system

Abstract

We provide the system. [Solution] Means for acquiring dynamic information and location identification information from an information acquisition device, A means of acquiring environmental condition information from an external information provision device, A means for analyzing collected dynamic information, location identification information, and environmental condition information to evaluate the user's situation and preferences, A means of suggesting the optimal action to the user based on the analyzed results, Means of notifying the proposed action, An autonomous support system including...

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the current technology, there is a problem that it is difficult to anticipate various needs of users and autonomously make individualized proposals. In particular, in order to appropriately integrate users' health conditions, preferences, and surrounding environmental information and make proposals in real time, advanced data collection and analysis capabilities are required, and there is a lack of systems to achieve this.

Means for Solving the Problems

[0005] This invention provides a means for collecting biometric information, location information, and environmental information from wearable devices and external information providers, and for evaluating the user's state and preferences by analyzing this information. Furthermore, by constructing a means for generating and notifying the user of optimal suggestions based on this evaluation, the invention provides a system that can proactively respond to the diverse needs of the user. Specifically, it enables the provision of clothing suggestions that take into account the user's schedule and environmental information, and restaurant recommendations based on their health condition.

[0006] An "information gathering device" is a device used to acquire a user's biometric information and location information, and wearable devices fall into this category.

[0007] An "external information provider device" refers to a system or service that provides environmental information, such as a weather forecast API or a restaurant information service.

[0008] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate and blood sugar levels.

[0009] "Location information" refers to data that indicates the user's current location and is obtained through methods such as GPS.

[0010] "Environmental information" refers to data that shows the user's surroundings, such as temperature, weather, and information about nearby facilities.

[0011] "Analysis" is the process of processing collected data to determine the user's state and preferences.

[0012] A "suggestion" refers to instructions or advice provided to the user based on the results of the analysis.

[0013] "Notifications" refer to means of informing users of generated suggestions, and include displays on the device and audio guidance. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[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 an autonomous system for providing personalized support tailored to the diverse needs of users. This system is configured to utilize a combination of an information gathering device and an external information provision device.

[0036] First, the user's device connects with a wearable device that acts as an information gathering device, periodically acquiring the user's biometric and location information. This allows for real-time recording of, for example, the user's heart rate, activity level, and current location.

[0037] Next, the device connects to external information providers such as weather APIs and nearby facility information services via the internet to obtain environmental information about the user's surroundings. This information includes temperature, probability of precipitation, and menus from nearby restaurants.

[0038] Subsequently, the server receives biometric information, location information, and external information transmitted from the terminal, and uses a dedicated analysis engine to evaluate the user's current state and preferences. During this analysis process, past data is also referenced, and the user's personal profile is updated.

[0039] Based on the analysis results, the server generates suggestions for actions appropriate to the user. For example, if the user is going out on a cold day, the server will recommend warm clothing and send a notification. Also, if the user's heart rate increases during lunchtime, it will send a message recommending a nearby Japanese restaurant.

[0040] When the terminal receives a proposal notification from the server, it displays it clearly to the user. The user can review the proposal and choose whether to accept it or not. The user's choice is recorded by the terminal and used to improve the accuracy of future proposals.

[0041] In this way, the system responds to the diverse needs and circumstances of users and autonomously provides optimal support. For example, it can provide support in a variety of situations, such as preparing before going out, selecting healthy meals, and suggesting action plans based on the weather.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user's device periodically acquires biometric and location information from the wearable device. Specifically, it collects heart rate and activity levels via Bluetooth, and current location using GPS, and temporarily stores this data.

[0045] Step 2:

[0046] The device accesses external information providers via the internet to obtain weather and nearby facility information. For example, it might use a weather API to obtain temperature and precipitation probability, or use a restaurant information service to obtain details about nearby dining options.

[0047] Step 3:

[0048] The device sends this data to the server. The data is transferred quickly and reliably through a secure communication protocol.

[0049] Step 4:

[0050] The server analyzes the user's current state and preferences based on biometric, location, and environmental information it receives. Specifically, it uses machine learning algorithms to compare current data with past data and evaluate the user's behavioral patterns.

[0051] Step 5:

[0052] Based on the analysis results, the server generates optimal suggestions for the user. For example, it might recommend wearing a thick coat when going out on a cold day, or suggest a nearby Japanese restaurant for lunch.

[0053] Step 6:

[0054] The server sends the generated suggestions to the terminal. The suggestions are processed to be sent in a format that is easy for the user to understand.

[0055] Step 7:

[0056] The device notifies the user of the proposal and displays it clearly on the screen. The user can review the notification and choose to accept the proposal.

[0057] Step 8:

[0058] If the user makes a selection, the terminal records the result. This record is sent to the server and used to optimize future suggestions.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, there is a demand for personalized support tailored to each individual's lifestyle and health condition, yet autonomous systems to achieve this are insufficient. To solve this problem, a method is needed that can analyze biometric information and environmental conditions in real time and quickly provide optimal suggestions to individuals.

[0062] 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.

[0063] In this invention, the server includes a communication device for acquiring biometric and location information, means for collecting environmental conditions using an external information provision function, and means for analyzing the acquired biometric data, location data, and environmental conditions to evaluate the individual's current state and preferences. This makes it possible to provide personalized suggestions and respond to the diverse needs of users.

[0064] A "communication device" is a device that has the function of sending and receiving data between a user and an information provider in order to acquire biometric information and location information.

[0065] The "external information provision function" refers to a data acquisition function for collecting environmental conditions via the internet, etc., which allows for obtaining weather information and facility information.

[0066] "Biometric data" refers to data that indicates a user's health status and activity level, including heart rate and exercise level.

[0067] "Location data" refers to information indicating the user's current location, and is obtained using technologies such as GPS.

[0068] "Environmental conditions" refer to information that describes the situation around the user, such as weather and the level of congestion at a facility.

[0069] "Analysis" refers to data processing techniques used to evaluate an individual's state and preferences by processing acquired biometric data, location data, and environmental conditions.

[0070] "Personalized suggestions" refer to suggestions made to guide users towards the most optimal actions or choices based on analysis results.

[0071] A "display device" is a device used to visually show suggestions and notifications from a server to the user, and includes smartphones and displays.

[0072] This system possesses advanced data processing capabilities to provide appropriate support tailored to the individual needs of each user. The implementation utilizes communication devices, external information provision functions, and an analysis engine to provide users with optimal suggestions in real time. Specifically, the system is configured as follows:

[0073] Users acquire their own biometric data using wearable sensors and smart devices. This data includes heart rate and activity levels and is transmitted to the terminal. The terminal uses the internet to access external information services such as weather APIs and facility information services to obtain current environmental conditions. This information includes temperature, probability of precipitation, and menu information for nearby facilities.

[0074] The server receives biometric data, location data, and environmental conditions transmitted from the terminal. It then runs a dedicated analysis engine to analyze the user's state and preferences by referencing past data. The analysis uses common programming languages ​​and, for example, data science libraries in Python.

[0075] Based on the analysis results, the server suggests the optimal course of action for the user. This suggestion is generated according to the user's activity and environment, and may include notifications recommending warm clothing on cold days or messages recommending low-calorie restaurants at specific times of day.

[0076] The terminal receives suggestions from the server and displays them visually to the user. The user can review these suggestions and decide whether to accept them, and the terminal records the result of that decision. The recorded data is sent to the server to improve the accuracy of future suggestions.

[0077] As a concrete example, you can input the following prompt sentence into a generation AI model and obtain suggestions:

[0078] "What are some recommended ways to stay hydrated while running?"

[0079] "Could you recommend a restaurant suitable for lunch today?"

[0080] This system configuration allows users to receive advice tailored to their lifestyle, which can be expected to improve their health management and overall quality of life.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user puts on a wearable device and begins their daily activities. The input here is biometric information such as the user's heart rate and activity level. The device periodically collects this data and transmits it to the terminal. Specifically, the device sends signals using Bluetooth or Wi-Fi. The output at this stage is the biometric data stored on the terminal.

[0084] Step 2:

[0085] The device acquires the user's location information using GPS functionality in parallel with receiving biometric data. The input consists of location coordinates and time information. Based on this data, the device identifies the user's current location and records it along with the biometric information. The output is integrated data with added location information, which is used for later analysis.

[0086] Step 3:

[0087] The device accesses external information services such as weather APIs and nearby facilities information services via the internet. Input requires the user's current location and surrounding environmental information. The device collects the acquired temperature, weather, and detailed information about nearby facilities, and stores it as the current environmental conditions. The output is environmental information associated with the location data.

[0088] Step 4:

[0089] The server receives integrated data (biometric data, location data, and environmental information) transmitted from the terminal. The input is this integrated data. The server uses this data to analyze it using data analysis tools such as Python in order to estimate the user's state and preferences. Specifically, it performs trend analysis of time-series data and comparisons with past history. The output is an evaluation result indicating the user's health status and preferences at that time.

[0090] Step 5:

[0091] The server generates optimal action suggestions for the user based on the analysis results. The input is the analyzed evaluation results. The server uses a generative AI model to generate suggestion messages. For example, if the analysis determines that the user is fatigued, the server will create a message recommending rest. The output is a suggestion message optimized for each individual user.

[0092] Step 6:

[0093] The terminal receives suggestion messages sent from the server and presents them visually to the user. The input is the suggestion message from the server. The terminal notifies the user of this message by displaying it on the screen or reading it aloud. The output is the suggestion information received by the user. The user uses this suggestion to decide on an action and records the result of their choice on the terminal.

[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] Users' daily activities and health conditions are diverse, making it challenging to provide appropriate support tailored to their specific needs. In particular, offering concrete action suggestions that consider real-time dynamic and environmental information presents a new technological challenge. Therefore, there is a need to develop a comprehensive system that advises actions optimized for each user's individual circumstances.

[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 acquiring dynamic information and location identification information, means for acquiring external situation information, and means for analyzing the collected information to evaluate the user's situation and preferences. This makes it possible to provide the user with optimal action suggestions in real time.

[0099] An "information acquisition device" is a device used to acquire dynamic information and location identification information.

[0100] An "external information provision device" refers to a device and system for acquiring environmental condition information.

[0101] "Dynamic information" refers to data related to the user's biometric information and behavior.

[0102] "Location identification information" refers to data used to determine the user's current location.

[0103] "Environmental condition information" refers to information about external factors and the surrounding environment.

[0104] "Analysis" is the process of evaluating collected information to understand the user's situation and preferences.

[0105] "Action suggestions" are proposed optimal actions or choices presented to the user based on the analysis results.

[0106] "Notification" refers to the means and process for communicating action suggestions to users.

[0107] An "autonomous support system" is a system that analyzes dynamic and environmental information to provide action suggestions in order to support the user's life.

[0108] The system for realizing this invention consists of a device for acquiring dynamic information and location identification information, a device for providing external situation information, and a server for analyzing the information. The server acquires dynamic information such as the user's biometric information and current location from a wearable device, and collects external situation information such as weather and surrounding environment information from an external information providing device.

[0109] Based on the input information, the server uses an analysis engine written in the Python programming language to evaluate the user's current situation and preferences. This analysis involves referencing past data to gain a more precise understanding of the user's behavior patterns and preferences. An API is built using the Django framework, and the acquired information is processed and managed on the server.

[0110] Based on the analysis results, the system proposes the most appropriate action for the user. This proposal might include, for example, notifying the user to bring an umbrella if the weather is changing. This proposal is communicated to the user through displays on consumer robots and other devices.

[0111] As a concrete example, this system detects that a user's heart rate is at a resting state during the morning hours, and when external environmental data predicts rain, it sends a notification suggesting action such as, "Please take an umbrella with you when you go out today."

[0112] Examples of prompt statements in this system include:

[0113] One example is: "Generate actions to suggest to the user under the following conditions: The user is in the living room. Heart rate is 90 beats / minute. Outside temperature is 18°C, and the probability of precipitation is 80%." Based on such prompts, the AI ​​model generates appropriate action suggestions for the user.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The terminal collects user movement information and location identification information from the wearable device. Specifically, sensors detect the user's heart rate, steps, and location information and transmit it to the terminal via Bluetooth communication. The input to this process is sensor data from the wearable device, and the output is biometric and location information stored in the terminal.

[0117] Step 2:

[0118] The terminal acquires external information from external information providers. Specifically, it obtains current weather data from a weather API via the internet and collects nearby facility information from surrounding information services. It sends API requests as input and receives environmental data (temperature, probability of precipitation, facility information) as output.

