system

Abstract

We provide the system. [Solution] A means for receiving usage data that is periodically collected from user terminals via a digital communication network, An analysis means including a generative model that analyzes the aforementioned usage data and estimates the user's emotional state, Based on the user's emotional state obtained from the aforementioned analysis means, a proposal means selects and provides an appropriate care method. 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is important for an individual to appropriately grasp their own mental health status and receive care based on it, but many people do not have the means to do so. In addition, there is a problem that conventional uniform mental care methods cannot sufficiently respond to individual situations and have limited effects. As a result, many people have difficulty receiving appropriate mental care. To solve this problem, there is a need for a system that can detect individual stresses and emotional changes unconsciously by the user and provide personalized care.

Means for Solving the Problems

[0005] This invention provides an analysis means that receives usage data transmitted from a user terminal and analyzes the user's emotional state in real time using a generative model. Furthermore, it provides a system that includes a suggestion means that automatically selects and provides appropriate care to each individual user according to the analyzed emotional state. With this system, users can unconsciously receive support that is in line with their mental health condition, and an environment can be created in which they can receive mental care without feeling any psychological barriers.

[0006] A "user terminal" is an electronic device used to send and receive data via a digital communication network.

[0007] "Digital communication network" refers to the entire network infrastructure used to send and receive data as electronic signals.

[0008] "Usage data" refers to data that represents the user's behavior and status, such as SNS usage, message content, and notification responses, collected from the user's device.

[0009] A "generative model" refers to an artificial intelligence algorithm used to analyze collected data and predict or estimate specific outcomes.

[0010] "Analysis means" refers to the functions of software and hardware used to process received data and evaluate and interpret its contents.

[0011] "Emotional state" refers to an individual's mental state and is classified into categories such as positive, negative, and neutral.

[0012] "Suggestion method" refers to a system element that has the function of selecting a care method suitable for the user based on the analysis results and presenting it to the user.

[0013] "Notification means" refers to technical means used to transmit information to users, and includes various methods using voice, text, and visuals. [Brief explanation of the drawing]

[0014] [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]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (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.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number 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, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that analyzes a user's emotional state in real time using a generative model based on usage data automatically collected from a user's device, such as a smartphone, and proposes appropriate care. The main functions of this system and their processing flow are described below.

[0036] In this system, the terminal first continuously collects usage data related to the user's daily activities, specifically, the frequency of SNS usage, message content, and notification response time. The collected data is then transmitted to the server via a secure communication protocol.

[0037] The server temporarily stores the received data and analyzes it using a pre-trained generative model. This model utilizes natural language processing techniques to detect the user's emotions from messages and text. It integrates multiple emotional factors to estimate the current emotional state.

[0038] Next, the server automatically suggests the most appropriate care method to support the user's emotional state based on the analysis results. This suggestion becomes more accurate by using insights from a database of experts. For example, if the user's stress level is high, it may recommend stress-reducing music or encourage them to take a short break.

[0039] Finally, the device notifies the user of care suggestions received from the server, prompting them to take specific action. The notification appears as a push message, and the user can open the relaxation app by clicking the link included in the notification if necessary.

[0040] For example, if a user's social media interactions increase and contain a lot of negative tones, the server might estimate that the user's stress levels are rising. In that case, the device might send a notification to the user recommending the use of an app that includes breathing exercises to help them relax.

[0041] This system helps users receive appropriate care as needed, reducing psychological burden and supporting the maintenance of a healthy mental state, even without actively engaging in mental health care themselves.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device monitors and collects usage data related to the user's daily activities. This includes the frequency of use of social media apps, the content of messages, and the response time to notifications.

[0045] Step 2:

[0046] The device periodically sends the collected usage data to the server. This transmission is performed using a secure communication protocol.

[0047] Step 3:

[0048] The server temporarily stores the usage data received from the terminal and performs checks to verify the integrity and completeness of the data.

[0049] Step 4:

[0050] The server inputs the stored data into a generative model to analyze the user's emotional state. The generative model utilizes natural language processing techniques to classify the message content into emotional categories.

[0051] Step 5:

[0052] Based on the analysis, the server estimates the user's current emotional state. This estimation integrates numerous emotional factors to calculate the user's emotional score.

[0053] Step 6:

[0054] The server automatically selects the appropriate care method based on the emotional state. This selection incorporates insights from a database of experts, generating highly accurate recommendations.

[0055] Step 7:

[0056] The server sends the generated care suggestion to the terminal.

[0057] Step 8:

[0058] The device notifies the user of care suggestions received from the server. This notification includes a push message prompting specific action.

[0059] Step 9:

[0060] The user receives notifications from the device and takes the suggested care actions as needed. The results are reported to the server as feedback by the device.

[0061] (Example 1)

[0062] 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."

[0063] In recent years, problems caused by stress and emotional fluctuations have been increasing, highlighting the growing importance of individual users recognizing their own emotional states and providing appropriate care. However, many users lack the means to properly understand their own emotional states and therefore struggle to take appropriate action based on them. Consequently, there is a need for a system that analyzes users' emotional states in real time based on their behavioral data and automatically suggests appropriate care methods.

[0064] 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.

[0065] In this invention, the server includes means for receiving behavioral data periodically collected from a user device via an information and communication network, analysis means including a generative AI model that analyzes the behavioral data and estimates the user's emotional state, and proposal means for determining and providing an appropriate support method based on the user's emotional state obtained from the analysis means. This enables the user to receive appropriate care without having to actively manage their own emotional state.

[0066] A "user device" is an electronic device that a user directly operates to send and receive information.

[0067] An "information and communication network" is a network structure for electronically transferring digital data.

[0068] "Periodicly collected behavioral data" refers to data that is automatically collected at regular intervals, providing information about the user's behavior.

[0069] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and performs specific analyses or predictions.

[0070] "Analysis means" refers to a method or apparatus for using collected data to derive specific results.

[0071] "Emotional state" refers to information that indicates the user's psychological or emotional condition.

[0072] "Proposed means" refers to a method or device for providing users with appropriate actions or instructions based on the analysis results.

[0073] This invention is a system that analyzes user behavior data and proposes appropriate care. To achieve this, it operates based on the user's smart device (user device) and a server located in the cloud.

[0074] The devices, such as smartphones and tablets, collect data about the user's daily behavior. Specifically, this includes the frequency of use of social media apps, the content of messages, and the response time to notifications, and this data is obtained using APIs and sensors within the device.

[0075] The collected data is transmitted to the server via encrypted and secure communication. The server then temporarily stores the data and analyzes it using a pre-trained generative AI model. In particular, it focuses on extracting emotional patterns from text data using natural language processing technology. This generative AI model has the ability to integrate different emotional factors and infer the user's current emotional state.

[0076] Based on the analysis results, the server automatically determines and creates a suggestion for the care method best suited to the user. Expert opinions are stored in a database and referenced to improve the accuracy of the suggestions. For example, if a high level of stress is detected, relaxation activities will be recommended.

[0077] The suggestion is sent to the device as a push notification, prompting the user to take specific action. The user can then use the relaxation app by checking the notification and tapping the link within the app if necessary.

[0078] For example, when a user's communication on social media becomes more active and negative expressions increase, the server detects the increase in stress. In this case, the device displays a notification prompting the user to use an app that suggests breathing exercises.

[0079] Example of a prompt:

[0080] "Please analyze data on users who have recently been making an increase in negative comments on social media and suggest appropriate support measures."

[0081] This system allows users to receive appropriate care as needed, without having to actively manage their own emotional state, thereby reducing psychological burden and promoting the maintenance of a healthy mental state.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The device collects data about the user's daily activities. Specifically, this includes data such as the frequency of use of social media apps, the content of messages, and the response time to notifications. This data is acquired through sensors and APIs within the device and prepared as input. It is essential that this input data is reliably acquired for subsequent analysis.

[0085] Step 2:

[0086] The device transmits the collected data to the server using a secure communication protocol. Because the data content is privacy-related, encryption technology is used to ensure security during transmission. This transmitted data becomes the primary input information on the server.

[0087] Step 3:

[0088] The server temporarily stores the received data. Then, it analyzes the stored data using a generative AI model. This model utilizes natural language processing techniques to extract emotion-related patterns from the input messages and text data. For example, if a user's message contains many words indicating negative emotions, the data processing includes counting their frequency. The output of the analysis is inferred information about the user's emotional state.

[0089] Step 4:

[0090] The server generates appropriate care suggestions based on the analysis results. These suggestions are created by referencing a database containing expert knowledge. If high stress levels are detected, the server determines how to support the user, such as recommending relaxation techniques. The care suggestions are then ready to be sent to the terminal as output.

[0091] Step 5:

[0092] The device notifies the user of care suggestions received from the server. Specifically, these are displayed as push notifications, which the user can use as a trigger to take action. By tapping the link included in the notification, the user launches the suggested relaxation app and receives the provided care. As an output, the notification results in prompting the user to take action.

[0093] Through this step, users can automatically receive appropriate emotional care while utilizing the system.

[0094] (Application Example 1)

[0095] 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."

[0096] Traditional systems have struggled to provide content optimized for each user's emotional state, often resulting in the delivery of uniform content. Therefore, there is a growing need for systems that can deliver personalized content tailored to each user's emotions.

[0097] 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.

[0098] In this invention, the server includes means for receiving usage information periodically collected from user devices via an information network; analysis means including a generative model that analyzes the usage information and evaluates the individual's emotional state; and suggestion means for selecting and providing appropriate media content based on the individual's emotional state obtained from the analysis means. This makes it possible to provide personalized media content that corresponds to the user's emotional state.

[0099] A "user device" is a digital device used by individual users to manipulate and receive data.

[0100] An "information network" is a network used to send and receive digital data over a long distance.

[0101] "Usage information" refers to data collected through user devices that relates to the activities and behaviors of individual users.

[0102] A "generative model" is an artificial intelligence technology designed to analyze user data and make predictions based on a specific algorithm.

[0103] "Analysis means" refers to a device or method for detecting and analyzing the emotional state of individual users based on collected data.

[0104] "Proposed means" refers to a device or method for selecting and providing optimal media content to a user based on an analyzed emotional state.

[0105] "Media content" refers to personalized digital information, such as music, videos, and articles, that is delivered according to the user's emotional state.

[0106] This system consists of user devices, an information network, and a server. User devices are digital devices such as smartphones and tablets, which users use for their daily activities. The user devices transmit usage information to the server via the information network. This usage information includes user activity and message data.

[0107] The server operates by integrating multiple software programs. Specifically, it uses a generative AI model to analyze usage information and evaluate an individual's emotional state. This model utilizes natural language processing technology to identify emotions from messages and behavioral data. The server uses cloud technologies such as Google Cloud Platform to collect, store, and analyze data.

[0108] After performing the analysis, the server uses the suggested methods to select appropriate media content. This process employs machine learning algorithms to suggest content that best suits the user's emotional state. For example, this might include recommending relaxation apps such as Relax Melodies.

[0109] The user's device receives a notification suggesting selected media content. This notification appears as a push message on the user's screen, and by tapping the message, the user can immediately view the corresponding content. By providing an experience tailored to the user's emotions in this way, effects such as stress reduction and relaxation can be expected.

[0110] For example, if a user submits data suggesting work-related stress, the server can recommend music or short videos that are effective in relieving stress. Another example of a prompt used in a generative AI model might be, "Analyze the user's recent social media activity to understand their emotions and recommend relaxing content." This provides a more personalized and better user experience for each individual user.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user's smartphone collects usage data. The input is user activity data (e.g., frequency of SNS use, message content, notification response time). This data is obtained from sensors and applications within the device and structured in JSON format.

[0114] Step 2:

[0115] The terminal sends the collected data to the server using a secure protocol (e.g., HTTPS). The output is usage data packaged for transmission. The server receives this data and temporarily stores it in a database.

