system

Abstract

We provide the system. [Solution] A means of receiving user sentiment data and storing it in a database, A means for analyzing stored emotional data and generating verbalized emotional feedback, Means for providing analysis results to the user, A means of generating a graph that visualizes changes in emotions based on user emotion data, A means of displaying the generated graph to the user, A means of generating digital reports containing emotional data and selectively sharing them with healthcare professionals, A system that includes this.

Description

Technical Field

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 recent years, although the need for personal mental health care has been increasing, many people have difficulties in approaching for appropriate support. In particular, there are problems in understanding one's own emotions and collaborating with medical professionals at appropriate times. Conventional methods lack means for visually confirming changes in emotions and means for efficiently sharing information with medical professionals via digital reports. Against such a background, there is a demand for a solution that enables users to easily record their daily moods and effectively utilize the data, thereby deepening self-understanding and facilitating receiving support from medical professionals.

Means for Solving the Problems

[0005] This invention provides a system that allows users to easily input and record emotional data, analyze that data, and generate verbalized feedback. Based on the emotions selected by the user, it generates graphs that visualize changes in emotions on a weekly basis, allowing users to visually understand past emotional fluctuations. Furthermore, it is designed to generate digital reports based on emotional data and to securely share information with healthcare professionals with the user's permission. This promotes appropriate collaboration between users and healthcare professionals and contributes to improving mental health.

[0006] "User sentiment data" refers to information obtained when users select or input their daily moods and emotional states.

[0007] A "database" is an information storage system for efficiently saving and managing user sentiment data and analysis results.

[0008] "Verbalized emotional feedback" is feedback information that is provided to users in an easily understandable format by expressing their emotional data linguistically through analysis by an AI model.

[0009] A "graph that visualizes changes in emotion" is a graph generated to visually represent fluctuations in previously recorded emotional data over a certain period of time.

[0010] A "digital report" is a formal document containing user sentiment data and its analysis results, generated for the purpose of sharing information with medical professionals.

[0011] A "medical professional" is a professional such as a doctor or counselor who is qualified to provide specialized support for the user's mental health.

[0012] "Permission" refers to the act or authorization by which a user agrees to share emotional data and digital reports with third parties. [Brief explanation of the drawing]

[0013] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered 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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for users to record their daily emotions, receive feedback based on that data, and manage their mental health. The system consists of a terminal application for users to input their emotions, a server that processes, analyzes, and stores the emotional data, and an interface for generating and sharing digital reports.

[0035] Implementation of a terminal application

[0036] Users use their devices to record their daily moods. The device application is designed to present users with multiple mood options, making it easy to record their mood for the day. Once the user has entered their mood, the device sends the data to a server.

[0037] Server Embodiment

[0038] The server receives emotional data sent from the terminal and stores it in a database. This storage process records user-specific identification information and timestamps, enabling effective management of past emotional history. Furthermore, the server uses an AI model to analyze the data and generate feedback that verbalizes the user's feelings. This feedback is provided to the user to help them understand their current emotions.

[0039] Visualization of emotional changes and digital reporting

[0040] The server analyzes the user's past emotional data and generates a graph that visualizes changes in their emotions. This graph reflects past data and shows the evolution of emotions on a weekly basis. The graph is sent to the terminal, allowing the user to intuitively understand the shifts in their emotions.

[0041] Furthermore, the server generates digital reports based on important emotional data. These reports are designed to be shared with healthcare professionals as needed, and if the user grants permission, the digital reports are securely sent to healthcare professionals. This allows healthcare professionals to gain a more accurate understanding of the user's condition and provide appropriate support.

[0042] For example, if a user inputs the emotion "anxiety," the server stores this data and uses an AI model to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." Furthermore, an emotion graph shows the increase or decrease in anxiety over the past few days, and this information is included in a highly organized digital report and shared with healthcare professionals. In this way, users can deepen their self-understanding and receive appropriate mental health care.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user launches the terminal application and accesses a screen where they can select their daily mood.

[0046] Step 2:

[0047] The device displays multiple emotional options to the user, who then chooses the one that best suits their mood for the day.

[0048] Step 3:

[0049] The device generates a request to send the selected emotion data to the server. This request includes the user's identification information and a timestamp.

[0050] Step 4:

[0051] The server receives the sentiment data request sent from the terminal and checks its contents.

[0052] Step 5:

[0053] The server stores the received sentiment data in a database. During storage, it associates the user ID with a timestamp to prepare for future data analysis.

[0054] Step 6:

[0055] The server activates the AI ​​model and begins analysis using the stored sentiment data. This analysis generates verbalized feedback based on the sentiment data.

[0056] Step 7:

[0057] The server sends the generated feedback to the terminal.

[0058] Step 8:

[0059] The device displays feedback received from the server to the user, helping them understand their current emotional state.

[0060] Step 9:

[0061] The server periodically collects user sentiment data and analyzes the data from the past week to generate graphs that visualize changes in sentiment.

[0062] Step 10:

[0063] The server sends the generated emotion graph to the terminal.

[0064] Step 11:

[0065] The device displays an emotion graph to the user, allowing them to visually review the changes in their emotions over time.

[0066] Step 12:

[0067] The server generates digital reports based on emotional data and prepares them for sharing with medical professionals as needed.

[0068] Step 13:

[0069] The server confirms with the user their permission to share the digital report with medical professionals.

[0070] Step 14:

[0071] If the user grants permission, the server will send the report to a medical professional using a secure communication method.

[0072] Step 15:

[0073] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then the process ends.

[0074] (Example 1)

[0075] 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."

[0076] There is a lack of systems that allow individual users to properly record their daily emotions and understand their own mental health status. This makes it difficult for users to obtain concrete clues to understand their own emotional tendencies and problems. In addition, there is a need for technology that allows healthcare professionals to easily obtain reliable emotional information for more accurate diagnosis and guidance.

[0077] 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.

[0078] In this invention, the server includes means for receiving user mood information and storing it in an information aggregation device, means for analyzing the stored mood information and generating verbalized mood output, and means for generating an electronic report containing the mood information and selectively sharing it with medical professionals. This makes it easier for users to understand their own mental health status, and in addition, medical professionals can quickly obtain specific and reliable information about the user's emotions.

[0079] A "user" refers to an individual who uses this system to record their emotions and receive the results.

[0080] "Mood information" refers to data that users select and input to indicate their daily emotional state.

[0081] An "information aggregation device" refers to a device that includes a database for storing mood information received from users.

[0082] "Verbalized mood output" refers to feedback messages in natural language generated based on analyzed mood information.

[0083] An "electronic report" refers to a digital report that summarizes a user's mood information and the results of its analysis.

[0084] "Medical professionals" refer to professionals such as doctors and counselors who specialize in mental health care and diagnosis.

[0085] This invention is a system for users to record their daily emotions, receive feedback based on that information, and manage their own mental health. The system mainly consists of three components: a terminal application for recording the user's emotions, a server for processing, analyzing, and storing the emotional data, and an interface for generating and sharing digital reports.

[0086] The terminal application is installed on user devices such as smartphones and tablets, allowing users to select their emotions from a range of options including "joy," "sadness," and "anxiety." The selected emotion information is received by the terminal and then sent to a server. Encryption technology is used during this transmission to maintain the accuracy and security of the information.

[0087] The server stores the received emotional information in a database. During storage, information identifying each individual user and the date and time are recorded, ensuring accurate accumulation of chronological emotional data. The server then analyzes the emotional data using a generative AI model. This model compares past accumulated data with current input data to generate natural language feedback. For example, if a user inputs the emotion "anxiety," the AI ​​model uses that data to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." This feedback is sent to the user's device, helping them understand their own emotional state.

[0088] Furthermore, the server generates a graph of emotional changes based on the user's emotional history. This graph visualizes past emotional data on a weekly basis, allowing the user to intuitively understand emotional trends and fluctuations.

[0089] Furthermore, based on important emotional data, the server generates digital reports. If necessary and with user permission, these reports can be shared with healthcare professionals, who can provide appropriate support based on detailed user information. These reports are transmitted in a secure environment.

[0090] In this invention, a generative AI model plays a crucial role, and an example of a prompt based on user input is, "Please generate mental health feedback based on the emotional data entered by the user."

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] The user launches a terminal application on their smartphone or tablet. The terminal presents the user with emotional options such as "joy," "sadness," and "anxiety" on the screen. The user then selects their current emotion as input. The entered emotion is then prepared to be recorded along with the user ID and the date and time of input.

[0094] Step 2:

[0095] The device transmits the collected emotional information to the server. Encryption protocols are used during transmission to ensure the security of the information. Input data includes the specific type of emotion and a timestamp at the time of input.

[0096] Step 3:

[0097] The server receives emotion information sent from the terminal. Before this information is stored in the database, the type of emotion entered, the user ID, and the timestamp are verified. The received data is added based on each user's history, ensuring the integrity of the record.

[0098] Step 4:

[0099] The server uses a generative AI model to analyze the received emotional data. Using the input emotional information as a prompt, it generates natural language feedback through data calculations such as comparisons with past data. For example, if "anxiety" is input, the AI ​​model will output something like, "Your emotions are unstable today, and you seem to be feeling a lot of stress."

[0100] Step 5:

[0101] The server analyzes the user's emotional history and generates a graph that visualizes emotional fluctuations on a weekly basis. This graph shows the increase or decrease and patterns of each emotion, and the resulting visualized data is provided to the user.

[0102] Step 6:

[0103] The server generates digital reports based on important emotional data. These reports are securely shared with healthcare professionals of the user's choice, as needed. The reports include emotional trends and analysis results, providing a foundation for receiving assessments and recommendations from healthcare professionals as output.

[0104] (Application Example 1)

[0105] 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."

[0106] Modern commercial facilities must enhance the customer experience and increase customer satisfaction. However, there is a lack of effective means to collect data on customer emotions and experiences, and to use that data to improve operations and provide feedback. Furthermore, there is a need for methods to appropriately utilize individual customer emotional information and share it appropriately with experts in the medical field.

[0107] 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.

[0108] In this invention, the server includes means for receiving user emotional data and storing it in a database, means for analyzing the stored emotional data and generating verbalized emotional feedback, and means for receiving customer emotional data and promoting the improvement of the experience at the commercial facility. This makes it possible to efficiently collect and appropriately utilize feedback based on customers' emotions and experiences.

[0109] A "user" is an individual who uses a system to input emotional data and attempts to manage or improve their own emotional state.

[0110] "Emotional data" refers to information about a user's emotions that they input, and it serves as the basis for analysis and feedback generation.

[0111] A "database" is a memory system that continuously stores emotional data and generated feedback, and allows them to be retrieved as needed.

[0112] "Feedback" is information generated and provided to the user based on analyzed emotional data, and is intended to help the user understand their own emotional state.

[0113] An "information graph" is a visual representation of a user's emotional data, a chart that allows for an intuitive understanding of changes in their emotions.

[0114] "Digital records" are electronic documents that include emotional data and analysis results, facilitating information sharing among users and experts in specific fields.

[0115] A "commercial facility" is a place that provides goods and services to visitors, and is a place where improving the customer experience is required.

[0116] A "medical specialist" is a person who holds qualifications in the medical field and is able to provide advice and diagnoses based on the user's emotional state.

[0117] In order to implement this invention, the user's terminal, the server, and the commercial facility's management system must be closely coordinated.

[0118] First, users input their emotions through a simple interface using a smartphone or smart glasses. The device can be triggered by scanning a QR code (registered trademark) at specific locations within a commercial facility, allowing users to select and input emotions on the spot.

[0119] The input emotion data is instantly transmitted to the server. The server receives this data and stores it in a database. After the data is stored, a generative AI model is used to analyze the emotion data and generate verbalized feedback. This feedback is personalized to the user and is notified to the user's device.

[0120] Furthermore, the server uses the large amount of customer sentiment data it receives to perform data analysis in order to improve the customer experience at commercial facilities. This allows facility managers to instantly grasp the situation on-site using intuitive information graphs and take necessary corrective measures.

[0121] Furthermore, the feedback and digital records generated for individual users are securely shared with medical professionals with the user's permission. This allows professionals to gain a more detailed understanding of the user's emotional state and provide appropriate advice.

[0122] For example, if a user scans a QR code installed in a cafe and inputs the emotion "relaxed," the server will generate feedback such as, "It appears the customer is feeling relaxed." At the same time, this data can be used to consider adjusting the cafe's music and lighting.

[0123] An example of a prompt message would be: "Please enter sentiment data. For example, please provide specific feedback such as 'The atmosphere in the store is pleasant' or 'I liked the taste of this product.'"

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The user scans a QR code in the store with their device. This action launches an application on their smartphone or smart glasses, displaying a screen for emotion input. The input is the information from the QR code, and the output is an emotion selection interface.

[0127] Step 2:

[0128] The user selects and inputs their current emotion using the interface displayed on the device. The input is the emotion data selected by the user, and this data is used for subsequent analysis. The output is a notification that the emotion was selected correctly.

[0129] Step 3:

[0130] The terminal sends the input emotion data to the server. The input here is the user's emotion data, and the output is the data transfer to the server. This data serves as foundational information for later analysis.

[0131] Step 4:

[0132] The server stores the received emotion data in a database. The input is the emotion data sent from the terminal, and the output is a record that the data has been stored in the database. The saving process enables subsequent data analysis.

