system

Abstract

We provide the system. [Solution] A means of acquiring user data and sending it to the cloud, Means for securely storing and analyzing acquired data, A means of proposing the most suitable activities to the user based on the analysis results, A means of notifying the user of the proposal on their device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 modern society, there is an increasing demand for managing information overload, stress, and maintaining health in order for individuals to make appropriate decisions in a short time. Conventional technologies do not sufficiently provide a mechanism for quickly and effectively proposing activities suitable for individuals. As a result, users are often in a situation where they have to make their own decisions from many options, and there are many problems in which it is difficult to make an optimal choice.

Means for Solving the Problems

[0005] This invention provides a means for acquiring user data from a terminal and securely transmitting it to the cloud. The acquired data is stored in a secure environment, and advanced analytical methods are used to reveal the user's behavioral patterns and preferences. Furthermore, based on the analysis results, it provides a means for suggesting activities best suited to the user's lifestyle and health condition, and promptly provides this information by notifying the user of this suggestion on their device. The invention also includes a system for collecting user feedback to improve the accuracy of future suggestions.

[0006] "User data" refers to information generated or provided by the user, and includes location data, activity history, calendar events, and health-related data.

[0007] "Cloud" refers to a remote server environment for storing, managing, and processing data over the internet.

[0008] A "machine learning algorithm" is an automated computational method that learns patterns and features from large amounts of data to perform predictions and classifications.

[0009] "Feedback" refers to evaluations and opinions received from users regarding suggestions, and is information collected and analyzed to improve the quality of future suggestions. [Brief explanation of the drawing]

[0010] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0011] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0014] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0016] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0017] 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."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] 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.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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".

[0031] This invention realizes a private concierge system to improve the user's daily life. It is implemented in the following manner to generate and notify optimal suggestions based on user information.

[0032] Data collection and transmission

[0033] Device: The user's device automatically collects location information, calendar events, search history, health-related data, and more. This data is encrypted on the device and then securely transmitted to a cloud server.

[0034] Data storage and analysis

[0035] Server: Received data is stored in a highly secure environment. Here, the data is standardized in format and prepared for analysis. The server uses machine learning algorithms to analyze user behavior patterns and identify hobbies and health status.

[0036] Proposal generation and notification

[0037] Server: Based on the analysis results, it generates activity suggestions optimized for the user's needs and lifestyle. For example, if the user needs to refresh themselves, it might recommend a moderate walking route or suggest simple exercises to improve concentration.

[0038] Device: The suggestion will be notified to the user's device. The suggestion will include details to encourage action (e.g., time required, benefits, calories burned, etc.).

[0039] As a concrete example, consider a scenario where a user has recently been searching for a lot of health-related content. Based on this information, the server recommends nearby fitness events for the user's days off. This suggestion is notified to the user's device, allowing them to review the details and make a quick decision. Therefore, the present invention is an effective system that provides individually optimized suggestions to enrich the user's life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects the user's location information, calendar events, browser search history, and health-related data. The data is encrypted in real time and sent to a cloud server in a secure state.

[0043] Step 2:

[0044] The server securely stores the received data in the database. The stored data is then organized and converted into a format suitable for analysis. Data cleaning is also performed at this stage to remove unnecessary information.

[0045] Step 3:

[0046] The server uses machine learning algorithms to analyze the user's behavior patterns, hobbies, and health status. This allows it to identify what activities the user prefers and when suggestions would be most effective.

[0047] Step 4:

[0048] Based on the analysis results, the server suggests activities that are suitable for the user's lifestyle. The suggestions are generated in a way that is optimized for the user, taking into account factors such as budget, time required, and calories burned.

[0049] Step 5:

[0050] The device notifies the user of suggestions received from the server. The notification includes detailed information about the suggested activity (such as the time required, benefits, and relevant links).

[0051] Step 6:

[0052] The user enters feedback into the device after implementing the provided suggestion. This feedback includes satisfaction levels and opinions on the suggestion itself.

[0053] Step 7:

[0054] The server collects and analyzes user feedback, accumulating data for future suggestions. This feedback is used to further improve the accuracy of the suggestions.

[0055] (Example 1)

[0056] 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."

[0057] In modern society, efficiently providing suggestions tailored to individual lifestyles and needs is crucial for optimizing users' time and resources. However, effectively analyzing the vast amounts of data users obtain from numerous sources and deriving appropriate suggestions is not easy. Furthermore, utilizing data without compromising privacy is another challenge. Against this backdrop, there is a need for a system that automatically generates optimal suggestions based on individual user behavioral characteristics, while ensuring user privacy and providing highly accurate suggestions.