[0116] Step 3:

[0117] The server feeds the stored data into a generating AI model. The input is collected usage data. Based on this data, natural language processing techniques are used to analyze messages and behavioral data and estimate the user's emotional state. The output is the category of the estimated emotional state.

[0118] Step 4:

[0119] The server analyzes the estimated emotional state and selects appropriate media content. The input is a database of emotional state categories and suggested content. Using a machine learning algorithm, it searches for the content (e.g., music, video) that best matches the emotion and outputs the selection results.

[0120] Step 5:

[0121] The device sends a push notification to the user based on the selection results received from the server. The input consists of the selected content information and the user's contact information. The output is a push message displayed on the user's device, which includes a direct link to the suggested content.

[0122] Step 6:

[0123] The user checks the notification and taps the suggested content to launch the application. The input is the link included in the push notification. The user's action plays the relevant media content, and the output is the user's viewing experience.

[0124] 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.

[0125] This invention is a system that uses usage data collected from user terminals and emotional information obtained by an emotion engine to comprehensively analyze the user's emotional state through a generative model and propose appropriate care accordingly. The following describes how to implement this system in detail.

[0126] First, the device continuously collects data about the user's daily activities. This data includes social media activity, message content, notification response times, and emotional indicators such as facial expressions and voice patterns. The emotion engine uses the device's built-in camera and microphone to recognize emotions in real time from the user's facial expressions and voice.

[0127] The server receives data transmitted from the terminal and emotional information collected by the emotion engine. This data is stored in a database on the server and used as input for a generative model. The generative model utilizes natural language processing and computer vision technologies to analyze the user's text data and image / audio data, and integrates multiple emotional indicators in a short amount of time.

[0128] Next, the server estimates the user's overall emotional state based on the analysis results and automatically selects the most appropriate care method for the user. This suggestion integrates information from a database of experts and is customized to address the user's specific problems.

[0129] The device notifies the user of selected care suggestions. This includes notifications prompting the launch of relaxation apps and instructions for stress reduction techniques that can be easily implemented in daily life. For example, if the emotion engine and generative model detect that the user is experiencing stress at work, the device sends a notification suggesting a short relaxation period.

[0130] This system tracks users' emotional states in real time and promptly provides appropriate solutions as needed, thereby lowering psychological barriers and promoting users' mental well-being.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device collects data on the user's daily activities. This includes usage of social media apps, message data, response data to notifications, and facial expressions and voice patterns captured using the camera and microphone.

[0134] Step 2:

[0135] The emotion engine built into the device analyzes the user's facial expressions and voice changes through the camera and microphone, acquiring emotional information in real time. This data is categorized into emotional categories such as joy, anger, and sadness.

[0136] Step 3:

[0137] The device periodically sends collected usage data and sentiment information to the server. The data is encrypted before transmission to ensure privacy.

[0138] Step 4:

[0139] The server temporarily stores all data received from the terminal and verifies data integrity. After verification, it performs necessary preprocessing and prepares the data for supply to the generative model.

[0140] Step 5:

[0141] The server uses generative models to analyze data. It analyzes message data using natural language processing techniques, while simultaneously analyzing emotions from facial expressions and voice using computer vision techniques.

[0142] Step 6:

[0143] The server uses the analysis results to comprehensively estimate the user's current emotional state. This estimation takes into account the weight of each emotional factor and calculates an overall emotional score.

[0144] Step 7:

[0145] The server automatically selects the optimal care method based on the estimated emotional state. The selected care method is personalized for each user, incorporating insights from a database of experts.

[0146] Step 8:

[0147] The server sends the selected care method to the terminal and prepares to present it to the user. This includes suggestions tailored to the user's interests and circumstances.

[0148] Step 9:

[0149] The device notifies the user of care suggestions received from the server. Specifically, it displays a message on the screen and recommends necessary applications or actions.

[0150] Step 10:

[0151] The user chooses whether to accept the device notification and take action on the care instructions. If action is taken, feedback is sent back to the server by the device and used for subsequent analysis.

[0152] (Example 2)

[0153] 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".

[0154] In modern society, mental stress and emotional instability are increasingly impacting health. However, there is a lack of systems that can quickly detect this and propose appropriate care tailored to individual circumstances. Therefore, there is a need for technology that can accurately estimate a user's emotional state in real time and propose effective care.

[0155] 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.

[0156] In this invention, the server includes means for receiving behavioral data periodically collected from a user terminal via a digital communication network; means for analyzing facial expressions and voice using sensors built into the terminal and acquiring emotional information in real time; analysis means including a generative model that analyzes the behavioral data and emotional information and estimates the user's emotional state from multiple perspectives by combining natural language processing technology and image / voice analysis technology; and proposal means that, based on the emotional state obtained from the analysis means, select an appropriate care method from an expert knowledge base and individually customize it. This makes it possible to evaluate the user's emotions from multiple perspectives and to present care that can be responded to quickly and effectively and individually.

[0157] A "user terminal" is a portable or stationary electronic device that a user uses on a daily basis to enable data collection and communication.

[0158] A "digital communication network" is a network infrastructure for sending and receiving information in digital format.

[0159] "Behavioral data" refers to recorded information about a user's daily activities, including in the form of text, images, and audio.

[0160] A "sensor" is a device necessary for appropriately acquiring environmental information, and includes devices such as cameras and microphones.

[0161] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions and voice.

[0162] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0163] "Image and audio analysis technology" refers to processing techniques for extracting specific information from image and audio data and evaluating its meaning.

[0164] A "generative model" is an algorithm or system for generating new information from multiple data points.

[0165] A "specialist knowledge base" is a database in which knowledge and information in a specific field are systematically accumulated.

[0166] A "proposal method" refers to a method or system for selecting and presenting the optimal solution according to the user's needs.

[0167] This invention is a system that operates in combination with a user terminal and a server to improve the mental health of the user. First, the terminal operates as a portable electronic device such as a smartphone or tablet to continuously collect data on the user's daily activities. The camera and microphone built into this terminal function as sensors, analyzing the user's facial expressions and voice patterns to acquire emotional information.

[0168] Next, the device transmits the collected behavioral data and emotional information to a server via a digital communication network. The server stores the received data in a database. This data is then analyzed using a generative AI model. The generative AI model utilizes natural language processing techniques to classify text data into emotional categories and uses image and audio analysis techniques to comprehensively estimate the user's emotional state.

[0169] Based on the analysis results, the server selects the most appropriate care method from its expert knowledge base. This selection process is customized to the user's individual needs. The selected care method is then sent back to the device and notified to the user. The notification includes a link to an app that encourages the implementation of intuitive relaxation techniques, as well as voice-guided instructions.

[0170] For example, if the device detects that a user is experiencing stress in a busy work environment, it will display a message prompting them to take a deep breath or a notification encouraging them to launch an app that provides short relaxing music.

[0171] An example of a prompt statement would be the command, "Suggest an appropriate relaxation method based on the user's current emotional state," which is used as input to the system. This invention allows for real-time tracking of the user's emotional state and the provision of prompt, personalized care as needed.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The device collects user behavioral and emotional data. It uses sensor information from its built-in camera and microphone as input. For data processing, it analyzes facial expressions using an image recognition algorithm and analyzes voice tone using speech recognition technology. As a result of these processes, it outputs raw data associated with the user's emotional state.

[0175] Step 2:

[0176] The terminal transmits collected behavioral data and emotional information to the server via a digital communication network. The input is the collected raw data, which is transmitted using network communication. As part of the data processing, the data is encrypted according to security protocols and then output to the server.

[0177] Step 3:

[0178] The server stores the received data in a database and inputs it into the generative AI model. The data sent from the terminal is used as input. Data processing involves normalization and cleaning of the data, converting it into an analyzable format. This results in a tidy dataset being output to the generative AI model.

[0179] Step 4:

[0180] The server analyzes the user's emotional state using a generative AI model. Normalized data from a database is used as input. The generative AI model utilizes natural language processing and image / speech analysis techniques to perform data calculations that integrate multiple emotional indicators. The output provides a multifaceted evaluation of the user's emotional state.

[0181] Step 5:

[0182] The server selects appropriate care methods from an expert knowledge base based on the obtained emotional state. It uses the analysis results of the emotional state as input to perform data calculations to identify the optimal care method from the knowledge base. As a result, a customized care method is output.

[0183] Step 6:

[0184] The device notifies the user of the selected care method. It receives specific instructions for the care method from the server as input. The notification output includes visual notification pop-ups and voice guidance to encourage the user to easily follow the instructions.

[0185] (Application Example 2)

[0186] 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 device 14 will be referred to as the "terminal."

[0187] In modern society, the stress and anxiety that users experience in their daily lives are increasing, and these can threaten an individual's mental and physical health. In particular, rapid emotional changes can be a harbinger of security threats or incidents, making early detection and appropriate response crucial. However, current systems lack efficient means to analyze an individual's emotional state in real time and quickly propose security measures.

[0188] 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.

[0189] In this invention, the server includes means for receiving usage data and sentiment indicators collected periodically from a user terminal via a digital communication network; analysis means including a generative model for analyzing the sentiment indicators in real time and estimating the user's emotional state; and proposal means for selecting and providing security measures based on the user's emotional state obtained from the analysis means. This makes it possible to quickly grasp the user's emotional state and immediately present appropriate security measures.

[0190] A "user terminal" is a device that functions as an interface with the user and has the function of collecting sentiment indicators and usage data.

[0191] A "digital communication network" is an infrastructure for transmitting data, providing a path for exchanging information between user terminals and servers.

[0192] "Usage data" refers to data that includes information about users' digital activities and behaviors, and is used for analyzing their emotional state.

[0193] "Emotional indicators" refer to information such as a user's facial expressions and voice patterns, and serve as criteria for evaluating a user's emotional state.

[0194] A "generative model" is a model that uses algorithms to analyze a user's text, voice, and video data to estimate their emotional state.

[0195] "Analysis means" refers to a device or system that has the function of analyzing and estimating emotional states based on collected usage data and emotional indicators.

[0196] "Proposed means" refers to a device or function that selects and provides appropriate countermeasures based on the user's emotional state obtained through analysis.

[0197] "Security measures" include proposed guidelines and actions to ensure user safety, and are implemented in response to emotional changes.

[0198] This system consists of a user terminal, a server, and a communication network. The user terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice. This allows for obtaining an indicator of the user's emotions in daily life. The data collected as this emotional indicator is transmitted to the server via the digital communication network.

[0199] The server first analyzes image data using the Python-based OpenCV library and converts the audio data to text using the Google Cloud Speech-to-Text API. Next, a generative model using TENSORFLOW® estimates the user's emotional state using this data. This analysis method makes it possible to estimate emotions in real time from the user's facial expressions and voice.

[0200] Based on the estimated emotional state, the server proposes appropriate security measures. These proposals are provided as notifications to the user's terminal. These proposals include specific countermeasures, such as sending notifications to close friends or security companies when the user feels uneasy. As a result, it is possible to respond quickly and flexibly according to the user's emotional state, contributing to ensuring personal safety.

[0201] This system can quickly detect, for example, a user's sudden anxiety in the evening and send timely and accurate notifications to pre-designated emergency contacts. This allows users to live their daily lives with a focus on safety.

[0202] The following prompt statements can be used as input to the generative model.

[0203] "Please explain the steps to detect if a user is experiencing stress and send an emergency notification to their friends."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The device captures the user's facial expressions with a camera and records their voice with a microphone. The input consists of facial image data and audio data, while the output consists of raw image and audio files.

[0207] Step 2:

[0208] The device analyzes the collected facial image data using the OpenCV library. The input is facial image data, and the output is features that indicate the user's emotions (e.g., percentage of smiles, frequency of anxious expressions). This analysis enables real-time detection of changes in facial expressions.

[0209] Step 3:

[0210] The device converts audio data into text via the Google Cloud Speech-to-Text API. The input is audio data, and the output is the audio content in text format. This makes it possible to obtain sentiment indicators from the audio.