[0133] Step 5:

[0134] The server uses a generation AI model to analyze stored sentiment data and generate verbalized feedback. The input is sentiment data stored in a database, and the output is the generated feedback message. The feedback provides the user with emotional insights.

[0135] Step 6:

[0136] The server generates an information graph based on emotional data. The input is analyzed emotional data, and the output is a visual information graph. This graph allows administrators and users to intuitively understand the transition of emotions.

[0137] Step 7:

[0138] The server sends the generated feedback and information graphs to the user's terminal. The input is data generated by the server, and the output is a notification to the user's terminal. The user receives this and uses it as feedback for themselves or the facility.

[0139] Step 8:

[0140] The server, based on user permission, shares digital records with medical professionals as needed. Input is the user's feedback data, and output is confirmation of data transfer to professionals. Authorized sharing allows for expert advice to be incorporated.

[0141] 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.

[0142] This invention is a mental health support system that combines an emotion engine for recognizing and evaluating user emotions. The system consists of a terminal for user emotion input, a server for analyzing and recognizing emotion data, and an interface for visualizing and sharing the obtained data.

[0143] Terminal embodiment

[0144] Users input their daily emotions using a device. The device displays multiple emotion options on the screen and accepts the user's selection. The device is also configured to capture real-time user data and input it into the emotion engine. Specifically, sensors on the device can detect emotions from the user's voice and facial expressions.

[0145] Server Embodiment

[0146] The server receives emotion data sent from the terminal and analysis results from the emotion engine. This data is stored in a database and managed as the user's mental health history. The emotion engine on the server automatically recognizes emotions using the user's real-time data and generates the recognition results as feedback for analysis. It also compares the recorded emotion data with the recognized emotion data and generates feedback to the user evaluating the degree of emotional consistency.

[0147] Embodiments of Emotion Visualization and Sharing

[0148] The server generates graphs that visualize changes in emotions based on past emotional data. These graphs are designed to allow users to visually check emotional fluctuations on a weekly basis. The generated graphs and feedback are then sent to the device and presented to the user. Furthermore, the server can also generate digital reports, which can be securely shared with healthcare professionals with the user's permission.

[0149] For example, if a user is feeling "sad," they select and input that emotion on their device. Simultaneously, the device's sensors analyze the user's voice tone and facial expressions, and the emotion engine confirms that they are feeling "sad." The server analyzes the degree of agreement of this information and provides feedback to the user, such as, "Today's emotion recognition shows a high degree of agreement." In this way, users can objectively understand their own emotional state and receive appropriate mental health care.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The user launches a terminal application and selects their emotion from several presented options. Simultaneously, they activate voice input and camera functions to collect their voice and facial expression data.

[0153] Step 2:

[0154] The device generates a request to send the emotion data selected by the user to the server. This request includes the user's identification information, the selected emotion, and the collected voice and facial expression data.

[0155] Step 3:

[0156] The terminal sends the generated request to the server and registers the emotion data in the system.

[0157] Step 4:

[0158] The server receives sentiment data sent from the terminal and stores it in a database. User identification information and timestamps are also recorded there.

[0159] Step 5:

[0160] The emotion engine installed on the server analyzes the received audio and facial expression data to automatically recognize the user's emotions. The recognition results are classified as emotions.

[0161] Step 6:

[0162] The server evaluates the degree of agreement between the sentiment data selected by the user and the sentiment data recognized by the sentiment engine. This evaluation is used as part of the feedback provided to the user.

[0163] Step 7:

[0164] Based on the evaluation results, the server generates feedback such as "Today's sentiment recognition shows a high degree of accuracy" and sends it to the terminal.

[0165] Step 8:

[0166] The device displays feedback received from the server to the user, providing objective information about their own emotions.

[0167] Step 9:

[0168] The server generates a weekly emotion change graph based on the user's past emotional data. This graph visually shows the fluctuations in emotions.

[0169] Step 10:

[0170] The server sends the generated graph to the terminal, allowing the user to visually confirm changes in their emotions.

[0171] Step 11:

[0172] The server aggregates important emotional data as needed and generates digital reports. These reports are formatted for sharing with healthcare professionals with the user's permission.

[0173] Step 12:

[0174] If a user allows sharing of a digital report with a medical professional, the server will send the report to the medical professional using a secure protocol.

[0175] Step 13:

[0176] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then terminates the process.

[0177] (Example 2)

[0178] 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".

[0179] In modern society, individual mental health is a critical issue, but there is a lack of adequate systems for objectively understanding one's own emotional state and providing appropriate care. In particular, there is a need for effective means to recognize daily emotional changes in real time, visualize those changes, and share them.

[0180] 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.

[0181] In this invention, the server includes means for acquiring user emotional information and transmitting it to an information processing device; the information processing device includes means for analyzing the received emotional information using a generating AI model and storing the analysis results in an information recording device; and means for evaluating the degree of emotional consistency based on the stored analysis results. This enables users to intuitively understand changes in their own emotions, safely share information with professionals as needed, and receive appropriate mental health care.

[0182] A "user" refers to an individual who uses the system to input their emotional state and receive the analysis results.

[0183] "Emotional information" refers to data about emotions obtained from the user, including selected emotions, voice, and information based on facial expressions.

[0184] An "information processing device" refers to a computer that receives emotional information from users and analyzes it.

[0185] A "generative AI model" refers to an algorithm used to analyze emotional information, specifically a model that utilizes machine learning techniques.

[0186] "Analysis results" refer to the output of emotional information after processing by the generative AI model, and include evaluations of the degree of emotional agreement and changes.

[0187] "Information recording device" refers to data storage used to save analysis results.

[0188] "Emotional agreement" refers to an indicator that shows the degree of agreement between the emotional information entered by the user and the analysis results.

[0189] "Information visualization methods" refer to means of visually representing changes in emotions and degrees of agreement, and providing this information to users.

[0190] "Visualization results" refer to visual outputs such as graphs generated by information visualization methods.

[0191] An "information report" is a report that summarizes emotional information and its analysis results, and is intended to be shared between users and experts.

[0192] "Information sharing means" refers to methods for securely sharing generated information reports with experts.

[0193] This invention is a mental health support system that recognizes, analyzes, and visualizes a user's emotional information. The system mainly consists of a terminal for inputting the user's emotional information, a server for analyzing the emotional information, and an interface for presenting the analysis results to the user.

[0194] Users can input their emotions from a selection of options using devices such as smartphones and tablets. These devices are equipped with voice recognition and image sensors, which allow them to acquire emotional information from the user's real-time voice tone and facial expressions and input it into the device.

[0195] The server receives emotional information sent by the user and analyzes it using a generative AI model. This generative AI model processes and evaluates the received emotional information and obtains analysis results. These results include an evaluation of the degree of emotional consistency and change. The analysis results are stored in an information recording device and can be used for later monitoring and feedback.

[0196] The analysis results are further generated as graphs using information visualization tools for visualization purposes. These graphs are designed to allow for an intuitive understanding of changes in the user's mental health. Furthermore, the generated information report can be shared with professionals through information sharing tools, with the user's permission.

[0197] For example, if a user feels "at ease," they can select and input that emotion on their device. Simultaneously, the device's sensors analyze the voice and facial expressions, and the acquired information is sent to a server. The server uses a generative AI model to analyze the data and provides feedback to the user, such as, "Your emotions today haven't changed significantly from yesterday."

[0198] An example of a prompt message is, "Analyze the user's emotional information and generate an emotional consistency rating based on a generative AI model." This system aims to improve mental health by providing users with feedback based on changes in their emotions and their analysis.

[0199] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0200] Step 1:

[0201] The user inputs their daily emotions into the device. Specifically, the user selects their current emotion from emotion options displayed on the device screen, and their voice and facial expressions are collected through voice recognition and the camera. The input in this step consists of the emotion data selected by the user and the voice and facial expression data acquired by the sensors. The output is a dataset in which this emotion information has been formatted.

[0202] Step 2:

[0203] The terminal sends the acquired sentiment information to the server. A process takes place in which the dataset, formatted as sentiment information, is sent to the server via the internet. In this step, the input is the formatted sentiment information provided by the terminal, and the output is the sentiment information converted into a format usable by the server.

[0204] Step 3:

[0205] The server analyzes emotional information using a generative AI model. The server takes the received data as input and performs data analysis based on the generative AI model. Here, data calculations are used to evaluate the degree of agreement and change in the user's emotions. In this step, the input is the emotional information that arrives on the server side, and the output is the analysis result.

[0206] Step 4:

[0207] The server saves the analysis results to a database. The analyzed data is stored in an information recording device because it is managed over the long term as the user's mental health history. The input for this step is the analysis results, and the output is the analyzed data stored in the database.

[0208] Step 5:

[0209] The server generates a graph that visualizes changes in emotions based on the analysis results and past emotional information. The server uses accumulated analysis data as input and employs information visualization methods based on past trends and accuracy evaluations. This output is a graph that allows the user to visually understand the changes in their emotions.

[0210] Step 6:

[0211] The server sends the generated graph to the terminal for display to the user. The terminal receives the graph sent from the server and provides it to the user. In this step, the input is the generated visualization graph, and the output is the graph displayed on the screen to the user.

[0212] Step 7:

[0213] Based on user permission, the server shares a digital report containing emotional information and analysis results with experts. Authorized information reports are securely shared with medical professionals by selectively transmitting them through the information sharing mechanism. The inputs for this step are the permission for information sharing and the report content, while the output is the shared digital report.

[0214] (Application Example 2)

[0215] 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".

[0216] Improving customer satisfaction is a crucial challenge in modern commercial facilities. However, instantly understanding the diverse emotions of customers and adjusting services accordingly is considered difficult. Traditional methods do not involve real-time collection and analysis of customer emotion data, making rapid response difficult. As a result, potential customer dissatisfaction is often overlooked, leading to a failure to provide optimal customer service.

[0217] 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.

[0218] In this invention, the server includes means for receiving user emotion data and storing it in an information database, means for analyzing the stored emotion data and generating verbalized emotional feedback, means for providing the analysis results to the user, and means for analyzing the customer's facial expressions and voice and providing feedback to the staff in real time. This enables a rapid understanding of the customer's emotions and the provision of optimal service based on that understanding.

[0219] "User emotional data" refers to information that reflects the user's emotional state, such as facial expressions, voice, and selected emotions.

[0220] An "information database" is a collection point of digital information used to store and manage user sentiment data and analysis results.

[0221] "Verbalized emotional feedback" refers to the provision of information in language that is easy for users to understand, based on emotional data.

[0222] "Charts and graphs" are visual materials such as graphs and charts used to visually represent changes in emotional data.

[0223] A "digital report" is an electronic report document that compiles user sentiment data and analysis results.

[0224] A "professional" is a medical or psychological professional involved in improving a user's mental health and analyzing their emotions.

[0225] A "feedback method" is a way of providing useful information to users and store staff based on analyzed emotional information.

[0226] This embodiment is a system that analyzes user emotions and allows store staff to adjust their customer service in real time. Specifically, users record their emotions through their smartphones or smart glasses, and the system is designed to provide appropriate feedback based on those emotions.

[0227] Smart glasses and smartphones, which are the devices used in this system, collect the user's facial expressions and voice using their built-in cameras and microphones. This data is transmitted to a server in real time. The server uses "Google Cloud Vision API" or "Microsoft Azure Face API" to analyze the user's emotions from the received data. The results of this analysis are generated as verbalized feedback and reflected in visual charts and graphs.

[0228] The server provides the generated feedback to store staff, allowing for individualized customer service within the store. For example, if the system analyzes a customer's feelings of surprise or delight upon seeing a new product, this information is immediately communicated to the staff, promoting more proactive service. This system enables stores to provide services that result in higher customer satisfaction.

[0229] Furthermore, the generated digital reports can be shared with experts, allowing stores to obtain analytical data that can lead to improvements in customer service. This enables the development of strategies to enhance the customer experience.

[0230] As a concrete example, the following prompt statement is used:

[0231] "Evaluate the overall atmosphere of the store based on customer facial expression data and propose what service improvements are needed."

[0232] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0233] Step 1:

[0234] The device, either smart glasses or a smartphone, uses a camera and microphone to collect the user's facial expressions and voice in real time. The input is the user's visual and audio data, and the output is the collected raw data. Sensors on the device are utilized at this stage.

[0235] Step 2:

[0236] The terminal sends the collected data to the server. The input is the raw data obtained in step 1, and the output is the state of completion of data transmission to the server. At this point, the data transmission protocol is applied.

[0237] Step 3:

[0238] The server analyzes the received data. In this step, it receives audio and visual data as input and performs sentiment analysis using the Google Cloud Vision API or Microsoft Azure Face API. The output is a category of emotion (e.g., "happy," "surprised"). Data processing includes a sentiment recognition process by an AI model.

[0239] Step 4:

[0240] The server generates analysis results as feedback. The input is the emotion category from step 3, and the output is verbalized feedback and visualized charts. Based on the emotion data, natural language generation technology is used to construct user-oriented feedback.

[0241] Step 5:

[0242] The server provides the generated feedback to the store clerk in real time. The input is feedback and visualized charts, and the output is the display of information on the clerk's terminal. The information is displayed in an intuitively easy-to-understand format.