[0119] Step 3:

[0120] The server receives dynamic and external situational information transmitted from the terminal and performs evaluation using an analysis engine. Inputs include biometric information, location information, and environmental information from the terminal, and output is an evaluation result tailored to the user's situation. Specifically, Python is used to analyze the data and estimate the user's health status and preferences in real time.

[0121] Step 4:

[0122] The server uses a generation AI model based on the analysis results to generate optimal action suggestions for the user. Here, a prompt is input to the AI ​​model, and the resulting action suggestion is output as a response. For example, if the prompt "It's raining today, should I take an umbrella?" is sent to the generation AI model, the suggestion "You should take an umbrella" is generated.

[0123] Step 5:

[0124] The device receives action suggestions from the server and notifies the user. The UI is used to visually display or audibly communicate these action suggestions. The input here is the action suggestion from the server, and the output is the notification message to the user. Specific actions include displaying "Let's go out with an umbrella" on the screen or providing the same message via voice guidance.

[0125] 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.

[0126] This invention aims to construct a system for recognizing a user's emotional state and providing personalized support based on that state. This system incorporates an information gathering device, an external information provision device, and an emotion engine.

[0127] First, the user's device acquires biometric information through a wearable device acting as an information gathering device. This data includes heart rate, skin electrical activity, and location information. Furthermore, the device collects weather and nearby facility information from external information providers via the internet to understand the environment.

[0128] Next, all collected data is sent to a server and processed by a dedicated analysis engine. This analysis evaluates the user's current situation and preferences, and updates them in comparison with past data.

[0129] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes physiological changes such as heart rate and skin reactions to identify the user's emotions in real time. This allows the system to determine whether the user is stressed or relaxed.

[0130] Based on the analysis results and the emotion engine's judgment, the server generates the most appropriate suggestions for the user. For example, if the server detects that the user is stressed, it will recommend music that is effective in relieving stress and notify the user's device. Furthermore, it will suggest clothing appropriate for the weather and recommend dining locations that take the user's health condition into consideration.

[0131] When the terminal receives a suggestion from the server, it notifies the user visually or audibly. The user can then review and implement these suggestions. This allows the system to address the user's diverse needs and provide optimal support tailored to their emotions and health condition.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user's device acquires biometric information such as heart rate, skin electrical activity, and current location through a wearable device. This data is temporarily stored on the device.

[0135] Step 2:

[0136] The device accesses external information providers via the internet to obtain weather information and information about nearby facilities. This allows it to collect information such as the current temperature, probability of precipitation, and information about nearby restaurants.

[0137] Step 3:

[0138] The device collects biometric and environmental information and transmits it to the server. The data is encrypted and transmitted to the server via a secure communication channel.

[0139] Step 4:

[0140] The server uses the received data to evaluate the user's state and preferences using a dedicated analysis engine. This analysis references historical information, including past data, and updates the user profile accordingly.

[0141] Step 5:

[0142] The server's emotion engine analyzes biometric information to identify the user's emotional state. For example, an excessive increase in heart rate may indicate stress, and this can be detected to determine that the user is in a stressed state.

[0143] Step 6:

[0144] The server generates optimal suggestions for the user based on analysis results and the emotion engine's judgment. For example, it might recommend relaxing music to alleviate stress or suggest dining locations that prioritize mental and physical health.

[0145] Step 7:

[0146] The server generates suggestions and sends them to the device as notifications. The suggestions are displayed in a format that is easy for the user to understand and act upon.

[0147] Step 8:

[0148] The user reviews the suggestions displayed on the device, accepts them, and proceeds with execution. The device records the user's selection and sends feedback back to the server. This feedback is used to improve the accuracy of future suggestions.

[0149] (Example 2)

[0150] 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".

[0151] Conventional information gathering systems only acquire users' biometric and location data individually and make simple suggestions, making it difficult to provide personalized support that takes into account the user's emotional state. Therefore, they face the challenge of not being able to provide optimal suggestions in real time that meet the diverse needs of users.

[0152] 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.

[0153] In this invention, the server includes means for acquiring biometric data and location data from information gathering devices, means for acquiring surrounding information from an external information provision mechanism, means for analyzing the collected biometric data, location data, and surrounding information to evaluate the user's state and preferences, means for generating the most suitable suggestions for the user based on the identified emotional state and the analyzed results, and means for notifying the user of the generated suggestions. This makes it possible to provide personalized suggestions that take into account the user's emotional state and health condition in real time.

[0154] An "information gathering device" is a device used to acquire biometric data and location data from users.

[0155] "Biometric data" refers to data that indicates the user's physiological state, such as heart rate and skin electrical activity.

[0156] "Location data" refers to data that indicates the user's current geographical location.

[0157] An "external information provision system" is a system that provides local information, such as weather and information about nearby facilities, via the internet.

[0158] "Surrounding information" refers to environmental information related to the user's current location, including weather and information about nearby facilities.

[0159] "Analysis" is the process of processing a series of data to evaluate the user's state and preferences.

[0160] "Preferences" refer to the personal tastes and tendencies of the user.

[0161] "Emotional state" refers to the mental or emotional state of the user.

[0162] A "suggestion" is information intended to encourage users to take action or make choices.

[0163] "Notification" refers to the act of communicating a generated proposal to the user visually or audibly.

[0164] This invention is a system that understands the user's current situation and provides personalized suggestions in real time. The system includes a wearable device (terminal) for information collection, through which biometric data is acquired. Specifically, heart rate and skin electrical activity are detected by this device. This device can be a smartwatch or a fitness tracker, among others.

[0165] The user's device obtains weather and information about nearby facilities from external information providers via the internet. This often utilizes existing weather information APIs and map information services. The device integrates this information and transmits it to the server along with biometric data.

[0166] The server is equipped with a dedicated analysis engine that processes the collected data. The analysis engine uses AI algorithms and generative AI models to identify the user's emotional state. The emotion engine analyzes physiological indicators such as heart rate to determine whether the user is stressed or relaxed. Based on this determination, it generates appropriate suggestions.

[0167] The server generates optimal suggestions based on the user's emotional state and collected information, and sends them to the device. For example, if the system determines that the user is feeling stressed in the morning, the server will suggest relaxing music and notify the device. Notifications can be made via voice through a voice assistant, depending on the user's preferences.

[0168] For example, if the system detects an increase in heart rate while a user is running, it will determine that this is due to exercise rather than physiological stress. This allows the system to suggest post-exercise cool-down methods and encourage hydration.

[0169] Examples of prompts for a generative AI model include the following:

[0170] "The user's heart rate is elevated. Please suggest a suitable way to relax in this situation. Music or an activity that can be done nearby would be ideal."

[0171] This configuration allows the system to provide personalized support tailored to the user's emotions and health condition.

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

[0173] Step 1:

[0174] The user's device acquires the user's biometric data in real time using a wearable device. The data obtained as input includes heart rate, skin electrical activity, and location information. This data is used to indicate the user's current physiological state. Specifically, the device collects data from the wearable device via Bluetooth or Wi-Fi.

[0175] Step 2:

[0176] The user's device obtains weather information and nearby facility information from external information providers via the internet. The input is the user's current location, and the output is environmental information associated with that location. Specifically, the device calls existing APIs to check the weather and operating status of facilities in the relevant area.

[0177] Step 3:

[0178] The user's device transmits acquired biometric data and environmental information to the server. The input is aggregated data, and the output is that data being transmitted to the server via the network. Specifically, the device encrypts the data using SSL / TLS or similar methods and sends it to the specified address on the server.

[0179] Step 4:

[0180] The server uses an analysis engine to analyze the user's state based on the received data. The input consists of biometric data and environmental information, and the output is an analysis result indicating the user's emotional state and health status. The analysis engine utilizes machine learning models to process the data while comparing it with past data. Specifically, the algorithm detects rapid changes in heart rate and maps them to emotions such as stress and joy.

[0181] Step 5:

[0182] The server uses a generative AI model to generate suggestions tailored to the user. The input consists of analysis results and emotional states, while the output is specific suggestions for the user. Specifically, the generative AI model creates prompts and generates scenarios suggesting music, relaxation methods, or appropriate actions.

[0183] Step 6:

[0184] The server notifies the user's terminal of the generated suggestions. The input is the content of the suggestions, and the output is the user's terminal receiving those suggestions. Specifically, the notification is displayed on the terminal's screen or output audibly through a voice assistant. The terminal launches the appropriate application and displays the message to convey the content to the user.

[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 a "server" and the smart device 14 as a "terminal".

[0187] In modern society, users are exposed to a variety of stressors, and personalized support tailored to their individual emotional states is required. However, conventional technologies have made it difficult to recognize users' emotions in real time and provide appropriate suggestions based on that recognition. Therefore, the present invention aims to recognize users' emotional states and propose optimal activities for stress reduction and health promotion.

[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 acquiring biometric information and location information from an information gathering device, means for acquiring environmental information from an external information providing device, means for analyzing the collected biometric information, location information, and environmental information and evaluating the user's state and preferences, means for generating optimal suggestions for the user based on the analysis results, means for notifying the user of the generated suggestions, and means for recognizing the user's emotional state and suggesting relaxation activities if the user is feeling stressed. This enables appropriate and personalized support according to the user's emotions and health condition.

[0190] An "information gathering device" is a device used to acquire biometric and location information, and includes wearable devices and sensor technologies.

[0191] An "external information provider device" is a device for acquiring environmental information, which uses databases and APIs via the internet to provide weather information and information about surrounding facilities.

[0192] "Emotional state" is an indicator that shows the psychological and physiological changes of the user, and is evaluated in relation to physiological data such as heart rate and skin response.

[0193] "Suggestions" are recommendations for actions and choices provided to users by the analysis engine, including recommendations for music playback, appropriate clothing choices, and healthy dining locations.

[0194] An "analysis engine" is a software component that evaluates the user's state and preferences based on acquired biometric, location, and environmental information, and performs analysis using data processing and algorithms.

[0195] "Notification means" refers to a method of conveying generated suggestions or information to the user, presenting them on the device in a visual or auditory format.

[0196] "Relaxation activities" refer to actions and experiences that help users reduce stress and promote relaxation, and include the use of music, aromatherapy, and other such activities.

[0197] This system uses information gathering devices, external information providers, an analysis engine, and notification means to recognize the user's emotional state and provide personalized support. The server uses wearable devices as information gathering devices to acquire biometric information such as heart rate and skin electrical activity, as well as location information. It also utilizes an internet connection as an external information provider to acquire weather information and information about nearby facilities.

[0198] The acquired data is sent to a cloud-based server and processed by an analysis engine. Specifically, biometric information, location information, and environmental information are compared to evaluate the user's current state and preferences. This evaluation uses an emotion recognition AI engine. The emotion recognition AI engine, for example, uses IBM's Emotion Analysis to identify the user's emotions in real time based on physiological data.

[0199] Based on the analyzed results, the server determines whether the user is stressed or relaxed and generates suggestions accordingly. These suggestions are then notified to the user's device via smart home devices, etc. For example, when the user is stressed, relaxing music can be played or an aroma diffuser can be activated. Furthermore, recommendations for places to eat, clothing, and other items can be provided based on the user's health condition and the weather.

[0200] For example, if an increase in heart rate is detected in the evening, the server will suggest relaxing jazz music, automatically set a playlist, and start playing it. Furthermore, if a colder temperature is forecast for the next day, it can send a notification such as, "We recommend wearing warmer clothing tomorrow."

[0201] Examples of prompts from the generative AI model include, "Please come up with ideas for providing the best relaxation methods for when the user is feeling stressed," and "Based on emotion recognition data, please provide suggestions that are useful in daily life."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device acquires biometric information such as the user's heart rate and skin electrical activity through a wearable device, and also collects location information. This data is sent to the server as initial input. In this step, information is collected through an API for data acquisition.

[0205] Step 2:

[0206] The server uses external information providers to acquire environmental information such as weather and nearby facilities via the network. This environmental information is retrieved from the database, thereby supplementing the input data.

[0207] Step 3:

[0208] The server inputs biometric information, location information, and acquired environmental information into an emotion recognition AI engine. This engine evaluates the user's current emotional state in real time. During this process, the data is processed using multiple algorithms to obtain output regarding the user's emotional state.

[0209] Step 4:

[0210] The server inputs emotional state data obtained from the emotion recognition AI engine into the analysis engine to evaluate the user's state and preferences. This analysis compares and analyzes the user's past behavioral history and preference data with the current emotional state data to generate output that forms the basis for appropriate suggestions.

[0211] Step 5:

[0212] The server generates optimal suggestions based on the analysis. These suggestions are created using a generative AI model to identify music playlists and lifestyle suggestions that match the user's emotional state and environmental conditions. The generated suggestions are then sent to the device as instructions.