[0211] Step 4:

[0212] The server inputs the analyzed facial feature vectors and text data into a generating AI model to estimate the user's overall emotional state. The input consists of feature vectors and text data, and the output is the estimated emotional state. This step involves data processing, enabling multifaceted emotional analysis.

[0213] Step 5:

[0214] The server selects appropriate security measures based on the estimated emotional state of the user. The input is emotional state data, and the output is recommended security measures (e.g., notifying emergency contacts). During this process, the system consults a database of experts to fine-tune the measures.

[0215] Step 6:

[0216] The server sends a notification to the terminal regarding the selected security measures. The input is the content of the security measures, and the output is the notification displayed on the terminal. This notification allows the user to quickly understand and implement the appropriate measures.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is a system that analyzes a user's emotional state in real time using a generative model, based on usage data automatically collected from a user's device such as a smartphone, and proposes appropriate care. The main functions of this system and their processing flow are described below.

[0234] In this system, the terminal first continuously collects usage data related to the user's daily activities, specifically, the frequency of SNS use, message content, and notification response time. The collected data is then transmitted to the server via a secure communication protocol.

[0235] The server temporarily stores the received data and analyzes it using a pre-trained generative model. This model utilizes natural language processing techniques to detect the user's emotions from messages and text. It integrates multiple emotional factors to estimate the current emotional state.

[0236] Next, the server automatically suggests the most appropriate care method to support the user's emotional state based on the analysis results. This suggestion becomes more accurate by using insights from a database of experts. For example, if the user's stress level is high, it may recommend stress-reducing music or encourage them to take a short break.

[0237] Finally, the device notifies the user of care suggestions received from the server, prompting them to take specific action. The notification appears as a push message, and the user can open the relaxation app by clicking the link included in the notification if necessary.

[0238] For example, if a user's social media interactions increase and contain a lot of negative tones, the server might estimate that the user's stress levels are rising. In that case, the device might send a notification to the user recommending the use of an app that includes breathing exercises to help them relax.

[0239] This system helps users receive appropriate care as needed, reducing psychological burden and supporting the maintenance of a healthy mental state, even without actively engaging in mental health care themselves.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The device monitors and collects usage data related to the user's daily activities. This includes the frequency of use of social media apps, the content of messages, and the response time to notifications.

[0243] Step 2:

[0244] The device periodically sends the collected usage data to the server. This transmission is performed using a secure communication protocol.

[0245] Step 3:

[0246] The server temporarily stores the usage data received from the terminal and performs checks to verify the integrity and completeness of the data.

[0247] Step 4:

[0248] The server inputs the stored data into a generative model to analyze the user's emotional state. The generative model utilizes natural language processing techniques to classify the message content into emotional categories.

[0249] Step 5:

[0250] Based on the analysis, the server estimates the user's current emotional state. This estimation integrates numerous emotional factors to calculate the user's emotional score.

[0251] Step 6:

[0252] The server automatically selects the appropriate care method based on the emotional state. This selection incorporates insights from a database of experts, generating highly accurate recommendations.

[0253] Step 7:

[0254] The server sends the generated care suggestion to the terminal.

[0255] Step 8:

[0256] The device notifies the user of care suggestions received from the server. This notification includes a push message prompting specific action.

[0257] Step 9:

[0258] The user receives notifications from the device and takes the suggested care actions as needed. The results are reported to the server as feedback by the device.

[0259] (Example 1)

[0260] 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 as the "terminal".

[0261] In recent years, problems caused by stress and emotional fluctuations have been increasing, highlighting the growing importance of individual users recognizing their own emotional states and providing appropriate care. However, many users lack the means to properly understand their own emotional states and therefore struggle to take appropriate action based on them. Consequently, there is a need for a system that analyzes users' emotional states in real time based on their behavioral data and automatically suggests appropriate care methods.

[0262] 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.

[0263] In this invention, the server includes means for receiving behavioral data periodically collected from a user device via an information and communication network; analysis means including a generative AI model that analyzes the behavioral data and estimates the user's emotional state; and proposal means that determines and provides an appropriate support method based on the user's emotional state obtained from the analysis means. This enables the user to receive appropriate care without having to actively manage their own emotional state.

[0264] A "user device" is an electronic device that a user directly operates to send and receive information.

[0265] An "information and communication network" is a network structure for electronically transferring digital data.

[0266] "Periodicly collected behavioral data" refers to data that is automatically collected at regular intervals, providing information about the user's behavior.

[0267] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and performs specific analyses or predictions.

[0268] "Analysis means" refers to a method or apparatus for using collected data to derive specific results.

[0269] "Emotional state" refers to information that indicates the user's psychological or emotional condition.

[0270] "Proposed means" refers to a method or device for providing users with appropriate actions or instructions based on the analysis results.

[0271] This invention is a system that analyzes user behavior data and proposes appropriate care. To achieve this, it operates based on the user's smart device (user device) and a server located in the cloud.

[0272] The devices, such as smartphones and tablets, collect data about the user's daily behavior. Specifically, this includes the frequency of use of social media apps, the content of messages, and the response time to notifications, and this data is obtained using APIs and sensors within the device.

[0273] The collected data is transmitted to the server via encrypted and secure communication. The server then temporarily stores the data and analyzes it using a pre-trained generative AI model. In particular, it focuses on extracting emotional patterns from text data using natural language processing technology. This generative AI model has the ability to integrate different emotional factors and infer the user's current emotional state.

[0274] Based on the analysis results, the server automatically determines and creates a suggestion for the care method best suited to the user. Expert opinions are stored in a database and referenced to improve the accuracy of the suggestions. For example, if a high level of stress is detected, relaxation activities will be recommended.

[0275] The suggestion is sent to the device as a push notification, prompting the user to take specific action. The user can then use the relaxation app by checking the notification and tapping the link within the app if necessary.

[0276] For example, when a user's communication on social media becomes more active and negative expressions increase, the server detects the increase in stress. In this case, the device displays a notification prompting the user to use an app that suggests breathing exercises.

[0277] Example of a prompt:

[0278] "Please analyze data on users who have recently been making an increase in negative comments on social media and suggest appropriate support measures."

[0279] This system allows users to receive appropriate care as needed, without having to actively manage their own emotional state, thereby reducing psychological burden and promoting the maintenance of a healthy mental state.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The device collects data about the user's daily activities. Specifically, this includes data such as the frequency of use of social media apps, the content of messages, and the response time to notifications. This data is acquired through sensors and APIs within the device and prepared as input. It is essential that this input data is reliably acquired for subsequent analysis.

[0283] Step 2:

[0284] The terminal uses a secure communication protocol to send the collected data to the server. Since the content of the data involves privacy, security is ensured by encrypting the data before transmission. This transmitted data becomes the primary input information on the server.

[0285] Step 3:

[0286] The server temporarily stores the received data. Then, it analyzes the stored data using a generated AI model. This model utilizes natural language processing technology to extract patterns related to emotions from the input messages and text data. For example, when the user's message contains many words indicating negative emotions, data processing such as counting the frequency is performed. The output of the analysis is speculative information regarding the user's emotional state.

[0287] Step 4:

[0288] The server generates appropriate care proposals based on the analysis results. These proposals are created by referring to a database filled with experts' knowledge. When it is determined that the stress is high, the server decides on support methods such as recommending relaxation techniques to the user. The care proposals are ready to be sent to the terminal as output.

[0289] Step 5:

[0290] The terminal notifies the user of the care proposals received from the server. Specifically, it is displayed as a push notification, and the user can take action triggered by this. By tapping on the link included in the notification, the user can launch the recommended relaxation app and receive the provided care. As output, the notification brings about the result of prompting the user's action.

[0291] Through this step, the user can automatically receive appropriate emotional care while utilizing the system.

[0292] (Application Example 1)

[0293] 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."

[0294] Traditional systems have struggled to provide content optimized for each user's emotional state, often resulting in the delivery of uniform content. Therefore, there is a growing need for systems that can deliver personalized content tailored to each user's emotions.

[0295] 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.

[0296] In this invention, the server includes means for receiving usage information periodically collected from user devices via an information network; analysis means including a generative model that analyzes the usage information and evaluates the individual's emotional state; and suggestion means for selecting and providing appropriate media content based on the individual's emotional state obtained from the analysis means. This makes it possible to provide personalized media content that corresponds to the user's emotional state.

[0297] A "user device" is a digital device used by individual users to manipulate and receive data.

[0298] An "information network" is a network used to send and receive digital data over a long distance.

[0299] "Usage information" refers to data collected through user devices that relates to the activities and behaviors of individual users.

[0300] A "generative model" is an artificial intelligence technology designed to analyze user data and make predictions based on a specific algorithm.

[0301] "Analysis means" refers to a device or method for detecting and analyzing the emotional state of individual users based on collected data.

[0302] "Proposed means" refers to a device or method for selecting and providing optimal media content to a user based on an analyzed emotional state.

[0303] "Media content" refers to personalized digital information, such as music, videos, and articles, that is delivered according to the user's emotional state.

[0304] This system consists of user devices, an information network, and a server. User devices are digital devices such as smartphones and tablets, which users use for their daily activities. The user devices transmit usage information to the server via the information network. This usage information includes user activity and message data.

[0305] The server operates by integrating multiple software programs. Specifically, it uses a generative AI model to analyze usage information and evaluate an individual's emotional state. This model utilizes natural language processing techniques to identify emotions from messages and behavioral data. The server uses cloud technologies such as Google Cloud Platform to collect, store, and analyze data.

[0306] After performing the analysis, the server uses the suggested methods to select appropriate media content. This process employs machine learning algorithms to suggest content that best suits the user's emotional state. For example, this might include recommending relaxation apps such as Relax Melodies.

[0307] A notification for proposing selected media content is sent to the user device. This notification is displayed as a push message on the user's screen, and by tapping on the message, the user can immediately enjoy the corresponding content. In this way, by providing an experience tailored to the user's emotions, effects such as stress relief and relaxation can be expected.

[0308] As a specific example, when the user sends data suggesting stress at work, the server can recommend music or short videos that are effective for stress relief. Also, as an example of a prompt sentence used in the generative AI model, it is something like "Analyze the emotions from the user's recent SNS activities and recommend relaxing content." This provides a better user experience customized for individual users.

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user's smartphone collects usage data. The input is the user's activity data (e.g., SNS usage frequency, message content, notification response time). These data are obtained from sensors and applications within the device and structured in JSON format.

[0312] Step 2:

[0313] The data collected by the terminal is sent to the server using a secure protocol (e.g., HTTPS). What is output is the usage data packaged for transmission. On the server side, this data is received and temporarily stored in a database.

[0314] Step 3:

[0315] The server feeds the stored data into a generating AI model. The input is collected usage data. Based on this data, natural language processing techniques are used to analyze messages and behavioral data and estimate the user's emotional state. The output is the category of the estimated emotional state.

[0316] Step 4:

[0317] The server analyzes the estimated emotional state and selects appropriate media content. The input is a database of emotional state categories and suggested content. Using a machine learning algorithm, it searches for the content (e.g., music, video) that best matches the emotion and outputs the selection results.

[0318] Step 5:

[0319] The device sends a push notification to the user based on the selection results received from the server. The input consists of the selected content information and the user's contact information. The output is a push message displayed on the user's device, which includes a direct link to the suggested content.

[0320] Step 6:

[0321] The user checks the notification and taps the suggested content to launch the application. The input is the link included in the push notification. The user's action plays the relevant media content, and the output is the user's viewing experience.

[0322] 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.

[0323] This invention is a system that uses usage data collected from user terminals and emotional information obtained by an emotion engine to comprehensively analyze the user's emotional state through a generative model and propose appropriate care accordingly. The following describes how to implement this system in detail.

[0324] First, the device continuously collects data about the user's daily activities. This data includes social media activity, message content, notification response times, and emotional indicators such as facial expressions and voice patterns. The emotion engine uses the device's built-in camera and microphone to recognize emotions in real time from the user's facial expressions and voice.

