operate on a general cloud platform and analyzes the user's psychological state using a machine learning algorithm.

[0097] Based on the evaluation results, the server selects a suitable mental training program for the user and provides it to the device via the cloud. This device is envisioned to be a smartphone or smart glasses, and is used to obtain real-time feedback from the user through an application. The device also has the function of recording progress and collecting feedback from the user.

[0098] Users can run programs through a terminal and receive real-time psychological support activities. In this process, location information is sent to the system, and the server uses that information to suggest personalized activities. For example, if a user feels stressed in a particular store, the terminal will suggest relaxation techniques and visual images of green spaces.

[0099] As a concrete example, if a user feels stressed or anxious due to a crowded store in a specific environment, the system generates and suggests ways to relax using a programming language. An example of a prompt would be, "Please provide ways for the user to relax in a crowded environment." In this way, the invention can support and improve the user's psychological well-being in real time.

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The user provides input information through an application on their device. This input information includes their psychological state and current location. This information is sent from the device to the server.

[0103] Step 2:

[0104] The server receives the input information and feeds it into a generating AI model for analysis. This analysis process uses machine learning algorithms to evaluate the user's psychological state. The evaluation results are obtained as output from this step.

[0105] Step 3:

[0106] The server generates a mental training program tailored to the user based on the evaluation results. Here, a generating AI model devises psychological support activities based on prompt messages and sends them as a program. This program is then provided to the terminal as output.

[0107] Step 4:

[0108] The device presents the user with the received mental training program and encourages them to perform it. The user implements this program in their daily life and records the resulting improvement in their psychological state on the device.

[0109] Step 5:

[0110] The user enters their opinions and feedback on the program they have run into a terminal. The terminal sends this feedback to a server, where it is recorded as new data to evaluate the program's effectiveness.

[0111] Step 6:

[0112] The server uses feedback data and location information to analyze the success of the psychological support activities and makes necessary adjustments. If needed, it generates a new program and sends it back to the terminal. This cycle is repeated, allowing the user to continuously receive personalized support.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] To implement this invention, it is necessary to build a system in which the user, terminal, and server work together. This system aims to more accurately support the user's psychological health using an emotion engine.

[0115] First, the user inputs information using an interface to improve their mental health. This input data is then sent from the terminal to the server. This input data includes text-based messages and answers to questions.

[0116] Next, the server inputs the received data into an emotion engine to recognize the user's emotional state. This emotion engine uses natural language processing techniques to analyze the input text and has the ability to identify emotions such as joy, sadness, and anger. The emotional information recognized by the emotion engine is then passed to a generative model, which also evaluates the user's psychological state.

[0117] Next, the server uses information about the user's emotional state to create a mental training program optimized for the user's current psychological condition. This program is customized according to the psychological challenges the user needs to address and may include elements of cognitive behavioral therapy.

[0118] This program is sent to the device, and the user is shown how to run it. The user performs daily training based on this program and records their progress and results on the device. The device sends this data to the server, which evaluates the effectiveness of the program based on the feedback received and makes adjustments if necessary.

[0119] As a concrete example, consider a case where a user inputs "I've been feeling very sad lately." The emotion engine recognizes the emotion of "sadness," and the generative model performs an analysis. As a result, the server presents the user with a mental training program that encourages positive self-dialogue. Through this program, the user can receive appropriate approaches to improve their emotional health.

[0120] In this way, the system provides training that accurately reflects the user's emotions, enabling efficient support for maintaining and improving mental health.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] Users access the platform through their devices and input information about their mental health situation and the emotions they are experiencing. A free-form text input interface is provided for this process.

[0124] Step 2:

[0125] The terminal sends the entered text data to the server. This transmission occurs in real time, and the data is securely stored on the server.

[0126] Step 3:

[0127] The server passes the received text data to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's text, evaluate their emotional state, and identify emotions such as "joy," "sadness," and "anger."

[0128] Step 4:

[0129] The server processes a generative model based on the emotional state recognized by the emotion engine and input data from other users. This generative model uses machine learning algorithms to evaluate the user's psychological state in detail.

[0130] Step 5:

[0131] The server creates a personalized mental training program for each user based on their evaluation results. This program is individualized and incorporates elements that promote psychological well-being, including cognitive behavioral therapy.

[0132] Step 6:

[0133] The terminal displays a mental training program sent from the server to the user. The user can perform the instructed training activities and practice each step.

[0134] Step 7:

[0135] Users input the program's execution results and any perceived changes into their device, and this feedback is recorded. This feedback includes comments on the achievements and effects of the training performed.

[0136] Step 8:

[0137] The terminal sends the recorded feedback to the server. The server evaluates the program's adaptation based on this feedback and adjusts the content of the next session as needed.

[0138] Through these steps, the system supports the user's mental health and provides appropriate mental training.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In recent years, there has been growing interest in maintaining and improving mental health, but conventional psychological support systems have faced challenges in accurately responding to the emotional states of individual users. In particular, the need for providing individualized training plans based on changes in psychological state, and for effective support in implementing them, remains insufficiently met.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for an information processing device to acquire information data from a user via an information recording device, means for a data communication device to transmit the acquired information data to a data analysis device, analyze the data using an emotion engine to identify the user's emotional state, and means for a generative model to evaluate the user's psychological state using the analysis results and create and provide a psychological training plan tailored to the user's emotional state. This makes it possible to create and provide a customized psychological training plan for each individual user.

[0144] An "information processing device" is a device that collects information data from users and has the function of recording data entered through an interface.

[0145] An "information recording device" is a device that works in conjunction with an information processing device to store and manage information from users, and performs temporary or permanent recording of input data.

[0146] A "data communication device" is a device that transmits data recorded by an information recording device to a server, and is equipped with communication means for safely and quickly moving data.

[0147] A "data analysis device" is a device for analyzing received information data and performs calculations to identify emotional states using an emotion engine.

[0148] An "emotion engine" is an analytical technology that uses natural language processing to identify a user's emotions from input data, and has the ability to recognize emotions such as joy, sadness, and anger.

[0149] A "generative model" is an algorithm that uses machine learning to evaluate a user's psychological state, and then creates an individualized psychological training plan based on the analysis results.

[0150] A "psychological training plan" is a training program created based on the user's identified emotional state, and includes activities aimed at improving and maintaining their psychological health.

[0151] This invention is a system for supporting the psychological health of users, and mainly involves the coordinated functioning of hardware such as an information processing device, an information recording device, a data communication device, and a data analysis device, as well as software such as natural language processing technology, an emotion engine, and a generative model.

[0152] Users access a mental health interface using an information processing device and input information about their mental state in text format. This information is securely stored in an information recording device. Next, a data communication device encrypts the stored data and transmits it to a server. Security measures are in place to ensure the confidentiality of the data during this process.

[0153] The server analyzes the received information using a data analysis device and identifies the user's emotional state using an emotion engine. The emotion engine utilizes natural language processing techniques to classify the user's emotions from the input text, identifying feelings such as joy, sadness, and anger. The emotional data is then passed to a generative AI model to evaluate the user's psychological state. Based on this evaluation, the server creates and provides a psychological training plan tailored to each individual user.

[0154] For example, if a user inputs "I've been feeling very sad lately," the emotion engine analyzes the sentence and identifies the emotion of "sadness." The generative AI model uses this information to evaluate the user's psychological tendencies, and the server provides the user with a specific psychological training plan that encourages positive self-dialogue. By providing this training plan, the user can efficiently improve their emotional health.

[0155] Examples of prompts include, "I've been feeling down lately. How can I manage these feelings?" and "I can't control my anger. How can I express these feelings in a healthy way?"

[0156] By implementing such a system, users can receive specific approaches tailored to their emotional state, which can contribute to maintaining and improving their psychological health.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user accesses a mental health interface using their device and enters information about their mental state. The input data is in text format, and an example would be "I've been feeling very depressed lately." At this point, the device temporarily records the entered information and prepares to send it to the server.

[0160] Step 2:

[0161] The terminal transmits user input data to the server via a data communication device. The data is encrypted and transmitted securely. The data received by the server is text information related to the user's state of mind.

[0162] Step 3:

[0163] The server uses a data analysis device to analyze the received input data. The analysis device uses an emotion engine to process the input text based on natural language processing techniques and identify the user's emotional state. This process identifies emotions such as "sadness" from the text. The identified emotion is generated as output and passed to the next processing stage.

[0164] Step 4:

[0165] The server passes the emotional information identified by the emotion engine to a generative AI model to evaluate the user's psychological state. The generative AI model compares this data with past data to analyze the user's psychological tendencies and problems. As a result of the analysis, a detailed evaluation report on the user's psychological state is generated.

[0166] Step 5:

[0167] The server creates an optimal psychological training plan for the user based on its evaluation of the generated AI model. This training plan includes elements of cognitive behavioral therapy, and specifically includes tasks that promote positive self-dialogue. This training plan is generated as output and sent to the terminal.

[0168] Step 6:

[0169] The terminal displays the training plan sent from the server to the user. The user performs daily training according to this plan and records the results and progress on the terminal. The recorded data becomes input for the next feedback process.

[0170] Step 7:

[0171] The terminal sends user feedback and achievement data to the server. The server uses this feedback information to evaluate the effectiveness of the training plan and adjust the program content as needed. This feedback loop allows the overall system approach to continuously improve.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] In physical stores, it is difficult to effectively analyze customers' emotions and psychological states and optimize the store environment and services in real time based on that analysis. Traditional methods fail to capture customer feedback on the spot and respond immediately, resulting in insufficient improvement of the customer experience.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes means for providing means to receive predetermined data from a user, means for analyzing the received data and using a generative model to evaluate the user's psychological state, and means for collecting customer facial expression data in a physical store and analyzing emotions in real time. This makes it possible to optimize the store environment to instantly reflect customer emotions.

[0177] A "user" is someone who uses the system to receive an evaluation of their emotions and psychological state, and then executes a program based on that evaluation.