[0058] 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.

[0059] In this invention, the server includes means for collecting user activity information, encrypting it, and transmitting it to the cloud; means for securely storing and preparing the received information in a unified format; means for analyzing the user's behavioral characteristics using machine learning techniques; means for using a generative AI model that generates optimized suggestions based on the analysis results; and means for notifying the user's device of the generated suggestions. This makes it possible to automatically generate and notify each individual user of the optimal suggestions tailored to their needs while protecting their privacy.

[0060] "User activity information" refers to data that shows an individual's activity history and status, such as location information, calendar events, search history, and health-related data.

[0061] "Encryption" is the process of ensuring the confidentiality of information by transforming data into a form that cannot be easily understood by third parties.

[0062] "Sending to the cloud" refers to transferring data to a cloud computing environment via the internet for processing and storage.

[0063] A "unified format" refers to converting different data formats into a consistent, standardized format, thereby enabling consistent analysis and processing.

[0064] "Machine learning techniques" are technologies that use algorithms to learn patterns and rules from large amounts of data to perform data analysis and prediction.

[0065] "User behavioral characteristics" refer to the unique properties of a user's behavior, such as their behavioral patterns, habits, interests, and concerns.

[0066] A "generative AI model" is a model equipped with an algorithm that uses artificial intelligence technology to analyze data and automatically generate new suggestions and content.

[0067] "Notifying a suggestion" means communicating information to the user's device in real time or in a timely manner so that the user can take action based on that information.

[0068] This invention provides a private concierge system for optimizing the user's daily life. This system automatically collects and analyzes the user's activity information and provides individually optimized suggestions to improve their daily life.

[0069] Data collection and transmission

[0070] Device: The user's device, such as a smartphone or tablet, automatically collects location information, calendar events, search history, and health-related data. This data is securely processed on the device using advanced encryption technology (e.g., AES encryption). Once encrypted, the data is transmitted to the cloud server via the internet using the SSL / TLS protocol.

[0071] Data storage and analysis

[0072] Server: Servers in the cloud securely store received data in a database. Since data may be transmitted in different formats, it is converted to a unified format. For example, time data is standardized to ISO 8601 format. The standardized data is then analyzed using machine learning algorithms to extract user behavioral characteristics and preferences. Machine learning frameworks used include TENSORFLOW® and PyTorch.

[0073] Proposal generation and notification

[0074] Server: Based on accumulated data analysis, it uses generative AI models (e.g., GPT-3® or similar generative models) to generate activity suggestions optimized for the user. This process determines specific activities based on the user's past behavior data and current state. The generated suggestions are sent to the user's device as push notifications.

[0075] Device: The device receiving the suggestion will display a real-time notification to the user, allowing them to view the details of the suggestion. The user can then review the presented information and choose their next course of action.

[0076] Specific example

[0077] For example, consider a situation where a user has recently been searching for a lot of health-related information. The server uses this information to suggest local fitness events that the user can attend this weekend. This suggestion is notified to the user's device, allowing the user to quickly review the event details and decide whether to participate.

[0078] Example of a prompt

[0079] Based on user data, the following prompt is input into the AI ​​model: "Consider recent search history and calendar appointments, and suggest the most suitable fitness activities."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Data collection

[0083] Terminal: The user's device automatically collects location information, calendar events, search history, and health-related data. Inputs are various types of data stored on the device, which are in various formats. Specifically, location information is obtained from the smartphone's sensors, and data from calendar and health apps is accessed using an Application Programming Interface (API). Outputs are encrypted data for use in other processing steps.

[0084] Step 2:

[0085] Data transmission

[0086] Terminal: Sends encrypted user data to the cloud server. The input is the data encrypted in step 1, which is sent using a secure communication protocol (SSL / TLS). Specifically, it calls an API for data transmission, establishes a secure communication channel, and then packets the data and sends it. The output is the data that has been successfully transferred to the cloud server.

[0087] Step 3:

[0088] Data storage and preparation

[0089] Server: Receives data and stores it in a cloud-based database. The input is encrypted data sent from the terminal, which is then converted for format standardization. Specifically, a data format conversion tool is used to standardize date and time formats and perform unit conversions. The output is a database record formatted in a unified format.

[0090] Step 4:

[0091] Data Analysis

[0092] Server: Analyzes data using machine learning algorithms to identify user behavior patterns. Input is data in a unified format, which is then fed into the analysis model. Specifically, it uses frameworks such as TensorFlow or PyTorch to train a neural network and learn user behavior tendencies. Output is information about user behavior patterns and interest characteristics.