[0325] The server receives data transmitted from the terminal and emotional information collected by the emotion engine. This data is stored in a database on the server and used as input for a generative model. The generative model utilizes natural language processing and computer vision technologies to analyze the user's text data and image / audio data, and integrates multiple emotional indicators in a short amount of time.

[0326] Next, the server estimates the user's overall emotional state based on the analysis results and automatically selects the most appropriate care method for the user. This suggestion integrates information from a database of experts and is customized to address the user's specific problems.

[0327] The device notifies the user of selected care suggestions. This includes notifications prompting the launch of relaxation apps and instructions for stress reduction techniques that can be easily implemented in daily life. For example, if the emotion engine and generative model detect that the user is experiencing stress at work, the device sends a notification suggesting a short relaxation period.

[0328] This system tracks users' emotional states in real time and promptly provides appropriate solutions as needed, thereby lowering psychological barriers and promoting users' mental well-being.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The device collects data on the user's daily activities. This includes usage of social media apps, message data, response data to notifications, and facial expressions and voice patterns captured using the camera and microphone.

[0332] Step 2:

[0333] The emotion engine built into the device analyzes the user's facial expressions and voice changes through the camera and microphone, acquiring emotional information in real time. This data is categorized into emotional categories such as joy, anger, and sadness.

[0334] Step 3:

[0335] The device periodically sends collected usage data and sentiment information to the server. The data is encrypted before transmission to ensure privacy.

[0336] Step 4:

[0337] The server temporarily stores all data received from the terminal and verifies data integrity. After verification, it performs necessary preprocessing and prepares the data for supply to the generative model.

[0338] Step 5:

[0339] The server uses generative models to analyze data. It analyzes message data using natural language processing techniques, while simultaneously analyzing emotions from facial expressions and voice using computer vision techniques.

[0340] Step 6:

[0341] The server uses the analysis results to comprehensively estimate the user's current emotional state. This estimation takes into account the weight of each emotional factor and calculates an overall emotional score.

[0342] Step 7:

[0343] The server automatically selects the optimal care method based on the estimated emotional state. The selected care method is personalized for each user, incorporating insights from a database of experts.

[0344] Step 8:

[0345] The server sends the selected care method to the terminal and prepares to present it to the user. This includes suggestions tailored to the user's interests and circumstances.

[0346] Step 9:

[0347] The device notifies the user of care suggestions received from the server. Specifically, it displays a message on the screen and recommends necessary applications or actions.

[0348] Step 10:

[0349] The user chooses whether to accept the device notification and take action on the care instructions. If action is taken, feedback is sent back to the server by the device and used for subsequent analysis.

[0350] (Example 2)

[0351] 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".

[0352] In modern society, mental stress and emotional instability are increasingly impacting health. However, there is a lack of systems that can quickly detect this and propose appropriate care tailored to individual circumstances. Therefore, there is a need for technology that can accurately estimate a user's emotional state in real time and propose effective care.

[0353] 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.

[0354] In this invention, the server includes means for receiving behavioral data periodically collected from a user terminal via a digital communication network; means for analyzing facial expressions and voice using sensors built into the terminal and acquiring emotional information in real time; analysis means including a generative model that analyzes the behavioral data and emotional information and estimates the user's emotional state from multiple perspectives by combining natural language processing technology and image / voice analysis technology; and proposal means that, based on the emotional state obtained from the analysis means, select an appropriate care method from an expert knowledge base and individually customize it. This makes it possible to evaluate the user's emotions from multiple perspectives and to present care that can be responded to quickly and effectively and individually.

[0355] A "user terminal" is a portable or stationary electronic device that a user uses on a daily basis to enable data collection and communication.

[0356] A "digital communication network" is a network infrastructure for sending and receiving information in digital format.

[0357] "Behavioral data" refers to recorded information about a user's daily activities, including in the form of text, images, and audio.

[0358] A "sensor" is a device necessary for appropriately acquiring environmental information, and includes devices such as cameras and microphones.

[0359] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions and voice.

[0360] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0361] "Image and audio analysis technology" refers to processing techniques for extracting specific information from image and audio data and evaluating its meaning.

[0362] A "generative model" is an algorithm or system for generating new information from multiple data points.

[0363] A "specialist knowledge base" is a database in which knowledge and information in a specific field are systematically accumulated.

[0364] A "proposal method" refers to a method or system for selecting and presenting the optimal solution according to the user's needs.

[0365] This invention is a system that operates in combination with a user terminal and a server to improve the mental health of the user. First, the terminal operates as a portable electronic device such as a smartphone or tablet to continuously collect data on the user's daily activities. The camera and microphone built into this terminal function as sensors, analyzing the user's facial expressions and voice patterns to acquire emotional information.

[0366] Next, the device transmits the collected behavioral data and emotional information to a server via a digital communication network. The server stores the received data in a database. This data is then analyzed using a generative AI model. The generative AI model utilizes natural language processing techniques to classify text data into emotional categories and uses image and audio analysis techniques to comprehensively estimate the user's emotional state.

[0367] Based on the analysis results, the server selects the most appropriate care method from its expert knowledge base. This selection process is customized to the user's individual needs. The selected care method is then sent back to the device and notified to the user. The notification includes a link to an app that encourages the implementation of intuitive relaxation techniques, as well as voice-guided instructions.

[0368] For example, if the device detects that a user is experiencing stress in a busy work environment, it will display a message prompting them to take a deep breath or a notification encouraging them to launch an app that provides short relaxing music.

[0369] An example of a prompt statement would be the command, "Suggest an appropriate relaxation method based on the user's current emotional state," which is used as input to the system. This invention allows for real-time tracking of the user's emotional state and the provision of prompt, personalized care as needed.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The device collects user behavioral and emotional data. It uses sensor information from its built-in camera and microphone as input. For data processing, it analyzes facial expressions using an image recognition algorithm and analyzes voice tone using speech recognition technology. As a result of these processes, it outputs raw data associated with the user's emotional state.

[0373] Step 2:

[0374] The terminal transmits collected behavioral data and emotional information to the server via a digital communication network. The input is the collected raw data, which is transmitted using network communication. As part of the data processing, the data is encrypted according to security protocols and then output to the server.

[0375] Step 3:

[0376] The server stores the received data in a database and inputs it into the generative AI model. The data sent from the terminal is used as input. Data processing involves normalization and cleaning of the data, converting it into an analyzable format. This results in a tidy dataset being output to the generative AI model.

[0377] Step 4:

[0378] The server analyzes the user's emotional state using a generative AI model. Normalized data from a database is used as input. The generative AI model utilizes natural language processing and image / speech analysis techniques to perform data calculations that integrate multiple emotional indicators. The output provides a multifaceted evaluation of the user's emotional state.

[0379] Step 5:

[0380] The server selects appropriate care methods from an expert knowledge base based on the obtained emotional state. It uses the analysis results of the emotional state as input to perform data calculations to identify the optimal care method from the knowledge base. As a result, a customized care method is output.

[0381] Step 6:

[0382] The device notifies the user of the selected care method. It receives specific instructions for the care method from the server as input. The notification output includes visual notification pop-ups and voice guidance to encourage the user to easily follow the instructions.

[0383] (Application Example 2)

[0384] 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."

[0385] In modern society, the stress and anxiety that users experience in their daily lives are increasing, and these can threaten an individual's mental and physical health. In particular, rapid emotional changes can be a harbinger of security threats or incidents, making early detection and appropriate response crucial. However, current systems lack efficient means to analyze an individual's emotional state in real time and quickly propose security measures.

[0386] 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.

[0387] In this invention, the server includes means for receiving usage data and sentiment indicators collected periodically from a user terminal via a digital communication network; analysis means including a generative model for analyzing the sentiment indicators in real time and estimating the user's emotional state; and proposal means for selecting and providing security measures based on the user's emotional state obtained from the analysis means. This makes it possible to quickly grasp the user's emotional state and immediately present appropriate security measures.

[0388] A "user terminal" is a device that functions as an interface with the user and has the function of collecting sentiment indicators and usage data.

[0389] A "digital communication network" is an infrastructure for transmitting data, providing a path for exchanging information between user terminals and servers.

[0390] "Usage data" refers to data that includes information about users' digital activities and behaviors, and is used for analyzing their emotional state.

[0391] "Emotional indicators" refer to information such as a user's facial expressions and voice patterns, and serve as criteria for evaluating a user's emotional state.

[0392] A "generative model" is a model that uses algorithms to analyze a user's text, voice, and video data to estimate their emotional state.

[0393] "Analysis means" refers to a device or system that has the function of analyzing and estimating emotional states based on collected usage data and emotional indicators.

[0394] "Proposed means" refers to a device or function that selects and provides appropriate countermeasures based on the user's emotional state obtained through analysis.

[0395] "Security measures" include proposed guidelines and actions to ensure user safety, and are measures taken in response to emotional changes.

[0396] This system consists of a user terminal, a server, and a communication network. The user terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice. This allows for obtaining an indicator of the user's emotions in daily life. The data collected as this emotional indicator is transmitted to the server via the digital communication network.

[0397] The server first analyzes image data using the Python-based OpenCV library and converts the audio data to text using the Google Cloud Speech-to-Text API. Next, a generative model using TensorFlow estimates the user's emotional state using this data. This analysis method makes it possible to estimate emotions in real time from the user's facial expressions and voice.

[0398] Based on the estimated emotional state, the server proposes appropriate security measures. These proposals are provided as notifications to the user's terminal. These proposals include specific countermeasures, such as sending notifications to close friends or security companies when the user feels uneasy. As a result, it is possible to respond quickly and flexibly according to the user's emotional state, contributing to ensuring personal safety.

[0399] This system can quickly detect, for example, a user experiencing sudden anxiety in the evening, and send timely and accurate notifications to pre-designated emergency contacts. This allows users to live their daily lives with a focus on safety.

[0400] The following prompt statements can be used as input to the generative model.

[0401] "Please explain the steps to detect if a user is experiencing stress and send an emergency notification to their friends."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The device captures the user's facial expressions with a camera and records their voice with a microphone. The input consists of facial image data and audio data, while the output consists of raw image and audio files.

[0405] Step 2:

[0406] The device analyzes the collected facial image data using the OpenCV library. The input is facial image data, and the output is features that indicate the user's emotions (e.g., percentage of smiles, frequency of anxious expressions). This analysis enables real-time detection of changes in facial expressions.

[0407] Step 3:

[0408] The device converts audio data into text via the Google Cloud Speech-to-Text API. The input is audio data, and the output is the audio content in text format. This makes it possible to obtain sentiment indicators from the audio.

[0409] Step 4:

[0410] The server inputs the analyzed facial feature vectors and text data into a generating AI model to estimate the user's overall emotional state. The input consists of feature vectors and text data, and the output is the estimated emotional state. This step involves data processing, enabling multifaceted emotional analysis.

[0411] Step 5:

[0412] The server selects appropriate security measures based on the estimated emotional state of the user. The input is emotional state data, and the output is recommended security measures (e.g., notifying emergency contacts). During this process, the system consults a database of experts to fine-tune the measures.

[0413] Step 6:

[0414] The server sends a notification to the terminal regarding the selected security measures. The input is the content of the security measures, and the output is the notification displayed on the terminal. This notification allows the user to quickly understand and implement the appropriate measures.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention is a system that analyzes a user's emotional state in real time using a generative model, based on usage data automatically collected from a user's device such as a smartphone, and proposes appropriate care. The main functions of this system and their processing flow are described below.

[0432] In this system, the terminal first continuously collects usage data related to the user's daily activities, specifically, the frequency of SNS use, message content, and notification response time. The collected data is then transmitted to the server via a secure communication protocol.

[0433] The server temporarily stores the received data and analyzes it using a pre-trained generative model. This model utilizes natural language processing techniques to detect the user's emotions from messages and text. It integrates multiple emotional factors to estimate the current emotional state.