[0213] Step 6:

[0214] Based on the received suggestions, the device notifies the user of the suggestions visually or audibly. In this step, specific actions are taken by the user using the display or speaker. For example, music may be played or a widget displaying the suggestions may be launched.

[0215] Step 7:

[0216] Users can review the suggestions notified via their device and decide whether to accept them. Depending on the user's choice, the system can also collect additional data to improve future suggestions.

[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 an autonomous system for providing personalized support tailored to the diverse needs of users. This system is configured to utilize a combination of an information gathering device and an external information provision device.

[0234] First, the user's device connects with a wearable device that acts as an information gathering device, periodically acquiring the user's biometric and location information. This allows for real-time recording of, for example, the user's heart rate, activity level, and current location.

[0235] Next, the device connects to external information providers such as weather APIs and nearby facility information services via the internet to obtain environmental information about the user's surroundings. This information includes temperature, probability of precipitation, and menus from nearby restaurants.

[0236] Subsequently, the server receives biometric information, location information, and external information transmitted from the terminal, and uses a dedicated analysis engine to evaluate the user's current state and preferences. During this analysis process, past data is also referenced, and the user's personal profile is updated.

[0237] Based on the analysis results, the server generates suggestions for actions appropriate to the user. For example, if the user is going out on a cold day, the server will recommend warm clothing and send a notification. Also, if the user's heart rate increases during lunchtime, it will send a message recommending a nearby Japanese restaurant.

[0238] When the terminal receives a proposal notification from the server, it displays it clearly to the user. The user can review the proposal and choose whether to accept it or not. The user's choice is recorded by the terminal and used to improve the accuracy of future proposals.

[0239] In this way, the system responds to the diverse needs and circumstances of users and autonomously provides optimal support. For example, it can provide support in a variety of situations, such as preparing before going out, selecting healthy meals, and suggesting action plans based on the weather.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user's device periodically acquires biometric and location information from the wearable device. Specifically, it collects heart rate and activity levels via Bluetooth, and current location using GPS, and temporarily stores this data.

[0243] Step 2:

[0244] The device accesses external information providers via the internet to obtain weather and nearby facility information. For example, it might use a weather API to obtain temperature and precipitation probability, or use a restaurant information service to obtain details about nearby dining options.

[0245] Step 3:

[0246] The device sends this data to the server. The data is transferred quickly and reliably through a secure communication protocol.

[0247] Step 4:

[0248] The server analyzes the user's current state and preferences based on biometric, location, and environmental information it receives. Specifically, it uses machine learning algorithms to compare current data with past data and evaluate the user's behavioral patterns.

[0249] Step 5:

[0250] Based on the analysis results, the server generates optimal suggestions for the user. For example, it might recommend wearing a thick coat when going out on a cold day, or suggest a nearby Japanese restaurant for lunch.

[0251] Step 6:

[0252] The server sends the generated suggestions to the terminal. The suggestions are processed to be sent in a format that is easy for the user to understand.

[0253] Step 7:

[0254] The device notifies the user of the proposal and displays it clearly on the screen. The user can review the notification and choose to accept the proposal.

[0255] Step 8:

[0256] If the user makes a selection, the terminal records the result. This record is sent to the server and used to optimize future suggestions.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] In modern society, there is a demand for personalized support tailored to each individual's lifestyle and health condition, yet autonomous systems to achieve this are insufficient. To solve this problem, a method is needed that can analyze biometric information and environmental conditions in real time and quickly provide optimal suggestions to individuals.

[0260] 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.

[0261] In this invention, the server includes a communication device for acquiring biometric and location information, means for collecting environmental conditions using an external information provision function, and means for analyzing the acquired biometric data, location data, and environmental conditions to evaluate the individual's current state and preferences. This makes it possible to provide personalized suggestions and respond to the diverse needs of users.

[0262] A "communication device" is a device that has the function of sending and receiving data between a user and an information provider in order to acquire biometric information and location information.

[0263] The "external information provision function" refers to a data acquisition function for collecting environmental conditions via the internet, etc., which allows for obtaining weather information and facility information.

[0264] "Biometric data" refers to data that indicates a user's health status and activity level, including heart rate and exercise level.

[0265] "Location data" refers to information indicating the user's current location, and is obtained using technologies such as GPS.

[0266] "Environmental conditions" refer to information that describes the situation around the user, such as weather and the level of congestion at a facility.

[0267] "Analysis" refers to data processing techniques used to evaluate an individual's state and preferences by processing acquired biometric data, location data, and environmental conditions.

[0268] "Personalized suggestions" refer to suggestions made to guide users towards the most optimal actions or choices based on analysis results.

[0269] A "display device" is a device used to visually show suggestions and notifications from a server to the user, and includes smartphones and displays.

[0270] This system possesses advanced data processing capabilities to provide appropriate support tailored to the individual needs of each user. The implementation utilizes communication devices, external information provision functions, and an analysis engine to provide users with optimal suggestions in real time. Specifically, the system is configured as follows:

[0271] Users acquire their own biometric data using wearable sensors and smart devices. This data includes heart rate and activity levels and is transmitted to the terminal. The terminal uses the internet to access external information services such as weather APIs and facility information services to obtain current environmental conditions. This information includes temperature, probability of precipitation, and menu information for nearby facilities.

[0272] The server receives biometric data, location data, and environmental conditions transmitted from the terminal. It then runs a dedicated analysis engine to analyze the user's state and preferences by referencing past data. The analysis uses common programming languages ​​and, for example, data science libraries in Python.

[0273] Based on the analysis results, the server suggests the optimal course of action for the user. This suggestion is generated according to the user's activity and environment, and may include notifications recommending warm clothing on cold days or messages recommending low-calorie restaurants at specific times of day.

[0274] The terminal receives suggestions from the server and displays them visually to the user. The user can review these suggestions and decide whether to accept them, and the terminal records the result of that decision. The recorded data is sent to the server to improve the accuracy of future suggestions.

[0275] As a concrete example, you can input the following prompt sentence into a generation AI model and obtain suggestions:

[0276] "What are some recommended ways to stay hydrated while running?"

[0277] "Could you recommend a restaurant suitable for lunch today?"

[0278] This system configuration allows users to receive advice tailored to their lifestyle, which can be expected to improve their health management and overall quality of life.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user puts on a wearable device and begins their daily activities. The input here is biometric information such as the user's heart rate and activity level. The device periodically collects this data and transmits it to the terminal. Specifically, the device sends signals using Bluetooth or Wi-Fi. The output at this stage is the biometric data stored on the terminal.

[0282] Step 2:

[0283] The terminal acquires the user's location information using the GPS function in parallel with the reception of biometric information. The input is information on position coordinates and time. Based on this data, the terminal identifies where the user is currently located and records it together with the biometric information. The output is integrated data with the location information added, which is used for analysis later.

[0284] Step 3:

[0285] The terminal accesses external information providing functions such as weather APIs and nearby facility information services through the Internet. The input requires the user's current location and environmental information around it. The terminal collects the acquired temperature, weather, and detailed information of nearby facilities and stores them as the current environmental conditions. The output is environmental information associated with the location data.

[0286] Step 4:

[0287] The server receives the integrated data (biometric data, location data, environmental information) sent from the terminal. The input is this integrated data. The server analyzes the data using data analysis tools such as Python to estimate the user's state and preferences. Specifically, trend analysis of time-series data and comparison with past history are performed. The output is an evaluation result indicating the user's health status and preferences at that time.

[0288] Step 5:

[0289] Based on the analysis results, the server generates an optimal action proposal for the user. The input is the analyzed evaluation result. The server utilizes a generation AI model to generate a proposal text. For example, if the analysis determines that the user is in a fatigued state, the server creates a message recommending rest. The output is a proposal message optimized for each individual user.

[0290] Step 6:

[0291] The terminal receives suggestion messages sent from the server and presents them visually to the user. The input is the suggestion message from the server. The terminal notifies the user of this message by displaying it on the screen or reading it aloud. The output is the suggestion information received by the user. The user uses this suggestion to decide on an action and records the result of their choice on the terminal.

[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] Users' daily activities and health conditions are diverse, making it challenging to provide appropriate support tailored to their specific needs. In particular, offering concrete action suggestions that consider real-time dynamic and environmental information presents a new technological challenge. Therefore, there is a need to develop a comprehensive system that advises actions optimized for each user's individual circumstances.

[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 acquiring dynamic information and location identification information, means for acquiring external situation information, and means for analyzing the collected information to evaluate the user's situation and preferences. This makes it possible to provide the user with optimal action suggestions in real time.

[0297] An "information acquisition device" is a device used to acquire dynamic information and location identification information.

[0298] An "external information provision device" refers to a device and system for acquiring environmental condition information.

[0299] "Dynamic information" refers to data related to the user's biometric information and behavior.

[0300] "Location identification information" is data for grasping the current location of the user.

[0301] "Environmental situation information" refers to information on external factors and the surrounding environment.

[0302] "Analysis" is a process for evaluating the collected information and understanding the user's situation and preferences.

[0303] "Action proposal" is a plan for optimal actions and choices presented to the user based on the analysis results.

[0304] "Notification" is a means and process for conveying the action proposal to the user.

[0305] "Autonomous support system" is a system that analyzes dynamic information and environmental information and makes action proposals to support the user's life.

[0306] The system for realizing this invention consists of a device for acquiring dynamic information and location identification information, a device for providing external situation information, and a server for analyzing information. The server acquires dynamic information such as the user's biometric information and current location from wearable devices, and collects external situation information such as weather and surrounding environment information from external information providing devices.

[0307] Based on this input information, the server uses an analysis engine using the Python programming language to evaluate the user's current situation and preferences. In this analysis, by referring to past data, the user's behavior patterns and preferences can be grasped more precisely. An API is constructed using the Django framework, and the acquired information is processed and managed by the server.

[0308] Based on the analysis results, an optimal action is proposed to the user. The proposed content includes, for example, notifying the user to bring an umbrella when the weather changes. This proposal is transmitted to the user through the display of a consumer robot or other devices.

[0309] As a concrete example, this system detects that a user's heart rate is at a resting state during the morning hours, and when external environmental data predicts rain, it sends a notification suggesting action such as, "Please take an umbrella with you when you go out today."

[0310] Examples of prompt statements in this system include:

[0311] One example is: "Generate actions to suggest to the user under the following conditions: The user is in the living room. Heart rate is 90 beats / minute. Outside temperature is 18°C, and the probability of precipitation is 80%." Based on such prompts, the AI ​​model generates appropriate action suggestions for the user.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The terminal collects user movement information and location identification information from the wearable device. Specifically, sensors detect the user's heart rate, steps, and location information and transmit it to the terminal via Bluetooth communication. The input to this process is sensor data from the wearable device, and the output is biometric and location information stored in the terminal.

[0315] Step 2:

[0316] The terminal acquires external information from external information providers. Specifically, it obtains current weather data from a weather API via the internet and collects nearby facility information from surrounding information services. It sends API requests as input and receives environmental data (temperature, probability of precipitation, facility information) as output.

[0317] Step 3:

[0318] The server receives dynamic and external situational information transmitted from the terminal and performs evaluation using an analysis engine. Inputs include biometric information, location information, and environmental information from the terminal, and output is an evaluation result tailored to the user's situation. Specifically, Python is used to analyze the data and estimate the user's health status and preferences in real time.

[0319] Step 4:

[0320] The server uses a generation AI model based on the analysis results to generate optimal action suggestions for the user. Here, a prompt is input to the AI ​​model, and the resulting action suggestion is output as a response. For example, if the prompt "It's raining today, should I take an umbrella?" is sent to the generation AI model, the suggestion "You should take an umbrella" is generated.

[0321] Step 5:

[0322] The device receives action suggestions from the server and notifies the user. The UI is used to visually display or audibly communicate these action suggestions. The input here is the action suggestion from the server, and the output is the notification message to the user. Specific actions include displaying "Let's go out with an umbrella" on the screen or providing the same message via voice guidance.

[0323] 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.

[0324] This invention aims to construct a system for recognizing a user's emotional state and providing personalized support based on that state. This system incorporates an information gathering device, an external information provision device, and an emotion engine.

[0325] First, the user's device acquires biometric information through a wearable device acting as an information gathering device. This data includes heart rate, skin electrical activity, and location information. Furthermore, the device collects weather and nearby facility information from external information providers via the internet to understand the environment.

[0326] Next, all collected data is sent to a server and processed by a dedicated analysis engine. This analysis evaluates the user's current situation and preferences, and updates them in comparison with past data.