[0243] Step 6:

[0244] Users and staff use feedback to adjust in-store customer service approaches. Input is feedback information received by staff, and output is improvement actions taken in customer service. This ensures that appropriate service is provided to customers.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] [Second Embodiment]

[0249] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0250] 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.

[0251] 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).

[0252] 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.

[0253] 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.

[0254] 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).

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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".

[0261] This invention is a system for users to record their daily emotions, receive feedback based on that data, and manage their mental health. The system consists of a terminal application for users to input their emotions, a server that processes, analyzes, and stores the emotional data, and an interface for generating and sharing digital reports.

[0262] Implementation of a terminal application

[0263] Users use their devices to record their daily moods. The device application is designed to present users with multiple mood options, making it easy to record their mood for the day. Once the user has entered their mood, the device sends the data to a server.

[0264] Server Embodiment

[0265] The server receives emotional data sent from the terminal and stores it in a database. This storage process records user-specific identification information and timestamps, enabling effective management of past emotional history. Furthermore, the server uses an AI model to analyze the data and generate feedback that verbalizes the user's feelings. This feedback is provided to the user to help them understand their current emotions.

[0266] Visualization of emotional changes and digital reporting

[0267] The server analyzes the user's past emotional data and generates a graph that visualizes changes in their emotions. This graph reflects past data and shows the evolution of emotions on a weekly basis. The graph is sent to the terminal, allowing the user to intuitively understand the shifts in their emotions.

[0268] Furthermore, the server generates digital reports based on important emotional data. These reports are designed to be shared with healthcare professionals as needed, and if the user grants permission, the digital reports are securely sent to healthcare professionals. This allows healthcare professionals to gain a more accurate understanding of the user's condition and provide appropriate support.

[0269] For example, if a user inputs the emotion "anxiety," the server stores this data and uses an AI model to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." Furthermore, an emotion graph shows the increase or decrease in anxiety over the past few days, and this information is included in a highly organized digital report and shared with healthcare professionals. In this way, users can deepen their self-understanding and receive appropriate mental health care.

[0270] The following describes the processing flow.

[0271] Step 1:

[0272] The user launches the terminal application and accesses a screen where they can select their daily mood.

[0273] Step 2:

[0274] The device displays multiple emotional options to the user, who then chooses the one that best suits their mood for the day.

[0275] Step 3:

[0276] The device generates a request to send the selected emotion data to the server. This request includes the user's identification information and a timestamp.

[0277] Step 4:

[0278] The server receives the sentiment data request sent from the terminal and checks its contents.

[0279] Step 5:

[0280] The server stores the received sentiment data in a database. During storage, it associates the user ID with a timestamp to prepare for future data analysis.

[0281] Step 6:

[0282] The server starts the AI model and begins the analysis using the stored emotion data. This analysis generates verbal feedback based on the emotion data.

[0283] Step 7:

[0284] The server sends the generated feedback to the terminal.

[0285] Step 8:

[0286] The terminal displays the feedback received from the server to the user, promoting understanding of the current emotional state.

[0287] Step 9:

[0288] The server periodically aggregates the user's emotion data, analyzes the data for the past week, and generates a graph visualizing the emotional changes.

[0289] Step 10:

[0290] The server sends the generated emotion graph to the terminal.

[0291] Step 11:

[0292] The terminal displays the emotion graph to the user, enabling the user to visually confirm the past emotional transitions.

[0293] Step 12:

[0294] The server creates a digital report based on the emotion data and prepares to share it with medical experts if necessary.

[0295] Step 13:

[0296] The server checks with the user for permission to share the digital report with medical experts.

[0297] Step 14:

[0298] If the user grants permission, the server will send the report to a medical professional using a secure communication method.

[0299] Step 15:

[0300] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then the process ends.

[0301] (Example 1)

[0302] 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."

[0303] There is a lack of systems that allow individual users to properly record their daily emotions and understand their own mental health status. This makes it difficult for users to obtain concrete clues to understand their own emotional tendencies and problems. In addition, there is a need for technology that allows healthcare professionals to easily obtain reliable emotional information for more accurate diagnosis and guidance.

[0304] 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.

[0305] In this invention, the server includes means for receiving user mood information and storing it in an information aggregation device, means for analyzing the stored mood information and generating verbalized mood output, and means for generating an electronic report containing the mood information and selectively sharing it with medical professionals. This makes it easier for users to understand their own mental health status, and in addition, medical professionals can quickly obtain specific and reliable information about the user's emotions.

[0306] A "user" refers to an individual who uses this system to record their emotions and receive the results.

[0307] "Mood information" refers to the data selected and input by the user to indicate their daily emotional state.

[0308] "Information aggregation device" refers to a device that includes a database for storing mood information received from the user.

[0309] "Verbalized mood product" refers to a feedback message in natural language generated based on the analyzed mood information.

[0310] "Electronic report" refers to a digital format report that summarizes the user's mood information and its analysis results.

[0311] "Medical professional" refers to a professional such as a doctor or counselor who specializes in mental health care and diagnosis.

[0312] The present invention is a system for the user to record their daily emotions, receive feedback based on that information, and manage their own mental health state. This system mainly consists of three components: a terminal application for recording the user's emotions, a server for processing, analyzing, and storing the emotional data, and an interface for generating and sharing digital reports.

[0313] The terminal application is installed on a user terminal such as a smartphone or tablet, enabling the user to select their emotions from emotional options such as "happiness", "sadness", "anxiety", etc. The selected emotional information is received by the terminal and then transmitted to the server. In this transmission, encryption technology is used to maintain the accuracy and security of the information.

[0314] The server stores the received emotional information in a database. During storage, information identifying each individual user and the date and time are recorded, ensuring accurate accumulation of chronological emotional data. The server then analyzes the emotional data using a generative AI model. This model compares past accumulated data with current input data to generate natural language feedback. For example, if a user inputs the emotion "anxiety," the AI ​​model uses that data to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." This feedback is sent to the user's device, helping them understand their own emotional state.

[0315] Furthermore, the server generates a graph of emotional changes based on the user's emotional history. This graph visualizes past emotional data on a weekly basis, allowing the user to intuitively understand emotional trends and fluctuations.

[0316] Furthermore, based on important emotional data, the server generates digital reports. If necessary and with user permission, these reports can be shared with healthcare professionals, who can provide appropriate support based on detailed user information. These reports are transmitted in a secure environment.

[0317] In this invention, a generative AI model plays a crucial role, and an example of a prompt based on user input is, "Please generate mental health feedback based on the emotional data entered by the user."

[0318] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0319] Step 1:

[0320] The user launches a terminal application on their smartphone or tablet. The terminal presents the user with emotional options such as "joy," "sadness," and "anxiety" on the screen. The user then selects their current emotion as input. The entered emotion is then prepared to be recorded along with the user ID and the date and time of input.

[0321] Step 2:

[0322] The device transmits the collected emotional information to the server. Encryption protocols are used during transmission to ensure the security of the information. Input data includes the specific type of emotion and a timestamp at the time of input.

[0323] Step 3:

[0324] The server receives emotion information sent from the terminal. Before this information is stored in the database, the type of emotion entered, the user ID, and the timestamp are verified. The received data is added based on each user's history, ensuring the integrity of the record.

[0325] Step 4:

[0326] The server uses a generative AI model to analyze the received emotional data. Using the input emotional information as a prompt, it generates natural language feedback through data calculations such as comparisons with past data. For example, if "anxiety" is input, the AI ​​model will output something like, "Your emotions are unstable today, and you seem to be feeling a lot of stress."

[0327] Step 5:

[0328] The server analyzes the user's emotional history and generates a graph that visualizes emotional fluctuations on a weekly basis. This graph shows the increase or decrease and patterns of each emotion, and the resulting visualized data is provided to the user.

[0329] Step 6:

[0330] The server generates digital reports based on important emotional data. These reports are securely shared with healthcare professionals of the user's choice, as needed. The reports include emotional trends and analysis results, providing a foundation for receiving assessments and recommendations from healthcare professionals as output.

[0331] (Application Example 1)

[0332] 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."

[0333] Modern commercial facilities must enhance the customer experience and increase customer satisfaction. However, there is a lack of effective means to collect data on customer emotions and experiences, and to use that data to improve operations and provide feedback. Furthermore, there is a need for methods to appropriately utilize individual customer emotional information and share it appropriately with experts in the medical field.

[0334] 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.

[0335] In this invention, the server includes means for receiving user emotional data and storing it in a database, means for analyzing the stored emotional data and generating verbalized emotional feedback, and means for receiving customer emotional data and promoting the improvement of the experience at the commercial facility. This makes it possible to efficiently collect and appropriately utilize feedback based on customers' emotions and experiences.

[0336] A "user" is an individual who uses a system to input emotional data and attempts to manage or improve their own emotional state.

[0337] "Emotional data" refers to information about a user's emotions that they input, and it serves as the basis for analysis and feedback generation.

[0338] A "database" is a memory system that continuously stores emotional data and generated feedback, and allows them to be retrieved as needed.

[0339] "Feedback" is information generated and provided to the user based on analyzed emotional data, and is intended to help the user understand their own emotional state.

[0340] An "information graph" is a visual representation of a user's emotional data, a chart that allows for an intuitive understanding of changes in their emotions.

[0341] "Digital records" are electronic documents that include emotional data and analysis results, facilitating information sharing among users and experts in specific fields.

[0342] A "commercial facility" is a place that provides goods and services to visitors, and is a place where improving the customer experience is required.

[0343] A "medical specialist" is a person who holds qualifications in the medical field and is able to provide advice and diagnoses based on the user's emotional state.

[0344] In order to implement this invention, the user's terminal, the server, and the commercial facility's management system must be closely coordinated.

[0345] First, users input their emotions through a simple interface using a smartphone or smart glasses. The device can be triggered by scanning a QR code at specific locations within the commercial facility, allowing users to select and input their emotions on the spot.

[0346] The input emotion data is instantly transmitted to the server. The server receives this data and stores it in a database. After the data is stored, a generative AI model is used to analyze the emotion data and generate verbalized feedback. This feedback is personalized to the user and is notified to the user's device.

[0347] Furthermore, the server uses the large amount of customer sentiment data it receives to perform data analysis in order to improve the customer experience at commercial facilities. This allows facility managers to instantly grasp the situation on-site using intuitive information graphs and take necessary corrective measures.

[0348] Furthermore, the feedback and digital records generated for individual users are securely shared with medical professionals with the user's permission. This allows professionals to gain a more detailed understanding of the user's emotional state and provide appropriate advice.

[0349] For example, if a user scans a QR code installed in a cafe and inputs the emotion "relaxed," the server will generate feedback such as, "It appears the customer is feeling relaxed." At the same time, this data can be used to consider adjusting the cafe's music and lighting.

[0350] An example of a prompt message would be: "Please enter sentiment data. For example, please provide specific feedback such as 'The atmosphere in the store is pleasant' or 'I liked the taste of this product.'"

[0351] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0352] Step 1:

[0353] The user scans a QR code in the store with their device. This action launches an application on their smartphone or smart glasses, displaying a screen for emotion input. The input is the information from the QR code, and the output is an emotion selection interface.

[0354] Step 2:

[0355] The user selects and inputs their current emotion using the interface displayed on the device. The input is the emotion data selected by the user, and this data is used for subsequent analysis. The output is a notification that the emotion was selected correctly.

[0356] Step 3:

[0357] The terminal sends the input emotion data to the server. The input here is the user's emotion data, and the output is the data transfer to the server. This data serves as foundational information for later analysis.

[0358] Step 4:

[0359] The server stores the received emotion data in a database. The input is the emotion data sent from the terminal, and the output is a record that the data has been stored in the database. The saving process enables subsequent data analysis.

[0360] Step 5:

[0361] The server uses a generation AI model to analyze stored sentiment data and generate verbalized feedback. The input is sentiment data stored in a database, and the output is the generated feedback message. The feedback provides the user with emotional insights.

[0362] Step 6:

[0363] The server generates an information graph based on emotional data. The input is analyzed emotional data, and the output is a visual information graph. This graph allows administrators and users to intuitively understand the transition of emotions.

[0364] Step 7:

[0365] The server sends the generated feedback and information graphs to the user's terminal. The input is data generated by the server, and the output is a notification to the user's terminal. The user receives this and uses it as feedback for themselves or the facility.

[0366] Step 8:

[0367] The server, based on user permission, shares digital records with medical professionals as needed. Input is the user's feedback data, and output is confirmation of data transfer to professionals. Authorized sharing allows for expert advice to be incorporated.

[0368] 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.

[0369] This invention is a mental health support system that combines an emotion engine for recognizing and evaluating user emotions. The system consists of a terminal for user emotion input, a server for analyzing and recognizing emotion data, and an interface for visualizing and sharing the obtained data.

[0370] Terminal embodiment

[0371] Users input their daily emotions using a device. The device displays multiple emotion options on the screen and accepts the user's selection. The device is also configured to capture real-time user data and input it into the emotion engine. Specifically, sensors on the device can detect emotions from the user's voice and facial expressions.

[0372] Server Embodiment

[0373] The server receives emotion data sent from the terminal and analysis results from the emotion engine. This data is stored in a database and managed as the user's mental health history. The emotion engine on the server automatically recognizes emotions using the user's real-time data and generates the recognition results as feedback for analysis. It also compares the recorded emotion data with the recognized emotion data and generates feedback to the user evaluating the degree of emotional consistency.