[0178] A "generative model" is a model used to analyze user input data and evaluate their psychological state, and it is trained using machine learning algorithms.

[0179] A "training program" is a program selected based on the user's psychological state, with the aim of improving the user's mental health through their participation.

[0180] A "physical store" is a physical sales location where goods and services are directly provided to customers, and it is also a place where data necessary for analyzing the psychological state of customers is collected.

[0181] "Real-time analysis" is a process that instantly evaluates customers' facial expressions and emotions, and quickly incorporates the necessary data into feedback.

[0182] To implement this invention, a system must be built in cooperation with the user, terminal, and server. The server receives user input data, evaluates the user's psychological state using a generative model, and provides a suitable training program. In physical stores, it is necessary to analyze customer emotions in real time using hardware such as smart glasses.

[0183] Specifically, smart glasses (e.g., Google Glass®) capture the customer's facial expressions and send the data to a server. The server uses an emotion engine (e.g., natural language processing tools such as NLTK) to perform emotion analysis and immediately provides feedback to the store. Based on the analyzed data, the store can adjust in-store displays and change customer service methods.

[0184] As a concrete example, suppose smart glasses recognize a customer's "dissatisfied" expression while they are looking at a display. Based on this information, the system can provide the store staff with real-time suggestions for price changes or revisions to explanation methods.

[0185] Examples of prompts for a generative AI model are as follows:

[0186] "Analyze the customer's facial expressions and voice over a 10-second period to evaluate their emotions. Then, generate store display improvement strategies that correspond to those emotions."

[0187] In this way, the real-time evaluation of psychological state and the presentation of countermeasures realized by the system of the present invention make it possible to improve the customer experience.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The smart glasses (device) capture the customer's facial expressions and voice in real time within a physical store. The input is customer facial expression and voice data, and the output is digitized data. Specifically, data is collected through a camera and microphone built into the glasses.

[0191] Step 2:

[0192] The terminal transmits the collected facial and audio data to the server. The input here is digitized facial and audio data, and the output is data transfer to the server. Specifically, the data is securely transmitted via a network connection.

[0193] Step 3:

[0194] The server analyzes the received data using an emotion engine. The input is facial expression and voice data sent from the terminal, and the output is the analyzed emotion information. For data processing, natural language processing techniques are used to identify emotional states (joy, sadness, anger, etc.).

[0195] Step 4:

[0196] The server provides real-time feedback to store staff based on the analysis results. The input is analyzed sentiment information, and the output is specific improvement suggestions. Specifically, advice on adjusting the store environment is displayed on smart glasses.

[0197] Step 5:

[0198] Store staff (users) adjust in-store displays and customer service methods based on feedback from the server. The input here is feedback information, and the output is an improvement in the customer experience. Specifically, this involves rearranging displays and improving product descriptions.

[0199] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0215] In order to implement this invention, a server, a terminal, and a user must work together in coordination. This system aims to provide psychological support to the user in their daily life.

[0216] The server collects input data through user interaction and feeds it into a generative model. The generative model analyzes this data to assess the psychological challenges the user faces. Based on the assessment, the server creates a mental training program tailored to the user. This program provides specific activities to promote the user's mental well-being.

[0217] The terminal displays a mental training program received from the server to the user. The user can execute the program through the terminal. The progress during execution is recorded in real time from the terminal to the server. User feedback regarding the program's effectiveness is also collected via the terminal.

[0218] As a concrete example of implementation, let's consider a case where the user aims to reduce stress. First, the user inputs information into the platform, such as "I've been feeling stressed lately because I have a lot of work." Based on this information, the server uses a generative model to analyze it and proposes a mental training program focused on "stress management." This program includes daily mindfulness meditation and relaxation exercises. The user performs these activities on their device and leaves feedback such as their impressions afterward.

[0219] In this way, the system of the present invention makes it possible to continuously provide personalized support to support the mental health of users.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] Users access the platform using their devices and provide input data about their current mental health status and the problems they are facing.

[0223] Step 2:

[0224] The terminal transmits input data received from the user to the server in real time. During this process, the data is transferred quickly while maintaining its accuracy.

[0225] Step 3:

[0226] The server receives the transferred data and inputs it into a generative model for analysis. The generative model uses machine learning algorithms to evaluate the user's psychological state and stress factors.

[0227] Step 4:

[0228] Based on the evaluation results, the server creates a mental training program best suited to each user. This program includes exercises and activities that support psychological well-being.

[0229] Step 5:

[0230] A mental training program created on the server is sent to the terminal, and the terminal displays its contents to the user. The user can then perform the training on a daily basis according to the program.

[0231] Step 6:

[0232] Users input their perceived effects and feedback into their device after running the program. This includes a record of the activity performed and their impressions.

[0233] Step 7:

[0234] The terminal then sends the collected feedback and progress updates back to the server. The server uses this information to evaluate the program's effectiveness and make adjustments as needed.

[0235] Through this series of processes, the system provides personalized support to maintain and improve the user's mental health.

[0236] (Example 1)

[0237] Next, we will describe Example 1. 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."

[0238] In modern society, many people face various psychological challenges, but opportunities to receive individualized support are limited. In particular, there is a problem in that it is difficult for users to accurately understand their own mental state and receive appropriate psychological training based on that understanding. A system is needed to efficiently address this challenge.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0240] In this invention, the server includes means for providing a device for receiving information from users, means for using a generation system that processes the received information and evaluates the user's mental state, and means for creating and providing a psychological training plan tailored to the user based on the evaluation results. This makes it possible to provide individualized psychological support to each user and promote their mental health.

[0241] A "device for receiving information from users" is a technical means that provides an interface for users to input their psychological state and related information into the system, and for accurately collecting that information.

[0242] A "generation system" refers to a mechanism used to analyze received information, evaluate the user's mental state, and perform individual data processing based on that evaluation.

[0243] A "psychological training plan" is a collective term for a series of activities and exercises designed to promote the mental health of users, and is specifically tailored to the individual needs of each user.

[0244] "Data processing techniques" refer to algorithms and models used to quickly and efficiently analyze large amounts of data and derive useful conclusions from it.

[0245] "Thought regulation techniques" refer to a type of psychotherapy technique used to guide users' perceptions and behaviors in a more appropriate direction, and specifically those that include elements of cognitive behavioral therapy.

[0246] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together. The server first provides a device to receive information from the user, allowing the user to input information about their psychological state. For example, the user inputs a situation such as, "I've been feeling stressed lately because I have a lot of work."

[0247] Subsequently, the terminal sends the collected user information to the server. The server uses a generation system to process the received information and evaluate the user's mental state. This generation system is built using machine learning libraries such as TensorFlow and performs analysis using sophisticated data processing techniques.

[0248] Based on the evaluation results obtained from the generation system, the server automatically creates a psychological training plan tailored to the user. This psychological training plan might include, for example, training on the theme of "stress management," which could include mindfulness meditation and relaxation exercises.

[0249] The terminal displays a psychological training plan provided by the server to the user and supports its implementation. The user can carry out the plan and record their progress through the terminal. After implementation, the user is asked to input feedback on their impressions and the effects into the terminal. This feedback is collected by the server, and the effectiveness of the program is continuously evaluated, with adjustments made as needed.

[0250] A concrete example of a prompt message would be, "I'm feeling stressed, what are some ways to cope?" In this way, the invention represents a form for implementing a system that can provide personalized psychological support to each user and promote mental health.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] Users input their psychological state and related information into the system via a terminal. This information includes specific details about their emotions and the stress they are experiencing. The input data is collected by the terminal in text format.

[0254] Step 2:

[0255] The terminal sends user input data to the server. The server receives this data and performs preprocessing for text analysis. This preprocessing involves standardizing the data and cleaning it using natural language processing techniques as needed. The output is preprocessed text data.

[0256] Step 3:

[0257] The server inputs pre-processed text data into a generative AI model and begins the analysis. The generative AI model uses machine learning algorithms to numerically or categorically evaluate the user's mental state. The output of this analysis is the evaluation of the user's psychological state.

[0258] Step 4:

[0259] The server creates an optimal psychological training plan for the user based on evaluation results obtained from the generated AI model. This plan is created by selecting an appropriate template from predefined training templates and customizing it to the user's specific needs. The output is an individualized psychological training plan.

[0260] Step 5:

[0261] The server sends the generated psychological training plan to the terminal. The terminal displays the received training plan to the user, allowing the user to perform each step of the plan. The display uses text, images, audio guides, etc., to aid the user's understanding.

[0262] Step 6:

[0263] The user implements a psychological training plan via a terminal and records their progress throughout the process. The terminal transmits the user's operation logs and progress data to a server in real time for storage.

[0264] Step 7:

[0265] After completing the psychological training plan, users input feedback on their impressions and perceived effects into a terminal. The terminal sends this feedback to a server, which evaluates the effectiveness of the training plan based on the collected data and modifies the plan as needed. The output is the improved training plan and analysis results based on the feedback.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] In modern society, the psychological burden and stress that individuals face in their daily lives and commercial spaces are increasing. In particular, in commercial environments, customers' psychological states significantly influence their experiences and consumption behavior, making effective psychological support essential. However, existing systems struggle to provide real-time psychological support tailored to individual circumstances. To address this challenge, there is a need to develop a system that provides personalized psychological support activities in real time, based on the user's current location and psychological state.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes means for providing communication means to receive input information from the user, means for using a generation algorithm to analyze the received input information and evaluate the user's psychological state, and means for providing personalized psychological support activities to the user in real time using location information. This enables effective psychological support in a commercial environment.

[0271] "User" refers to an individual who uses the system and is a subject who requires emotional support.

[0272] "Input information" refers to data that the user provides to the system, including psychological state and location information.

[0273] "Communication means" refers to the function for exchanging information between a user and a server, and the technology for sending and receiving data via a network connection.

[0274] A "generative algorithm" is a computer program that analyzes user input information to evaluate their psychological state.

[0275] A "mental training program" refers to a series of activities and exercises designed to improve the user's mental health.