[0327] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes physiological changes such as heart rate and skin reactions to identify the user's emotions in real time. This allows the system to determine whether the user is stressed or relaxed.

[0328] Based on the analysis results and the emotion engine's judgment, the server generates the most appropriate suggestions for the user. For example, if the server detects that the user is stressed, it will recommend music that is effective in relieving stress and notify the user's device. Furthermore, it will suggest clothing appropriate for the weather and recommend dining locations that take the user's health condition into consideration.

[0329] When the terminal receives a suggestion from the server, it notifies the user visually or audibly. The user can then review and implement these suggestions. This allows the system to address the user's diverse needs and provide optimal support tailored to their emotions and health condition.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user's device acquires biometric information such as heart rate, skin electrical activity, and current location through a wearable device. This data is temporarily stored on the device.

[0333] Step 2:

[0334] The device accesses external information providers via the internet to obtain weather information and information about nearby facilities. This allows it to collect information such as the current temperature, probability of precipitation, and information about nearby restaurants.

[0335] Step 3:

[0336] The device collects biometric and environmental information and transmits it to the server. The data is encrypted and transmitted to the server via a secure communication channel.

[0337] Step 4:

[0338] The server uses the received data to evaluate the user's state and preferences using a dedicated analysis engine. This analysis references historical information, including past data, and updates the user profile accordingly.

[0339] Step 5:

[0340] The server's emotion engine analyzes biometric information to identify the user's emotional state. For example, an excessive increase in heart rate may indicate stress, and this can be detected to determine that the user is in a stressed state.

[0341] Step 6:

[0342] The server generates optimal suggestions for the user based on analysis results and the emotion engine's judgment. For example, it might recommend relaxing music to alleviate stress or suggest dining locations that prioritize mental and physical health.

[0343] Step 7:

[0344] The server generates suggestions and sends them to the device as notifications. The suggestions are displayed in a format that is easy for the user to understand and act upon.

[0345] Step 8:

[0346] The user reviews the suggestions displayed on the device, accepts them, and proceeds with execution. The device records the user's selection and sends feedback back to the server. This feedback is used to improve the accuracy of future suggestions.

[0347] (Example 2)

[0348] 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".

[0349] Conventional information gathering systems only acquire users' biometric and location data individually and make simple suggestions, making it difficult to provide personalized support that takes into account the user's emotional state. Therefore, they face the challenge of not being able to provide optimal suggestions in real time that meet the diverse needs of users.

[0350] 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.

[0351] In this invention, the server includes means for acquiring biometric data and location data from information gathering devices, means for acquiring surrounding information from an external information provision mechanism, means for analyzing the collected biometric data, location data, and surrounding information to evaluate the user's state and preferences, means for generating the most suitable suggestions for the user based on the identified emotional state and the analyzed results, and means for notifying the user of the generated suggestions. This makes it possible to provide personalized suggestions that take into account the user's emotional state and health condition in real time.

[0352] An "information gathering device" is a device used to acquire biometric data and location data from users.

[0353] "Biometric data" refers to data that indicates the user's physiological state, such as heart rate and skin electrical activity.

[0354] "Location data" refers to data that indicates the user's current geographical location.

[0355] An "external information provision system" is a system that provides local information, such as weather and information about nearby facilities, via the internet.

[0356] "Surrounding information" refers to environmental information related to the user's current location, including weather and information about nearby facilities.

[0357] "Analysis" is the process of processing a series of data to evaluate the user's state and preferences.

[0358] "Preferences" refer to the personal tastes and tendencies of the user.

[0359] "Emotional state" refers to the mental or emotional state of the user.

[0360] A "suggestion" is information intended to encourage users to take action or make choices.

[0361] "Notification" refers to the act of communicating a generated proposal to the user visually or audibly.

[0362] This invention is a system that understands the user's current situation and provides personalized suggestions in real time. The system includes a wearable device (terminal) for information collection, through which biometric data is acquired. Specifically, heart rate and skin electrical activity are detected by this device. This device can be a smartwatch or a fitness tracker, among others.

[0363] The user's device obtains weather and information about nearby facilities from external information providers via the internet. This often utilizes existing weather information APIs and map information services. The device integrates this information and transmits it to the server along with biometric data.

[0364] The server is equipped with a dedicated analysis engine that processes the collected data. The analysis engine uses AI algorithms and generative AI models to identify the user's emotional state. The emotion engine analyzes physiological indicators such as heart rate to determine whether the user is stressed or relaxed. Based on this determination, it generates appropriate suggestions.

[0365] The server generates optimal suggestions based on the user's emotional state and collected information, and sends them to the device. For example, if the system determines that the user is feeling stressed in the morning, the server will suggest relaxing music and notify the device. Notifications can be made via voice through a voice assistant, depending on the user's preferences.

[0366] For example, if the system detects an increase in heart rate while a user is running, it will determine that this is due to exercise rather than physiological stress. This allows the system to suggest post-exercise cool-down methods and encourage hydration.

[0367] Examples of prompts for a generative AI model include the following:

[0368] "The user's heart rate is elevated. Please suggest a suitable way to relax in this situation. Music or an activity that can be done nearby would be ideal."

[0369] This configuration allows the system to provide personalized support tailored to the user's emotions and health condition.

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

[0371] Step 1:

[0372] The user's device acquires the user's biometric data in real time using a wearable device. The data obtained as input includes heart rate, skin electrical activity, and location information. This data is used to indicate the user's current physiological state. Specifically, the device collects data from the wearable device via Bluetooth or Wi-Fi.

[0373] Step 2:

[0374] The user's device obtains weather information and nearby facility information from external information providers via the internet. The input is the user's current location, and the output is environmental information associated with that location. Specifically, the device calls existing APIs to check the weather and operating status of facilities in the relevant area.

[0375] Step 3:

[0376] The user's device transmits acquired biometric data and environmental information to the server. The input is aggregated data, and the output is that data being transmitted to the server via the network. Specifically, the device encrypts the data using SSL / TLS or similar methods and sends it to the specified address on the server.

[0377] Step 4:

[0378] The server uses an analysis engine to analyze the user's state based on the received data. The input consists of biometric data and environmental information, and the output is an analysis result indicating the user's emotional state and health status. The analysis engine utilizes machine learning models to process the data while comparing it with past data. Specifically, the algorithm detects rapid changes in heart rate and maps them to emotions such as stress and joy.

[0379] Step 5:

[0380] The server uses a generative AI model to generate suggestions tailored to the user. The input consists of analysis results and emotional states, while the output is specific suggestions for the user. Specifically, the generative AI model creates prompts and generates scenarios suggesting music, relaxation methods, or appropriate actions.

[0381] Step 6:

[0382] The server notifies the user's terminal of the generated suggestions. The input is the content of the suggestions, and the output is the user's terminal receiving those suggestions. Specifically, the notification is displayed on the terminal's screen or output audibly through a voice assistant. The terminal launches the appropriate application and displays the message to convey the content to the user.

[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, users are exposed to a variety of stressors, and personalized support tailored to their individual emotional states is required. However, conventional technologies have made it difficult to recognize users' emotions in real time and provide appropriate suggestions based on that recognition. Therefore, the present invention aims to recognize users' emotional states and propose optimal activities for stress reduction and health promotion.

[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 acquiring biometric information and location information from an information gathering device, means for acquiring environmental information from an external information providing device, means for analyzing the collected biometric information, location information, and environmental information and evaluating the user's state and preferences, means for generating optimal suggestions for the user based on the analysis results, means for notifying the user of the generated suggestions, and means for recognizing the user's emotional state and suggesting relaxation activities if the user is feeling stressed. This enables appropriate and personalized support according to the user's emotions and health condition.

[0388] An "information gathering device" is a device used to acquire biometric and location information, and includes wearable devices and sensor technologies.

[0389] An "external information provider device" is a device for acquiring environmental information, which uses databases and APIs via the internet to provide weather information and information about surrounding facilities.

[0390] "Emotional state" is an indicator that shows the psychological and physiological changes of the user, and is evaluated in relation to physiological data such as heart rate and skin response.

[0391] "Suggestions" are recommendations for actions and choices provided to users by the analysis engine, including recommendations for music playback, appropriate clothing choices, and healthy dining locations.

[0392] An "analysis engine" is a software component that evaluates the user's state and preferences based on acquired biometric, location, and environmental information, and performs analysis using data processing and algorithms.

[0393] "Notification means" refers to a method of conveying generated suggestions or information to the user, presenting them on the device in a visual or auditory format.

[0394] "Relaxation activities" refer to actions and experiences that help users reduce stress and promote relaxation, and include the use of music, aromatherapy, and other such activities.

[0395] This system uses information gathering devices, external information providers, an analysis engine, and notification means to recognize the user's emotional state and provide personalized support. The server uses wearable devices as information gathering devices to acquire biometric information such as heart rate and skin electrical activity, as well as location information. It also utilizes an internet connection as an external information provider to acquire weather information and information about nearby facilities.

[0396] The acquired data is sent to a cloud-based server and processed by an analysis engine. Specifically, biometric information, location information, and environmental information are compared to evaluate the user's current state and preferences. This evaluation uses an emotion recognition AI engine. The emotion recognition AI engine, for example, uses IBM's Emotion Analysis to identify the user's emotions in real time based on physiological data.

[0397] Based on the analyzed results, the server determines whether the user is stressed or relaxed and generates suggestions accordingly. These suggestions are then notified to the user's device via smart home devices, etc. For example, when the user is stressed, relaxing music can be played or an aroma diffuser can be activated. Furthermore, recommendations for places to eat, clothing, and other items can be provided based on the user's health condition and the weather.

[0398] For example, if an increase in heart rate is detected in the evening, the server will suggest relaxing jazz music, automatically set a playlist, and start playing it. Furthermore, if a colder temperature is forecast for the next day, it can send a notification such as, "We recommend wearing warmer clothing tomorrow."

[0399] Examples of prompts from the generative AI model include, "Please come up with ideas for providing the best relaxation methods for when the user is feeling stressed," and "Based on emotion recognition data, please provide suggestions that are useful in daily life."

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The device acquires biometric information such as the user's heart rate and skin electrical activity through a wearable device, and also collects location information. This data is sent to the server as initial input. In this step, information is collected through an API for data acquisition.

[0403] Step 2:

[0404] The server uses external information providers to acquire environmental information such as weather and nearby facilities via the network. This environmental information is retrieved from the database, thereby supplementing the input data.

[0405] Step 3:

[0406] The server inputs biometric information, location information, and acquired environmental information into an emotion recognition AI engine. This engine evaluates the user's current emotional state in real time. During this process, the data is processed using multiple algorithms to obtain output regarding the user's emotional state.

[0407] Step 4:

[0408] The server inputs emotional state data obtained from the emotion recognition AI engine into the analysis engine to evaluate the user's state and preferences. This analysis compares and analyzes the user's past behavioral history and preference data with the current emotional state data to generate output that forms the basis for appropriate suggestions.

[0409] Step 5:

[0410] The server generates optimal suggestions based on the analysis. These suggestions are created using a generative AI model to identify music playlists and lifestyle suggestions that match the user's emotional state and environmental conditions. The generated suggestions are then sent to the device as instructions.

[0411] Step 6:

[0412] Based on the received suggestions, the device notifies the user of the suggestions visually or audibly. In this step, specific actions are taken by the user using the display or speaker. For example, music may be played or a widget displaying the suggestions may be launched.

[0413] Step 7:

[0414] Users can review the suggestions notified via their device and decide whether to accept them. Depending on the user's choice, the system can also collect additional data to improve future suggestions.

[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 an autonomous system for providing personalized support tailored to the diverse needs of users. This system is configured to utilize a combination of an information gathering device and an external information provision device.

[0432] First, the user's device connects with a wearable device that acts as an information gathering device, periodically acquiring the user's biometric and location information. This allows for real-time recording of, for example, the user's heart rate, activity level, and current location.

[0433] Next, the device connects to external information providers such as weather APIs and nearby facility information services via the internet to obtain environmental information about the user's surroundings. This information includes temperature, probability of precipitation, and menus from nearby restaurants.

[0434] Subsequently, the server receives biometric information, location information, and external information transmitted from the terminal, and uses a dedicated analysis engine to evaluate the user's current state and preferences. During this analysis process, past data is also referenced, and the user's personal profile is updated.

[0435] Based on the analysis results, the server generates suggestions for actions appropriate to the user. For example, if the user is going out on a cold day, the server will recommend warm clothing and send a notification. Also, if the user's heart rate increases during lunchtime, it will send a message recommending a nearby Japanese restaurant.