[0374] Embodiments of Emotion Visualization and Sharing

[0375] The server generates graphs that visualize changes in emotions based on past emotional data. These graphs are designed to allow users to visually check emotional fluctuations on a weekly basis. The generated graphs and feedback are then sent to the device and presented to the user. Furthermore, the server can also generate digital reports, which can be securely shared with healthcare professionals with the user's permission.

[0376] For example, if a user is feeling "sad," they select and input that emotion on their device. Simultaneously, the device's sensors analyze the user's voice tone and facial expressions, and the emotion engine confirms that they are feeling "sad." The server analyzes the degree of agreement of this information and provides feedback to the user, such as, "Today's emotion recognition shows a high degree of agreement." In this way, users can objectively understand their own emotional state and receive appropriate mental health care.

[0377] The following describes the processing flow.

[0378] Step 1:

[0379] The user launches a terminal application and selects their emotion from several presented options. Simultaneously, they activate voice input and camera functions to collect their voice and facial expression data.

[0380] Step 2:

[0381] The device generates a request to send the emotion data selected by the user to the server. This request includes the user's identification information, the selected emotion, and the collected voice and facial expression data.

[0382] Step 3:

[0383] The terminal sends the generated request to the server and registers the emotion data in the system.

[0384] Step 4:

[0385] The server receives sentiment data sent from the terminal and stores it in a database. User identification information and timestamps are also recorded there.

[0386] Step 5:

[0387] The emotion engine installed on the server analyzes the received audio and facial expression data to automatically recognize the user's emotions. The recognition results are classified as emotions.

[0388] Step 6:

[0389] The server evaluates the degree of agreement between the sentiment data selected by the user and the sentiment data recognized by the sentiment engine. This evaluation is used as part of the feedback provided to the user.

[0390] Step 7:

[0391] Based on the evaluation results, the server generates feedback such as "Today's sentiment recognition shows a high degree of accuracy" and sends it to the terminal.

[0392] Step 8:

[0393] The device displays feedback received from the server to the user, providing objective information about their own emotions.

[0394] Step 9:

[0395] The server generates a weekly emotion change graph based on the user's past emotional data. This graph visually shows the fluctuations in emotions.

[0396] Step 10:

[0397] The server sends the generated graph to the terminal, allowing the user to visually confirm changes in their emotions.

[0398] Step 11:

[0399] The server aggregates important emotional data as needed and generates digital reports. These reports are formatted for sharing with healthcare professionals with the user's permission.

[0400] Step 12:

[0401] If a user allows sharing of a digital report with a medical professional, the server will send the report to the medical professional using a secure protocol.

[0402] Step 13:

[0403] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then terminates the process.

[0404] (Example 2)

[0405] 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".

[0406] In modern society, individual mental health is a critical issue, but there is a lack of adequate systems for objectively understanding one's own emotional state and providing appropriate care. In particular, there is a need for effective means to recognize daily emotional changes in real time, visualize those changes, and share them.

[0407] 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.

[0408] In this invention, the server includes means for acquiring user emotional information and transmitting it to an information processing device; the information processing device includes means for analyzing the received emotional information using a generating AI model and storing the analysis results in an information recording device; and means for evaluating the degree of emotional consistency based on the stored analysis results. This enables users to intuitively understand changes in their own emotions, safely share information with professionals as needed, and receive appropriate mental health care.

[0409] A "user" refers to an individual who uses the system to input their emotional state and receive the analysis results.

[0410] "Emotional information" refers to data about emotions obtained from the user, including selected emotions, voice, and information based on facial expressions.

[0411] An "information processing device" refers to a computer that receives emotional information from users and analyzes it.

[0412] A "generative AI model" refers to an algorithm used to analyze emotional information, specifically a model that utilizes machine learning techniques.

[0413] "Analysis results" refer to the output of emotional information after processing by the generative AI model, and include evaluations of the degree of emotional agreement and changes.

[0414] "Information recording device" refers to data storage used to save analysis results.

[0415] "Emotional agreement" refers to an indicator that shows the degree of agreement between the emotional information entered by the user and the analysis results.

[0416] "Information visualization methods" refer to means of visually representing changes in emotions and degrees of agreement, and providing this information to users.

[0417] "Visualization results" refer to visual outputs such as graphs generated by information visualization methods.

[0418] An "information report" is a report that summarizes emotional information and its analysis results, and is intended to be shared between users and experts.

[0419] "Information sharing means" refers to methods for securely sharing generated information reports with experts.

[0420] This invention is a mental health support system that recognizes, analyzes, and visualizes a user's emotional information. The system mainly consists of a terminal for inputting the user's emotional information, a server for analyzing the emotional information, and an interface for presenting the analysis results to the user.

[0421] Users can input their emotions from a selection of options using devices such as smartphones and tablets. These devices are equipped with voice recognition and image sensors, which allow them to acquire emotional information from the user's real-time voice tone and facial expressions and input it into the device.

[0422] The server receives emotional information sent by the user and analyzes it using a generative AI model. This generative AI model processes and evaluates the received emotional information and obtains analysis results. These results include an evaluation of the degree of emotional consistency and change. The analysis results are stored in an information recording device and can be used for later monitoring and feedback.

[0423] The analysis results are further generated as graphs using information visualization tools for visualization purposes. These graphs are designed to allow for an intuitive understanding of changes in the user's mental health. Furthermore, the generated information report can be shared with professionals through information sharing tools, with the user's permission.

[0424] For example, if a user feels "at ease," they can select and input that emotion on their device. Simultaneously, the device's sensors analyze the voice and facial expressions, and the acquired information is sent to a server. The server uses a generative AI model to analyze the data and provides feedback to the user, such as, "Your emotions today haven't changed significantly from yesterday."

[0425] An example of a prompt message is, "Analyze the user's emotional information and generate an emotional consistency rating based on a generative AI model." This system aims to improve mental health by providing users with feedback based on changes in their emotions and their analysis.

[0426] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0427] Step 1:

[0428] The user inputs their daily emotions into the device. Specifically, the user selects their current emotion from emotion options displayed on the device screen, and their voice and facial expressions are collected through voice recognition and the camera. The input in this step consists of the emotion data selected by the user and the voice and facial expression data acquired by the sensors. The output is a dataset in which this emotion information has been formatted.

[0429] Step 2:

[0430] The terminal sends the acquired sentiment information to the server. A process takes place in which the dataset, formatted as sentiment information, is sent to the server via the internet. In this step, the input is the formatted sentiment information provided by the terminal, and the output is the sentiment information converted into a format usable by the server.

[0431] Step 3:

[0432] The server analyzes emotional information using a generative AI model. The server takes the received data as input and performs data analysis based on the generative AI model. Here, data calculations are used to evaluate the degree of agreement and change in the user's emotions. In this step, the input is the emotional information that arrives on the server side, and the output is the analysis result.

[0433] Step 4:

[0434] The server saves the analysis results to a database. The analyzed data is stored in an information recording device because it is managed over the long term as the user's mental health history. The input for this step is the analysis results, and the output is the analyzed data stored in the database.

[0435] Step 5:

[0436] The server generates a graph that visualizes changes in emotions based on the analysis results and past emotional information. The server uses accumulated analysis data as input and employs information visualization methods based on past trends and accuracy evaluations. This output is a graph that allows the user to visually understand the changes in their emotions.

[0437] Step 6:

[0438] The server sends the generated graph to the terminal for display to the user. The terminal receives the graph sent from the server and provides it to the user. In this step, the input is the generated visualization graph, and the output is the graph displayed on the screen to the user.

[0439] Step 7:

[0440] Based on user permission, the server shares a digital report containing emotional information and analysis results with experts. Authorized information reports are securely shared with medical professionals by selectively transmitting them through the information sharing mechanism. The inputs for this step are the permission for information sharing and the report content, while the output is the shared digital report.

[0441] (Application Example 2)

[0442] 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."

[0443] Improving customer satisfaction is a crucial challenge in modern commercial facilities. However, instantly understanding the diverse emotions of customers and adjusting services accordingly is considered difficult. Traditional methods do not involve real-time collection and analysis of customer emotion data, making rapid response difficult. As a result, potential customer dissatisfaction is often overlooked, leading to a failure to provide optimal customer service.

[0444] 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.

[0445] In this invention, the server includes means for receiving user emotion data and storing it in an information database, means for analyzing the stored emotion data and generating verbalized emotional feedback, means for providing the analysis results to the user, and means for analyzing the customer's facial expressions and voice and providing feedback to the staff in real time. This enables a rapid understanding of the customer's emotions and the provision of optimal service based on that understanding.

[0446] "User emotional data" refers to information that reflects the user's emotional state, such as facial expressions, voice, and selected emotions.

[0447] An "information database" is a collection point of digital information used to store and manage user sentiment data and analysis results.

[0448] "Verbalized emotional feedback" refers to the provision of information in language that is easy for users to understand, based on emotional data.

[0449] "Charts and graphs" are visual materials such as graphs and charts used to visually represent changes in emotional data.

[0450] A "digital report" is an electronic report document that compiles user sentiment data and analysis results.

[0451] A "professional" is a medical or psychological professional involved in improving a user's mental health and analyzing their emotions.

[0452] A "feedback method" is a way of providing useful information to users and store staff based on analyzed emotional information.

[0453] This embodiment is a system that analyzes user emotions and allows store staff to adjust their customer service in real time. Specifically, users record their emotions through their smartphones or smart glasses, and the system is designed to provide appropriate feedback based on those emotions.

[0454] Smart glasses and smartphones, which are the devices used in this system, collect the user's facial expressions and voice using their built-in cameras and microphones. This data is transmitted to a server in real time. The server uses the Google Cloud Vision API or Microsoft Azure Face API to analyze the user's emotions from the received data. The results of this analysis are generated as verbalized feedback and reflected in visual charts and graphs.

[0455] The server provides the generated feedback to store staff, allowing for individualized customer service within the store. For example, if the system analyzes a customer's feelings of surprise or delight upon seeing a new product, this information is immediately communicated to the staff, promoting more proactive service. This system enables stores to provide services that result in higher customer satisfaction.

[0456] Furthermore, the generated digital reports can be shared with experts, allowing stores to obtain analytical data that can lead to improvements in customer service. This enables the development of strategies to enhance the customer experience.

[0457] As a concrete example, the following prompt statement is used:

[0458] "Evaluate the overall atmosphere of the store based on customer facial expression data and propose what service improvements are needed."

[0459] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0460] Step 1:

[0461] The device, either smart glasses or a smartphone, uses a camera and microphone to collect the user's facial expressions and voice in real time. The input is the user's visual and audio data, and the output is the collected raw data. Sensors on the device are utilized at this stage.

[0462] Step 2:

[0463] The terminal sends the collected data to the server. The input is the raw data obtained in step 1, and the output is the state of completion of data transmission to the server. At this point, the data transmission protocol is applied.

[0464] Step 3:

[0465] The server analyzes the received data. In this step, it receives audio and visual data as input and performs sentiment analysis using the Google Cloud Vision API or Microsoft Azure Face API. The output is a category of emotion (e.g., "happy," "surprised"). Data processing includes a sentiment recognition process by an AI model.

[0466] Step 4:

[0467] The server generates analysis results as feedback. The input is the emotion category from step 3, and the output is verbalized feedback and visualized charts. Based on the emotion data, natural language generation technology is used to construct user-oriented feedback.

[0468] Step 5:

[0469] The server provides the generated feedback to the store clerk in real time. The input is feedback and visualized charts, and the output is the display of information on the clerk's terminal. The information is displayed in an intuitively easy-to-understand format.

[0470] Step 6:

[0471] Users and staff use feedback to adjust in-store customer service approaches. Input is feedback information received by staff, and output is improvement actions taken in customer service. This ensures that appropriate service is provided to customers.

[0472] 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.

[0473] 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.

[0474] 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.

[0475] [Third Embodiment]

[0476] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0477] 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.

[0478] 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).

[0479] 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.

[0480] 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.

[0481] 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).

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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".

[0488] This invention is a system for users to record their daily emotions, receive feedback based on that data, and manage their mental health. The system consists of a terminal application for users to input their emotions, a server that processes, analyzes, and stores the emotional data, and an interface for generating and sharing digital reports.

[0489] Implementation of a terminal application

[0490] Users use their devices to record their daily moods. The device application is designed to present users with multiple mood options, making it easy to record their mood for the day. Once the user has entered their mood, the device sends the data to a server.

[0491] Server Embodiment

[0492] The server receives emotional data sent from the terminal and stores it in a database. This storage process records user-specific identification information and timestamps, enabling effective management of past emotional history. Furthermore, the server uses an AI model to analyze the data and generate feedback that verbalizes the user's feelings. This feedback is provided to the user to help them understand their current emotions.

[0493] Visualization of emotional changes and digital reporting

[0494] The server analyzes the user's past emotional data and generates a graph that visualizes changes in their emotions. This graph reflects past data and shows the evolution of emotions on a weekly basis. The graph is sent to the terminal, allowing the user to intuitively understand the shifts in their emotions.

[0495] Furthermore, the server generates digital reports based on important emotional data. These reports are designed to be shared with healthcare professionals as needed, and if the user grants permission, the digital reports are securely sent to healthcare professionals. This allows healthcare professionals to gain a more accurate understanding of the user's condition and provide appropriate support.