[0434] Next, the server automatically suggests the most appropriate care method to support the user's emotional state based on the analysis results. This suggestion becomes more accurate by using insights from a database of experts. For example, if the user's stress level is high, it may recommend stress-reducing music or encourage them to take a short break.

[0435] Finally, the device notifies the user of care suggestions received from the server, prompting them to take specific action. The notification appears as a push message, and the user can open the relaxation app by clicking the link included in the notification if necessary.

[0436] For example, if a user's social media interactions increase and contain a lot of negative tones, the server might estimate that the user's stress levels are rising. In that case, the device might send a notification to the user recommending the use of an app that includes breathing exercises to help them relax.

[0437] This system helps users receive appropriate care as needed, reducing psychological burden and supporting the maintenance of a healthy mental state, even without actively engaging in mental health care themselves.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The device monitors and collects usage data related to the user's daily activities. This includes the frequency of use of social media apps, the content of messages, and the response time to notifications.

[0441] Step 2:

[0442] The device periodically sends the collected usage data to the server. This transmission is performed using a secure communication protocol.

[0443] Step 3:

[0444] The server temporarily stores the usage data received from the terminal and performs checks to verify the integrity and completeness of the data.

[0445] Step 4:

[0446] The server inputs the stored data into a generative model to analyze the user's emotional state. The generative model utilizes natural language processing techniques to classify the message content into emotional categories.

[0447] Step 5:

[0448] Based on the analysis, the server estimates the user's current emotional state. This estimation integrates numerous emotional factors to calculate the user's emotional score.

[0449] Step 6:

[0450] The server automatically selects the appropriate care method based on the emotional state. This selection incorporates insights from a database of experts, generating highly accurate recommendations.

[0451] Step 7:

[0452] The server sends the generated care suggestion to the terminal.

[0453] Step 8:

[0454] The device notifies the user of care suggestions received from the server. This notification includes a push message prompting specific action.

[0455] Step 9:

[0456] The user receives notifications from the device and takes the suggested care actions as needed. The results are reported to the server as feedback by the device.

[0457] (Example 1)

[0458] 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."

[0459] In recent years, problems caused by stress and emotional fluctuations have been increasing, highlighting the growing importance of individual users recognizing their own emotional states and providing appropriate care. However, many users lack the means to properly understand their own emotional states and therefore struggle to take appropriate action based on them. Consequently, there is a need for a system that analyzes users' emotional states in real time based on their behavioral data and automatically suggests appropriate care methods.

[0460] 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.

[0461] In this invention, the server includes means for receiving behavioral data periodically collected from a user device via an information and communication network; analysis means including a generative AI model that analyzes the behavioral data and estimates the user's emotional state; and proposal means that determines and provides an appropriate support method based on the user's emotional state obtained from the analysis means. This enables the user to receive appropriate care without having to actively manage their own emotional state.

[0462] A "user device" is an electronic device that a user directly operates to send and receive information.

[0463] An "information and communication network" is a network structure for electronically transferring digital data.

[0464] "Periodicly collected behavioral data" refers to data that is automatically collected at regular intervals, providing information about the user's behavior.

[0465] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and performs specific analyses or predictions.

[0466] "Analysis means" refers to a method or apparatus for using collected data to derive specific results.

[0467] "Emotional state" refers to information that indicates the user's psychological or emotional condition.

[0468] "Proposed means" refers to a method or device for providing users with appropriate actions or instructions based on the analysis results.

[0469] This invention is a system that analyzes user behavior data and proposes appropriate care. To achieve this, it operates based on the user's smart device (user device) and a server located in the cloud.

[0470] The devices, such as smartphones and tablets, collect data about the user's daily behavior. Specifically, this includes the frequency of use of social media apps, the content of messages, and the response time to notifications, and this data is obtained using APIs and sensors within the device.

[0471] The collected data is transmitted to the server via encrypted and secure communication. The server then temporarily stores the data and analyzes it using a pre-trained generative AI model. In particular, it focuses on extracting emotional patterns from text data using natural language processing technology. This generative AI model has the ability to integrate different emotional factors and infer the user's current emotional state.

[0472] Based on the analysis results, the server automatically determines and creates a suggestion for the care method best suited to the user. Expert opinions are stored in a database and referenced to improve the accuracy of the suggestions. For example, if a high level of stress is detected, relaxation activities will be recommended.

[0473] The suggestion is sent to the device as a push notification, prompting the user to take specific action. The user can then use the relaxation app by checking the notification and tapping the link within the app if necessary.

[0474] For example, when a user's communication on social media becomes more active and negative expressions increase, the server detects the increase in stress. In this case, the device displays a notification prompting the user to use an app that suggests breathing exercises.

[0475] Example of a prompt:

[0476] "Please analyze data on users who have recently been making an increase in negative comments on social media and suggest appropriate support measures."

[0477] This system allows users to receive appropriate care as needed, without having to actively manage their own emotional state, thereby reducing psychological burden and promoting the maintenance of a healthy mental state.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The device collects data about the user's daily activities. Specifically, this includes data such as the frequency of use of social media apps, the content of messages, and the response time to notifications. This data is acquired through sensors and APIs within the device and prepared as input. It is essential that this input data is reliably acquired for subsequent analysis.

[0481] Step 2:

[0482] The device transmits the collected data to the server using a secure communication protocol. Because the data content is privacy-related, encryption technology is used to ensure security during transmission. This transmitted data becomes the primary input information on the server.

[0483] Step 3:

[0484] The server temporarily stores the received data. Then, it analyzes the stored data using a generative AI model. This model utilizes natural language processing techniques to extract emotion-related patterns from the input messages and text data. For example, if a user's message contains many words indicating negative emotions, the data processing includes counting their frequency. The output of the analysis is inferred information about the user's emotional state.

[0485] Step 4:

[0486] The server generates appropriate care suggestions based on the analysis results. These suggestions are created by referencing a database containing expert knowledge. If high stress levels are detected, the server determines how to support the user, such as recommending relaxation techniques. The care suggestions are then ready to be sent to the terminal as output.

[0487] Step 5:

[0488] The device notifies the user of care suggestions received from the server. Specifically, these are displayed as push notifications, which the user can use as a trigger to take action. By tapping the link included in the notification, the user launches the suggested relaxation app and receives the provided care. As an output, the notification results in prompting the user to take action.

[0489] Through this step, users can automatically receive appropriate emotional care while utilizing the system.

[0490] (Application Example 1)

[0491] 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."

[0492] Traditional systems have struggled to provide content optimized for each user's emotional state, often resulting in the delivery of uniform content. Therefore, there is a growing need for systems that can deliver personalized content tailored to each user's emotions.

[0493] 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.

[0494] In this invention, the server includes means for receiving usage information periodically collected from user devices via an information network; analysis means including a generative model that analyzes the usage information and evaluates the individual's emotional state; and suggestion means for selecting and providing appropriate media content based on the individual's emotional state obtained from the analysis means. This makes it possible to provide personalized media content that corresponds to the user's emotional state.

[0495] A "user device" is a digital device used by individual users to manipulate and receive data.

[0496] An "information network" is a network used to send and receive digital data over a long distance.

[0497] "Usage information" refers to data collected through user devices that relates to the activities and behaviors of individual users.

[0498] A "generative model" is an artificial intelligence technology designed to analyze user data and make predictions based on a specific algorithm.

[0499] "Analysis means" refers to a device or method for detecting and analyzing the emotional state of individual users based on collected data.

[0500] "Proposed means" refers to a device or method for selecting and providing optimal media content to a user based on an analyzed emotional state.

[0501] "Media content" refers to personalized digital information, such as music, videos, and articles, that is delivered according to the user's emotional state.

[0502] This system consists of user devices, an information network, and a server. User devices are digital devices such as smartphones and tablets, which users use for their daily activities. The user devices transmit usage information to the server via the information network. This usage information includes user activity and message data.

[0503] The server operates by integrating multiple software programs. Specifically, it uses a generative AI model to analyze usage information and evaluate an individual's emotional state. This model utilizes natural language processing techniques to identify emotions from messages and behavioral data. The server uses cloud technologies such as Google Cloud Platform to collect, store, and analyze data.

[0504] After performing the analysis, the server uses the suggested methods to select appropriate media content. This process employs machine learning algorithms to suggest content that best suits the user's emotional state. For example, this might include recommending relaxation apps such as Relax Melodies.

[0505] The user's device receives a notification suggesting selected media content. This notification appears as a push message on the user's screen, and by tapping the message, the user can immediately view the corresponding content. By providing an experience tailored to the user's emotions in this way, effects such as stress reduction and relaxation can be expected.

[0506] For example, if a user submits data suggesting work-related stress, the server can recommend music or short videos that are effective in relieving stress. Another example of a prompt used in a generative AI model might be, "Analyze the user's recent social media activity to understand their emotions and recommend relaxing content." This provides a more personalized and better user experience for each individual user.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user's smartphone collects usage data. The input is user activity data (e.g., frequency of SNS use, message content, notification response time). This data is obtained from sensors and applications within the device and structured in JSON format.

[0510] Step 2:

[0511] The terminal sends the collected data to the server using a secure protocol (e.g., HTTPS). The output is usage data packaged for transmission. The server receives this data and temporarily stores it in a database.

[0512] Step 3:

[0513] The server feeds the stored data into a generating AI model. The input is collected usage data. Based on this data, natural language processing techniques are used to analyze messages and behavioral data and estimate the user's emotional state. The output is the category of the estimated emotional state.

[0514] Step 4:

[0515] The server analyzes the estimated emotional state and selects appropriate media content. The input is a database of emotional state categories and suggested content. Using a machine learning algorithm, it searches for the content (e.g., music, video) that best matches the emotion and outputs the selection results.

[0516] Step 5:

[0517] The device sends a push notification to the user based on the selection results received from the server. The input consists of the selected content information and the user's contact information. The output is a push message displayed on the user's device, which includes a direct link to the suggested content.

[0518] Step 6:

[0519] The user checks the notification and taps the suggested content to launch the application. The input is the link included in the push notification. The user's action plays the relevant media content, and the output is the user's viewing experience.

[0520] 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.

[0521] This invention is a system that uses usage data collected from user terminals and emotional information obtained by an emotion engine to comprehensively analyze the user's emotional state through a generative model and propose appropriate care accordingly. The following describes how to implement this system in detail.

[0522] First, the device continuously collects data about the user's daily activities. This data includes social media activity, message content, notification response times, and emotional indicators such as facial expressions and voice patterns. The emotion engine uses the device's built-in camera and microphone to recognize emotions in real time from the user's facial expressions and voice.

[0523] The server receives data transmitted from the terminal and emotional information collected by the emotion engine. This data is stored in a database on the server and used as input for a generative model. The generative model utilizes natural language processing and computer vision technologies to analyze the user's text data and image / audio data, and integrates multiple emotional indicators in a short amount of time.

[0524] Next, the server estimates the user's overall emotional state based on the analysis results and automatically selects the most appropriate care method for the user. This suggestion integrates information from a database of experts and is customized to address the user's specific problems.

[0525] The device notifies the user of selected care suggestions. This includes notifications prompting the launch of relaxation apps and instructions for stress reduction techniques that can be easily implemented in daily life. For example, if the emotion engine and generative model detect that the user is experiencing stress at work, the device sends a notification suggesting a short relaxation period.

[0526] This system tracks users' emotional states in real time and promptly provides appropriate solutions as needed, thereby lowering psychological barriers and promoting users' mental well-being.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The device collects data on the user's daily activities. This includes usage of social media apps, message data, response data to notifications, and facial expressions and voice patterns captured using the camera and microphone.

[0530] Step 2:

[0531] The emotion engine built into the device analyzes the user's facial expressions and voice changes through the camera and microphone, acquiring emotional information in real time. This data is categorized into emotional categories such as joy, anger, and sadness.

[0532] Step 3:

[0533] The device periodically sends collected usage data and sentiment information to the server. The data is encrypted before transmission to ensure privacy.