[0436] When the terminal receives a proposal notification from the server, it displays it clearly to the user. The user can review the proposal and choose whether to accept it or not. The user's choice is recorded by the terminal and used to improve the accuracy of future proposals.

[0437] In this way, the system responds to the diverse needs and circumstances of users and autonomously provides optimal support. For example, it can provide support in a variety of situations, such as preparing before going out, selecting healthy meals, and suggesting action plans based on the weather.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The user's device periodically acquires biometric and location information from the wearable device. Specifically, it collects heart rate and activity levels via Bluetooth, and current location using GPS, and temporarily stores this data.

[0441] Step 2:

[0442] The device accesses external information providers via the internet to obtain weather and nearby facility information. For example, it might use a weather API to obtain temperature and precipitation probability, or use a restaurant information service to obtain details about nearby dining options.

[0443] Step 3:

[0444] The device sends this data to the server. The data is transferred quickly and reliably through a secure communication protocol.

[0445] Step 4:

[0446] The server analyzes the user's current state and preferences based on biometric, location, and environmental information it receives. Specifically, it uses machine learning algorithms to compare current data with past data and evaluate the user's behavioral patterns.

[0447] Step 5:

[0448] Based on the analysis results, the server generates optimal suggestions for the user. For example, it might recommend wearing a thick coat when going out on a cold day, or suggest a nearby Japanese restaurant for lunch.

[0449] Step 6:

[0450] The server sends the generated suggestions to the terminal. The suggestions are processed to be sent in a format that is easy for the user to understand.

[0451] Step 7:

[0452] The device notifies the user of the proposal and displays it clearly on the screen. The user can review the notification and choose to accept the proposal.

[0453] Step 8:

[0454] If the user makes a selection, the terminal records the result. This record is sent to the server and used to optimize future suggestions.

[0455] (Example 1)

[0456] 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."

[0457] In modern society, there is a demand for personalized support tailored to each individual's lifestyle and health condition, yet autonomous systems to achieve this are insufficient. To solve this problem, a method is needed that can analyze biometric information and environmental conditions in real time and quickly provide optimal suggestions to individuals.

[0458] 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.

[0459] In this invention, the server includes a communication device for acquiring biometric and location information, means for collecting environmental conditions using an external information provision function, and means for analyzing the acquired biometric data, location data, and environmental conditions to evaluate the individual's current state and preferences. This makes it possible to provide personalized suggestions and respond to the diverse needs of users.

[0460] A "communication device" is a device that has the function of sending and receiving data between a user and an information provider in order to acquire biometric information and location information.

[0461] The "external information provision function" refers to a data acquisition function for collecting environmental conditions via the internet, etc., which allows for obtaining weather information and facility information.

[0462] "Biometric data" refers to data that indicates a user's health status and activity level, including heart rate and exercise level.

[0463] "Location data" refers to information indicating the user's current location, and is obtained using technologies such as GPS.

[0464] "Environmental conditions" refer to information that describes the situation around the user, such as weather and the level of congestion at a facility.

[0465] "Analysis" refers to data processing techniques used to evaluate an individual's state and preferences by processing acquired biometric data, location data, and environmental conditions.

[0466] "Personalized suggestions" refer to suggestions made to guide users towards the most optimal actions or choices based on analysis results.

[0467] A "display device" is a device used to visually show suggestions and notifications from a server to the user, and includes smartphones and displays.

[0468] This system possesses advanced data processing capabilities to provide appropriate support tailored to the individual needs of each user. The implementation utilizes communication devices, external information provision functions, and an analysis engine to provide users with optimal suggestions in real time. Specifically, the system is configured as follows:

[0469] Users acquire their own biometric data using wearable sensors and smart devices. This data includes heart rate and activity levels and is transmitted to the terminal. The terminal uses the internet to access external information services such as weather APIs and facility information services to obtain current environmental conditions. This information includes temperature, probability of precipitation, and menu information for nearby facilities.

[0470] The server receives biometric data, location data, and environmental conditions transmitted from the terminal. It then runs a dedicated analysis engine to analyze the user's state and preferences by referencing past data. The analysis uses common programming languages ​​and, for example, data science libraries in Python.

[0471] Based on the analysis results, the server suggests the optimal course of action for the user. This suggestion is generated according to the user's activity and environment, and may include notifications recommending warm clothing on cold days or messages recommending low-calorie restaurants at specific times of day.

[0472] The terminal receives suggestions from the server and displays them visually to the user. The user can review these suggestions and decide whether to accept them, and the terminal records the result of that decision. The recorded data is sent to the server to improve the accuracy of future suggestions.

[0473] As a concrete example, you can input the following prompt sentence into a generation AI model and obtain suggestions:

[0474] "What are some recommended ways to stay hydrated while running?"

[0475] "Could you recommend a restaurant suitable for lunch today?"

[0476] This system configuration allows users to receive advice tailored to their lifestyle, which can be expected to improve their health management and overall quality of life.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user puts on a wearable device and begins their daily activities. The input here is biometric information such as the user's heart rate and activity level. The device periodically collects this data and transmits it to the terminal. Specifically, the device sends signals using Bluetooth or Wi-Fi. The output at this stage is the biometric data stored on the terminal.

[0480] Step 2:

[0481] The device acquires the user's location information using GPS functionality in parallel with receiving biometric data. The input consists of location coordinates and time information. Based on this data, the device identifies the user's current location and records it along with the biometric information. The output is integrated data with added location information, which is used for later analysis.

[0482] Step 3:

[0483] The device accesses external information services such as weather APIs and nearby facilities information services via the internet. Input requires the user's current location and surrounding environmental information. The device collects the acquired temperature, weather, and detailed information about nearby facilities, and stores it as the current environmental conditions. The output is environmental information associated with the location data.

[0484] Step 4:

[0485] The server receives integrated data (biometric data, location data, and environmental information) transmitted from the terminal. The input is this integrated data. The server uses this data to analyze it using data analysis tools such as Python in order to estimate the user's state and preferences. Specifically, it performs trend analysis of time-series data and comparisons with past history. The output is an evaluation result indicating the user's health status and preferences at that time.

[0486] Step 5:

[0487] The server generates optimal action suggestions for the user based on the analysis results. The input is the analyzed evaluation results. The server uses a generative AI model to generate suggestion messages. For example, if the analysis determines that the user is fatigued, the server will create a message recommending rest. The output is a suggestion message optimized for each individual user.

[0488] Step 6:

[0489] The terminal receives suggestion messages sent from the server and presents them visually to the user. The input is the suggestion message from the server. The terminal notifies the user of this message by displaying it on the screen or reading it aloud. The output is the suggestion information received by the user. The user uses this suggestion to decide on an action and records the result of their choice on the terminal.

[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] Users' daily activities and health conditions are diverse, making it challenging to provide appropriate support tailored to their specific needs. In particular, offering concrete action suggestions that consider real-time dynamic and environmental information presents a new technological challenge. Therefore, there is a need to develop a comprehensive system that advises actions optimized for each user's individual circumstances.

[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 acquiring dynamic information and location identification information, means for acquiring external situation information, and means for analyzing the collected information to evaluate the user's situation and preferences. This makes it possible to provide the user with optimal action suggestions in real time.

[0495] An "information acquisition device" is a device used to acquire dynamic information and location identification information.

[0496] An "external information provision device" refers to a device and system for acquiring environmental condition information.

[0497] "Dynamic information" refers to data related to the user's biometric information and behavior.

[0498] "Location identification information" refers to data used to determine the user's current location.

[0499] "Environmental condition information" refers to information about external factors and the surrounding environment.

[0500] "Analysis" is the process of evaluating collected information to understand the user's situation and preferences.

[0501] "Action suggestions" are proposed optimal actions or choices presented to the user based on the analysis results.

[0502] "Notification" refers to the means and process for communicating action suggestions to users.

[0503] An "autonomous support system" is a system that analyzes dynamic and environmental information to provide action suggestions in order to support the user's life.

[0504] The system for realizing this invention consists of a device for acquiring dynamic information and location identification information, a device for providing external situation information, and a server for analyzing the information. The server acquires dynamic information such as the user's biometric information and current location from a wearable device, and collects external situation information such as weather and surrounding environment information from an external information providing device.

[0505] Based on the input information, the server uses an analysis engine written in the Python programming language to evaluate the user's current situation and preferences. This analysis involves referencing past data to gain a more precise understanding of the user's behavior patterns and preferences. An API is built using the Django framework, and the acquired information is processed and managed on the server.

[0506] Based on the analysis results, the system proposes the most appropriate action for the user. This proposal might include, for example, notifying the user to bring an umbrella if the weather is changing. This proposal is communicated to the user through displays on consumer robots and other devices.

[0507] As a concrete example, this system detects that a user's heart rate is at a resting state during the morning hours, and when external environmental data predicts rain, it sends a notification suggesting action such as, "Please take an umbrella with you when you go out today."

[0508] Examples of prompt statements in this system include:

[0509] One example is: "Generate actions to suggest to the user under the following conditions: The user is in the living room. Heart rate is 90 beats / minute. Outside temperature is 18°C, and the probability of precipitation is 80%." Based on such prompts, the AI ​​model generates appropriate action suggestions for the user.

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The terminal collects user movement information and location identification information from the wearable device. Specifically, sensors detect the user's heart rate, steps, and location information and transmit it to the terminal via Bluetooth communication. The input to this process is sensor data from the wearable device, and the output is biometric and location information stored in the terminal.

[0513] Step 2:

[0514] The terminal acquires external information from external information providers. Specifically, it obtains current weather data from a weather API via the internet and collects nearby facility information from surrounding information services. It sends API requests as input and receives environmental data (temperature, probability of precipitation, facility information) as output.

[0515] Step 3:

[0516] The server receives dynamic and external situational information transmitted from the terminal and performs evaluation using an analysis engine. Inputs include biometric information, location information, and environmental information from the terminal, and output is an evaluation result tailored to the user's situation. Specifically, Python is used to analyze the data and estimate the user's health status and preferences in real time.

[0517] Step 4:

[0518] The server uses a generation AI model based on the analysis results to generate optimal action suggestions for the user. Here, a prompt is input to the AI ​​model, and the resulting action suggestion is output as a response. For example, if the prompt "It's raining today, should I take an umbrella?" is sent to the generation AI model, the suggestion "You should take an umbrella" is generated.

[0519] Step 5:

[0520] The device receives action suggestions from the server and notifies the user. The UI is used to visually display or audibly communicate these action suggestions. The input here is the action suggestion from the server, and the output is the notification message to the user. Specific actions include displaying "Let's go out with an umbrella" on the screen or providing the same message via voice guidance.

[0521] 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.

[0522] This invention aims to construct a system for recognizing a user's emotional state and providing personalized support based on that state. This system incorporates an information gathering device, an external information provision device, and an emotion engine.

[0523] First, the user's device acquires biometric information through a wearable device acting as an information gathering device. This data includes heart rate, skin electrical activity, and location information. Furthermore, the device collects weather and nearby facility information from external information providers via the internet to understand the environment.

[0524] Next, all collected data is sent to a server and processed by a dedicated analysis engine. This analysis evaluates the user's current situation and preferences, and updates them in comparison with past data.

[0525] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes physiological changes such as heart rate and skin reactions to identify the user's emotions in real time. This allows the system to determine whether the user is stressed or relaxed.

[0526] Based on the analysis results and the emotion engine's judgment, the server generates the most appropriate suggestions for the user. For example, if the server detects that the user is stressed, it will recommend music that is effective in relieving stress and notify the user's device. Furthermore, it will suggest clothing appropriate for the weather and recommend dining locations that take the user's health condition into consideration.

[0527] When the terminal receives a suggestion from the server, it notifies the user visually or audibly. The user can then review and implement these suggestions. This allows the system to address the user's diverse needs and provide optimal support tailored to their emotions and health condition.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The user's device acquires biometric information such as heart rate, skin electrical activity, and current location through a wearable device. This data is temporarily stored on the device.

[0531] Step 2:

[0532] The device accesses external information providers via the internet to obtain weather information and information about nearby facilities. This allows it to collect information such as the current temperature, probability of precipitation, and information about nearby restaurants.

[0533] Step 3:

[0534] The device collects biometric and environmental information and transmits it to the server. The data is encrypted and transmitted to the server via a secure communication channel.

[0535] Step 4:

[0536] The server uses the received data to evaluate the user's state and preferences using a dedicated analysis engine. This analysis references historical information, including past data, and updates the user profile accordingly.

[0537] Step 5:

[0538] The server's emotion engine analyzes biometric information to identify the user's emotional state. For example, an excessive increase in heart rate may indicate stress, and this can be detected to determine that the user is in a stressed state.