[0093] Step 5:

[0094] Proposal generation

[0095] Server: Automatically generates suggestions using a generative AI model based on user behavior patterns. The input is the output of data analysis, which creates prompt sentences that instruct the generative AI model. Specifically, it prompts the generative AI model with these sentences and generates text containing relevant information. The output is activity suggestions optimized for user needs.

[0096] Step 6:

[0097] proposal notification

[0098] Terminal: The system delivers suggestions to the user's device via push notifications. The input is suggestions sent from the server, and a notification message is created using the notification API. Specifically, the system displays the suggestion information in the user interface, making it easy to view the details. The output consists of the notification received by the user and the user's reaction to the notification content.

[0099] (Application Example 1)

[0100] 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."

[0101] A challenge faced by modern consumers is the difficulty in selecting the most suitable diet and supply methods from a vast array of options, based on their preferences and health conditions. Furthermore, there is the problem of not being able to quickly obtain dietary suggestions and supply methods that fit their lifestyle. Current systems fail to effectively utilize user information to provide individually optimized suggestions, leading to decreased user satisfaction.

[0102] 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.

[0103] In this invention, the server includes means for acquiring user information and transmitting it to an information processing device, means for securely storing and analyzing the acquired information, means for making optimal suggestions to the user based on the analysis results, means for suggesting and notifying the user of meal options based on their preferences, and means for providing supply means based on the suggestions. As a result, the user can receive meal suggestions that match their individual preferences and health needs, and obtain prompt supply means based on those suggestions.

[0104] A "user" refers to an individual or group that uses the system, and is the subject of information acquisition and proposals.

[0105] An "information processing device" refers to a computer system or server used to receive and process acquired data.

[0106] "Information" refers to all data, including user preferences, behavioral history, health status, and related data.

[0107] "Storage" refers to the secure accumulation of information in databases or storage devices, making it available for later use.

[0108] "Analysis" refers to the process of interpreting and understanding user preferences and behavioral patterns using acquired information.

[0109] A "suggestion" refers to a list of activities or options provided to the user based on the analysis results, and is intended to improve user convenience.

[0110] "Notification" refers to a message or alert function used to inform users of the content of a proposal.

[0111] "Means of supply" refers to the methods and mechanisms for quickly providing services or products to users.

[0112] To realize this invention, a private concierge system will be constructed that primarily utilizes a server and user terminals.

[0113] Program Overview

[0114] The server securely collects information from the user's device and receives various types of data by sending it to the cloud. For example, location information, meal history, and health information are data collected by the user using smartphones or wearable devices. This information is received in an encrypted form by the server, which is an information processing device, and the data is organized using pandas.

[0115] The organized data is analyzed using machine learning algorithms such as scikit-learn. The purpose of the analysis is to identify user preferences, health status, and behavioral patterns, and to form optimal suggestions for the user. The generated suggestions are sent to the user's device to prompt action, such as suggesting nearby suppliers of nutritious meals or recommending the use of delivery services.

[0116] For example, if a user has just started jogging, the system could suggest restaurants that offer high-protein meals. The server would then notify the user of this suggestion on their smartphone and provide links for making reservations or ordering delivery.

[0117] An example of a prompt message might be, "I've started jogging, so I'm looking for a restaurant nearby that serves high-protein meals. Please also let me know if there are any delivery options available."

[0118] Thus, the present invention aims to improve the quality of life by enabling the provision of services tailored to the individual needs and circumstances of users.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The device automatically collects information such as the user's location, activity history, eating history, and health status. This information is encrypted on the device and sent to the information processing unit. The input consists of various pieces of information collected from the device, and the output is sent to the receiving server as encrypted information.

[0122] Step 2:

[0123] The server receives encrypted information and securely stores it in a database. During this process, the information is formatted using pandas and processed into a form suitable for analysis. The input is encrypted information, and the output is formatted data.

[0124] Step 3:

[0125] The server analyzes the prepared data using scikit-learn. Machine learning algorithms are employed to identify user preferences, behavioral patterns, and health status. The input is the prepared data, and the output is the analysis results.

[0126] Step 4:

[0127] The server generates optimal suggestions for the user based on the analysis results. For example, it provides meal plans tailored to the user's health condition and information about nearby suppliers. The input is the analysis results, and the output is the generated suggestions.

[0128] Step 5:

[0129] The server notifies the user's device of the generated suggestions. These suggestions include links and detailed information to encourage action. The input is the generated suggestions, and the output is the notification sent to the user's device.