[0534] Step 4:

[0535] The server temporarily stores all data received from the terminal and verifies data integrity. After verification, it performs necessary preprocessing and prepares the data for supply to the generative model.

[0536] Step 5:

[0537] The server uses generative models to analyze data. It analyzes message data using natural language processing techniques, while simultaneously analyzing emotions from facial expressions and voice using computer vision techniques.

[0538] Step 6:

[0539] The server uses the analysis results to comprehensively estimate the user's current emotional state. This estimation takes into account the weight of each emotional factor and calculates an overall emotional score.

[0540] Step 7:

[0541] The server automatically selects the optimal care method based on the estimated emotional state. The selected care method is personalized for each user, incorporating insights from a database of experts.

[0542] Step 8:

[0543] The server sends the selected care method to the terminal and prepares to present it to the user. This includes suggestions tailored to the user's interests and circumstances.

[0544] Step 9:

[0545] The device notifies the user of care suggestions received from the server. Specifically, it displays a message on the screen and recommends necessary applications or actions.

[0546] Step 10:

[0547] The user chooses whether to accept the device notification and take action on the care instructions. If action is taken, feedback is sent back to the server by the device and used for subsequent analysis.

[0548] (Example 2)

[0549] 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."

[0550] In modern society, mental stress and emotional instability are increasingly impacting health. However, there is a lack of systems that can quickly detect this and propose appropriate care tailored to individual circumstances. Therefore, there is a need for technology that can accurately estimate a user's emotional state in real time and propose effective care.

[0551] 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.

[0552] In this invention, the server includes means for receiving behavioral data periodically collected from a user terminal via a digital communication network; means for analyzing facial expressions and voice using sensors built into the terminal and acquiring emotional information in real time; analysis means including a generative model that analyzes the behavioral data and emotional information and estimates the user's emotional state from multiple perspectives by combining natural language processing technology and image / voice analysis technology; and proposal means that, based on the emotional state obtained from the analysis means, select an appropriate care method from an expert knowledge base and individually customize it. This makes it possible to evaluate the user's emotions from multiple perspectives and to present care that can be responded to quickly and effectively and individually.

[0553] A "user terminal" is a portable or stationary electronic device that a user uses on a daily basis to enable data collection and communication.

[0554] A "digital communication network" is a network infrastructure for sending and receiving information in digital format.

[0555] "Behavioral data" refers to recorded information about a user's daily activities, including in the form of text, images, and audio.

[0556] A "sensor" is a device necessary for appropriately acquiring environmental information, and includes devices such as cameras and microphones.

[0557] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions and voice.

[0558] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0559] "Image and audio analysis technology" refers to processing techniques for extracting specific information from image and audio data and evaluating its meaning.

[0560] A "generative model" is an algorithm or system for generating new information from multiple data points.

[0561] A "specialist knowledge base" is a database in which knowledge and information in a specific field are systematically accumulated.

[0562] A "proposal method" refers to a method or system for selecting and presenting the optimal solution according to the user's needs.

[0563] This invention is a system that operates in combination with a user terminal and a server to improve the mental health of the user. First, the terminal operates as a portable electronic device such as a smartphone or tablet to continuously collect data on the user's daily activities. The camera and microphone built into this terminal function as sensors, analyzing the user's facial expressions and voice patterns to acquire emotional information.

[0564] Next, the device transmits the collected behavioral data and emotional information to a server via a digital communication network. The server stores the received data in a database. This data is then analyzed using a generative AI model. The generative AI model utilizes natural language processing techniques to classify text data into emotional categories and uses image and audio analysis techniques to comprehensively estimate the user's emotional state.

[0565] Based on the analysis results, the server selects the most appropriate care method from its expert knowledge base. This selection process is customized to the user's individual needs. The selected care method is then sent back to the device and notified to the user. The notification includes a link to an app that encourages the implementation of intuitive relaxation techniques, as well as voice-guided instructions.

[0566] For example, if the device detects that a user is experiencing stress in a busy work environment, it will display a message prompting them to take a deep breath or a notification encouraging them to launch an app that provides short relaxing music.

[0567] An example of a prompt statement would be the command, "Suggest an appropriate relaxation method based on the user's current emotional state," which is used as input to the system. This invention allows for real-time tracking of the user's emotional state and the provision of prompt, personalized care as needed.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The device collects user behavioral and emotional data. It uses sensor information from its built-in camera and microphone as input. For data processing, it analyzes facial expressions using an image recognition algorithm and analyzes voice tone using speech recognition technology. As a result of these processes, it outputs raw data associated with the user's emotional state.

[0571] Step 2:

[0572] The terminal transmits collected behavioral data and emotional information to the server via a digital communication network. The input is the collected raw data, which is transmitted using network communication. As part of the data processing, the data is encrypted according to security protocols and then output to the server.

[0573] Step 3:

[0574] The server stores the received data in a database and inputs it into the generative AI model. The data sent from the terminal is used as input. Data processing involves normalization and cleaning of the data, converting it into an analyzable format. This results in a tidy dataset being output to the generative AI model.

[0575] Step 4:

[0576] The server analyzes the user's emotional state using a generative AI model. Normalized data from a database is used as input. The generative AI model utilizes natural language processing and image / speech analysis techniques to perform data calculations that integrate multiple emotional indicators. The output provides a multifaceted evaluation of the user's emotional state.

[0577] Step 5:

[0578] The server selects appropriate care methods from an expert knowledge base based on the obtained emotional state. It uses the analysis results of the emotional state as input to perform data calculations to identify the optimal care method from the knowledge base. As a result, a customized care method is output.

[0579] Step 6:

[0580] The device notifies the user of the selected care method. It receives specific instructions for the care method from the server as input. The notification output includes visual notification pop-ups and voice guidance to encourage the user to easily follow the instructions.

[0581] (Application Example 2)

[0582] 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."

[0583] In modern society, the stress and anxiety that users experience in their daily lives are increasing, and these can threaten an individual's mental and physical health. In particular, rapid emotional changes can be a harbinger of security threats or incidents, making early detection and appropriate response crucial. However, current systems lack efficient means to analyze an individual's emotional state in real time and quickly propose security measures.

[0584] 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.

[0585] In this invention, the server includes means for receiving usage data and sentiment indicators collected periodically from a user terminal via a digital communication network; analysis means including a generative model for analyzing the sentiment indicators in real time and estimating the user's emotional state; and proposal means for selecting and providing security measures based on the user's emotional state obtained from the analysis means. This makes it possible to quickly grasp the user's emotional state and immediately present appropriate security measures.

[0586] A "user terminal" is a device that functions as an interface with the user and has the function of collecting sentiment indicators and usage data.

[0587] A "digital communication network" is an infrastructure for transmitting data, providing a path for exchanging information between user terminals and servers.

[0588] "Usage data" refers to data that includes information about users' digital activities and behaviors, and is used for analyzing their emotional state.

[0589] "Emotional indicators" refer to information such as a user's facial expressions and voice patterns, and serve as criteria for evaluating a user's emotional state.

[0590] A "generative model" is a model that uses algorithms to analyze a user's text, voice, and video data to estimate their emotional state.

[0591] "Analysis means" refers to a device or system that has the function of analyzing and estimating emotional states based on collected usage data and emotional indicators.

[0592] "Proposed means" refers to a device or function that selects and provides appropriate countermeasures based on the user's emotional state obtained through analysis.

[0593] "Security measures" include proposed guidelines and actions to ensure user safety, and are measures taken in response to emotional changes.

[0594] This system consists of a user terminal, a server, and a communication network. The user terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice. This allows for obtaining an indicator of the user's emotions in daily life. The data collected as this emotional indicator is transmitted to the server via the digital communication network.

[0595] The server first analyzes image data using the Python-based OpenCV library and converts the audio data to text using the Google Cloud Speech-to-Text API. Next, a generative model using TensorFlow estimates the user's emotional state using this data. This analysis method makes it possible to estimate emotions in real time from the user's facial expressions and voice.

[0596] Based on the estimated emotional state, the server proposes appropriate security measures. These proposals are provided as notifications to the user's terminal. These proposals include specific countermeasures, such as sending notifications to close friends or security companies when the user feels uneasy. As a result, it is possible to respond quickly and flexibly according to the user's emotional state, contributing to ensuring personal safety.

[0597] This system can quickly detect, for example, a user experiencing sudden anxiety in the evening, and send timely and accurate notifications to pre-designated emergency contacts. This allows users to live their daily lives with a focus on safety.

[0598] The following prompt statements can be used as input to the generative model.

[0599] "Please explain the steps to detect if a user is experiencing stress and send an emergency notification to their friends."

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The device captures the user's facial expressions with a camera and records their voice with a microphone. The input consists of facial image data and audio data, while the output consists of raw image and audio files.

[0603] Step 2:

[0604] The device analyzes the collected facial image data using the OpenCV library. The input is facial image data, and the output is features that indicate the user's emotions (e.g., percentage of smiles, frequency of anxious expressions). This analysis enables real-time detection of changes in facial expressions.

[0605] Step 3:

[0606] The device converts audio data into text via the Google Cloud Speech-to-Text API. The input is audio data, and the output is the audio content in text format. This makes it possible to obtain sentiment indicators from the audio.

[0607] Step 4:

[0608] The server inputs the analyzed facial feature vectors and text data into a generating AI model to estimate the user's overall emotional state. The input consists of feature vectors and text data, and the output is the estimated emotional state. This step involves data processing, enabling multifaceted emotional analysis.

[0609] Step 5:

[0610] The server selects appropriate security measures based on the estimated emotional state of the user. The input is emotional state data, and the output is recommended security measures (e.g., notifying emergency contacts). During this process, the system consults a database of experts to fine-tune the measures.

[0611] Step 6:

[0612] The server sends a notification to the terminal regarding the selected security measures. The input is the content of the security measures, and the output is the notification displayed on the terminal. This notification allows the user to quickly understand and implement the appropriate measures.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] This invention is a system that analyzes a user's emotional state in real time using a generative model, based on usage data automatically collected from a user's device such as a smartphone, and proposes appropriate care. The main functions of this system and their processing flow are described below.

[0631] In this system, the terminal first continuously collects usage data related to the user's daily activities, specifically, the frequency of SNS use, message content, and notification response time. The collected data is then transmitted to the server via a secure communication protocol.

[0632] The server temporarily stores the received data and analyzes it using a pre-trained generative model. This model utilizes natural language processing techniques to detect the user's emotions from messages and text. It integrates multiple emotional factors to estimate the current emotional state.

[0633] Next, the server automatically suggests the most appropriate care method to support the user's emotional state based on the analysis results. This suggestion becomes more accurate by using insights from a database of experts. For example, if the user's stress level is high, it may recommend stress-reducing music or encourage them to take a short break.

[0634] Finally, the device notifies the user of care suggestions received from the server, prompting them to take specific action. The notification appears as a push message, and the user can open the relaxation app by clicking the link included in the notification if necessary.

[0635] For example, if a user's social media interactions increase and contain a lot of negative tones, the server might estimate that the user's stress levels are rising. In that case, the device might send a notification to the user recommending the use of an app that includes breathing exercises to help them relax.

[0636] This system helps users receive appropriate care as needed, reducing psychological burden and supporting the maintenance of a healthy mental state, even without actively engaging in mental health care themselves.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The device monitors and collects usage data related to the user's daily activities. This includes the frequency of use of social media apps, the content of messages, and the response time to notifications.

[0640] Step 2:

[0641] The device periodically sends the collected usage data to the server. This transmission is performed using a secure communication protocol.

[0642] Step 3:

[0643] The server temporarily stores the usage data received from the terminal and performs checks to verify the integrity and completeness of the data.

[0644] Step 4:

[0645] The server inputs the stored data into a generative model to analyze the user's emotional state. The generative model utilizes natural language processing techniques to classify the message content into emotional categories.

[0646] Step 5:

[0647] Based on the analysis, the server estimates the user's current emotional state. This estimation integrates numerous emotional factors to calculate the user's emotional score.