[0539] Step 6:

[0540] The server generates optimal suggestions for the user based on analysis results and the emotion engine's judgment. For example, it might recommend relaxing music to alleviate stress or suggest dining locations that prioritize mental and physical health.

[0541] Step 7:

[0542] The server generates suggestions and sends them to the device as notifications. The suggestions are displayed in a format that is easy for the user to understand and act upon.

[0543] Step 8:

[0544] The user reviews the suggestions displayed on the device, accepts them, and proceeds with execution. The device records the user's selection and sends feedback back to the server. This feedback is used to improve the accuracy of future suggestions.

[0545] (Example 2)

[0546] 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."

[0547] Conventional information gathering systems only acquire users' biometric and location data individually and make simple suggestions, making it difficult to provide personalized support that takes into account the user's emotional state. Therefore, they face the challenge of not being able to provide optimal suggestions in real time that meet the diverse needs of users.

[0548] 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.

[0549] In this invention, the server includes means for acquiring biometric data and location data from information gathering devices, means for acquiring surrounding information from an external information provision mechanism, means for analyzing the collected biometric data, location data, and surrounding information to evaluate the user's state and preferences, means for generating the most suitable suggestions for the user based on the identified emotional state and the analyzed results, and means for notifying the user of the generated suggestions. This makes it possible to provide personalized suggestions that take into account the user's emotional state and health condition in real time.

[0550] An "information gathering device" is a device used to acquire biometric data and location data from users.

[0551] "Biometric data" refers to data that indicates the user's physiological state, such as heart rate and skin electrical activity.

[0552] "Location data" refers to data that indicates the user's current geographical location.

[0553] An "external information provision system" is a system that provides local information, such as weather and information about nearby facilities, via the internet.

[0554] "Surrounding information" refers to environmental information related to the user's current location, including weather and information about nearby facilities.

[0555] "Analysis" is the process of processing a series of data to evaluate the user's state and preferences.

[0556] "Preferences" refer to the personal tastes and tendencies of the user.

[0557] "Emotional state" refers to the mental or emotional state of the user.

[0558] A "suggestion" is information intended to encourage users to take action or make choices.

[0559] "Notification" refers to the act of communicating a generated proposal to the user visually or audibly.

[0560] This invention is a system that understands the user's current situation and provides personalized suggestions in real time. The system includes a wearable device (terminal) for information collection, through which biometric data is acquired. Specifically, heart rate and skin electrical activity are detected by this device. This device can be a smartwatch or a fitness tracker, among others.

[0561] The user's device obtains weather and information about nearby facilities from external information providers via the internet. This often utilizes existing weather information APIs and map information services. The device integrates this information and transmits it to the server along with biometric data.

[0562] The server is equipped with a dedicated analysis engine that processes the collected data. The analysis engine uses AI algorithms and generative AI models to identify the user's emotional state. The emotion engine analyzes physiological indicators such as heart rate to determine whether the user is stressed or relaxed. Based on this determination, it generates appropriate suggestions.

[0563] The server generates optimal suggestions based on the user's emotional state and collected information, and sends them to the device. For example, if the system determines that the user is feeling stressed in the morning, the server will suggest relaxing music and notify the device. Notifications can be made via voice through a voice assistant, depending on the user's preferences.

[0564] For example, if the system detects an increase in heart rate while a user is running, it will determine that this is due to exercise rather than physiological stress. This allows the system to suggest post-exercise cool-down methods and encourage hydration.

[0565] Examples of prompts for a generative AI model include the following:

[0566] "The user's heart rate is elevated. Please suggest a suitable way to relax in this situation. Music or an activity that can be done nearby would be ideal."

[0567] This configuration allows the system to provide personalized support tailored to the user's emotions and health condition.

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

[0569] Step 1:

[0570] The user's device acquires the user's biometric data in real time using a wearable device. The data obtained as input includes heart rate, skin electrical activity, and location information. This data is used to indicate the user's current physiological state. Specifically, the device collects data from the wearable device via Bluetooth or Wi-Fi.

[0571] Step 2:

[0572] The user's device obtains weather information and nearby facility information from external information providers via the internet. The input is the user's current location, and the output is environmental information associated with that location. Specifically, the device calls existing APIs to check the weather and operating status of facilities in the relevant area.

[0573] Step 3:

[0574] The user's device transmits acquired biometric data and environmental information to the server. The input is aggregated data, and the output is that data being transmitted to the server via the network. Specifically, the device encrypts the data using SSL / TLS or similar methods and sends it to the specified address on the server.

[0575] Step 4:

[0576] The server uses an analysis engine to analyze the user's state based on the received data. The input consists of biometric data and environmental information, and the output is an analysis result indicating the user's emotional state and health status. The analysis engine utilizes machine learning models to process the data while comparing it with past data. Specifically, the algorithm detects rapid changes in heart rate and maps them to emotions such as stress and joy.

[0577] Step 5:

[0578] The server uses a generative AI model to generate suggestions tailored to the user. The input consists of analysis results and emotional states, while the output is specific suggestions for the user. Specifically, the generative AI model creates prompts and generates scenarios suggesting music, relaxation methods, or appropriate actions.

[0579] Step 6:

[0580] The server notifies the user's terminal of the generated suggestions. The input is the content of the suggestions, and the output is the user's terminal receiving those suggestions. Specifically, the notification is displayed on the terminal's screen or output audibly through a voice assistant. The terminal launches the appropriate application and displays the message to convey the content to the user.

[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, users are exposed to a variety of stressors, and personalized support tailored to their individual emotional states is required. However, conventional technologies have made it difficult to recognize users' emotions in real time and provide appropriate suggestions based on that recognition. Therefore, the present invention aims to recognize users' emotional states and propose optimal activities for stress reduction and health promotion.

[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 acquiring biometric information and location information from an information gathering device, means for acquiring environmental information from an external information providing device, means for analyzing the collected biometric information, location information, and environmental information and evaluating the user's state and preferences, means for generating optimal suggestions for the user based on the analysis results, means for notifying the user of the generated suggestions, and means for recognizing the user's emotional state and suggesting relaxation activities if the user is feeling stressed. This enables appropriate and personalized support according to the user's emotions and health condition.

[0586] An "information gathering device" is a device used to acquire biometric and location information, and includes wearable devices and sensor technologies.

[0587] An "external information provider device" is a device for acquiring environmental information, which uses databases and APIs via the internet to provide weather information and information about surrounding facilities.

[0588] "Emotional state" is an indicator that shows the psychological and physiological changes of the user, and is evaluated in relation to physiological data such as heart rate and skin response.

[0589] "Suggestions" are recommendations for actions and choices provided to users by the analysis engine, including recommendations for music playback, appropriate clothing choices, and healthy dining locations.

[0590] An "analysis engine" is a software component that evaluates the user's state and preferences based on acquired biometric, location, and environmental information, and performs analysis using data processing and algorithms.

[0591] "Notification means" refers to a method of conveying generated suggestions or information to the user, presenting them on the device in a visual or auditory format.

[0592] "Relaxation activities" refer to actions and experiences that help users reduce stress and promote relaxation, and include the use of music, aromatherapy, and other such activities.

[0593] This system uses information gathering devices, external information providers, an analysis engine, and notification means to recognize the user's emotional state and provide personalized support. The server uses wearable devices as information gathering devices to acquire biometric information such as heart rate and skin electrical activity, as well as location information. It also utilizes an internet connection as an external information provider to acquire weather information and information about nearby facilities.

[0594] The acquired data is sent to a cloud-based server and processed by an analysis engine. Specifically, biometric information, location information, and environmental information are compared to evaluate the user's current state and preferences. This evaluation uses an emotion recognition AI engine. The emotion recognition AI engine, for example, uses IBM's Emotion Analysis to identify the user's emotions in real time based on physiological data.

[0595] Based on the analyzed results, the server determines whether the user is stressed or relaxed and generates suggestions accordingly. These suggestions are then notified to the user's device via smart home devices, etc. For example, when the user is stressed, relaxing music can be played or an aroma diffuser can be activated. Furthermore, recommendations for places to eat, clothing, and other items can be provided based on the user's health condition and the weather.

[0596] For example, if an increase in heart rate is detected in the evening, the server will suggest relaxing jazz music, automatically set a playlist, and start playing it. Furthermore, if a colder temperature is forecast for the next day, it can send a notification such as, "We recommend wearing warmer clothing tomorrow."

[0597] Examples of prompts from the generative AI model include, "Please come up with ideas for providing the best relaxation methods for when the user is feeling stressed," and "Based on emotion recognition data, please provide suggestions that are useful in daily life."

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The device acquires biometric information such as the user's heart rate and skin electrical activity through a wearable device, and also collects location information. This data is sent to the server as initial input. In this step, information is collected through an API for data acquisition.

[0601] Step 2:

[0602] The server uses external information providers to acquire environmental information such as weather and nearby facilities via the network. This environmental information is retrieved from the database, thereby supplementing the input data.

[0603] Step 3:

[0604] The server inputs biometric information, location information, and acquired environmental information into an emotion recognition AI engine. This engine evaluates the user's current emotional state in real time. During this process, the data is processed using multiple algorithms to obtain output regarding the user's emotional state.

[0605] Step 4:

[0606] The server inputs emotional state data obtained from the emotion recognition AI engine into the analysis engine to evaluate the user's state and preferences. This analysis compares and analyzes the user's past behavioral history and preference data with the current emotional state data to generate output that forms the basis for appropriate suggestions.

[0607] Step 5:

[0608] The server generates optimal suggestions based on the analysis. These suggestions are created using a generative AI model to identify music playlists and lifestyle suggestions that match the user's emotional state and environmental conditions. The generated suggestions are then sent to the device as instructions.

[0609] Step 6:

[0610] Based on the received suggestions, the device notifies the user of the suggestions visually or audibly. In this step, specific actions are taken by the user using the display or speaker. For example, music may be played or a widget displaying the suggestions may be launched.

[0611] Step 7:

[0612] Users can review the suggestions notified via their device and decide whether to accept them. Depending on the user's choice, the system can also collect additional data to improve future suggestions.

[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 an autonomous system for providing personalized support tailored to the diverse needs of users. This system is configured to utilize a combination of an information gathering device and an external information provision device.

[0631] First, the user's device connects with a wearable device that acts as an information gathering device, periodically acquiring the user's biometric and location information. This allows for real-time recording of, for example, the user's heart rate, activity level, and current location.

[0632] Next, the device connects to external information providers such as weather APIs and nearby facility information services via the internet to obtain environmental information about the user's surroundings. This information includes temperature, probability of precipitation, and menus from nearby restaurants.

[0633] Subsequently, the server receives biometric information, location information, and external information transmitted from the terminal, and uses a dedicated analysis engine to evaluate the user's current state and preferences. During this analysis process, past data is also referenced, and the user's personal profile is updated.

[0634] Based on the analysis results, the server generates suggestions for actions appropriate to the user. For example, if the user is going out on a cold day, the server will recommend warm clothing and send a notification. Also, if the user's heart rate increases during lunchtime, it will send a message recommending a nearby Japanese restaurant.

[0635] When the terminal receives a proposal notification from the server, it displays it clearly to the user. The user can review the proposal and choose whether to accept it or not. The user's choice is recorded by the terminal and used to improve the accuracy of future proposals.

[0636] In this way, the system responds to the diverse needs and circumstances of users and autonomously provides optimal support. For example, it can provide support in a variety of situations, such as preparing before going out, selecting healthy meals, and suggesting action plans based on the weather.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The user's device periodically acquires biometric and location information from the wearable device. Specifically, it collects heart rate and activity levels via Bluetooth, and current location using GPS, and temporarily stores this data.

[0640] Step 2:

[0641] The device accesses external information providers via the internet to obtain weather and nearby facility information. For example, it might use a weather API to obtain temperature and precipitation probability, or use a restaurant information service to obtain details about nearby dining options.

[0642] Step 3:

[0643] The device sends this data to the server. The data is transferred quickly and reliably through a secure communication protocol.

[0644] Step 4:

[0645] The server analyzes the user's current state and preferences based on biometric, location, and environmental information it receives. Specifically, it uses machine learning algorithms to compare current data with past data and evaluate the user's behavioral patterns.

[0646] Step 5:

[0647] Based on the analysis results, the server generates optimal suggestions for the user. For example, it might recommend wearing a thick coat when going out on a cold day, or suggest a nearby Japanese restaurant for lunch.

[0648] Step 6:

[0649] The server sends the generated suggestions to the terminal. The suggestions are processed to be sent in a format that is easy for the user to understand.