[0276] "Location information" refers to data indicating the user's current geographical location, and is used to provide support in real time.

[0277] "Psychological support activities" refer to specific actions and advice provided to reduce the user's stress and improve their psychological state.

[0278] The system necessary to implement this invention mainly consists of a server, a terminal, and a user. The server has a communication means to receive input information provided by the user and uses a generation algorithm to analyze the information. The generation algorithm can operate on a general cloud platform and analyzes the user's psychological state using a machine learning algorithm.

[0279] Based on the evaluation results, the server selects a mental training program suitable for the user and provides it to the terminal through the cloud. This terminal is assumed to be a smartphone or smart glasses and is used to obtain real-time feedback from the user through an application. The terminal also has functions for recording progress and collecting opinions from the user.

[0280] The user can execute the program through the terminal and receive psychological support activities provided in real time. In this process, location information is sent to the system, and the server uses this information to propose personalized activities. For example, when the user feels stressed inside a specific store, the terminal proposes relaxation techniques or visual green space images.

[0281] As a specific example, when the user feels "crowded and tense inside the store" in a specific environment, the system generates a relaxation method in a programming language and proposes it to the user. An example of a prompt sentence is "Please provide a method for the user to relax in a crowded environment." In this way, the invention can support and improve the user's mental health in real time.

[0282] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0283] Step 1:

[0284] The user provides input information through the application of the terminal. The input information includes the mental state and the current location information. This information is sent from the terminal to the server.

[0285] Step 2:

[0286] The server acquires the received input information, inputs the data into the generative AI model for analysis. In this analysis process, a machine learning algorithm is used to evaluate the user's mental state. As the output of this step, an evaluation result is obtained.

[0287] Step 3:

[0288] The server generates a mental training program suitable for the user based on the evaluation result. Here, the generative AI model devises psychological support activities based on the prompt text and transmits them as a program. The output is that this program is provided to the terminal.

[0289] Step 4:

[0290] The terminal presents the received mental training program to the user and prompts for execution. The user implements this program in daily life and records the improvement in the obtained mental state on the terminal.

[0291] Step 5:

[0292] The user inputs opinions and feedback on the executed program into the terminal. The terminal transmits this feedback to the server and records it as new data for evaluating the effectiveness of the program.

[0293] Step 6:

[0294] The server uses the feedback data and location information to analyze how successful the psychological support activities are and make necessary adjustments. If necessary, a new program is generated and transmitted to the terminal again. By repeating this cycle, the user can continuously receive personalized support.

[0295] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0296] To implement this invention, it is necessary to build a system in which the user, terminal, and server work together. This system aims to more accurately support the user's psychological health using an emotion engine.

[0297] First, the user inputs information using an interface to improve their mental health. This input data is then sent from the terminal to the server. This input data includes text-based messages and answers to questions.

[0298] Next, the server inputs the received data into an emotion engine to recognize the user's emotional state. This emotion engine uses natural language processing techniques to analyze the input text and has the ability to identify emotions such as joy, sadness, and anger. The emotional information recognized by the emotion engine is then passed to a generative model, which also evaluates the user's psychological state.

[0299] Next, the server uses information about the user's emotional state to create a mental training program optimized for the user's current psychological condition. This program is customized according to the psychological challenges the user needs to address and may include elements of cognitive behavioral therapy.

[0300] This program is sent to the device, and the user is shown how to run it. The user performs daily training based on this program and records their progress and results on the device. The device sends this data to the server, which evaluates the effectiveness of the program based on the feedback received and makes adjustments if necessary.

[0301] As a specific example, consider the case where a user inputs "I've been feeling very sad lately." The emotion engine recognizes the emotion of "sadness," and the generation model conducts an analysis. As a result, the server presents a mental training program that encourages the user to engage in positive self-talk. Through this program, the user can receive an appropriate approach to improving emotional health.

[0302] In this way, this system provides training that accurately reflects the user's emotions and realizes efficient support for maintaining and improving mental health.

[0303] The processing flow will be described below.

[0304] Step 1:

[0305] The user accesses the platform through the terminal and inputs information about their own situation regarding mental health and the emotions they are feeling. At this time, an interface for freely entering text is provided.

[0306] Step 2:

[0307] The terminal sends the input text data to the server. This transmission is performed in real time, and the data is securely stored on the server.

[0308] Step 3:

[0309] The server passes the received text data to the emotion engine. The emotion engine analyzes the user's text using natural language processing technology, evaluates the emotional state, and identifies emotions such as "joy," "sadness," "anger," etc.

[0310] Step 4:

[0311] The server processes a generative model based on the emotional state recognized by the emotion engine and input data from other users. This generative model uses machine learning algorithms to evaluate the user's psychological state in detail.

[0312] Step 5:

[0313] The server creates a personalized mental training program for each user based on their evaluation results. This program is individualized and incorporates elements that promote psychological well-being, including cognitive behavioral therapy.

[0314] Step 6:

[0315] The terminal displays a mental training program sent from the server to the user. The user can perform the instructed training activities and practice each step.

[0316] Step 7:

[0317] Users input the program's execution results and any perceived changes into their device, and this feedback is recorded. This feedback includes comments on the achievements and effects of the training performed.

[0318] Step 8:

[0319] The terminal sends the recorded feedback to the server. The server evaluates the program's adaptation based on this feedback and adjusts the content of the next session as needed.

[0320] Through these steps, the system supports the user's mental health and provides appropriate mental training.

[0321] (Example 2)

[0322] Next, we will describe Example 2. 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".

[0323] In recent years, there has been growing interest in maintaining and improving mental health, but conventional psychological support systems have faced challenges in accurately responding to the emotional states of individual users. In particular, the need for providing individualized training plans based on changes in psychological state, and for effective support in implementing them, remains insufficiently met.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for an information processing device to acquire information data from a user via an information recording device, means for a data communication device to transmit the acquired information data to a data analysis device, analyze the data using an emotion engine to identify the user's emotional state, and means for a generative model to evaluate the user's psychological state using the analysis results and create and provide a psychological training plan tailored to the user's emotional state. This makes it possible to create and provide a customized psychological training plan for each individual user.

[0326] An "information processing device" is a device that collects information data from users and has the function of recording data entered through an interface.

[0327] An "information recording device" is a device that works in conjunction with an information processing device to store and manage information from users, and performs temporary or permanent recording of input data.

[0328] A "data communication device" is a device that transmits data recorded by an information recording device to a server, and is equipped with communication means for safely and quickly moving data.

[0329] A "data analysis device" is a device for analyzing received information data and performs calculations to identify emotional states using an emotion engine.

[0330] An "emotion engine" is an analytical technology that uses natural language processing to identify a user's emotions from input data, and has the ability to recognize emotions such as joy, sadness, and anger.

[0331] A "generative model" is an algorithm that uses machine learning to evaluate a user's psychological state, and then creates an individualized psychological training plan based on the analysis results.

[0332] A "psychological training plan" is a training program created based on the user's identified emotional state, and includes activities aimed at improving and maintaining their psychological health.

[0333] This invention is a system for supporting the psychological health of users, and mainly involves the coordinated functioning of hardware such as an information processing device, an information recording device, a data communication device, and a data analysis device, as well as software such as natural language processing technology, an emotion engine, and a generative model.

[0334] Users access a mental health interface using an information processing device and input information about their mental state in text format. This information is securely stored in an information recording device. Next, a data communication device encrypts the stored data and transmits it to a server. Security measures are in place to ensure the confidentiality of the data during this process.

[0335] The server analyzes the received information using a data analysis device and identifies the user's emotional state using an emotion engine. The emotion engine utilizes natural language processing techniques to classify the user's emotions from the input text, identifying feelings such as joy, sadness, and anger. The emotional data is then passed to a generative AI model to evaluate the user's psychological state. Based on this evaluation, the server creates and provides a psychological training plan tailored to each individual user.

[0336] For example, if a user inputs "I've been feeling very sad lately," the emotion engine analyzes the sentence and identifies the emotion of "sadness." The generative AI model uses this information to evaluate the user's psychological tendencies, and the server provides the user with a specific psychological training plan that encourages positive self-dialogue. By providing this training plan, the user can efficiently improve their emotional health.

[0337] Examples of prompts include, "I've been feeling down lately. How can I manage these feelings?" and "I can't control my anger. How can I express these feelings in a healthy way?"

[0338] By implementing such a system, users can receive specific approaches tailored to their emotional state, which can contribute to maintaining and improving their psychological health.

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] The user accesses a mental health interface using their device and enters information about their mental state. The input data is in text format, and an example would be "I've been feeling very depressed lately." At this point, the device temporarily records the entered information and prepares to send it to the server.

[0342] Step 2:

[0343] The terminal transmits user input data to the server via a data communication device. The data is encrypted and transmitted securely. The data received by the server is text information related to the user's state of mind.

[0344] Step 3:

[0345] The server uses a data analysis device to analyze the received input data. The analysis device uses an emotion engine to process the input text based on natural language processing techniques and identify the user's emotional state. This process identifies emotions such as "sadness" from the text. The identified emotion is generated as output and passed to the next processing stage.

[0346] Step 4:

[0347] The server passes the emotional information identified by the emotion engine to a generative AI model to evaluate the user's psychological state. The generative AI model compares this data with past data to analyze the user's psychological tendencies and problems. As a result of the analysis, a detailed evaluation report on the user's psychological state is generated.

[0348] Step 5:

[0349] The server creates an optimal psychological training plan for the user based on its evaluation of the generated AI model. This training plan includes elements of cognitive behavioral therapy, and specifically includes tasks that promote positive self-dialogue. This training plan is generated as output and sent to the terminal.

[0350] Step 6:

[0351] The terminal displays the training plan sent from the server to the user. The user performs daily training according to this plan and records the results and progress on the terminal. The recorded data becomes input for the next feedback process.

[0352] Step 7:

[0353] The terminal sends user feedback and achievement data to the server. The server uses this feedback information to evaluate the effectiveness of the training plan and adjust the program content as needed. This feedback loop allows the overall system approach to continuously improve.