[0648] Step 6:

[0649] The server automatically selects the appropriate care method based on the emotional state. This selection incorporates insights from a database of experts, generating highly accurate recommendations.

[0650] Step 7:

[0651] The server sends the generated care suggestion to the terminal.

[0652] Step 8:

[0653] The device notifies the user of care suggestions received from the server. This notification includes a push message prompting specific action.

[0654] Step 9:

[0655] The user receives notifications from the device and takes the suggested care actions as needed. The results are reported to the server as feedback by the device.

[0656] (Example 1)

[0657] 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".

[0658] In recent years, problems caused by stress and emotional fluctuations have been increasing, highlighting the growing importance of individual users recognizing their own emotional states and providing appropriate care. However, many users lack the means to properly understand their own emotional states and therefore struggle to take appropriate action based on them. Consequently, there is a need for a system that analyzes users' emotional states in real time based on their behavioral data and automatically suggests appropriate care methods.

[0659] 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.

[0660] In this invention, the server includes means for receiving behavioral data periodically collected from a user device via an information and communication network; analysis means including a generative AI model that analyzes the behavioral data and estimates the user's emotional state; and proposal means that determines and provides an appropriate support method based on the user's emotional state obtained from the analysis means. This enables the user to receive appropriate care without having to actively manage their own emotional state.

[0661] A "user device" is an electronic device that a user directly operates to send and receive information.

[0662] An "information and communication network" is a network structure for electronically transferring digital data.

[0663] "Periodicly collected behavioral data" refers to data that is automatically collected at regular intervals, providing information about the user's behavior.

[0664] A "generative AI model" is an artificial intelligence algorithm that learns from large amounts of data and performs specific analyses or predictions.

[0665] "Analysis means" refers to a method or apparatus for using collected data to derive specific results.

[0666] "Emotional state" refers to information that indicates the user's psychological or emotional condition.

[0667] "Proposed means" refers to a method or device for providing users with appropriate actions or instructions based on the analysis results.

[0668] This invention is a system that analyzes user behavior data and proposes appropriate care. To achieve this, it operates based on the user's smart device (user device) and a server located in the cloud.

[0669] The devices, such as smartphones and tablets, collect data about the user's daily behavior. Specifically, this includes the frequency of use of social media apps, the content of messages, and the response time to notifications, and this data is obtained using APIs and sensors within the device.

[0670] The collected data is transmitted to the server via encrypted and secure communication. The server then temporarily stores the data and analyzes it using a pre-trained generative AI model. In particular, it focuses on extracting emotional patterns from text data using natural language processing technology. This generative AI model has the ability to integrate different emotional factors and infer the user's current emotional state.

[0671] Based on the analysis results, the server automatically determines and creates a suggestion for the care method best suited to the user. Expert opinions are stored in a database and referenced to improve the accuracy of the suggestions. For example, if a high level of stress is detected, relaxation activities will be recommended.

[0672] The suggestion is sent to the device as a push notification, prompting the user to take specific action. The user can then use the relaxation app by checking the notification and tapping the link within the app if necessary.

[0673] For example, when a user's communication on social media becomes more active and negative expressions increase, the server detects the increase in stress. In this case, the device displays a notification prompting the user to use an app that suggests breathing exercises.

[0674] Example of a prompt:

[0675] "Please analyze data on users who have recently been making an increase in negative comments on social media and suggest appropriate support measures."

[0676] This system allows users to receive appropriate care as needed, without having to actively manage their own emotional state, thereby reducing psychological burden and promoting the maintenance of a healthy mental state.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The device collects data about the user's daily activities. Specifically, this includes data such as the frequency of use of social media apps, the content of messages, and the response time to notifications. This data is acquired through sensors and APIs within the device and prepared as input. It is essential that this input data is reliably acquired for subsequent analysis.

[0680] Step 2:

[0681] The device transmits the collected data to the server using a secure communication protocol. Because the data content is privacy-related, encryption technology is used to ensure security during transmission. This transmitted data becomes the primary input information on the server.

[0682] Step 3:

[0683] The server temporarily stores the received data. Then, it analyzes the stored data using a generative AI model. This model utilizes natural language processing techniques to extract emotion-related patterns from the input messages and text data. For example, if a user's message contains many words indicating negative emotions, the data processing includes counting their frequency. The output of the analysis is inferred information about the user's emotional state.

[0684] Step 4:

[0685] The server generates appropriate care suggestions based on the analysis results. These suggestions are created by referencing a database containing expert knowledge. If high stress levels are detected, the server determines how to support the user, such as recommending relaxation techniques. The care suggestions are then ready to be sent to the terminal as output.

[0686] Step 5:

[0687] The device notifies the user of care suggestions received from the server. Specifically, these are displayed as push notifications, which the user can use as a trigger to take action. By tapping the link included in the notification, the user launches the suggested relaxation app and receives the provided care. As an output, the notification results in prompting the user to take action.

[0688] Through this step, users can automatically receive appropriate emotional care while utilizing the system.

[0689] (Application Example 1)

[0690] 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".

[0691] Traditional systems have struggled to provide content optimized for each user's emotional state, often resulting in the delivery of uniform content. Therefore, there is a growing need for systems that can deliver personalized content tailored to each user's emotions.

[0692] 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.

[0693] In this invention, the server includes means for receiving usage information periodically collected from user devices via an information network; analysis means including a generative model that analyzes the usage information and evaluates the individual's emotional state; and suggestion means for selecting and providing appropriate media content based on the individual's emotional state obtained from the analysis means. This makes it possible to provide personalized media content that corresponds to the user's emotional state.

[0694] A "user device" is a digital device used by individual users to manipulate and receive data.

[0695] An "information network" is a network used to send and receive digital data over a long distance.

[0696] "Usage information" refers to data collected through user devices that relates to the activities and behaviors of individual users.

[0697] A "generative model" is an artificial intelligence technology designed to analyze user data and make predictions based on a specific algorithm.

[0698] "Analysis means" refers to a device or method for detecting and analyzing the emotional state of individual users based on collected data.

[0699] "Proposed means" refers to a device or method for selecting and providing optimal media content to a user based on an analyzed emotional state.

[0700] "Media content" refers to personalized digital information, such as music, videos, and articles, that is delivered according to the user's emotional state.

[0701] This system consists of user devices, an information network, and a server. User devices are digital devices such as smartphones and tablets, which users use for their daily activities. The user devices transmit usage information to the server via the information network. This usage information includes user activity and message data.

[0702] The server operates by integrating multiple software programs. Specifically, it uses a generative AI model to analyze usage information and evaluate an individual's emotional state. This model utilizes natural language processing techniques to identify emotions from messages and behavioral data. The server uses cloud technologies such as Google Cloud Platform to collect, store, and analyze data.

[0703] After performing the analysis, the server uses the suggested methods to select appropriate media content. This process employs machine learning algorithms to suggest content that best suits the user's emotional state. For example, this might include recommending relaxation apps such as Relax Melodies.

[0704] The user's device receives a notification suggesting selected media content. This notification appears as a push message on the user's screen, and by tapping the message, the user can immediately view the corresponding content. By providing an experience tailored to the user's emotions in this way, effects such as stress reduction and relaxation can be expected.

[0705] For example, if a user submits data suggesting work-related stress, the server can recommend music or short videos that are effective in relieving stress. Another example of a prompt used in a generative AI model might be, "Analyze the user's recent social media activity to understand their emotions and recommend relaxing content." This provides a more personalized and better user experience for each individual user.

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The user's smartphone collects usage data. The input is user activity data (e.g., frequency of SNS use, message content, notification response time). This data is obtained from sensors and applications within the device and structured in JSON format.

[0709] Step 2:

[0710] The terminal sends the collected data to the server using a secure protocol (e.g., HTTPS). The output is usage data packaged for transmission. The server receives this data and temporarily stores it in a database.

[0711] Step 3:

[0712] The server feeds the stored data into a generating AI model. The input is collected usage data. Based on this data, natural language processing techniques are used to analyze messages and behavioral data and estimate the user's emotional state. The output is the category of the estimated emotional state.

[0713] Step 4:

[0714] The server analyzes the estimated emotional state and selects appropriate media content. The input is a database of emotional state categories and suggested content. Using a machine learning algorithm, it searches for the content (e.g., music, video) that best matches the emotion and outputs the selection results.

[0715] Step 5:

[0716] The device sends a push notification to the user based on the selection results received from the server. The input consists of the selected content information and the user's contact information. The output is a push message displayed on the user's device, which includes a direct link to the suggested content.

[0717] Step 6:

[0718] The user checks the notification and taps the suggested content to launch the application. The input is the link included in the push notification. The user's action plays the relevant media content, and the output is the user's viewing experience.

[0719] 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.

[0720] This invention is a system that uses usage data collected from user terminals and emotional information obtained by an emotion engine to comprehensively analyze the user's emotional state through a generative model and propose appropriate care accordingly. The following describes how to implement this system in detail.

[0721] First, the device continuously collects data about the user's daily activities. This data includes social media activity, message content, notification response times, and emotional indicators such as facial expressions and voice patterns. The emotion engine uses the device's built-in camera and microphone to recognize emotions in real time from the user's facial expressions and voice.

[0722] The server receives data transmitted from the terminal and emotional information collected by the emotion engine. This data is stored in a database on the server and used as input for a generative model. The generative model utilizes natural language processing and computer vision technologies to analyze the user's text data and image / audio data, and integrates multiple emotional indicators in a short amount of time.

[0723] Next, the server estimates the user's overall emotional state based on the analysis results and automatically selects the most appropriate care method for the user. This suggestion integrates information from a database of experts and is customized to address the user's specific problems.

[0724] The device notifies the user of selected care suggestions. This includes notifications prompting the launch of relaxation apps and instructions for stress reduction techniques that can be easily implemented in daily life. For example, if the emotion engine and generative model detect that the user is experiencing stress at work, the device sends a notification suggesting a short relaxation period.

[0725] This system tracks users' emotional states in real time and promptly provides appropriate solutions as needed, thereby lowering psychological barriers and promoting users' mental well-being.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The device collects data on the user's daily activities. This includes usage of social media apps, message data, response data to notifications, and facial expressions and voice patterns captured using the camera and microphone.

[0729] Step 2:

[0730] The emotion engine built into the device analyzes the user's facial expressions and voice changes through the camera and microphone, acquiring emotional information in real time. This data is categorized into emotional categories such as joy, anger, and sadness.

[0731] Step 3:

[0732] The device periodically sends collected usage data and sentiment information to the server. The data is encrypted before transmission to ensure privacy.

[0733] Step 4:

[0734] The server temporarily stores all data received from the terminal and verifies data integrity. After verification, it performs necessary preprocessing and prepares the data for supply to the generative model.

[0735] Step 5:

[0736] The server uses generative models to analyze data. It analyzes message data using natural language processing techniques, while simultaneously analyzing emotions from facial expressions and voice using computer vision techniques.

[0737] Step 6:

[0738] The server uses the analysis results to comprehensively estimate the user's current emotional state. This estimation takes into account the weight of each emotional factor and calculates an overall emotional score.

[0739] Step 7:

[0740] The server automatically selects the optimal care method based on the estimated emotional state. The selected care method is personalized for each user, incorporating insights from a database of experts.

[0741] Step 8:

[0742] The server sends the selected care method to the terminal and prepares to present it to the user. This includes suggestions tailored to the user's interests and circumstances.

[0743] Step 9:

[0744] The device notifies the user of care suggestions received from the server. Specifically, it displays a message on the screen and recommends necessary applications or actions.

[0745] Step 10:

[0746] The user chooses whether to accept the device notification and take action on the care instructions. If action is taken, feedback is sent back to the server by the device and used for subsequent analysis.

[0747] (Example 2)

[0748] 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".

[0749] In modern society, mental stress and emotional instability are increasingly impacting health. However, there is a lack of systems that can quickly detect this and propose appropriate care tailored to individual circumstances. Therefore, there is a need for technology that can accurately estimate a user's emotional state in real time and propose effective care.