[0496] For example, if a user inputs the emotion "anxiety," the server stores this data and uses an AI model to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." Furthermore, an emotion graph shows the increase or decrease in anxiety over the past few days, and this information is included in a highly organized digital report and shared with healthcare professionals. In this way, users can deepen their self-understanding and receive appropriate mental health care.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The user launches the terminal application and accesses a screen where they can select their daily mood.

[0500] Step 2:

[0501] The device displays multiple emotional options to the user, who then chooses the one that best suits their mood for the day.

[0502] Step 3:

[0503] The device generates a request to send the selected emotion data to the server. This request includes the user's identification information and a timestamp.

[0504] Step 4:

[0505] The server receives the sentiment data request sent from the terminal and checks its contents.

[0506] Step 5:

[0507] The server stores the received sentiment data in a database. During storage, it associates the user ID with a timestamp to prepare for future data analysis.

[0508] Step 6:

[0509] The server activates the AI ​​model and begins analysis using the stored sentiment data. This analysis generates verbalized feedback based on the sentiment data.

[0510] Step 7:

[0511] The server sends the generated feedback to the terminal.

[0512] Step 8:

[0513] The device displays feedback received from the server to the user, helping them understand their current emotional state.

[0514] Step 9:

[0515] The server periodically collects user sentiment data and analyzes the data from the past week to generate graphs that visualize changes in sentiment.

[0516] Step 10:

[0517] The server sends the generated emotion graph to the terminal.

[0518] Step 11:

[0519] The device displays an emotion graph to the user, allowing them to visually review the changes in their emotions over time.

[0520] Step 12:

[0521] The server generates digital reports based on emotional data and prepares them for sharing with medical professionals as needed.

[0522] Step 13:

[0523] The server confirms with the user their permission to share the digital report with medical professionals.

[0524] Step 14:

[0525] If the user grants permission, the server will send the report to a medical professional using a secure communication method.

[0526] Step 15:

[0527] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then the process ends.

[0528] (Example 1)

[0529] 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."

[0530] There is a lack of systems that allow individual users to properly record their daily emotions and understand their own mental health status. This makes it difficult for users to obtain concrete clues to understand their own emotional tendencies and problems. In addition, there is a need for technology that allows healthcare professionals to easily obtain reliable emotional information for more accurate diagnosis and guidance.

[0531] 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.

[0532] In this invention, the server includes means for receiving user mood information and storing it in an information aggregation device, means for analyzing the stored mood information and generating verbalized mood output, and means for generating an electronic report containing the mood information and selectively sharing it with medical professionals. This makes it easier for users to understand their own mental health status, and in addition, medical professionals can quickly obtain specific and reliable information about the user's emotions.

[0533] A "user" refers to an individual who uses this system to record their emotions and receive the results.

[0534] "Mood information" refers to data that users select and input to indicate their daily emotional state.

[0535] An "information aggregation device" refers to a device that includes a database for storing mood information received from users.

[0536] "Verbalized mood output" refers to feedback messages in natural language generated based on analyzed mood information.

[0537] An "electronic report" refers to a digital report that summarizes a user's mood information and the results of its analysis.

[0538] "Medical professionals" refer to professionals such as doctors and counselors who specialize in mental health care and diagnosis.

[0539] This invention is a system for users to record their daily emotions, receive feedback based on that information, and manage their own mental health. The system mainly consists of three components: a terminal application for recording the user's emotions, a server for processing, analyzing, and storing the emotional data, and an interface for generating and sharing digital reports.

[0540] The terminal application is installed on user devices such as smartphones and tablets, allowing users to select their emotions from a range of options including "joy," "sadness," and "anxiety." The selected emotion information is received by the terminal and then sent to a server. Encryption technology is used during this transmission to maintain the accuracy and security of the information.

[0541] The server stores the received emotional information in a database. During storage, information identifying each individual user and the date and time are recorded, ensuring accurate accumulation of chronological emotional data. The server then analyzes the emotional data using a generative AI model. This model compares past accumulated data with current input data to generate natural language feedback. For example, if a user inputs the emotion "anxiety," the AI ​​model uses that data to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." This feedback is sent to the user's device, helping them understand their own emotional state.

[0542] Furthermore, the server generates a graph of emotional changes based on the user's emotional history. This graph visualizes past emotional data on a weekly basis, allowing the user to intuitively understand emotional trends and fluctuations.

[0543] Furthermore, based on important emotional data, the server generates digital reports. If necessary and with user permission, these reports can be shared with healthcare professionals, who can provide appropriate support based on detailed user information. These reports are transmitted in a secure environment.

[0544] In this invention, a generative AI model plays a crucial role, and an example of a prompt based on user input is, "Please generate mental health feedback based on the emotional data entered by the user."

[0545] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0546] Step 1:

[0547] The user launches a terminal application on their smartphone or tablet. The terminal presents the user with emotional options such as "joy," "sadness," and "anxiety" on the screen. The user then selects their current emotion as input. The entered emotion is then prepared to be recorded along with the user ID and the date and time of input.

[0548] Step 2:

[0549] The device transmits the collected emotional information to the server. Encryption protocols are used during transmission to ensure the security of the information. Input data includes the specific type of emotion and a timestamp at the time of input.

[0550] Step 3:

[0551] The server receives emotion information sent from the terminal. Before this information is stored in the database, the type of emotion entered, the user ID, and the timestamp are verified. The received data is added based on each user's history, ensuring the integrity of the record.

[0552] Step 4:

[0553] The server uses a generative AI model to analyze the received emotional data. Using the input emotional information as a prompt, it generates natural language feedback through data calculations such as comparisons with past data. For example, if "anxiety" is input, the AI ​​model will output something like, "Your emotions are unstable today, and you seem to be feeling a lot of stress."

[0554] Step 5:

[0555] The server analyzes the user's emotional history and generates a graph that visualizes emotional fluctuations on a weekly basis. This graph shows the increase or decrease and patterns of each emotion, and the resulting visualized data is provided to the user.

[0556] Step 6:

[0557] The server generates digital reports based on important emotional data. These reports are securely shared with healthcare professionals of the user's choice, as needed. The reports include emotional trends and analysis results, providing a foundation for receiving assessments and recommendations from healthcare professionals as output.

[0558] (Application Example 1)

[0559] 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."

[0560] Modern commercial facilities must enhance the customer experience and increase customer satisfaction. However, there is a lack of effective means to collect data on customer emotions and experiences, and to use that data to improve operations and provide feedback. Furthermore, there is a need for methods to appropriately utilize individual customer emotional information and share it appropriately with experts in the medical field.

[0561] 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.

[0562] In this invention, the server includes means for receiving user emotional data and storing it in a database, means for analyzing the stored emotional data and generating verbalized emotional feedback, and means for receiving customer emotional data and promoting the improvement of the experience at the commercial facility. This makes it possible to efficiently collect and appropriately utilize feedback based on customers' emotions and experiences.

[0563] A "user" is an individual who uses a system to input emotional data and attempts to manage or improve their own emotional state.

[0564] "Emotional data" refers to information about a user's emotions that they input, and it serves as the basis for analysis and feedback generation.

[0565] A "database" is a memory system that continuously stores emotional data and generated feedback, and allows them to be retrieved as needed.

[0566] "Feedback" is information generated and provided to the user based on analyzed emotional data, and is intended to help the user understand their own emotional state.

[0567] An "information graph" is a visual representation of a user's emotional data, a chart that allows for an intuitive understanding of changes in their emotions.

[0568] "Digital records" are electronic documents that include emotional data and analysis results, facilitating information sharing among users and experts in specific fields.

[0569] A "commercial facility" is a place that provides goods and services to visitors, and is a place where improving the customer experience is required.

[0570] A "medical specialist" is a person who holds qualifications in the medical field and is able to provide advice and diagnoses based on the user's emotional state.

[0571] In order to implement this invention, the user's terminal, the server, and the commercial facility's management system must be closely coordinated.

[0572] First, users input their emotions through a simple interface using a smartphone or smart glasses. The device can be triggered by scanning a QR code at specific locations within the commercial facility, allowing users to select and input their emotions on the spot.

[0573] The input emotion data is instantly transmitted to the server. The server receives this data and stores it in a database. After the data is stored, a generative AI model is used to analyze the emotion data and generate verbalized feedback. This feedback is personalized to the user and is notified to the user's device.

[0574] Furthermore, the server uses the large amount of customer sentiment data it receives to perform data analysis in order to improve the customer experience at commercial facilities. This allows facility managers to instantly grasp the situation on-site using intuitive information graphs and take necessary corrective measures.

[0575] Furthermore, the feedback and digital records generated for individual users are securely shared with medical professionals with the user's permission. This allows professionals to gain a more detailed understanding of the user's emotional state and provide appropriate advice.

[0576] For example, if a user scans a QR code installed in a cafe and inputs the emotion "relaxed," the server will generate feedback such as, "It appears the customer is feeling relaxed." At the same time, this data can be used to consider adjusting the cafe's music and lighting.

[0577] An example of a prompt message would be: "Please enter sentiment data. For example, please provide specific feedback such as 'The atmosphere in the store is pleasant' or 'I liked the taste of this product.'"

[0578] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0579] Step 1:

[0580] The user scans a QR code in the store with their device. This action launches an application on their smartphone or smart glasses, displaying a screen for emotion input. The input is the information from the QR code, and the output is an emotion selection interface.

[0581] Step 2:

[0582] The user selects and inputs their current emotion using the interface displayed on the device. The input is the emotion data selected by the user, and this data is used for subsequent analysis. The output is a notification that the emotion was selected correctly.

[0583] Step 3:

[0584] The terminal sends the input emotion data to the server. The input here is the user's emotion data, and the output is the data transfer to the server. This data serves as foundational information for later analysis.

[0585] Step 4:

[0586] The server stores the received emotion data in a database. The input is the emotion data sent from the terminal, and the output is a record that the data has been stored in the database. The saving process enables subsequent data analysis.

[0587] Step 5:

[0588] The server uses a generation AI model to analyze stored sentiment data and generate verbalized feedback. The input is sentiment data stored in a database, and the output is the generated feedback message. The feedback provides the user with emotional insights.

[0589] Step 6:

[0590] The server generates an information graph based on emotional data. The input is analyzed emotional data, and the output is a visual information graph. This graph allows administrators and users to intuitively understand the transition of emotions.

[0591] Step 7:

[0592] The server sends the generated feedback and information graphs to the user's terminal. The input is data generated by the server, and the output is a notification to the user's terminal. The user receives this and uses it as feedback for themselves or the facility.

[0593] Step 8:

[0594] The server, based on user permission, shares digital records with medical professionals as needed. Input is the user's feedback data, and output is confirmation of data transfer to professionals. Authorized sharing allows for expert advice to be incorporated.

[0595] 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.

[0596] This invention is a mental health support system that combines an emotion engine for recognizing and evaluating user emotions. The system consists of a terminal for user emotion input, a server for analyzing and recognizing emotion data, and an interface for visualizing and sharing the obtained data.

[0597] Terminal embodiment

[0598] Users input their daily emotions using a device. The device displays multiple emotion options on the screen and accepts the user's selection. The device is also configured to capture real-time user data and input it into the emotion engine. Specifically, sensors on the device can detect emotions from the user's voice and facial expressions.

[0599] Server Embodiment

[0600] The server receives emotion data sent from the terminal and analysis results from the emotion engine. This data is stored in a database and managed as the user's mental health history. The emotion engine on the server automatically recognizes emotions using the user's real-time data and generates the recognition results as feedback for analysis. It also compares the recorded emotion data with the recognized emotion data and generates feedback to the user evaluating the degree of emotional consistency.

[0601] Embodiments of Emotion Visualization and Sharing

[0602] The server generates graphs that visualize changes in emotions based on past emotional data. These graphs are designed to allow users to visually check emotional fluctuations on a weekly basis. The generated graphs and feedback are then sent to the device and presented to the user. Furthermore, the server can also generate digital reports, which can be securely shared with healthcare professionals with the user's permission.

[0603] For example, if a user is feeling "sad," they select and input that emotion on their device. Simultaneously, the device's sensors analyze the user's voice tone and facial expressions, and the emotion engine confirms that they are feeling "sad." The server analyzes the degree of agreement of this information and provides feedback to the user, such as, "Today's emotion recognition shows a high degree of agreement." In this way, users can objectively understand their own emotional state and receive appropriate mental health care.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The user launches a terminal application and selects their emotion from several presented options. Simultaneously, they activate voice input and camera functions to collect their voice and facial expression data.

[0607] Step 2:

[0608] The device generates a request to send the emotion data selected by the user to the server. This request includes the user's identification information, the selected emotion, and the collected voice and facial expression data.

[0609] Step 3:

[0610] The terminal sends the generated request to the server and registers the emotion data in the system.

[0611] Step 4:

[0612] The server receives sentiment data sent from the terminal and stores it in a database. User identification information and timestamps are also recorded there.

[0613] Step 5:

[0614] The emotion engine installed on the server analyzes the received audio and facial expression data to automatically recognize the user's emotions. The recognition results are classified as emotions.