[0354] (Application Example 2)

[0355] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0356] In physical stores, it is difficult to effectively analyze customers' emotions and psychological states and optimize the store environment and services in real time based on that analysis. Traditional methods fail to capture customer feedback on the spot and respond immediately, resulting in insufficient improvement of the customer experience.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0358] In this invention, the server includes means for providing means to receive predetermined data from a user, means for analyzing the received data and using a generative model to evaluate the user's psychological state, and means for collecting customer facial expression data in a physical store and analyzing emotions in real time. This makes it possible to optimize the store environment to instantly reflect customer emotions.

[0359] A "user" is someone who uses the system to receive an evaluation of their emotions and psychological state, and then executes a program based on that evaluation.

[0360] A "generative model" is a model used to analyze user input data and evaluate their psychological state, and it is trained using machine learning algorithms.

[0361] A "training program" is a program selected based on the user's psychological state, with the aim of improving the user's mental health through their participation.

[0362] A "physical store" is a physical sales location where goods and services are directly provided to customers, and it is also a place where data necessary for analyzing the psychological state of customers is collected.

[0363] "Real-time analysis" is a process that instantly evaluates customers' facial expressions and emotions, and quickly incorporates the necessary data into feedback.

[0364] To implement this invention, a system must be built in cooperation with the user, terminal, and server. The server receives user input data, evaluates the user's psychological state using a generative model, and provides a suitable training program. In physical stores, it is necessary to analyze customer emotions in real time using hardware such as smart glasses.

[0365] Specifically, smart glasses (e.g., Google Glass) capture the customer's facial expressions and send the data to a server. The server uses an emotion engine (e.g., a natural language processing tool such as NLTK) to perform emotion analysis and immediately provides feedback to the store. The store can then adjust in-store displays and change customer service methods based on the analyzed data.

[0366] As a concrete example, suppose smart glasses recognize a customer's "dissatisfied" expression while they are looking at a display. Based on this information, the system can provide the store staff with real-time suggestions for price changes or revisions to explanation methods.

[0367] Examples of prompts for a generative AI model are as follows:

[0368] "Analyze the customer's facial expressions and voice over a 10-second period to evaluate their emotions. Then, generate store display improvement strategies that correspond to those emotions."

[0369] In this way, the real-time evaluation of psychological state and the presentation of countermeasures realized by the system of the present invention make it possible to improve the customer experience.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The smart glasses (device) capture the customer's facial expressions and voice in real time within a physical store. The input is customer facial expression and voice data, and the output is digitized data. Specifically, data is collected through a camera and microphone built into the glasses.

[0373] Step 2:

[0374] The terminal transmits the collected facial and audio data to the server. The input here is digitized facial and audio data, and the output is data transfer to the server. Specifically, the data is securely transmitted via a network connection.

[0375] Step 3:

[0376] The server analyzes the received data using an emotion engine. The input is facial expression and voice data sent from the terminal, and the output is the analyzed emotion information. For data processing, natural language processing techniques are used to identify emotional states (joy, sadness, anger, etc.).

[0377] Step 4:

[0378] The server provides real-time feedback to store staff based on the analysis results. The input is analyzed sentiment information, and the output is specific improvement suggestions. Specifically, advice on adjusting the store environment is displayed on smart glasses.

[0379] Step 5:

[0380] Store staff (users) adjust in-store displays and customer service methods based on feedback from the server. The input here is feedback information, and the output is an improvement in the customer experience. Specifically, this involves rearranging displays and improving product descriptions.

[0381] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0382] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0384] [Third Embodiment]

[0385] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0386] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0387] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0388] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0389] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0390] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0391] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0392] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0393] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0394] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0395] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0396] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0397] In order to implement this invention, a server, a terminal, and a user must work together in coordination. This system aims to provide psychological support to the user in their daily life.

[0398] The server collects input data through user interaction and feeds it into a generative model. The generative model analyzes this data to assess the psychological challenges the user faces. Based on the assessment, the server creates a mental training program tailored to the user. This program provides specific activities to promote the user's mental well-being.

[0399] The terminal displays a mental training program received from the server to the user. The user can execute the program through the terminal. The progress during execution is recorded in real time from the terminal to the server. User feedback regarding the program's effectiveness is also collected via the terminal.

[0400] As a concrete example of implementation, let's consider a case where the user aims to reduce stress. First, the user inputs information into the platform, such as "I've been feeling stressed lately because I have a lot of work." Based on this information, the server uses a generative model to analyze it and proposes a mental training program focused on "stress management." This program includes daily mindfulness meditation and relaxation exercises. The user performs these activities on their device and leaves feedback such as their impressions afterward.

[0401] In this way, the system of the present invention makes it possible to continuously provide personalized support to support the mental health of users.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] Users access the platform using their devices and provide input data about their current mental health status and the problems they are facing.

[0405] Step 2:

[0406] The terminal transmits input data received from the user to the server in real time. During this process, the data is transferred quickly while maintaining its accuracy.

[0407] Step 3:

[0408] The server receives the transferred data and inputs it into a generative model for analysis. The generative model uses machine learning algorithms to evaluate the user's psychological state and stress factors.

[0409] Step 4:

[0410] Based on the evaluation results, the server creates a mental training program best suited to each user. This program includes exercises and activities that support psychological well-being.

[0411] Step 5:

[0412] A mental training program created on the server is sent to the terminal, and the terminal displays its contents to the user. The user can then perform the training on a daily basis according to the program.

[0413] Step 6:

[0414] Users input their perceived effects and feedback into their device after running the program. This includes a record of the activity performed and their impressions.

[0415] Step 7:

[0416] The terminal then sends the collected feedback and progress updates back to the server. The server uses this information to evaluate the program's effectiveness and make adjustments as needed.

[0417] Through this series of processes, the system provides personalized support to maintain and improve the user's mental health.

[0418] (Example 1)

[0419] Next, we will describe Example 1. 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."

[0420] In modern society, many people face various psychological challenges, but opportunities to receive individualized support are limited. In particular, there is a problem in that it is difficult for users to accurately understand their own mental state and receive appropriate psychological training based on that understanding. A system is needed to efficiently address this challenge.

[0421] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0422] In this invention, the server includes means for providing a device for receiving information from users, means for using a generation system that processes the received information and evaluates the user's mental state, and means for creating and providing a psychological training plan tailored to the user based on the evaluation results. This makes it possible to provide individualized psychological support to each user and promote their mental health.

[0423] A "device for receiving information from users" is a technical means that provides an interface for users to input their psychological state and related information into the system, and for accurately collecting that information.

[0424] A "generation system" refers to a mechanism used to analyze received information, evaluate the user's mental state, and perform individual data processing based on that evaluation.

[0425] A "psychological training plan" is a collective term for a series of activities and exercises designed to promote the mental health of users, and is specifically tailored to the individual needs of each user.

[0426] "Data processing techniques" refer to algorithms and models used to quickly and efficiently analyze large amounts of data and derive useful conclusions from it.

[0427] "Thought regulation techniques" refer to a type of psychotherapy technique used to guide users' perceptions and behaviors in a more appropriate direction, and specifically those that include elements of cognitive behavioral therapy.

[0428] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together. The server first provides a device to receive information from the user, allowing the user to input information about their psychological state. For example, the user inputs a situation such as, "I've been feeling stressed lately because I have a lot of work."

[0429] Subsequently, the terminal sends the collected user information to the server. The server uses a generation system to process the received information and evaluate the user's mental state. This generation system is built using machine learning libraries such as TensorFlow and performs analysis using sophisticated data processing techniques.

[0430] Based on the evaluation results obtained from the generation system, the server automatically creates a psychological training plan tailored to the user. This psychological training plan might include, for example, training on the theme of "stress management," which could include mindfulness meditation and relaxation exercises.

[0431] The terminal displays a psychological training plan provided by the server to the user and supports its implementation. The user can carry out the plan and record their progress through the terminal. After implementation, the user is asked to input feedback on their impressions and the effects into the terminal. This feedback is collected by the server, and the effectiveness of the program is continuously evaluated, with adjustments made as needed.

[0432] A concrete example of a prompt message would be, "I'm feeling stressed, what are some ways to cope?" In this way, the invention represents a form for implementing a system that can provide personalized psychological support to each user and promote mental health.

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] Users input their psychological state and related information into the system via a terminal. This information includes specific details about their emotions and the stress they are experiencing. The input data is collected by the terminal in text format.

[0436] Step 2:

[0437] The terminal sends user input data to the server. The server receives this data and performs preprocessing for text analysis. This preprocessing involves standardizing the data and cleaning it using natural language processing techniques as needed. The output is preprocessed text data.

[0438] Step 3:

[0439] The server inputs pre-processed text data into a generative AI model and begins the analysis. The generative AI model uses machine learning algorithms to numerically or categorically evaluate the user's mental state. The output of this analysis is the evaluation of the user's psychological state.

[0440] Step 4:

[0441] The server creates an optimal psychological training plan for the user based on evaluation results obtained from the generated AI model. This plan is created by selecting an appropriate template from predefined training templates and customizing it to the user's specific needs. The output is an individualized psychological training plan.

[0442] Step 5:

[0443] The server sends the generated psychological training plan to the terminal. The terminal displays the received training plan to the user, allowing the user to perform each step of the plan. The display uses text, images, audio guides, etc., to aid the user's understanding.

[0444] Step 6:

[0445] The user implements a psychological training plan via a terminal and records their progress throughout the process. The terminal transmits the user's operation logs and progress data to a server in real time for storage.

[0446] Step 7:

[0447] After completing the psychological training plan, users input feedback on their impressions and perceived effects into a terminal. The terminal sends this feedback to a server, which evaluates the effectiveness of the training plan based on the collected data and modifies the plan as needed. The output is the improved training plan and analysis results based on the feedback.