[0750] 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.

[0751] In this invention, the server includes means for receiving behavioral data periodically collected from a user terminal via a digital communication network; means for analyzing facial expressions and voice using sensors built into the terminal and acquiring emotional information in real time; analysis means including a generative model that analyzes the behavioral data and emotional information and estimates the user's emotional state from multiple perspectives by combining natural language processing technology and image / voice analysis technology; and proposal means that, based on the emotional state obtained from the analysis means, select an appropriate care method from an expert knowledge base and individually customize it. This makes it possible to evaluate the user's emotions from multiple perspectives and to present care that can be responded to quickly and effectively and individually.

[0752] A "user terminal" is a portable or stationary electronic device that a user uses on a daily basis to enable data collection and communication.

[0753] A "digital communication network" is a network infrastructure for sending and receiving information in digital format.

[0754] "Behavioral data" refers to recorded information about a user's daily activities, including in the form of text, images, and audio.

[0755] A "sensor" is a device necessary for appropriately acquiring environmental information, and includes devices such as cameras and microphones.

[0756] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions and voice.

[0757] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0758] "Image and audio analysis technology" refers to processing techniques for extracting specific information from image and audio data and evaluating its meaning.

[0759] A "generative model" is an algorithm or system for generating new information from multiple data points.

[0760] A "specialist knowledge base" is a database in which knowledge and information in a specific field are systematically accumulated.

[0761] A "proposal method" refers to a method or system for selecting and presenting the optimal solution according to the user's needs.

[0762] This invention is a system that operates in combination with a user terminal and a server to improve the mental health of the user. First, the terminal operates as a portable electronic device such as a smartphone or tablet to continuously collect data on the user's daily activities. The camera and microphone built into this terminal function as sensors, analyzing the user's facial expressions and voice patterns to acquire emotional information.

[0763] Next, the device transmits the collected behavioral data and emotional information to a server via a digital communication network. The server stores the received data in a database. This data is then analyzed using a generative AI model. The generative AI model utilizes natural language processing techniques to classify text data into emotional categories and uses image and audio analysis techniques to comprehensively estimate the user's emotional state.

[0764] Based on the analysis results, the server selects the most appropriate care method from its expert knowledge base. This selection process is customized to the user's individual needs. The selected care method is then sent back to the device and notified to the user. The notification includes a link to an app that encourages the implementation of intuitive relaxation techniques, as well as voice-guided instructions.

[0765] For example, if the device detects that a user is experiencing stress in a busy work environment, it will display a message prompting them to take a deep breath or a notification encouraging them to launch an app that provides short relaxing music.

[0766] An example of a prompt statement would be the command, "Suggest an appropriate relaxation method based on the user's current emotional state," which is used as input to the system. This invention allows for real-time tracking of the user's emotional state and the provision of prompt, personalized care as needed.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] The device collects user behavioral and emotional data. It uses sensor information from its built-in camera and microphone as input. For data processing, it analyzes facial expressions using an image recognition algorithm and analyzes voice tone using speech recognition technology. As a result of these processes, it outputs raw data associated with the user's emotional state.

[0770] Step 2:

[0771] The terminal transmits collected behavioral data and emotional information to the server via a digital communication network. The input is the collected raw data, which is transmitted using network communication. As part of the data processing, the data is encrypted according to security protocols and then output to the server.

[0772] Step 3:

[0773] The server stores the received data in a database and inputs it into the generative AI model. The data sent from the terminal is used as input. Data processing involves normalization and cleaning of the data, converting it into an analyzable format. This results in a tidy dataset being output to the generative AI model.

[0774] Step 4:

[0775] The server analyzes the user's emotional state using a generative AI model. Normalized data from a database is used as input. The generative AI model utilizes natural language processing and image / speech analysis techniques to perform data calculations that integrate multiple emotional indicators. The output provides a multifaceted evaluation of the user's emotional state.

[0776] Step 5:

[0777] The server selects appropriate care methods from an expert knowledge base based on the obtained emotional state. It uses the analysis results of the emotional state as input to perform data calculations to identify the optimal care method from the knowledge base. As a result, a customized care method is output.

[0778] Step 6:

[0779] The device notifies the user of the selected care method. It receives specific instructions for the care method from the server as input. The notification output includes visual notification pop-ups and voice guidance to encourage the user to easily follow the instructions.

[0780] (Application Example 2)

[0781] 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".

[0782] In modern society, the stress and anxiety that users experience in their daily lives are increasing, and these can threaten an individual's mental and physical health. In particular, rapid emotional changes can be a harbinger of security threats or incidents, making early detection and appropriate response crucial. However, current systems lack efficient means to analyze an individual's emotional state in real time and quickly propose security measures.

[0783] 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.

[0784] In this invention, the server includes means for receiving usage data and sentiment indicators collected periodically from a user terminal via a digital communication network; analysis means including a generative model for analyzing the sentiment indicators in real time and estimating the user's emotional state; and proposal means for selecting and providing security measures based on the user's emotional state obtained from the analysis means. This makes it possible to quickly grasp the user's emotional state and immediately present appropriate security measures.

[0785] A "user terminal" is a device that functions as an interface with the user and has the function of collecting sentiment indicators and usage data.

[0786] A "digital communication network" is an infrastructure for transmitting data, providing a path for exchanging information between user terminals and servers.

[0787] "Usage data" refers to data that includes information about users' digital activities and behaviors, and is used for analyzing their emotional state.

[0788] "Emotional indicators" refer to information such as a user's facial expressions and voice patterns, and serve as criteria for evaluating a user's emotional state.

[0789] A "generative model" is a model that uses algorithms to analyze a user's text, voice, and video data to estimate their emotional state.

[0790] "Analysis means" refers to a device or system that has the function of analyzing and estimating emotional states based on collected usage data and emotional indicators.

[0791] "Proposed means" refers to a device or function that selects and provides appropriate countermeasures based on the user's emotional state obtained through analysis.

[0792] "Security measures" include proposed guidelines and actions to ensure user safety, and are measures taken in response to emotional changes.

[0793] This system consists of a user terminal, a server, and a communication network. The user terminal is equipped with a camera and microphone, which capture the user's facial expressions and voice. This allows for obtaining an indicator of the user's emotions in daily life. The data collected as this emotional indicator is transmitted to the server via the digital communication network.

[0794] The server first analyzes image data using the Python-based OpenCV library and converts the audio data to text using the Google Cloud Speech-to-Text API. Next, a generative model using TensorFlow estimates the user's emotional state using this data. This analysis method makes it possible to estimate emotions in real time from the user's facial expressions and voice.

[0795] Based on the estimated emotional state, the server proposes appropriate security measures. These proposals are provided as notifications to the user's terminal. These proposals include specific countermeasures, such as sending notifications to close friends or security companies when the user feels uneasy. As a result, it is possible to respond quickly and flexibly according to the user's emotional state, contributing to ensuring personal safety.

[0796] This system can quickly detect, for example, a user experiencing sudden anxiety in the evening, and send timely and accurate notifications to pre-designated emergency contacts. This allows users to live their daily lives with a focus on safety.

[0797] The following prompt statements can be used as input to the generative model.

[0798] "Please explain the steps to detect if a user is experiencing stress and send an emergency notification to their friends."

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The device captures the user's facial expressions with a camera and records their voice with a microphone. The input consists of facial image data and audio data, while the output consists of raw image and audio files.

[0802] Step 2:

[0803] The device analyzes the collected facial image data using the OpenCV library. The input is facial image data, and the output is features that indicate the user's emotions (e.g., percentage of smiles, frequency of anxious expressions). This analysis enables real-time detection of changes in facial expressions.

[0804] Step 3:

[0805] The device converts audio data into text via the Google Cloud Speech-to-Text API. The input is audio data, and the output is the audio content in text format. This makes it possible to obtain sentiment indicators from the audio.

[0806] Step 4:

[0807] The server inputs the analyzed facial feature vectors and text data into a generating AI model to estimate the user's overall emotional state. The input consists of feature vectors and text data, and the output is the estimated emotional state. This step involves data processing, enabling multifaceted emotional analysis.

[0808] Step 5:

[0809] The server selects appropriate security measures based on the estimated emotional state of the user. The input is emotional state data, and the output is recommended security measures (e.g., notifying emergency contacts). During this process, the system consults a database of experts to fine-tune the measures.

[0810] Step 6:

[0811] The server sends a notification to the terminal regarding the selected security measures. The input is the content of the security measures, and the output is the notification displayed on the terminal. This notification allows the user to quickly understand and implement the appropriate measures.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means for receiving usage data that is periodically collected from user terminals via a digital communication network,

[0836] An analysis means including a generative model that analyzes the aforementioned usage data and estimates the user's emotional state,

[0837] Based on the user's emotional state obtained from the aforementioned analysis means, a proposal means selects and provides an appropriate care method.

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the analysis means classifies message data into emotion categories using natural language processing technology.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the proposed means presents a selected care method via a notification means to the user.

[0843] "Example 1"

[0844] (Claim 1)

[0845] A means for receiving behavioral data periodically collected from a user device via an information and communication network,

[0846] Analysis means including a generative AI model that analyzes the aforementioned behavioral data and estimates the emotional state of the user,

[0847] Based on the emotional state of the user obtained from the aforementioned analysis means, a proposal means is provided to determine and offer an appropriate support method.

[0848] A means for notifying the user of the support method selected by the proposed means,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein the analysis means classifies text data into emotional factors using natural language processing technology.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the proposed means displays a selected support method via a user's device.

[0854] "Application Example 1"

[0855] (Claim 1)

[0856] A means for receiving usage information that is periodically collected from user devices via an information network,

[0857] An analysis means including a generative model that analyzes the aforementioned usage information and evaluates the emotional state of an individual,

[0858] A proposal means that selects and provides appropriate media content based on the individual's emotional state obtained from the aforementioned analysis means,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the analysis means uses natural language processing technology to organize message information into emotion classifications.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the proposed means presents selected media content via a notification device to an individual.

[0864] "Example 2 of combining an emotion engine"

[0865] (Claim 1)

[0866] A means for receiving behavioral data collected periodically from a user terminal via a digital communication network,

[0867] A method for analyzing facial expressions and voice using sensors built into the device and acquiring emotional information in real time,

[0868] Analysis means including a generative model that analyzes the aforementioned behavioral data and emotional information and estimates the user's emotional state from multiple perspectives by combining natural language processing technology and image / speech analysis technology,

[0869] Based on the emotional state obtained from the aforementioned analysis, a proposal means selects an appropriate care method from a specialist knowledge base and customizes it individually.

[0870] A communication means for notifying the user of the care method selected by the proposed means and encouraging its implementation,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the analysis means classifies acquired data into emotion categories using natural language processing technology and integrates multiple emotion indicators.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the proposed means presents a reasonably customized care method via a notification means to the user.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] A means for receiving usage data and sentiment indicators that are periodically collected from user terminals via a digital communication network,

[0879] Analysis means including a generative model that analyzes the aforementioned emotion indicators in real time and estimates the user's emotional state,

[0880] Based on the user's emotional state obtained from the aforementioned analysis means, a proposal means selects and provides security measures,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the analysis means analyzes audio information using a technique for processing audio data.

[0884] (Claim 3)

[0885] The system according to claim 1, wherein the proposed means presents selected security measures to the user via a notification device. [Explanation of symbols]

[0886] 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 for receiving usage data that is periodically collected from user terminals via a digital communication network, An analysis means including a generative model that analyzes the aforementioned usage data and estimates the user's emotional state, Based on the user's emotional state obtained from the aforementioned analysis means, a proposal means selects and provides an appropriate care method. A system that includes this.

2. The system according to claim 1, wherein the analysis means classifies message data into emotion categories using natural language processing technology.

3. The system according to claim 1, wherein the proposed means presents a selected care method via a notification means to the user.

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