[0615] Step 6:

[0616] The server evaluates the degree of agreement between the sentiment data selected by the user and the sentiment data recognized by the sentiment engine. This evaluation is used as part of the feedback provided to the user.

[0617] Step 7:

[0618] Based on the evaluation results, the server generates feedback such as "Today's sentiment recognition shows a high degree of accuracy" and sends it to the terminal.

[0619] Step 8:

[0620] The device displays feedback received from the server to the user, providing objective information about their own emotions.

[0621] Step 9:

[0622] The server generates a weekly emotion change graph based on the user's past emotional data. This graph visually shows the fluctuations in emotions.

[0623] Step 10:

[0624] The server sends the generated graph to the terminal, allowing the user to visually confirm changes in their emotions.

[0625] Step 11:

[0626] The server aggregates important emotional data as needed and generates digital reports. These reports are formatted for sharing with healthcare professionals with the user's permission.

[0627] Step 12:

[0628] If a user allows sharing of a digital report with a medical professional, the server will send the report to the medical professional using a secure protocol.

[0629] Step 13:

[0630] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then terminates the process.

[0631] (Example 2)

[0632] 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."

[0633] In modern society, individual mental health is a critical issue, but there is a lack of adequate systems for objectively understanding one's own emotional state and providing appropriate care. In particular, there is a need for effective means to recognize daily emotional changes in real time, visualize those changes, and share them.

[0634] 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.

[0635] In this invention, the server includes means for acquiring user emotional information and transmitting it to an information processing device; the information processing device includes means for analyzing the received emotional information using a generating AI model and storing the analysis results in an information recording device; and means for evaluating the degree of emotional consistency based on the stored analysis results. This enables users to intuitively understand changes in their own emotions, safely share information with professionals as needed, and receive appropriate mental health care.

[0636] A "user" refers to an individual who uses the system to input their emotional state and receive the analysis results.

[0637] "Emotional information" refers to data about emotions obtained from the user, including selected emotions, voice, and information based on facial expressions.

[0638] An "information processing device" refers to a computer that receives emotional information from users and analyzes it.

[0639] A "generative AI model" refers to an algorithm used to analyze emotional information, specifically a model that utilizes machine learning techniques.

[0640] "Analysis results" refer to the output of emotional information after processing by the generative AI model, and include evaluations of the degree of emotional agreement and changes.

[0641] "Information recording device" refers to data storage used to save analysis results.

[0642] "Emotional agreement" refers to an indicator that shows the degree of agreement between the emotional information entered by the user and the analysis results.

[0643] "Information visualization methods" refer to means of visually representing changes in emotions and degrees of agreement, and providing this information to users.

[0644] "Visualization results" refer to visual outputs such as graphs generated by information visualization methods.

[0645] An "information report" is a report that summarizes emotional information and its analysis results, and is intended to be shared between users and experts.

[0646] "Information sharing means" refers to methods for securely sharing generated information reports with experts.

[0647] This invention is a mental health support system that recognizes, analyzes, and visualizes a user's emotional information. The system mainly consists of a terminal for inputting the user's emotional information, a server for analyzing the emotional information, and an interface for presenting the analysis results to the user.

[0648] Users can input their emotions from a selection of options using devices such as smartphones and tablets. These devices are equipped with voice recognition and image sensors, which allow them to acquire emotional information from the user's real-time voice tone and facial expressions and input it into the device.

[0649] The server receives emotional information sent by the user and analyzes it using a generative AI model. This generative AI model processes and evaluates the received emotional information and obtains analysis results. These results include an evaluation of the degree of emotional consistency and change. The analysis results are stored in an information recording device and can be used for later monitoring and feedback.

[0650] The analysis results are further generated as graphs using information visualization tools for visualization purposes. These graphs are designed to allow for an intuitive understanding of changes in the user's mental health. Furthermore, the generated information report can be shared with professionals through information sharing tools, with the user's permission.

[0651] For example, if a user feels "at ease," they can select and input that emotion on their device. Simultaneously, the device's sensors analyze the voice and facial expressions, and the acquired information is sent to a server. The server uses a generative AI model to analyze the data and provides feedback to the user, such as, "Your emotions today haven't changed significantly from yesterday."

[0652] An example of a prompt message is, "Analyze the user's emotional information and generate an emotional consistency rating based on a generative AI model." This system aims to improve mental health by providing users with feedback based on changes in their emotions and their analysis.

[0653] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0654] Step 1:

[0655] The user inputs their daily emotions into the device. Specifically, the user selects their current emotion from emotion options displayed on the device screen, and their voice and facial expressions are collected through voice recognition and the camera. The input in this step consists of the emotion data selected by the user and the voice and facial expression data acquired by the sensors. The output is a dataset in which this emotion information has been formatted.

[0656] Step 2:

[0657] The terminal sends the acquired sentiment information to the server. A process takes place in which the dataset, formatted as sentiment information, is sent to the server via the internet. In this step, the input is the formatted sentiment information provided by the terminal, and the output is the sentiment information converted into a format usable by the server.

[0658] Step 3:

[0659] The server analyzes emotional information using a generative AI model. The server takes the received data as input and performs data analysis based on the generative AI model. Here, data calculations are used to evaluate the degree of agreement and change in the user's emotions. In this step, the input is the emotional information that arrives on the server side, and the output is the analysis result.

[0660] Step 4:

[0661] The server saves the analysis results to a database. The analyzed data is stored in an information recording device because it is managed over the long term as the user's mental health history. The input for this step is the analysis results, and the output is the analyzed data stored in the database.

[0662] Step 5:

[0663] The server generates a graph that visualizes changes in emotions based on the analysis results and past emotional information. The server uses accumulated analysis data as input and employs information visualization methods based on past trends and accuracy evaluations. This output is a graph that allows the user to visually understand the changes in their emotions.

[0664] Step 6:

[0665] The server sends the generated graph to the terminal for display to the user. The terminal receives the graph sent from the server and provides it to the user. In this step, the input is the generated visualization graph, and the output is the graph displayed on the screen to the user.

[0666] Step 7:

[0667] Based on user permission, the server shares a digital report containing emotional information and analysis results with experts. Authorized information reports are securely shared with medical professionals by selectively transmitting them through the information sharing mechanism. The inputs for this step are the permission for information sharing and the report content, while the output is the shared digital report.

[0668] (Application Example 2)

[0669] 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."

[0670] Improving customer satisfaction is a crucial challenge in modern commercial facilities. However, instantly understanding the diverse emotions of customers and adjusting services accordingly is considered difficult. Traditional methods do not involve real-time collection and analysis of customer emotion data, making rapid response difficult. As a result, potential customer dissatisfaction is often overlooked, leading to a failure to provide optimal customer service.

[0671] 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.

[0672] In this invention, the server includes means for receiving user emotion data and storing it in an information database, means for analyzing the stored emotion data and generating verbalized emotional feedback, means for providing the analysis results to the user, and means for analyzing the customer's facial expressions and voice and providing feedback to the staff in real time. This enables a rapid understanding of the customer's emotions and the provision of optimal service based on that understanding.

[0673] "User emotional data" refers to information that reflects the user's emotional state, such as facial expressions, voice, and selected emotions.

[0674] An "information database" is a collection point of digital information used to store and manage user sentiment data and analysis results.

[0675] "Verbalized emotional feedback" refers to the provision of information in language that is easy for users to understand, based on emotional data.

[0676] "Charts and graphs" are visual materials such as graphs and charts used to visually represent changes in emotional data.

[0677] A "digital report" is an electronic report document that compiles user sentiment data and analysis results.

[0678] A "professional" is a medical or psychological professional involved in improving a user's mental health and analyzing their emotions.

[0679] A "feedback method" is a way of providing useful information to users and store staff based on analyzed emotional information.

[0680] This embodiment is a system that analyzes user emotions and allows store staff to adjust their customer service in real time. Specifically, users record their emotions through their smartphones or smart glasses, and the system is designed to provide appropriate feedback based on those emotions.

[0681] Smart glasses and smartphones, which are the devices used in this system, collect the user's facial expressions and voice using their built-in cameras and microphones. This data is transmitted to a server in real time. The server uses the Google Cloud Vision API or Microsoft Azure Face API to analyze the user's emotions from the received data. The results of this analysis are generated as verbalized feedback and reflected in visual charts and graphs.

[0682] The server provides the generated feedback to store staff, allowing for individualized customer service within the store. For example, if the system analyzes a customer's feelings of surprise or delight upon seeing a new product, this information is immediately communicated to the staff, promoting more proactive service. This system enables stores to provide services that result in higher customer satisfaction.

[0683] Furthermore, the generated digital reports can be shared with experts, allowing stores to obtain analytical data that can lead to improvements in customer service. This enables the development of strategies to enhance the customer experience.

[0684] As a concrete example, the following prompt statement is used:

[0685] "Evaluate the overall atmosphere of the store based on customer facial expression data and propose what service improvements are needed."

[0686] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0687] Step 1:

[0688] The device, either smart glasses or a smartphone, uses a camera and microphone to collect the user's facial expressions and voice in real time. The input is the user's visual and audio data, and the output is the collected raw data. Sensors on the device are utilized at this stage.

[0689] Step 2:

[0690] The terminal sends the collected data to the server. The input is the raw data obtained in step 1, and the output is the state of completion of data transmission to the server. At this point, the data transmission protocol is applied.

[0691] Step 3:

[0692] The server analyzes the received data. In this step, it receives audio and visual data as input and performs sentiment analysis using the Google Cloud Vision API or Microsoft Azure Face API. The output is a category of emotion (e.g., "happy," "surprised"). Data processing includes a sentiment recognition process by an AI model.

[0693] Step 4:

[0694] The server generates analysis results as feedback. The input is the emotion category from step 3, and the output is verbalized feedback and visualized charts. Based on the emotion data, natural language generation technology is used to construct user-oriented feedback.

[0695] Step 5:

[0696] The server provides the generated feedback to the store clerk in real time. The input is feedback and visualized charts, and the output is the display of information on the clerk's terminal. The information is displayed in an intuitively easy-to-understand format.

[0697] Step 6:

[0698] Users and staff use feedback to adjust in-store customer service approaches. Input is feedback information received by staff, and output is improvement actions taken in customer service. This ensures that appropriate service is provided to customers.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] [Fourth Embodiment]

[0703] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0704] 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.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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).

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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".

[0716] This invention is a system for users to record their daily emotions, receive feedback based on that data, and manage their mental health. The system consists of a terminal application for users to input their emotions, a server that processes, analyzes, and stores the emotional data, and an interface for generating and sharing digital reports.

[0717] Implementation of a terminal application

[0718] Users use their devices to record their daily moods. The device application is designed to present users with multiple mood options, making it easy to record their mood for the day. Once the user has entered their mood, the device sends the data to a server.

[0719] Server Embodiment

[0720] The server receives emotional data sent from the terminal and stores it in a database. This storage process records user-specific identification information and timestamps, enabling effective management of past emotional history. Furthermore, the server uses an AI model to analyze the data and generate feedback that verbalizes the user's feelings. This feedback is provided to the user to help them understand their current emotions.

[0721] Visualization of emotional changes and digital reporting

[0722] The server analyzes the user's past emotional data and generates a graph that visualizes changes in their emotions. This graph reflects past data and shows the evolution of emotions on a weekly basis. The graph is sent to the terminal, allowing the user to intuitively understand the shifts in their emotions.

[0723] Furthermore, the server generates digital reports based on important emotional data. These reports are designed to be shared with healthcare professionals as needed, and if the user grants permission, the digital reports are securely sent to healthcare professionals. This allows healthcare professionals to gain a more accurate understanding of the user's condition and provide appropriate support.

[0724] For example, if a user inputs the emotion "anxiety," the server stores this data and uses an AI model to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." Furthermore, an emotion graph shows the increase or decrease in anxiety over the past few days, and this information is included in a highly organized digital report and shared with healthcare professionals. In this way, users can deepen their self-understanding and receive appropriate mental health care.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The user launches the terminal application and accesses a screen where they can select their daily mood.

[0728] Step 2:

[0729] The device displays multiple emotional options to the user, who then chooses the one that best suits their mood for the day.

[0730] Step 3:

[0731] The device generates a request to send the selected emotion data to the server. This request includes the user's identification information and a timestamp.

[0732] Step 4:

[0733] The server receives the sentiment data request sent from the terminal and checks its contents.

[0734] Step 5:

[0735] The server stores the received sentiment data in a database. During storage, it associates the user ID with a timestamp to prepare for future data analysis.

[0736] Step 6:

[0737] The server activates the AI ​​model and begins analysis using the stored sentiment data. This analysis generates verbalized feedback based on the sentiment data.

[0738] Step 7:

[0739] The server sends the generated feedback to the terminal.

[0740] Step 8:

[0741] The device displays feedback received from the server to the user, helping them understand their current emotional state.

[0742] Step 9:

[0743] The server periodically collects user sentiment data and analyzes the data from the past week to generate graphs that visualize changes in sentiment.

[0744] Step 10:

[0745] The server sends the generated emotion graph to the terminal.

[0746] Step 11:

[0747] The device displays an emotion graph to the user, allowing them to visually review the changes in their emotions over time.

[0748] Step 12:

[0749] The server generates digital reports based on emotional data and prepares them for sharing with medical professionals as needed.

[0750] Step 13:

[0751] The server confirms with the user their permission to share the digital report with medical professionals.