[0448] (Application Example 1)

[0449] Next, we will explain Application Example 1. In the following explanation, 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."

[0450] In modern society, the psychological burden and stress that individuals face in their daily lives and commercial spaces are increasing. In particular, in commercial environments, customers' psychological states significantly influence their experiences and consumption behavior, making effective psychological support essential. However, existing systems struggle to provide real-time psychological support tailored to individual circumstances. To address this challenge, there is a need to develop a system that provides personalized psychological support activities in real time, based on the user's current location and psychological state.

[0451] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0452] In this invention, the server includes means for providing communication means to receive input information from the user, means for using a generation algorithm to analyze the received input information and evaluate the user's psychological state, and means for providing personalized psychological support activities to the user in real time using location information. This enables effective psychological support in a commercial environment.

[0453] "User" refers to an individual who uses the system and is a subject who requires emotional support.

[0454] "Input information" refers to data that the user provides to the system, including psychological state and location information.

[0455] "Communication means" refers to the function for exchanging information between a user and a server, and the technology for sending and receiving data via a network connection.

[0456] A "generative algorithm" is a computer program that analyzes user input information to evaluate their psychological state.

[0457] A "mental training program" refers to a series of activities and exercises designed to improve the user's mental health.

[0458] "Location information" refers to data indicating the user's current geographical location, and is used to provide support in real time.

[0459] "Psychological support activities" refer to specific actions and advice provided to reduce the user's stress and improve their psychological state.

[0460] The system necessary to implement this invention mainly consists of a server, a terminal, and a user. The server has a communication means to receive input information provided by the user and uses a generation algorithm to analyze the information. The generation algorithm can operate on a general cloud platform and analyzes the user's psychological state using a machine learning algorithm.

[0461] Based on the evaluation results, the server selects a suitable mental training program for the user and provides it to the device via the cloud. This device is envisioned to be a smartphone or smart glasses, and is used to obtain real-time feedback from the user through an application. The device also has the function of recording progress and collecting feedback from the user.

[0462] Users can run programs through a terminal and receive real-time psychological support activities. In this process, location information is sent to the system, and the server uses that information to suggest personalized activities. For example, if a user feels stressed in a particular store, the terminal will suggest relaxation techniques and visual images of green spaces.

[0463] As a concrete example, if a user feels stressed or anxious due to a crowded store in a specific environment, the system generates and suggests ways to relax using a programming language. An example of a prompt would be, "Please provide ways for the user to relax in a crowded environment." In this way, the invention can support and improve the user's psychological well-being in real time.

[0464] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0465] Step 1:

[0466] The user provides input information through an application on their device. This input information includes their psychological state and current location. This information is sent from the device to the server.

[0467] Step 2:

[0468] The server receives the input information and feeds it into a generating AI model for analysis. This analysis process uses machine learning algorithms to evaluate the user's psychological state. The evaluation results are obtained as output from this step.

[0469] Step 3:

[0470] The server generates a mental training program tailored to the user based on the evaluation results. Here, a generating AI model devises psychological support activities based on prompt messages and sends them as a program. This program is then provided to the terminal as output.

[0471] Step 4:

[0472] The device presents the user with the received mental training program and encourages them to perform it. The user implements this program in their daily life and records the resulting improvement in their psychological state on the device.

[0473] Step 5:

[0474] The user enters their opinions and feedback on the program they have run into a terminal. The terminal sends this feedback to a server, where it is recorded as new data to evaluate the program's effectiveness.

[0475] Step 6:

[0476] The server uses feedback data and location information to analyze the success of the psychological support activities and makes necessary adjustments. If needed, it generates a new program and sends it back to the terminal. This cycle is repeated, allowing the user to continuously receive personalized support.

[0477] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0478] To implement this invention, it is necessary to build a system in which the user, terminal, and server work together. This system aims to more accurately support the user's psychological health using an emotion engine.

[0479] First, the user inputs information using an interface to improve their mental health. This input data is then sent from the terminal to the server. This input data includes text-based messages and answers to questions.

[0480] Next, the server inputs the received data into an emotion engine to recognize the user's emotional state. This emotion engine uses natural language processing techniques to analyze the input text and has the ability to identify emotions such as joy, sadness, and anger. The emotional information recognized by the emotion engine is then passed to a generative model, which also evaluates the user's psychological state.

[0481] Next, the server uses information about the user's emotional state to create a mental training program optimized for the user's current psychological condition. This program is customized according to the psychological challenges the user needs to address and may include elements of cognitive behavioral therapy.

[0482] This program is sent to the device, and the user is shown how to run it. The user performs daily training based on this program and records their progress and results on the device. The device sends this data to the server, which evaluates the effectiveness of the program based on the feedback received and makes adjustments if necessary.

[0483] As a concrete example, consider a case where a user inputs "I've been feeling very sad lately." The emotion engine recognizes the emotion of "sadness," and the generative model performs an analysis. As a result, the server presents the user with a mental training program that encourages positive self-dialogue. Through this program, the user can receive appropriate approaches to improve their emotional health.

[0484] In this way, the system provides training that accurately reflects the user's emotions, enabling efficient support for maintaining and improving mental health.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] Users access the platform through their devices and input information about their mental health situation and the emotions they are experiencing. A free-form text input interface is provided for this process.

[0488] Step 2:

[0489] The terminal sends the entered text data to the server. This transmission occurs in real time, and the data is securely stored on the server.

[0490] Step 3:

[0491] The server passes the received text data to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's text, evaluate their emotional state, and identify emotions such as "joy," "sadness," and "anger."

[0492] Step 4:

[0493] The server processes a generative model based on the emotional state recognized by the emotion engine and input data from other users. This generative model uses machine learning algorithms to evaluate the user's psychological state in detail.

[0494] Step 5:

[0495] The server creates a personalized mental training program for each user based on their evaluation results. This program is individualized and incorporates elements that promote psychological well-being, including cognitive behavioral therapy.

[0496] Step 6:

[0497] The terminal displays a mental training program sent from the server to the user. The user can perform the instructed training activities and practice each step.

[0498] Step 7:

[0499] Users input the program's execution results and any perceived changes into their device, and this feedback is recorded. This feedback includes comments on the achievements and effects of the training performed.

[0500] Step 8:

[0501] The terminal sends the recorded feedback to the server. The server evaluates the program's adaptation based on this feedback and adjusts the content of the next session as needed.

[0502] Through these steps, the system supports the user's mental health and provides appropriate mental training.

[0503] (Example 2)

[0504] Next, we will describe Example 2. 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."

[0505] In recent years, there has been growing interest in maintaining and improving mental health, but conventional psychological support systems have faced challenges in accurately responding to the emotional states of individual users. In particular, the need for providing individualized training plans based on changes in psychological state, and for effective support in implementing them, remains insufficiently met.

[0506] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0507] In this invention, the server includes means for an information processing device to acquire information data from a user via an information recording device, means for a data communication device to transmit the acquired information data to a data analysis device, analyze the data using an emotion engine to identify the user's emotional state, and means for a generative model to evaluate the user's psychological state using the analysis results and create and provide a psychological training plan tailored to the user's emotional state. This makes it possible to create and provide a customized psychological training plan for each individual user.

[0508] An "information processing device" is a device that collects information data from users and has the function of recording data entered through an interface.

[0509] An "information recording device" is a device that works in conjunction with an information processing device to store and manage information from users, and performs temporary or permanent recording of input data.

[0510] A "data communication device" is a device that transmits data recorded by an information recording device to a server, and is equipped with communication means for safely and quickly moving data.

[0511] A "data analysis device" is a device for analyzing received information data and performs calculations to identify emotional states using an emotion engine.

[0512] An "emotion engine" is an analytical technology that uses natural language processing to identify a user's emotions from input data, and has the ability to recognize emotions such as joy, sadness, and anger.

[0513] A "generative model" is an algorithm that uses machine learning to evaluate a user's psychological state, and then creates an individualized psychological training plan based on the analysis results.

[0514] A "psychological training plan" is a training program created based on the user's identified emotional state, and includes activities aimed at improving and maintaining their psychological health.

[0515] This invention is a system for supporting the psychological health of users, and mainly involves the coordinated functioning of hardware such as an information processing device, an information recording device, a data communication device, and a data analysis device, as well as software such as natural language processing technology, an emotion engine, and a generative model.

[0516] Users access a mental health interface using an information processing device and input information about their mental state in text format. This information is securely stored in an information recording device. Next, a data communication device encrypts the stored data and transmits it to a server. Security measures are in place to ensure the confidentiality of the data during this process.

[0517] The server analyzes the received information using a data analysis device and identifies the user's emotional state using an emotion engine. The emotion engine utilizes natural language processing techniques to classify the user's emotions from the input text, identifying feelings such as joy, sadness, and anger. The emotional data is then passed to a generative AI model to evaluate the user's psychological state. Based on this evaluation, the server creates and provides a psychological training plan tailored to each individual user.

[0518] For example, if a user inputs "I've been feeling very sad lately," the emotion engine analyzes the sentence and identifies the emotion of "sadness." The generative AI model uses this information to evaluate the user's psychological tendencies, and the server provides the user with a specific psychological training plan that encourages positive self-dialogue. By providing this training plan, the user can efficiently improve their emotional health.

[0519] Examples of prompts include, "I've been feeling down lately. How can I manage these feelings?" and "I can't control my anger. How can I express these feelings in a healthy way?"

[0520] By implementing such a system, users can receive specific approaches tailored to their emotional state, which can contribute to maintaining and improving their psychological health.

[0521] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0522] Step 1:

[0523] The user accesses a mental health interface using their device and enters information about their mental state. The input data is in text format, and an example would be "I've been feeling very depressed lately." At this point, the device temporarily records the entered information and prepares to send it to the server.

[0524] Step 2:

[0525] The terminal transmits user input data to the server via a data communication device. The data is encrypted and transmitted securely. The data received by the server is text information related to the user's state of mind.