[0650] Step 7:

[0651] The device notifies the user of the proposal and displays it clearly on the screen. The user can review the notification and choose to accept the proposal.

[0652] Step 8:

[0653] If the user makes a selection, the terminal records the result. This record is sent to the server and used to optimize future suggestions.

[0654] (Example 1)

[0655] 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".

[0656] In modern society, there is a demand for personalized support tailored to each individual's lifestyle and health condition, yet autonomous systems to achieve this are insufficient. To solve this problem, a method is needed that can analyze biometric information and environmental conditions in real time and quickly provide optimal suggestions to individuals.

[0657] 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.

[0658] In this invention, the server includes a communication device for acquiring biometric and location information, means for collecting environmental conditions using an external information provision function, and means for analyzing the acquired biometric data, location data, and environmental conditions to evaluate the individual's current state and preferences. This makes it possible to provide personalized suggestions and respond to the diverse needs of users.

[0659] A "communication device" is a device that has the function of sending and receiving data between a user and an information provider in order to acquire biometric information and location information.

[0660] The "external information provision function" refers to a data acquisition function for collecting environmental conditions via the internet, etc., which allows for obtaining weather information and facility information.

[0661] "Biometric data" refers to data that indicates a user's health status and activity level, including heart rate and exercise level.

[0662] "Location data" refers to information indicating the user's current location, and is obtained using technologies such as GPS.

[0663] "Environmental conditions" refer to information that describes the situation around the user, such as weather and the level of congestion at a facility.

[0664] "Analysis" refers to data processing techniques used to evaluate an individual's state and preferences by processing acquired biometric data, location data, and environmental conditions.

[0665] "Personalized suggestions" refer to suggestions made to guide users towards the most optimal actions or choices based on analysis results.

[0666] A "display device" is a device used to visually show suggestions and notifications from a server to the user, and includes smartphones and displays.

[0667] This system possesses advanced data processing capabilities to provide appropriate support tailored to the individual needs of each user. The implementation utilizes communication devices, external information provision functions, and an analysis engine to provide users with optimal suggestions in real time. Specifically, the system is configured as follows:

[0668] Users acquire their own biometric data using wearable sensors and smart devices. This data includes heart rate and activity levels and is transmitted to the terminal. The terminal uses the internet to access external information services such as weather APIs and facility information services to obtain current environmental conditions. This information includes temperature, probability of precipitation, and menu information for nearby facilities.

[0669] The server receives biometric data, location data, and environmental conditions transmitted from the terminal. It then runs a dedicated analysis engine to analyze the user's state and preferences by referencing past data. The analysis uses common programming languages ​​and, for example, data science libraries in Python.

[0670] Based on the analysis results, the server suggests the optimal course of action for the user. This suggestion is generated according to the user's activity and environment, and may include notifications recommending warm clothing on cold days or messages recommending low-calorie restaurants at specific times of day.

[0671] The terminal receives suggestions from the server and displays them visually to the user. The user can review these suggestions and decide whether to accept them, and the terminal records the result of that decision. The recorded data is sent to the server to improve the accuracy of future suggestions.

[0672] As a concrete example, you can input the following prompt sentence into a generation AI model and obtain suggestions:

[0673] "What are some recommended ways to stay hydrated while running?"

[0674] "Could you recommend a restaurant suitable for lunch today?"

[0675] This system configuration allows users to receive advice tailored to their lifestyle, which can be expected to improve their health management and overall quality of life.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The user puts on a wearable device and begins their daily activities. The input here is biometric information such as the user's heart rate and activity level. The device periodically collects this data and transmits it to the terminal. Specifically, the device sends signals using Bluetooth or Wi-Fi. The output at this stage is the biometric data stored on the terminal.

[0679] Step 2:

[0680] The device acquires the user's location information using GPS functionality in parallel with receiving biometric data. The input consists of location coordinates and time information. Based on this data, the device identifies the user's current location and records it along with the biometric information. The output is integrated data with added location information, which is used for later analysis.

[0681] Step 3:

[0682] The device accesses external information services such as weather APIs and nearby facilities information services via the internet. Input requires the user's current location and surrounding environmental information. The device collects the acquired temperature, weather, and detailed information about nearby facilities, and stores it as the current environmental conditions. The output is environmental information associated with the location data.

[0683] Step 4:

[0684] The server receives integrated data (biometric data, location data, and environmental information) transmitted from the terminal. The input is this integrated data. The server uses this data to analyze it using data analysis tools such as Python in order to estimate the user's state and preferences. Specifically, it performs trend analysis of time-series data and comparisons with past history. The output is an evaluation result indicating the user's health status and preferences at that time.

[0685] Step 5:

[0686] The server generates optimal action suggestions for the user based on the analysis results. The input is the analyzed evaluation results. The server uses a generative AI model to generate suggestion messages. For example, if the analysis determines that the user is fatigued, the server will create a message recommending rest. The output is a suggestion message optimized for each individual user.

[0687] Step 6:

[0688] The terminal receives suggestion messages sent from the server and presents them visually to the user. The input is the suggestion message from the server. The terminal notifies the user of this message by displaying it on the screen or reading it aloud. The output is the suggestion information received by the user. The user uses this suggestion to decide on an action and records the result of their choice on the terminal.

[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] Users' daily activities and health conditions are diverse, making it challenging to provide appropriate support tailored to their specific needs. In particular, offering concrete action suggestions that consider real-time dynamic and environmental information presents a new technological challenge. Therefore, there is a need to develop a comprehensive system that advises actions optimized for each user's individual circumstances.

[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 acquiring dynamic information and location identification information, means for acquiring external situation information, and means for analyzing the collected information to evaluate the user's situation and preferences. This makes it possible to provide the user with optimal action suggestions in real time.

[0694] An "information acquisition device" is a device used to acquire dynamic information and location identification information.

[0695] An "external information provision device" refers to a device and system for acquiring environmental condition information.

[0696] "Dynamic information" refers to data related to the user's biometric information and behavior.

[0697] "Location identification information" refers to data used to determine the user's current location.

[0698] "Environmental condition information" refers to information about external factors and the surrounding environment.

[0699] "Analysis" is the process of evaluating collected information to understand the user's situation and preferences.

[0700] "Action suggestions" are proposed optimal actions or choices presented to the user based on the analysis results.

[0701] "Notification" refers to the means and process for communicating action suggestions to users.

[0702] An "autonomous support system" is a system that analyzes dynamic and environmental information to provide action suggestions in order to support the user's life.

[0703] The system for realizing this invention consists of a device for acquiring dynamic information and location identification information, a device for providing external situation information, and a server for analyzing the information. The server acquires dynamic information such as the user's biometric information and current location from a wearable device, and collects external situation information such as weather and surrounding environment information from an external information providing device.

[0704] Based on the input information, the server uses an analysis engine written in the Python programming language to evaluate the user's current situation and preferences. This analysis involves referencing past data to gain a more precise understanding of the user's behavior patterns and preferences. An API is built using the Django framework, and the acquired information is processed and managed on the server.

[0705] Based on the analysis results, the system proposes the most appropriate action for the user. This proposal might include, for example, notifying the user to bring an umbrella if the weather is changing. This proposal is communicated to the user through displays on consumer robots and other devices.

[0706] As a concrete example, this system detects that a user's heart rate is at a resting state during the morning hours, and when external environmental data predicts rain, it sends a notification suggesting action such as, "Please take an umbrella with you when you go out today."

[0707] Examples of prompt statements in this system include:

[0708] One example is: "Generate actions to suggest to the user under the following conditions: The user is in the living room. Heart rate is 90 beats / minute. Outside temperature is 18°C, and the probability of precipitation is 80%." Based on such prompts, the AI ​​model generates appropriate action suggestions for the user.

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The terminal collects user movement information and location identification information from the wearable device. Specifically, sensors detect the user's heart rate, steps, and location information and transmit it to the terminal via Bluetooth communication. The input to this process is sensor data from the wearable device, and the output is biometric and location information stored in the terminal.

[0712] Step 2:

[0713] The terminal acquires external information from external information providers. Specifically, it obtains current weather data from a weather API via the internet and collects nearby facility information from surrounding information services. It sends API requests as input and receives environmental data (temperature, probability of precipitation, facility information) as output.

[0714] Step 3:

[0715] The server receives dynamic and external situational information transmitted from the terminal and performs evaluation using an analysis engine. Inputs include biometric information, location information, and environmental information from the terminal, and output is an evaluation result tailored to the user's situation. Specifically, Python is used to analyze the data and estimate the user's health status and preferences in real time.

[0716] Step 4:

[0717] The server uses a generation AI model based on the analysis results to generate optimal action suggestions for the user. Here, a prompt is input to the AI ​​model, and the resulting action suggestion is output as a response. For example, if the prompt "It's raining today, should I take an umbrella?" is sent to the generation AI model, the suggestion "You should take an umbrella" is generated.

[0718] Step 5:

[0719] The device receives action suggestions from the server and notifies the user. The UI is used to visually display or audibly communicate these action suggestions. The input here is the action suggestion from the server, and the output is the notification message to the user. Specific actions include displaying "Let's go out with an umbrella" on the screen or providing the same message via voice guidance.

[0720] 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.

[0721] This invention aims to construct a system for recognizing a user's emotional state and providing personalized support based on that state. This system incorporates an information gathering device, an external information provision device, and an emotion engine.

[0722] First, the user's device acquires biometric information through a wearable device acting as an information gathering device. This data includes heart rate, skin electrical activity, and location information. Furthermore, the device collects weather and nearby facility information from external information providers via the internet to understand the environment.

[0723] Next, all collected data is sent to a server and processed by a dedicated analysis engine. This analysis evaluates the user's current situation and preferences, and updates them in comparison with past data.

[0724] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes physiological changes such as heart rate and skin reactions to identify the user's emotions in real time. This allows the system to determine whether the user is stressed or relaxed.

[0725] Based on the analysis results and the emotion engine's judgment, the server generates the most appropriate suggestions for the user. For example, if the server detects that the user is stressed, it will recommend music that is effective in relieving stress and notify the user's device. Furthermore, it will suggest clothing appropriate for the weather and recommend dining locations that take the user's health condition into consideration.

[0726] When the terminal receives a suggestion from the server, it notifies the user visually or audibly. The user can then review and implement these suggestions. This allows the system to address the user's diverse needs and provide optimal support tailored to their emotions and health condition.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] The user's device acquires biometric information such as heart rate, skin electrical activity, and current location through a wearable device. This data is temporarily stored on the device.

[0730] Step 2:

[0731] The device accesses external information providers via the internet to obtain weather information and information about nearby facilities. This allows it to collect information such as the current temperature, probability of precipitation, and information about nearby restaurants.

[0732] Step 3:

[0733] The device collects biometric and environmental information and transmits it to the server. The data is encrypted and transmitted to the server via a secure communication channel.

[0734] Step 4:

[0735] The server uses the received data to evaluate the user's state and preferences using a dedicated analysis engine. This analysis references historical information, including past data, and updates the user profile accordingly.

[0736] Step 5:

[0737] The server's emotion engine analyzes biometric information to identify the user's emotional state. For example, an excessive increase in heart rate may indicate stress, and this can be detected to determine that the user is in a stressed state.

[0738] Step 6:

[0739] The server generates optimal suggestions for the user based on analysis results and the emotion engine's judgment. For example, it might recommend relaxing music to alleviate stress or suggest dining locations that prioritize mental and physical health.

[0740] Step 7:

[0741] The server generates suggestions and sends them to the device as notifications. The suggestions are displayed in a format that is easy for the user to understand and act upon.

[0742] Step 8:

[0743] The user reviews the suggestions displayed on the device, accepts them, and proceeds with execution. The device records the user's selection and sends feedback back to the server. This feedback is used to improve the accuracy of future suggestions.

[0744] (Example 2)

[0745] 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".

[0746] Conventional information gathering systems only acquire users' biometric and location data individually and make simple suggestions, making it difficult to provide personalized support that takes into account the user's emotional state. Therefore, they face the challenge of not being able to provide optimal suggestions in real time that meet the diverse needs of users.

[0747] 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.

[0748] In this invention, the server includes means for acquiring biometric data and location data from information gathering devices, means for acquiring surrounding information from an external information provision mechanism, means for analyzing the collected biometric data, location data, and surrounding information to evaluate the user's state and preferences, means for generating the most suitable suggestions for the user based on the identified emotional state and the analyzed results, and means for notifying the user of the generated suggestions. This makes it possible to provide personalized suggestions that take into account the user's emotional state and health condition in real time.

[0749] An "information gathering device" is a device used to acquire biometric data and location data from users.