[0752] Step 14:

[0753] If the user grants permission, the server will send the report to a medical professional using a secure communication method.

[0754] Step 15:

[0755] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then the process ends.

[0756] (Example 1)

[0757] 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".

[0758] There is a lack of systems that allow individual users to properly record their daily emotions and understand their own mental health status. This makes it difficult for users to obtain concrete clues to understand their own emotional tendencies and problems. In addition, there is a need for technology that allows healthcare professionals to easily obtain reliable emotional information for more accurate diagnosis and guidance.

[0759] 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.

[0760] In this invention, the server includes means for receiving user mood information and storing it in an information aggregation device, means for analyzing the stored mood information and generating verbalized mood output, and means for generating an electronic report containing the mood information and selectively sharing it with medical professionals. This makes it easier for users to understand their own mental health status, and in addition, medical professionals can quickly obtain specific and reliable information about the user's emotions.

[0761] A "user" refers to an individual who uses this system to record their emotions and receive the results.

[0762] "Mood information" refers to data that users select and input to indicate their daily emotional state.

[0763] An "information aggregation device" refers to a device that includes a database for storing mood information received from users.

[0764] "Verbalized mood output" refers to feedback messages in natural language generated based on analyzed mood information.

[0765] An "electronic report" refers to a digital report that summarizes a user's mood information and the results of its analysis.

[0766] "Medical professionals" refer to professionals such as doctors and counselors who specialize in mental health care and diagnosis.

[0767] This invention is a system for users to record their daily emotions, receive feedback based on that information, and manage their own mental health. The system mainly consists of three components: a terminal application for recording the user's emotions, a server for processing, analyzing, and storing the emotional data, and an interface for generating and sharing digital reports.

[0768] The terminal application is installed on user devices such as smartphones and tablets, allowing users to select their emotions from a range of options including "joy," "sadness," and "anxiety." The selected emotion information is received by the terminal and then sent to a server. Encryption technology is used during this transmission to maintain the accuracy and security of the information.

[0769] The server stores the received emotional information in a database. During storage, information identifying each individual user and the date and time are recorded, ensuring accurate accumulation of chronological emotional data. The server then analyzes the emotional data using a generative AI model. This model compares past accumulated data with current input data to generate natural language feedback. For example, if a user inputs the emotion "anxiety," the AI ​​model uses that data to generate feedback such as, "Your emotions are unstable today, and you seem to be feeling a lot of stress." This feedback is sent to the user's device, helping them understand their own emotional state.

[0770] Furthermore, the server generates a graph of emotional changes based on the user's emotional history. This graph visualizes past emotional data on a weekly basis, allowing the user to intuitively understand emotional trends and fluctuations.

[0771] Furthermore, based on important emotional data, the server generates digital reports. If necessary and with user permission, these reports can be shared with healthcare professionals, who can provide appropriate support based on detailed user information. These reports are transmitted in a secure environment.

[0772] In this invention, a generative AI model plays a crucial role, and an example of a prompt based on user input is, "Please generate mental health feedback based on the emotional data entered by the user."

[0773] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0774] Step 1:

[0775] The user launches a terminal application on their smartphone or tablet. The terminal presents the user with emotional options such as "joy," "sadness," and "anxiety" on the screen. The user then selects their current emotion as input. The entered emotion is then prepared to be recorded along with the user ID and the date and time of input.

[0776] Step 2:

[0777] The device transmits the collected emotional information to the server. Encryption protocols are used during transmission to ensure the security of the information. Input data includes the specific type of emotion and a timestamp at the time of input.

[0778] Step 3:

[0779] The server receives emotion information sent from the terminal. Before this information is stored in the database, the type of emotion entered, the user ID, and the timestamp are verified. The received data is added based on each user's history, ensuring the integrity of the record.

[0780] Step 4:

[0781] The server uses a generative AI model to analyze the received emotional data. Using the input emotional information as a prompt, it generates natural language feedback through data calculations such as comparisons with past data. For example, if "anxiety" is input, the AI ​​model will output something like, "Your emotions are unstable today, and you seem to be feeling a lot of stress."

[0782] Step 5:

[0783] The server analyzes the user's emotional history and generates a graph that visualizes emotional fluctuations on a weekly basis. This graph shows the increase or decrease and patterns of each emotion, and the resulting visualized data is provided to the user.

[0784] Step 6:

[0785] The server generates digital reports based on important emotional data. These reports are securely shared with healthcare professionals of the user's choice, as needed. The reports include emotional trends and analysis results, providing a foundation for receiving assessments and recommendations from healthcare professionals as output.

[0786] (Application Example 1)

[0787] 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".

[0788] Modern commercial facilities must enhance the customer experience and increase customer satisfaction. However, there is a lack of effective means to collect data on customer emotions and experiences, and to use that data to improve operations and provide feedback. Furthermore, there is a need for methods to appropriately utilize individual customer emotional information and share it appropriately with experts in the medical field.

[0789] 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.

[0790] In this invention, the server includes means for receiving user emotional data and storing it in a database, means for analyzing the stored emotional data and generating verbalized emotional feedback, and means for receiving customer emotional data and promoting the improvement of the experience at the commercial facility. This makes it possible to efficiently collect and appropriately utilize feedback based on customers' emotions and experiences.

[0791] A "user" is an individual who uses a system to input emotional data and attempts to manage or improve their own emotional state.

[0792] "Emotional data" refers to information about a user's emotions that they input, and it serves as the basis for analysis and feedback generation.

[0793] A "database" is a memory system that continuously stores emotional data and generated feedback, and allows them to be retrieved as needed.

[0794] "Feedback" is information generated and provided to the user based on analyzed emotional data, and is intended to help the user understand their own emotional state.

[0795] An "information graph" is a visual representation of a user's emotional data, a chart that allows for an intuitive understanding of changes in their emotions.

[0796] "Digital records" are electronic documents that include emotional data and analysis results, facilitating information sharing among users and experts in specific fields.

[0797] A "commercial facility" is a place that provides goods and services to visitors, and is a place where improving the customer experience is required.

[0798] A "medical specialist" is a person who holds qualifications in the medical field and is able to provide advice and diagnoses based on the user's emotional state.

[0799] In order to implement this invention, the user's terminal, the server, and the commercial facility's management system must be closely coordinated.

[0800] First, users input their emotions through a simple interface using a smartphone or smart glasses. The device can be triggered by scanning a QR code at specific locations within the commercial facility, allowing users to select and input their emotions on the spot.

[0801] The input emotion data is instantly transmitted to the server. The server receives this data and stores it in a database. After the data is stored, a generative AI model is used to analyze the emotion data and generate verbalized feedback. This feedback is personalized to the user and is notified to the user's device.

[0802] Furthermore, the server uses the large amount of customer sentiment data it receives to perform data analysis in order to improve the customer experience at commercial facilities. This allows facility managers to instantly grasp the situation on-site using intuitive information graphs and take necessary corrective measures.

[0803] Furthermore, the feedback and digital records generated for individual users are securely shared with medical professionals with the user's permission. This allows professionals to gain a more detailed understanding of the user's emotional state and provide appropriate advice.

[0804] For example, if a user scans a QR code installed in a cafe and inputs the emotion "relaxed," the server will generate feedback such as, "It appears the customer is feeling relaxed." At the same time, this data can be used to consider adjusting the cafe's music and lighting.

[0805] An example of a prompt message would be: "Please enter sentiment data. For example, please provide specific feedback such as 'The atmosphere in the store is pleasant' or 'I liked the taste of this product.'"

[0806] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0807] Step 1:

[0808] The user scans a QR code in the store with their device. This action launches an application on their smartphone or smart glasses, displaying a screen for emotion input. The input is the information from the QR code, and the output is an emotion selection interface.

[0809] Step 2:

[0810] The user selects and inputs their current emotion using the interface displayed on the device. The input is the emotion data selected by the user, and this data is used for subsequent analysis. The output is a notification that the emotion was selected correctly.

[0811] Step 3:

[0812] The terminal sends the input emotion data to the server. The input here is the user's emotion data, and the output is the data transfer to the server. This data serves as foundational information for later analysis.

[0813] Step 4:

[0814] The server stores the received emotion data in a database. The input is the emotion data sent from the terminal, and the output is a record that the data has been stored in the database. The saving process enables subsequent data analysis.

[0815] Step 5:

[0816] The server uses a generation AI model to analyze stored sentiment data and generate verbalized feedback. The input is sentiment data stored in a database, and the output is the generated feedback message. The feedback provides the user with emotional insights.

[0817] Step 6:

[0818] The server generates an information graph based on emotional data. The input is analyzed emotional data, and the output is a visual information graph. This graph allows administrators and users to intuitively understand the transition of emotions.

[0819] Step 7:

[0820] The server sends the generated feedback and information graphs to the user's terminal. The input is data generated by the server, and the output is a notification to the user's terminal. The user receives this and uses it as feedback for themselves or the facility.

[0821] Step 8:

[0822] The server, based on user permission, shares digital records with medical professionals as needed. Input is the user's feedback data, and output is confirmation of data transfer to professionals. Authorized sharing allows for expert advice to be incorporated.

[0823] 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.

[0824] This invention is a mental health support system that combines an emotion engine for recognizing and evaluating user emotions. The system consists of a terminal for user emotion input, a server for analyzing and recognizing emotion data, and an interface for visualizing and sharing the obtained data.

[0825] Terminal embodiment

[0826] Users input their daily emotions using a device. The device displays multiple emotion options on the screen and accepts the user's selection. The device is also configured to capture real-time user data and input it into the emotion engine. Specifically, sensors on the device can detect emotions from the user's voice and facial expressions.

[0827] Server Embodiment

[0828] The server receives emotion data sent from the terminal and analysis results from the emotion engine. This data is stored in a database and managed as the user's mental health history. The emotion engine on the server automatically recognizes emotions using the user's real-time data and generates the recognition results as feedback for analysis. It also compares the recorded emotion data with the recognized emotion data and generates feedback to the user evaluating the degree of emotional consistency.

[0829] Embodiments of Emotion Visualization and Sharing

[0830] The server generates graphs that visualize changes in emotions based on past emotional data. These graphs are designed to allow users to visually check emotional fluctuations on a weekly basis. The generated graphs and feedback are then sent to the device and presented to the user. Furthermore, the server can also generate digital reports, which can be securely shared with healthcare professionals with the user's permission.

[0831] For example, if a user is feeling "sad," they select and input that emotion on their device. Simultaneously, the device's sensors analyze the user's voice tone and facial expressions, and the emotion engine confirms that they are feeling "sad." The server analyzes the degree of agreement of this information and provides feedback to the user, such as, "Today's emotion recognition shows a high degree of agreement." In this way, users can objectively understand their own emotional state and receive appropriate mental health care.

[0832] The following describes the processing flow.

[0833] Step 1:

[0834] The user launches a terminal application and selects their emotion from several presented options. Simultaneously, they activate voice input and camera functions to collect their voice and facial expression data.

[0835] Step 2:

[0836] The device generates a request to send the emotion data selected by the user to the server. This request includes the user's identification information, the selected emotion, and the collected voice and facial expression data.

[0837] Step 3:

[0838] The terminal sends the generated request to the server and registers the emotion data in the system.

[0839] Step 4:

[0840] The server receives sentiment data sent from the terminal and stores it in a database. User identification information and timestamps are also recorded there.

[0841] Step 5:

[0842] The emotion engine installed on the server analyzes the received audio and facial expression data to automatically recognize the user's emotions. The recognition results are classified as emotions.

[0843] Step 6:

[0844] The server evaluates the degree of agreement between the sentiment data selected by the user and the sentiment data recognized by the sentiment engine. This evaluation is used as part of the feedback provided to the user.

[0845] Step 7:

[0846] Based on the evaluation results, the server generates feedback such as "Today's sentiment recognition shows a high degree of accuracy" and sends it to the terminal.

[0847] Step 8:

[0848] The device displays feedback received from the server to the user, providing objective information about their own emotions.

[0849] Step 9:

[0850] The server generates a weekly emotion change graph based on the user's past emotional data. This graph visually shows the fluctuations in emotions.

[0851] Step 10:

[0852] The server sends the generated graph to the terminal, allowing the user to visually confirm changes in their emotions.

[0853] Step 11:

[0854] The server aggregates important emotional data as needed and generates digital reports. These reports are formatted for sharing with healthcare professionals with the user's permission.

[0855] Step 12:

[0856] If a user allows sharing of a digital report with a medical professional, the server will send the report to the medical professional using a secure protocol.

[0857] Step 13:

[0858] The device notifies the user that the sharing of the digital report with the medical professional is complete, and then terminates the process.

[0859] (Example 2)

[0860] 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".

[0861] In modern society, individual mental health is a critical issue, but there is a lack of adequate systems for objectively understanding one's own emotional state and providing appropriate care. In particular, there is a need for effective means to recognize daily emotional changes in real time, visualize those changes, and share them.

[0862] 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.

[0863] In this invention, the server includes means for acquiring user emotional information and transmitting it to an information processing device; the information processing device includes means for analyzing the received emotional information using a generating AI model and storing the analysis results in an information recording device; and means for evaluating the degree of emotional consistency based on the stored analysis results. This enables users to intuitively understand changes in their own emotions, safely share information with professionals as needed, and receive appropriate mental health care.