[0526] Step 3:

[0527] The server uses a data analysis device to analyze the received input data. The analysis device uses an emotion engine to process the input text based on natural language processing techniques and identify the user's emotional state. This process identifies emotions such as "sadness" from the text. The identified emotion is generated as output and passed to the next processing stage.

[0528] Step 4:

[0529] The server passes the emotional information identified by the emotion engine to a generative AI model to evaluate the user's psychological state. The generative AI model compares this data with past data to analyze the user's psychological tendencies and problems. As a result of the analysis, a detailed evaluation report on the user's psychological state is generated.

[0530] Step 5:

[0531] The server creates an optimal psychological training plan for the user based on its evaluation of the generated AI model. This training plan includes elements of cognitive behavioral therapy, and specifically includes tasks that promote positive self-dialogue. This training plan is generated as output and sent to the terminal.

[0532] Step 6:

[0533] The terminal displays the training plan sent from the server to the user. The user performs daily training according to this plan and records the results and progress on the terminal. The recorded data becomes input for the next feedback process.

[0534] Step 7:

[0535] The terminal sends user feedback and achievement data to the server. The server uses this feedback information to evaluate the effectiveness of the training plan and adjust the program content as needed. This feedback loop allows the overall system approach to continuously improve.

[0536] (Application Example 2)

[0537] Next, we will explain application example 2. In the following explanation, 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."

[0538] In physical stores, it is difficult to effectively analyze customers' emotions and psychological states and optimize the store environment and services in real time based on that analysis. Traditional methods fail to capture customer feedback on the spot and respond immediately, resulting in insufficient improvement of the customer experience.

[0539] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0540] In this invention, the server includes means for providing means to receive predetermined data from a user, means for analyzing the received data and using a generative model to evaluate the user's psychological state, and means for collecting customer facial expression data in a physical store and analyzing emotions in real time. This makes it possible to optimize the store environment to instantly reflect customer emotions.

[0541] A "user" is someone who uses the system to receive an evaluation of their emotions and psychological state, and then executes a program based on that evaluation.

[0542] A "generative model" is a model used to analyze user input data and evaluate their psychological state, and it is trained using machine learning algorithms.

[0543] A "training program" is a program selected based on the user's psychological state, with the aim of improving the user's mental health through their participation.

[0544] A "physical store" is a physical sales location where goods and services are directly provided to customers, and it is also a place where data necessary for analyzing the psychological state of customers is collected.

[0545] "Real-time analysis" is a process that instantly evaluates customers' facial expressions and emotions, and quickly incorporates the necessary data into feedback.

[0546] To implement this invention, a system must be built in cooperation with the user, terminal, and server. The server receives user input data, evaluates the user's psychological state using a generative model, and provides a suitable training program. In physical stores, it is necessary to analyze customer emotions in real time using hardware such as smart glasses.

[0547] Specifically, smart glasses (e.g., Google Glass) capture the customer's facial expressions and send the data to a server. The server uses an emotion engine (e.g., a natural language processing tool such as NLTK) to perform emotion analysis and immediately provides feedback to the store. The store can then adjust in-store displays and change customer service methods based on the analyzed data.

[0548] As a concrete example, suppose smart glasses recognize a customer's "dissatisfied" expression while they are looking at a display. Based on this information, the system can provide the store staff with real-time suggestions for price changes or revisions to explanation methods.

[0549] Examples of prompts for a generative AI model are as follows:

[0550] "Analyze the customer's facial expressions and voice over a 10-second period to evaluate their emotions. Then, generate store display improvement strategies that correspond to those emotions."

[0551] In this way, the real-time evaluation of psychological state and the presentation of countermeasures realized by the system of the present invention make it possible to improve the customer experience.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The smart glasses (device) capture the customer's facial expressions and voice in real time within a physical store. The input is customer facial expression and voice data, and the output is digitized data. Specifically, data is collected through a camera and microphone built into the glasses.

[0555] Step 2:

[0556] The terminal transmits the collected facial and audio data to the server. The input here is digitized facial and audio data, and the output is data transfer to the server. Specifically, the data is securely transmitted via a network connection.

[0557] Step 3:

[0558] The server analyzes the received data using an emotion engine. The input is facial expression and voice data sent from the terminal, and the output is the analyzed emotion information. For data processing, natural language processing techniques are used to identify emotional states (joy, sadness, anger, etc.).

[0559] Step 4:

[0560] The server provides real-time feedback to store staff based on the analysis results. The input is analyzed sentiment information, and the output is specific improvement suggestions. Specifically, advice on adjusting the store environment is displayed on smart glasses.

[0561] Step 5:

[0562] Store staff (users) adjust in-store displays and customer service methods based on feedback from the server. The input here is feedback information, and the output is an improvement in the customer experience. Specifically, this involves rearranging displays and improving product descriptions.

[0563] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0564] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0565] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0566] [Fourth Embodiment]

[0567] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0568] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0569] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0570] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0571] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0572] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0573] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0574] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0575] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0576] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0577] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0578] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0579] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0580] In order to implement this invention, a server, a terminal, and a user must work together in coordination. This system aims to provide psychological support to the user in their daily life.

[0581] The server collects input data through user interaction and feeds it into a generative model. The generative model analyzes this data to assess the psychological challenges the user faces. Based on the assessment, the server creates a mental training program tailored to the user. This program provides specific activities to promote the user's mental well-being.

[0582] The terminal displays a mental training program received from the server to the user. The user can execute the program through the terminal. The progress during execution is recorded in real time from the terminal to the server. User feedback regarding the program's effectiveness is also collected via the terminal.

[0583] As a concrete example of implementation, let's consider a case where the user aims to reduce stress. First, the user inputs information into the platform, such as "I've been feeling stressed lately because I have a lot of work." Based on this information, the server uses a generative model to analyze it and proposes a mental training program focused on "stress management." This program includes daily mindfulness meditation and relaxation exercises. The user performs these activities on their device and leaves feedback such as their impressions afterward.

[0584] In this way, the system of the present invention makes it possible to continuously provide personalized support to support the mental health of users.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] Users access the platform using their devices and provide input data about their current mental health status and the problems they are facing.

[0588] Step 2:

[0589] The terminal transmits input data received from the user to the server in real time. During this process, the data is transferred quickly while maintaining its accuracy.

[0590] Step 3:

[0591] The server receives the transferred data and inputs it into a generative model for analysis. The generative model uses machine learning algorithms to evaluate the user's psychological state and stress factors.

[0592] Step 4:

[0593] Based on the evaluation results, the server creates a mental training program best suited to each user. This program includes exercises and activities that support psychological well-being.

[0594] Step 5:

[0595] A mental training program created on the server is sent to the terminal, and the terminal displays its contents to the user. The user can then perform the training on a daily basis according to the program.

[0596] Step 6:

[0597] Users input their perceived effects and feedback into their device after running the program. This includes a record of the activity performed and their impressions.

[0598] Step 7:

[0599] The terminal then sends the collected feedback and progress updates back to the server. The server uses this information to evaluate the program's effectiveness and make adjustments as needed.

[0600] Through this series of processes, the system provides personalized support to maintain and improve the user's mental health.

[0601] (Example 1)

[0602] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] In modern society, many people face various psychological challenges, but opportunities to receive individualized support are limited. In particular, there is a problem in that it is difficult for users to accurately understand their own mental state and receive appropriate psychological training based on that understanding. A system is needed to efficiently address this challenge.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0605] In this invention, the server includes means for providing a device for receiving information from users, means for using a generation system that processes the received information and evaluates the user's mental state, and means for creating and providing a psychological training plan tailored to the user based on the evaluation results. This makes it possible to provide individualized psychological support to each user and promote their mental health.

[0606] A "device for receiving information from users" is a technical means that provides an interface for users to input their psychological state and related information into the system, and for accurately collecting that information.

[0607] A "generation system" refers to a mechanism used to analyze received information, evaluate the user's mental state, and perform individual data processing based on that evaluation.

[0608] A "psychological training plan" is a collective term for a series of activities and exercises designed to promote the mental health of users, and is specifically tailored to the individual needs of each user.

[0609] "Data processing techniques" refer to algorithms and models used to quickly and efficiently analyze large amounts of data and derive useful conclusions from it.

[0610] "Thought regulation techniques" refer to a type of psychotherapy technique used to guide users' perceptions and behaviors in a more appropriate direction, and specifically those that include elements of cognitive behavioral therapy.

[0611] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together. The server first provides a device to receive information from the user, allowing the user to input information about their psychological state. For example, the user inputs a situation such as, "I've been feeling stressed lately because I have a lot of work."

[0612] Subsequently, the terminal sends the collected user information to the server. The server uses a generation system to process the received information and evaluate the user's mental state. This generation system is built using machine learning libraries such as TensorFlow and performs analysis using sophisticated data processing techniques.

[0613] Based on the evaluation results obtained from the generation system, the server automatically creates a psychological training plan tailored to the user. This psychological training plan might include, for example, training on the theme of "stress management," which could include mindfulness meditation and relaxation exercises.

[0614] The terminal displays a psychological training plan provided by the server to the user and supports its implementation. The user can carry out the plan and record their progress through the terminal. After implementation, the user is asked to input feedback on their impressions and the effects into the terminal. This feedback is collected by the server, and the effectiveness of the program is continuously evaluated, with adjustments made as needed.

[0615] A concrete example of a prompt message would be, "I'm feeling stressed, what are some ways to cope?" In this way, the invention represents a form for implementing a system that can provide personalized psychological support to each user and promote mental health.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] Users input their psychological state and related information into the system via a terminal. This information includes specific details about their emotions and the stress they are experiencing. The input data is collected by the terminal in text format.

[0619] Step 2:

[0620] The terminal sends user input data to the server. The server receives this data and performs preprocessing for text analysis. This preprocessing involves standardizing the data and cleaning it using natural language processing techniques as needed. The output is preprocessed text data.

[0621] Step 3:

[0622] The server inputs pre-processed text data into a generative AI model and begins the analysis. The generative AI model uses machine learning algorithms to numerically or categorically evaluate the user's mental state. The output of this analysis is the evaluation of the user's psychological state.