[0750] "Biometric data" refers to data that indicates the user's physiological state, such as heart rate and skin electrical activity.

[0751] "Location data" refers to data that indicates the user's current geographical location.

[0752] An "external information provision system" is a system that provides local information, such as weather and information about nearby facilities, via the internet.

[0753] "Surrounding information" refers to environmental information related to the user's current location, including weather and information about nearby facilities.

[0754] "Analysis" is the process of processing a series of data to evaluate the user's state and preferences.

[0755] "Preferences" refer to the personal tastes and tendencies of the user.

[0756] "Emotional state" refers to the mental or emotional state of the user.

[0757] A "suggestion" is information intended to encourage users to take action or make choices.

[0758] "Notification" refers to the act of communicating a generated proposal to the user visually or audibly.

[0759] This invention is a system that understands the user's current situation and provides personalized suggestions in real time. The system includes a wearable device (terminal) for information collection, through which biometric data is acquired. Specifically, heart rate and skin electrical activity are detected by this device. This device can be a smartwatch or a fitness tracker, among others.

[0760] The user's device obtains weather and information about nearby facilities from external information providers via the internet. This often utilizes existing weather information APIs and map information services. The device integrates this information and transmits it to the server along with biometric data.

[0761] The server is equipped with a dedicated analysis engine that processes the collected data. The analysis engine uses AI algorithms and generative AI models to identify the user's emotional state. The emotion engine analyzes physiological indicators such as heart rate to determine whether the user is stressed or relaxed. Based on this determination, it generates appropriate suggestions.

[0762] The server generates optimal suggestions based on the user's emotional state and collected information, and sends them to the device. For example, if the system determines that the user is feeling stressed in the morning, the server will suggest relaxing music and notify the device. Notifications can be made via voice through a voice assistant, depending on the user's preferences.

[0763] For example, if the system detects an increase in heart rate while a user is running, it will determine that this is due to exercise rather than physiological stress. This allows the system to suggest post-exercise cool-down methods and encourage hydration.

[0764] Examples of prompts for a generative AI model include the following:

[0765] "The user's heart rate is elevated. Please suggest a suitable way to relax in this situation. Music or an activity that can be done nearby would be ideal."

[0766] This configuration allows the system to provide personalized support tailored to the user's emotions and health condition.

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

[0768] Step 1:

[0769] The user's device acquires the user's biometric data in real time using a wearable device. The data obtained as input includes heart rate, skin electrical activity, and location information. This data is used to indicate the user's current physiological state. Specifically, the device collects data from the wearable device via Bluetooth or Wi-Fi.

[0770] Step 2:

[0771] The user's device obtains weather information and nearby facility information from external information providers via the internet. The input is the user's current location, and the output is environmental information associated with that location. Specifically, the device calls existing APIs to check the weather and operating status of facilities in the relevant area.

[0772] Step 3:

[0773] The user's device transmits acquired biometric data and environmental information to the server. The input is aggregated data, and the output is that data being transmitted to the server via the network. Specifically, the device encrypts the data using SSL / TLS or similar methods and sends it to the specified address on the server.

[0774] Step 4:

[0775] The server uses an analysis engine to analyze the user's state based on the received data. The input consists of biometric data and environmental information, and the output is an analysis result indicating the user's emotional state and health status. The analysis engine utilizes machine learning models to process the data while comparing it with past data. Specifically, the algorithm detects rapid changes in heart rate and maps them to emotions such as stress and joy.

[0776] Step 5:

[0777] The server uses a generative AI model to generate suggestions tailored to the user. The input consists of analysis results and emotional states, while the output is specific suggestions for the user. Specifically, the generative AI model creates prompts and generates scenarios suggesting music, relaxation methods, or appropriate actions.

[0778] Step 6:

[0779] The server notifies the user's terminal of the generated suggestions. The input is the content of the suggestions, and the output is the user's terminal receiving those suggestions. Specifically, the notification is displayed on the terminal's screen or output audibly through a voice assistant. The terminal launches the appropriate application and displays the message to convey the content to the user.

[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, users are exposed to a variety of stressors, and personalized support tailored to their individual emotional states is required. However, conventional technologies have made it difficult to recognize users' emotions in real time and provide appropriate suggestions based on that recognition. Therefore, the present invention aims to recognize users' emotional states and propose optimal activities for stress reduction and health promotion.

[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 acquiring biometric information and location information from an information gathering device, means for acquiring environmental information from an external information providing device, means for analyzing the collected biometric information, location information, and environmental information and evaluating the user's state and preferences, means for generating optimal suggestions for the user based on the analysis results, means for notifying the user of the generated suggestions, and means for recognizing the user's emotional state and suggesting relaxation activities if the user is feeling stressed. This enables appropriate and personalized support according to the user's emotions and health condition.

[0785] An "information gathering device" is a device used to acquire biometric and location information, and includes wearable devices and sensor technologies.

[0786] An "external information provider device" is a device for acquiring environmental information, which uses databases and APIs via the internet to provide weather information and information about surrounding facilities.

[0787] "Emotional state" is an indicator that shows the psychological and physiological changes of the user, and is evaluated in relation to physiological data such as heart rate and skin response.

[0788] "Suggestions" are recommendations for actions and choices provided to users by the analysis engine, including recommendations for music playback, appropriate clothing choices, and healthy dining locations.

[0789] An "analysis engine" is a software component that evaluates the user's state and preferences based on acquired biometric, location, and environmental information, and performs analysis using data processing and algorithms.

[0790] "Notification means" refers to a method of conveying generated suggestions or information to the user, presenting them on the device in a visual or auditory format.

[0791] "Relaxation activities" refer to actions and experiences that help users reduce stress and promote relaxation, and include the use of music, aromatherapy, and other such activities.

[0792] This system uses information gathering devices, external information providers, an analysis engine, and notification means to recognize the user's emotional state and provide personalized support. The server uses wearable devices as information gathering devices to acquire biometric information such as heart rate and skin electrical activity, as well as location information. It also utilizes an internet connection as an external information provider to acquire weather information and information about nearby facilities.

[0793] The acquired data is sent to a cloud-based server and processed by an analysis engine. Specifically, biometric information, location information, and environmental information are compared to evaluate the user's current state and preferences. This evaluation uses an emotion recognition AI engine. The emotion recognition AI engine, for example, uses IBM's Emotion Analysis to identify the user's emotions in real time based on physiological data.

[0794] Based on the analyzed results, the server determines whether the user is stressed or relaxed and generates suggestions accordingly. These suggestions are then notified to the user's device via smart home devices, etc. For example, when the user is stressed, relaxing music can be played or an aroma diffuser can be activated. Furthermore, recommendations for places to eat, clothing, and other items can be provided based on the user's health condition and the weather.

[0795] For example, if an increase in heart rate is detected in the evening, the server will suggest relaxing jazz music, automatically set a playlist, and start playing it. Furthermore, if a colder temperature is forecast for the next day, it can send a notification such as, "We recommend wearing warmer clothing tomorrow."

[0796] Examples of prompts from the generative AI model include, "Please come up with ideas for providing the best relaxation methods for when the user is feeling stressed," and "Based on emotion recognition data, please provide suggestions that are useful in daily life."

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The device acquires biometric information such as the user's heart rate and skin electrical activity through a wearable device, and also collects location information. This data is sent to the server as initial input. In this step, information is collected through an API for data acquisition.

[0800] Step 2:

[0801] The server uses external information providers to acquire environmental information such as weather and nearby facilities via the network. This environmental information is retrieved from the database, thereby supplementing the input data.

[0802] Step 3:

[0803] The server inputs biometric information, location information, and acquired environmental information into an emotion recognition AI engine. This engine evaluates the user's current emotional state in real time. During this process, the data is processed using multiple algorithms to obtain output regarding the user's emotional state.

[0804] Step 4:

[0805] The server inputs emotional state data obtained from the emotion recognition AI engine into the analysis engine to evaluate the user's state and preferences. This analysis compares and analyzes the user's past behavioral history and preference data with the current emotional state data to generate output that forms the basis for appropriate suggestions.

[0806] Step 5:

[0807] The server generates optimal suggestions based on the analysis. These suggestions are created using a generative AI model to identify music playlists and lifestyle suggestions that match the user's emotional state and environmental conditions. The generated suggestions are then sent to the device as instructions.

[0808] Step 6:

[0809] Based on the received suggestions, the device notifies the user of the suggestions visually or audibly. In this step, specific actions are taken by the user using the display or speaker. For example, music may be played or a widget displaying the suggestions may be launched.

[0810] Step 7:

[0811] Users can review the suggestions notified via their device and decide whether to accept them. Depending on the user's choice, the system can also collect additional data to improve future suggestions.

[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] Means for acquiring biometric information and location information from an information collection device,

[0836] A means of acquiring environmental information from an external information provider,

[0837] A means for analyzing collected biometric information, location information, and environmental information to evaluate the user's state and preferences,

[0838] A means for generating optimal suggestions for the user based on the analyzed results,

[0839] A means of notifying users of the generated suggestions,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which proposes the optimal clothing based on the analyzed results, taking into account scheduled information and environmental information.

[0843] (Claim 3)

[0844] The system according to claim 1, which evaluates the user's health status based on analyzed biometric information and suggests an appropriate place to eat.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A communication device for acquiring biometric information and location information,

[0848] A means of collecting environmental conditions using an external information provision function,

[0849] A means of analyzing acquired biometric data, location data, and environmental conditions to evaluate an individual's current state and preferences,

[0850] A means of presenting appropriate behavioral choices for individuals based on the analysis results,

[0851] A display device for notifying the presented action choice,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, which recommends appropriate attire based on the analysis results, taking into account planned activities and environmental conditions.

[0855] (Claim 3)

[0856] The system according to claim 1, which evaluates an individual's health status based on analyzed biometric data and proposes appropriate dining facilities.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] Means for acquiring dynamic information and location identification information from an information acquisition device,

[0860] A means of acquiring environmental condition information from an external information provision device,

[0861] A means for analyzing collected dynamic information, location identification information, and environmental condition information to evaluate the user's situation and preferences,

[0862] A means of suggesting the optimal action to the user based on the analyzed results,

[0863] Means of notifying the proposed action,

[0864] An autonomous support system including...

[0865] (Claim 2)

[0866] The autonomous support system according to claim 1, which proposes the optimal clothing selection considering scheduled information and environmental conditions based on the analyzed results.

[0867] (Claim 3)

[0868] The autonomous support system according to claim 1, which evaluates the user's health status based on analyzed dynamic information and proposes an appropriate meal provision facility.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] A means for acquiring biometric data and location data from information gathering devices,

[0872] Means of obtaining peripheral information from external information provision organizations,

[0873] A means for analyzing collected biometric data, location data, and surrounding information to evaluate the user's state and preferences,

[0874] A means for identifying the user's emotional state based on the analyzed results and physiological changes,

[0875] A means for generating the most suitable suggestions for the user based on identified emotional states and analyzed results,

[0876] A means of notifying users of the generated suggestions,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, which suggests optimal clothing considering scheduled information and surrounding information based on the analyzed results and identified emotional states.

[0880] (Claim 3)

[0881] The system according to claim 1, which evaluates the user's health status and suggests an appropriate restaurant based on analyzed biometric data and identified emotional states.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] Means for acquiring biometric information and location information from an information collection device,

[0885] A means of acquiring environmental information from an external information provider,

[0886] A means for analyzing collected biometric information, location information, and environmental information to evaluate the user's state and preferences,

[0887] A means for generating optimal suggestions for the user based on the analyzed results,

[0888] A means of notifying users of the generated suggestions,

[0889] A means of recognizing the user's emotional state and suggesting relaxation activities when the user is feeling stressed,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, which proposes the optimal clothing based on the analyzed results, taking into account scheduled information and environmental information.

[0893] (Claim 3)

[0894] The system according to claim 1, which evaluates the user's health status based on analyzed biometric information and suggests an appropriate place to eat or drink. [Explanation of symbols]

[0895] 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. Means for acquiring dynamic information and location identification information from an information acquisition device, A means of acquiring environmental condition information from an external information provision device, A means for analyzing collected dynamic information, location identification information, and environmental condition information to evaluate the user's situation and preferences, A means of suggesting the optimal action to the user based on the analyzed results, Means of notifying the proposed action, An autonomous support system including...

2. The autonomous support system according to claim 1, which proposes the optimal clothing selection considering planned information and environmental conditions based on the analyzed results.

3. The autonomous support system according to claim 1, which evaluates the user's health status based on analyzed dynamic information and proposes an appropriate meal provision facility.

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