[0864] A "user" refers to an individual who uses the system to input their emotional state and receive the analysis results.

[0865] "Emotional information" refers to data about emotions obtained from the user, including selected emotions, voice, and information based on facial expressions.

[0866] An "information processing device" refers to a computer that receives emotional information from users and analyzes it.

[0867] A "generative AI model" refers to an algorithm used to analyze emotional information, specifically a model that utilizes machine learning techniques.

[0868] "Analysis results" refer to the output of emotional information after processing by the generative AI model, and include evaluations of the degree of emotional agreement and changes.

[0869] "Information recording device" refers to data storage used to save analysis results.

[0870] "Emotional agreement" refers to an indicator that shows the degree of agreement between the emotional information entered by the user and the analysis results.

[0871] "Information visualization methods" refer to means of visually representing changes in emotions and degrees of agreement, and providing this information to users.

[0872] "Visualization results" refer to visual outputs such as graphs generated by information visualization methods.

[0873] An "information report" is a report that summarizes emotional information and its analysis results, and is intended to be shared between users and experts.

[0874] "Information sharing means" refers to methods for securely sharing generated information reports with experts.

[0875] This invention is a mental health support system that recognizes, analyzes, and visualizes a user's emotional information. The system mainly consists of a terminal for inputting the user's emotional information, a server for analyzing the emotional information, and an interface for presenting the analysis results to the user.

[0876] Users can input their emotions from a selection of options using devices such as smartphones and tablets. These devices are equipped with voice recognition and image sensors, which allow them to acquire emotional information from the user's real-time voice tone and facial expressions and input it into the device.

[0877] The server receives emotional information sent by the user and analyzes it using a generative AI model. This generative AI model processes and evaluates the received emotional information and obtains analysis results. These results include an evaluation of the degree of emotional consistency and change. The analysis results are stored in an information recording device and can be used for later monitoring and feedback.

[0878] The analysis results are further generated as graphs using information visualization tools for visualization purposes. These graphs are designed to allow for an intuitive understanding of changes in the user's mental health. Furthermore, the generated information report can be shared with professionals through information sharing tools, with the user's permission.

[0879] For example, if a user feels "at ease," they can select and input that emotion on their device. Simultaneously, the device's sensors analyze the voice and facial expressions, and the acquired information is sent to a server. The server uses a generative AI model to analyze the data and provides feedback to the user, such as, "Your emotions today haven't changed significantly from yesterday."

[0880] An example of a prompt message is, "Analyze the user's emotional information and generate an emotional consistency rating based on a generative AI model." This system aims to improve mental health by providing users with feedback based on changes in their emotions and their analysis.

[0881] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0882] Step 1:

[0883] The user inputs their daily emotions into the device. Specifically, the user selects their current emotion from emotion options displayed on the device screen, and their voice and facial expressions are collected through voice recognition and the camera. The input in this step consists of the emotion data selected by the user and the voice and facial expression data acquired by the sensors. The output is a dataset in which this emotion information has been formatted.

[0884] Step 2:

[0885] The terminal sends the acquired sentiment information to the server. A process takes place in which the dataset, formatted as sentiment information, is sent to the server via the internet. In this step, the input is the formatted sentiment information provided by the terminal, and the output is the sentiment information converted into a format usable by the server.

[0886] Step 3:

[0887] The server analyzes emotional information using a generative AI model. The server takes the received data as input and performs data analysis based on the generative AI model. Here, data calculations are used to evaluate the degree of agreement and change in the user's emotions. In this step, the input is the emotional information that arrives on the server side, and the output is the analysis result.

[0888] Step 4:

[0889] The server saves the analysis results to a database. The analyzed data is stored in an information recording device because it is managed over the long term as the user's mental health history. The input for this step is the analysis results, and the output is the analyzed data stored in the database.

[0890] Step 5:

[0891] The server generates a graph that visualizes changes in emotions based on the analysis results and past emotional information. The server uses accumulated analysis data as input and employs information visualization methods based on past trends and accuracy evaluations. This output is a graph that allows the user to visually understand the changes in their emotions.

[0892] Step 6:

[0893] The server sends the generated graph to the terminal for display to the user. The terminal receives the graph sent from the server and provides it to the user. In this step, the input is the generated visualization graph, and the output is the graph displayed on the screen to the user.

[0894] Step 7:

[0895] Based on user permission, the server shares a digital report containing emotional information and analysis results with experts. Authorized information reports are securely shared with medical professionals by selectively transmitting them through the information sharing mechanism. The inputs for this step are the permission for information sharing and the report content, while the output is the shared digital report.

[0896] (Application Example 2)

[0897] 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".

[0898] Improving customer satisfaction is a crucial challenge in modern commercial facilities. However, instantly understanding the diverse emotions of customers and adjusting services accordingly is considered difficult. Traditional methods do not involve real-time collection and analysis of customer emotion data, making rapid response difficult. As a result, potential customer dissatisfaction is often overlooked, leading to a failure to provide optimal customer service.

[0899] 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.

[0900] In this invention, the server includes means for receiving user emotion data and storing it in an information database, means for analyzing the stored emotion data and generating verbalized emotional feedback, means for providing the analysis results to the user, and means for analyzing the customer's facial expressions and voice and providing feedback to the staff in real time. This enables a rapid understanding of the customer's emotions and the provision of optimal service based on that understanding.

[0901] "User emotional data" refers to information that reflects the user's emotional state, such as facial expressions, voice, and selected emotions.

[0902] An "information database" is a collection point of digital information used to store and manage user sentiment data and analysis results.

[0903] "Verbalized emotional feedback" refers to the provision of information in language that is easy for users to understand, based on emotional data.

[0904] "Charts and graphs" are visual materials such as graphs and charts used to visually represent changes in emotional data.

[0905] A "digital report" is an electronic report document that compiles user sentiment data and analysis results.

[0906] A "professional" is a medical or psychological professional involved in improving a user's mental health and analyzing their emotions.

[0907] A "feedback method" is a way of providing useful information to users and store staff based on analyzed emotional information.

[0908] This embodiment is a system that analyzes user emotions and allows store staff to adjust their customer service in real time. Specifically, users record their emotions through their smartphones or smart glasses, and the system is designed to provide appropriate feedback based on those emotions.

[0909] Smart glasses and smartphones, which are the devices used in this system, collect the user's facial expressions and voice using their built-in cameras and microphones. This data is transmitted to a server in real time. The server uses the Google Cloud Vision API or Microsoft Azure Face API to analyze the user's emotions from the received data. The results of this analysis are generated as verbalized feedback and reflected in visual charts and graphs.

[0910] The server provides the generated feedback to store staff, allowing for individualized customer service within the store. For example, if the system analyzes a customer's feelings of surprise or delight upon seeing a new product, this information is immediately communicated to the staff, promoting more proactive service. This system enables stores to provide services that result in higher customer satisfaction.

[0911] Furthermore, the generated digital reports can be shared with experts, allowing stores to obtain analytical data that can lead to improvements in customer service. This enables the development of strategies to enhance the customer experience.

[0912] As a concrete example, the following prompt statement is used:

[0913] "Evaluate the overall atmosphere of the store based on customer facial expression data and propose what service improvements are needed."

[0914] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0915] Step 1:

[0916] The device, either smart glasses or a smartphone, uses a camera and microphone to collect the user's facial expressions and voice in real time. The input is the user's visual and audio data, and the output is the collected raw data. Sensors on the device are utilized at this stage.

[0917] Step 2:

[0918] The terminal sends the collected data to the server. The input is the raw data obtained in step 1, and the output is the state of completion of data transmission to the server. At this point, the data transmission protocol is applied.

[0919] Step 3:

[0920] The server analyzes the received data. In this step, it receives audio and visual data as input and performs sentiment analysis using the Google Cloud Vision API or Microsoft Azure Face API. The output is a category of emotion (e.g., "happy," "surprised"). Data processing includes a sentiment recognition process by an AI model.

[0921] Step 4:

[0922] The server generates analysis results as feedback. The input is the emotion category from step 3, and the output is verbalized feedback and visualized charts. Based on the emotion data, natural language generation technology is used to construct user-oriented feedback.

[0923] Step 5:

[0924] The server provides the generated feedback to the store clerk in real time. The input is feedback and visualized charts, and the output is the display of information on the clerk's terminal. The information is displayed in an intuitively easy-to-understand format.

[0925] Step 6:

[0926] Users and staff use feedback to adjust in-store customer service approaches. Input is feedback information received by staff, and output is improvement actions taken in customer service. This ensures that appropriate service is provided to customers.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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."

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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 as being incorporated by reference.

[0948] The following is further disclosed regarding the embodiments described above.

[0949] (Claim 1)

[0950] A means of receiving user sentiment data and storing it in a database,

[0951] A means for analyzing stored emotional data and generating verbalized emotional feedback,

[0952] Means for providing analysis results to the user,

[0953] A means of generating a graph that visualizes changes in emotions based on user emotion data,

[0954] A means of displaying the generated graph to the user,

[0955] A means of generating digital reports containing emotional data and selectively sharing them with healthcare professionals,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, further comprising means for visualizing changes in emotions on a weekly basis based on emotions selected by the user.

[0959] (Claim 3)

[0960] The system according to claim 1, wherein, when generating a digital report, it has means for securely sharing data with medical professionals with the user's permission.

[0961] "Example 1"

[0962] (Claim 1)

[0963] A means for receiving user mood information and storing it in an information aggregation device,

[0964] A means for analyzing accumulated mood information and generating verbalized expressions of mood,

[0965] Means for providing analysis results to the user,

[0966] A means for generating a diagram that visualizes changes in mood based on the user's mood information,

[0967] A means of displaying the generated diagram to the user,

[0968] A means of generating electronic reports containing mood information and selectively sharing them with healthcare professionals,

[0969] A system that includes this.

[0970] (Claim 2)

[0971] The system according to claim 1, further comprising means for visualizing changes in mood on a weekly basis based on a mood selected by the user.

[0972] (Claim 3)

[0973] The system according to claim 1, which, when generating electronic reports, has means for securely sharing information with medical professionals with the user's permission.

[0974] "Application Example 1"

[0975] (Claim 1)

[0976] A means of receiving user sentiment data and storing it in a database,

[0977] A means for analyzing stored emotional data and generating verbalized emotional feedback,

[0978] Means for providing analysis results to the user,

[0979] A means for generating an information graph that visualizes changes in emotions based on user emotion data,

[0980] A means of displaying the generated information graph to the user,

[0981] A means of generating digital records containing emotional data and selectively sharing them with experts in the medical field,

[0982] A means of receiving customer emotional data and promoting improvements to the experience at commercial facilities,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, further comprising means for visualizing changes in emotions on a weekly basis based on emotions selected by the user.

[0986] (Claim 3)

[0987] The system according to claim 1, which, when generating digital records, has means for securely sharing data with medical professionals with the user's permission.

[0988] "Example 2 of combining an emotion engine"

[0989] (Claim 1)

[0990] A means for acquiring user emotion information and transmitting it to an information processing device,

[0991] An information processing device includes means for analyzing received emotional information using a generating AI model and storing the analysis results in an information recording device,

[0992] A means to evaluate the degree of emotional agreement based on the saved analysis results,

[0993] An information visualization method that visualizes changes in emotions based on agreement evaluation and past emotional information,

[0994] A means of presenting the generated visualization results to the user,

[0995] A means of generating information reports that include emotional information and analysis results, and, with the user's permission, selectively and securely sharing them with experts,

[0996] A system that includes this.

[0997] (Claim 2)

[0998] The system according to claim 1, further comprising information visualization means for visualizing changes in emotions on a weekly basis based on the emotions selected by the user.

[0999] (Claim 3)

[1000] The system according to claim 1, wherein, when generating information reports, it has an information sharing means for securely sharing data with experts with the user's permission.

[1001] "Application example 2 when combining with an emotional engine"

[1002] (Claim 1)

[1003] A means of receiving user sentiment data and storing it in an information database,

[1004] A means for analyzing stored emotional data and generating verbalized emotional feedback,

[1005] Means for providing analysis results to the user,

[1006] A means for generating charts that visualize changes in emotions based on user emotion data,

[1007] A means of displaying the generated charts and graphs to the user,

[1008] A means of generating digital reports that include emotional data and selectively sharing them with experts,

[1009] A feedback system that analyzes customer facial expressions and voices and provides them to store staff in real time,

[1010] A system that includes this.

[1011] (Claim 2)

[1012] The system according to claim 1, further comprising means for visualizing changes in emotions on a weekly basis based on emotions selected by the user.

[1013] (Claim 3)

[1014] The system according to claim 1, which, when generating a digital report, has means for securely sharing information with experts with the user's permission. [Explanation of symbols]

[1015] 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 receiving user sentiment data and storing it in a database, A means for analyzing stored emotional data and generating verbalized emotional feedback, Means for providing analysis results to the user, A means of generating a graph that visualizes changes in emotions based on user emotion data, A means of displaying the generated graph to the user, A means of generating digital reports containing emotional data and selectively sharing them with healthcare professionals, A system that includes this.

2. The system according to claim 1, further comprising means for visualizing changes in emotions on a weekly basis based on emotions selected by the user.

3. The system according to claim 1, which, when generating a digital report, has means for securely sharing data with medical professionals with the user's permission.

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