[0623] Step 4:

[0624] The server creates an optimal psychological training plan for the user based on evaluation results obtained from the generated AI model. This plan is created by selecting an appropriate template from predefined training templates and customizing it to the user's specific needs. The output is an individualized psychological training plan.

[0625] Step 5:

[0626] The server sends the generated psychological training plan to the terminal. The terminal displays the received training plan to the user, allowing the user to perform each step of the plan. The display uses text, images, audio guides, etc., to aid the user's understanding.

[0627] Step 6:

[0628] The user implements a psychological training plan via a terminal and records their progress throughout the process. The terminal transmits the user's operation logs and progress data to a server in real time for storage.

[0629] Step 7:

[0630] After completing the psychological training plan, users input feedback on their impressions and perceived effects into a terminal. The terminal sends this feedback to a server, which evaluates the effectiveness of the training plan based on the collected data and modifies the plan as needed. The output is the improved training plan and analysis results based on the feedback.

[0631] (Application Example 1)

[0632] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] In modern society, the psychological burden and stress that individuals face in their daily lives and commercial spaces are increasing. In particular, in commercial environments, customers' psychological states significantly influence their experiences and consumption behavior, making effective psychological support essential. However, existing systems struggle to provide real-time psychological support tailored to individual circumstances. To address this challenge, there is a need to develop a system that provides personalized psychological support activities in real time, based on the user's current location and psychological state.

[0634] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0635] In this invention, the server includes means for providing communication means to receive input information from the user, means for using a generation algorithm to analyze the received input information and evaluate the user's psychological state, and means for providing personalized psychological support activities to the user in real time using location information. This enables effective psychological support in a commercial environment.

[0636] "User" refers to an individual who uses the system and is a subject who requires emotional support.

[0637] "Input information" refers to data that the user provides to the system, including psychological state and location information.

[0638] "Communication means" refers to the function for exchanging information between a user and a server, and the technology for sending and receiving data via a network connection.

[0639] A "generative algorithm" is a computer program that analyzes user input information to evaluate their psychological state.

[0640] A "mental training program" refers to a series of activities and exercises designed to improve the user's mental health.

[0641] "Location information" refers to data indicating the user's current geographical location, and is used to provide support in real time.

[0642] "Psychological support activities" refer to specific actions and advice provided to reduce the user's stress and improve their psychological state.

[0643] The system necessary to implement this invention mainly consists of a server, a terminal, and a user. The server has a communication means to receive input information provided by the user and uses a generation algorithm to analyze the information. The generation algorithm can operate on a general cloud platform and analyzes the user's psychological state using a machine learning algorithm.

[0644] Based on the evaluation results, the server selects a suitable mental training program for the user and provides it to the device via the cloud. This device is envisioned to be a smartphone or smart glasses, and is used to obtain real-time feedback from the user through an application. The device also has the function of recording progress and collecting feedback from the user.

[0645] Users can run programs through a terminal and receive real-time psychological support activities. In this process, location information is sent to the system, and the server uses that information to suggest personalized activities. For example, if a user feels stressed in a particular store, the terminal will suggest relaxation techniques and visual images of green spaces.

[0646] As a concrete example, if a user feels stressed or anxious due to a crowded store in a specific environment, the system generates and suggests ways to relax using a programming language. An example of a prompt would be, "Please provide ways for the user to relax in a crowded environment." In this way, the invention can support and improve the user's psychological well-being in real time.

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] The user provides input information through an application on their device. This input information includes their psychological state and current location. This information is sent from the device to the server.

[0650] Step 2:

[0651] The server receives the input information and feeds it into a generating AI model for analysis. This analysis process uses machine learning algorithms to evaluate the user's psychological state. The evaluation results are obtained as the output of this step.

[0652] Step 3:

[0653] The server generates a mental training program tailored to the user based on the evaluation results. Here, a generating AI model devises psychological support activities based on prompt messages and sends them as a program. This program is then provided to the terminal as output.

[0654] Step 4:

[0655] The device presents the user with the received mental training program and encourages them to perform it. The user implements this program in their daily life and records the resulting improvement in their psychological state on the device.

[0656] Step 5:

[0657] The user enters their opinions and feedback on the program they have run into a terminal. The terminal sends this feedback to a server, where it is recorded as new data to evaluate the program's effectiveness.

[0658] Step 6:

[0659] The server uses feedback data and location information to analyze the success of the psychological support activities and makes necessary adjustments. If needed, it generates a new program and sends it back to the terminal. This cycle is repeated, allowing the user to continuously receive personalized support.

[0660] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0661] To implement this invention, it is necessary to build a system in which the user, terminal, and server work together. This system aims to more accurately support the user's psychological health using an emotion engine.

[0662] First, the user inputs information using an interface to improve their mental health. This input data is then sent from the terminal to the server. This input data includes text-based messages and answers to questions.

[0663] Next, the server inputs the received data into an emotion engine to recognize the user's emotional state. This emotion engine uses natural language processing techniques to analyze the input text and has the ability to identify emotions such as joy, sadness, and anger. The emotional information recognized by the emotion engine is then passed to a generative model, which also evaluates the user's psychological state.

[0664] Next, the server uses information about the user's emotional state to create a mental training program optimized for the user's current psychological condition. This program is customized according to the psychological challenges the user needs to address and may include elements of cognitive behavioral therapy.

[0665] This program is sent to the device, and the user is shown how to run it. The user performs daily training based on this program and records their progress and results on the device. The device sends this data to the server, which evaluates the effectiveness of the program based on the feedback received and makes adjustments if necessary.

[0666] As a concrete example, consider a case where a user inputs "I've been feeling very sad lately." The emotion engine recognizes the emotion of "sadness," and the generative model performs an analysis. As a result, the server presents the user with a mental training program that encourages positive self-dialogue. Through this program, the user can receive appropriate approaches to improve their emotional health.

[0667] In this way, the system provides training that accurately reflects the user's emotions, enabling efficient support for maintaining and improving mental health.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] Users access the platform through their devices and input information about their mental health situation and the emotions they are experiencing. A free-form text input interface is provided for this process.

[0671] Step 2:

[0672] The terminal sends the entered text data to the server. This transmission occurs in real time, and the data is securely stored on the server.

[0673] Step 3:

[0674] The server passes the received text data to the emotion engine. The emotion engine uses natural language processing technology to analyze the user's text, evaluate their emotional state, and identify emotions such as "joy," "sadness," and "anger."

[0675] Step 4:

[0676] The server processes a generative model based on the emotional state recognized by the emotion engine and input data from other users. This generative model uses machine learning algorithms to evaluate the user's psychological state in detail.

[0677] Step 5:

[0678] The server creates a personalized mental training program for each user based on their evaluation results. This program is individualized and incorporates elements that promote psychological well-being, including cognitive behavioral therapy.

[0679] Step 6:

[0680] The terminal displays a mental training program sent from the server to the user. The user can perform the instructed training activities and practice each step.

[0681] Step 7:

[0682] Users input the program's execution results and any perceived changes into their device, and this feedback is recorded. This feedback includes comments on the achievements and effects of the training performed.

[0683] Step 8:

[0684] The terminal sends the recorded feedback to the server. The server evaluates the program's adaptation based on this feedback and adjusts the content of the next session as needed.

[0685] Through these steps, the system supports the user's mental health and provides appropriate mental training.

[0686] (Example 2)

[0687] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In recent years, there has been growing interest in maintaining and improving mental health, but conventional psychological support systems have faced challenges in accurately responding to the emotional states of individual users. In particular, the need for providing individualized training plans based on changes in psychological state, and for effective support in implementing them, remains insufficiently met.

[0689] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0690] In this invention, the server includes means for an information processing device to acquire information data from a user via an information recording device, means for a data communication device to transmit the acquired information data to a data analysis device, analyze the data using an emotion engine to identify the user's emotional state, and means for a generative model to evaluate the user's psychological state using the analysis results and create and provide a psychological training plan tailored to the user's emotional state. This makes it possible to create and provide a customized psychological training plan for each individual user.

[0691] An "information processing device" is a device that collects information data from users and has the function of recording data entered through an interface.

[0692] An "information recording device" is a device that works in conjunction with an information processing device to store and manage information from users, and performs temporary or permanent recording of input data.

[0693] A "data communication device" is a device that transmits data recorded by an information recording device to a server, and is equipped with communication means for safely and quickly moving data.

[0694] A "data analysis device" is a device for analyzing received information data and performs calculations to identify emotional states using an emotion engine.

[0695] An "emotion engine" is an analytical technology that uses natural language processing to identify a user's emotions from input data, and has the ability to recognize emotions such as joy, sadness, and anger.

[0696] A "generative model" is an algorithm that uses machine learning to evaluate a user's psychological state, and then creates an individualized psychological training plan based on the analysis results.

[0697] A "psychological training plan" is a training program created based on the user's identified emotional state, and includes activities aimed at improving and maintaining their psychological health.

[0698] This invention is a system for supporting the psychological health of users, and mainly involves the coordinated functioning of hardware such as an information processing device, information recording device, data communication device, and data analysis device, as well as software such as natural language processing technology, emotion engine, and generative model.

[0699] Users access a mental health interface using an information processing device and input information about their mental state in text format. This information is securely stored in an information recording device. Next, a data communication device encrypts the stored data and transmits it to a server. Security measures are in place to ensure the confidentiality of the data during this process.

[0700] The server analyzes the received information using a data analysis device and identifies the user's emotional state using an emotion engine. The emotion engine utilizes natural language processing techniques to classify the user's emotions from the input text, identifying feelings such as joy, sadness, and anger. The emotional data is then passed to a generative AI model to evaluate the user's psychological state. Based on this evaluation, the server creates and provides a psychological training plan tailored to each individual user.

[0701] For example, if a user inputs "I've been feeling very sad lately," the emotion engine analyzes the sentence and identifies the emotion of "sadness." The generative AI model uses this information to evaluate the user's psychological tendencies, and the server provides the user with a specific psychological training plan that encourages positive self-dialogue. By providing this training plan, the user can efficiently improve their emotional health.

[0702] Examples of prompts include, "I've been feeling down lately. How can I manage these feelings?" and "I can't control my anger. How can I express these feelings in a healthy way?"

[0703] By implementing such a system, users can receive specific approaches tailored to their emotional state, which can contribute to maintaining and improving their psychological health.

[0704] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0705] Step 1:

[0706] The user accesses a mental health interface using their device and enters information about their mental state. The input data is in text format, and an example would be "I've been feeling very depressed lately." At this point, the device temporarily records the entered information and prepares to send it to the server.

[0707] Step 2:

[0708] The terminal transmits user input data to the server via a data communication device. The data is encrypted and transmitted securely. The data received by the server is text information related to the user's state of mind.

[0709] Step 3:

[0710] The server uses a data analysis device to analyze the received input data. The analysis device uses an emotion engine to process the input text based on natural language processing techniques and identify the user's emotional state. This process identifies emotions such as "sadness" from the text. The identified emotion is generated as output and passed to the next processing stage.

[0711] Step 4:

[0712] The server passes the emotional information identified by the emotion engine to a generative AI model to evaluate the user's psychological state. The generative AI model compares this data with past data to analyze the user's psychological tendencies and problems. As a result of the analysis, a detailed evaluation report on the user's psychological state is generated.

[0713] Step 5:

[0714] The server creates an optimal psychological training plan for the user based on its evaluation of the generated AI model. This training plan includes elements of cognitive behavioral therapy, and specifically includes tasks that promote positive self-dialogue. This training plan is generated as output and sent to the terminal.

[0715] Step 6:

[0716] The terminal displays the training plan sent from the server to the user. The user performs daily training according to this plan and records the results and progress on the terminal. The recorded data becomes input for the next feedback process.

[0717] Step 7:

[0718] The terminal sends user feedback and achievement data to the server. The server uses this feedback information to evaluate the effectiveness of the training plan and adjust the program content as needed. This feedback loop allows the overall system approach to continuously improve.

[0719] (Application Example 2)

[0720] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] In physical stores, it is difficult to effectively analyze customers' emotions and psychological states and optimize the store environment and services in real time based on that analysis. Traditional methods fail to capture customer feedback on the spot and respond immediately, resulting in insufficient improvement of the customer experience.

[0722] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0723] In this invention, the server includes means for providing means to receive predetermined data from a user, means for analyzing the received data and using a generative model to evaluate the user's psychological state, and means for collecting customer facial expression data in a physical store and analyzing emotions in real time. This makes it possible to optimize the store environment to instantly reflect customer emotions.

[0724] A "user" is someone who uses the system to receive an evaluation of their emotions and psychological state, and then executes a program based on that evaluation.

[0725] A "generative model" is a model used to analyze user input data and evaluate their psychological state, and it is trained using machine learning algorithms.

[0726] A "training program" is a program selected based on the user's psychological state, with the aim of improving the user's mental health through their participation.

[0727] A "physical store" is a physical sales location where goods and services are directly provided to customers, and it is also a place where data necessary for analyzing the psychological state of customers is collected.

[0728] "Real-time analysis" is a process that instantly evaluates customers' facial expressions and emotions, and quickly incorporates the necessary data into feedback.

[0729] To implement this invention, a system must be built in cooperation with the user, terminal, and server. The server receives user input data, evaluates the user's psychological state using a generative model, and provides a suitable training program. In physical stores, it is necessary to analyze customer emotions in real time using hardware such as smart glasses.

[0730] Specifically, smart glasses (e.g., Google Glass) capture the customer's facial expressions and send the data to a server. The server uses an emotion engine (e.g., a natural language processing tool such as NLTK) to perform emotion analysis and immediately provides feedback to the store. The store can then adjust in-store displays and change customer service methods based on the analyzed data.

[0731] As a concrete example, suppose smart glasses recognize a customer's "dissatisfied" expression while they are looking at a display. Based on this information, the system can provide the store staff with real-time suggestions for price changes or revisions to explanation methods.

[0732] Examples of prompts for a generative AI model are as follows:

[0733] "Analyze the customer's facial expressions and voice over a 10-second period to evaluate their emotions. Then, generate store display improvement strategies that correspond to those emotions."

[0734] In this way, the real-time evaluation of psychological state and the presentation of countermeasures realized by the system of the present invention make it possible to improve the customer experience.

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] The smart glasses (device) capture the customer's facial expressions and voice in real time within a physical store. The input is customer facial expression and voice data, and the output is digitized data. Specifically, data is collected through a camera and microphone built into the glasses.

[0738] Step 2:

[0739] The terminal transmits the collected facial expression and audio data to the server. The input here is digitized facial expression and audio data, and the output is data transfer to the server. Specifically, the data is securely transmitted via a network connection.

[0740] Step 3:

[0741] The server analyzes the received data using an emotion engine. The input is facial expression and voice data sent from the terminal, and the output is the analyzed emotion information. For data processing, natural language processing techniques are used to identify emotional states (joy, sadness, anger, etc.).

[0742] Step 4:

[0743] The server provides real-time feedback to store staff based on the analysis results. The input is analyzed sentiment information, and the output is specific improvement suggestions. Specifically, advice on adjusting the store environment is displayed on smart glasses.

[0744] Step 5:

[0745] Store staff (users) adjust in-store displays and customer service methods based on feedback from the server. The input here is feedback information, and the output is an improvement in the customer experience. Specifically, this involves rearranging displays and improving product descriptions.

[0746] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0747] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0748] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0749] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0750] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0751] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0752] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0753] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0754] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0755] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0756] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0757] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0758] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0759] 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.

[0760] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0761] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0762] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0763] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0764] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0765] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0766] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0767] The following is further disclosed regarding the embodiments described above.

[0768] (Claim 1)

[0769] A means of providing an interface for receiving user input data,

[0770] A method that uses a generative model to analyze received input data and evaluate the user's psychological state,

[0771] Based on the evaluation results, a means of selecting and providing a mental training program suitable for the user,

[0772] A means to support the user in executing the provided program and to record its progress,

[0773] A system that includes means for collecting user feedback and evaluating and adjusting the effectiveness of a program.

[0774] (Claim 2)

[0775] The system according to claim 1, wherein the generative model uses a machine learning algorithm.

[0776] (Claim 3)

[0777] The system according to claim 1, wherein the mental training program uses cognitive adjustment techniques, including cognitive behavioral therapy.

[0778] "Example 1"

[0779] (Claim 1)

[0780] Means for providing a device that receives information from users,

[0781] A means of using a generation system that processes received information and evaluates the user's mental state,

[0782] Based on the evaluation results, a means to create and provide a psychological training plan tailored to the user,

[0783] A means to support users in implementing the provided plan and to record the progress,

[0784] An information processing device that includes means for collecting user feedback, evaluating the effectiveness of a plan, and adapting it accordingly.

[0785] (Claim 2)

[0786] The information processing apparatus according to claim 1, wherein the generation system utilizes data processing techniques.

[0787] (Claim 3)

[0788] The information processing device according to claim 1, wherein the psychological training plan uses thought regulation techniques including cognitive behavioral therapy.

[0789] "Application Example 1"

[0790] (Claim 1)

[0791] A means of providing a communication means for receiving input information from the user,

[0792] A method that uses a generation algorithm to analyze the received input information and evaluate the user's psychological state,

[0793] A means of selecting and providing a mental training program suitable for the user based on the evaluation results,

[0794] A means of instructing the user to execute the provided program and recording its progress,

[0795] A means of collecting feedback from users, evaluating the effectiveness of the program, and making adjustments,

[0796] A means of providing personalized psychological support activities to users in real time using location information,

[0797] A system that includes means for dynamically generating improvement measures based on response data and presenting them to the user.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the generation algorithm uses a machine learning algorithm.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the mental training program uses cognitive adjustment techniques, including cognitive behavioral therapy.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] An information processing device provides means for acquiring information data from a user via an information recording device,

[0805] A data communication device transmits acquired information data to a data analysis device, which analyzes the data using an emotion engine to identify the user's emotional state.

[0806] The generative model is a means of using the analysis results to evaluate the user's psychological state and creating and providing a psychological training plan tailored to the user's emotional state.

[0807] The information processing device provides means for assisting users in executing a provided plan and recording the implementation status,

[0808] A feedback collection system that includes means for gathering opinions from users, evaluating the effectiveness of training plans, and making modifications as necessary.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the generative model utilizes an automated learning algorithm.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the psychological training plan uses psychological adjustment techniques including cognitive behavioral therapy.

[0813] "Application example 2 when combining with an emotional engine"

[0814] (Claim 1)

[0815] A means of providing a means for receiving predetermined data from a user,

[0816] A method that uses a generative model to analyze the received data and evaluate the user's psychological state,

[0817] A means of selecting and providing the optimal training program for the user based on evaluation,

[0818] A means to support the execution of the provided program and record its progress,

[0819] A means of collecting user feedback, evaluating the program's effectiveness, and making adjustments,

[0820] A means of collecting customer facial expression data in physical stores and providing a method for analyzing emotions in real time,

[0821] A means of generating store improvement suggestions in real time based on analysis results.

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the generative model uses a learning algorithm.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the psychological training program uses cognitive adjustment techniques. [Explanation of symbols]

[0827] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing an interface for receiving user input data, A method that uses a generative model to analyze received input data and evaluate the user's psychological state, Based on the evaluation results, a means of selecting and providing a mental training program suitable for the user, A means to support the user in executing the provided program and to record its progress, A system that includes means for collecting user feedback and evaluating and adjusting the effectiveness of a program.

2. The system according to claim 1, wherein the generative model uses a machine learning algorithm.

3. The system according to claim 1, wherein the mental training program uses cognitive adjustment techniques, including cognitive behavioral therapy.

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