[0130] Step 6:

[0131] The user acts based on the suggestions they receive. They can book a restaurant or order delivery according to the suggestions. The input is the suggestions notified to the device, and the output is the user's actions.

[0132] 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.

[0133] This invention provides a private concierge system that takes into account the user's emotional state. Specifically, it uses an emotion engine to generate and adjust optimal suggestions based on user data.

[0134] Data collection and emotion recognition

[0135] Device: The user's device collects general location information, calendar events, browser search history, and health-related data, as well as emotional data through analysis of the user's voice tone and facial expressions. This data is encrypted and sent to a highly secure cloud server.

[0136] Data storage and analysis

[0137] Server: Securely stores received data, including emotional data. Machine learning algorithms are used to analyze user behavior patterns, preferences, health status, and emotional state. This analysis provides foundational data for tailoring responses to user needs.

[0138] Proposal generation and emotion-based adjustment

[0139] Server: Based on the analysis results, it generates activity suggestions for the user. Utilizing data from the emotion engine, it adjusts the content to match the user's emotions; for example, it provides relaxation-focused suggestions when the user needs relaxation, and exercise-based suggestions when the user desires active activities.

[0140] Device: Notifies the user of the suggested content on their device. The suggested content includes detailed information, such as sentiment-based benefits and reasons for recommendation, to support action.

[0141] As a concrete example, consider a case where the emotion engine determines that a user is experiencing stress in their busy daily life. In this case, the server suggests to the user that they book a nearby spa or relaxation room. Furthermore, by collecting the user's emotional feedback and incorporating it into future suggestions, more accurate and personalized suggestions can be provided. Thus, this invention is a system that enables the provision of sophisticated services tailored to the user's emotional state, thereby improving their quality of life.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The device collects the user's location information, calendar events, search history, and emotional data (voice tone, emotional state based on facial expression analysis). This data is encrypted and sent to a cloud server.

[0145] Step 2:

[0146] The server stores received data in a highly secure environment and standardizes the data format. The stored data includes both behavioral and emotional data.

[0147] Step 3:

[0148] The server utilizes machine learning algorithms to analyze the user's behavioral tendencies, hobbies, health status, and emotional state. The emotion engine evaluates the user's emotional changes in real time, forming the basis for ideal suggestions.

[0149] Step 4:

[0150] The server generates activity suggestions based on the analysis results. Depending on the emotional data, the suggestions flexibly respond to the user's immediate needs, such as prioritizing relaxation or activity.

[0151] Step 5:

[0152] The device notifies the user of the generated suggestions. The notification includes details of the suggestion (estimated time, reason for recommendation, relevant links, etc.) and sentiment-based benefits.

[0153] Step 6:

[0154] The user selects and performs a suggested activity and enters feedback about the results into the device. This feedback includes changes in emotions and satisfaction levels.

[0155] Step 7:

[0156] The server collects and analyzes user feedback, further optimizing the system for future suggestions. This feedback loop continuously improves the quality of the services provided.

[0157] (Example 2)

[0158] 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".

[0159] In modern society, accurately understanding the stress and emotional fluctuations experienced by individual users and providing personalized suggestions based on that understanding is not easy. Furthermore, conventional systems have faced the challenge of not being able to accurately understand users' emotions and provide the most suitable suggestions in response to them.

[0160] 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.

[0161] In this invention, the server includes means for collecting multiple sensory data, including voice and facial expression analysis, to identify the user's emotional state; means for encrypting the collected data and transmitting it to a data processing device; and means for the data processing device to analyze the user's behavior and emotional patterns using a machine learning algorithm. This enables sophisticated suggestions based on the user's emotional state.

[0162] "Emotional state" refers to the degree of stress or relaxation a user experiences, as well as their psychological and mental state, including emotions such as joy, anger, sadness, and happiness.

[0163] "Voice analysis" refers to technology that analyzes the tone, pitch, and speed of a user's voice to infer their emotions and physical condition.

[0164] "Facial expression analysis" refers to a technology that reads a user's facial expressions from image data and estimates their emotions based on the changes in those expressions.

[0165] "Sensory data" refers to a variety of data acquired to understand the user's state and situation, such as voice, facial expressions, location information, schedule, and browser history.

[0166] "Encryption" refers to the technology of transforming information using specific algorithms in order to protect data from unauthorized access and tampering from external sources.

[0167] A "data processing system" refers to the entire system that analyzes, stores, and processes received data, and cloud servers are the primary example of this.

[0168] "Machine learning algorithms" refer to computer technologies that recognize patterns based on historical data and predict future changes.

[0169] "Generating suggestions" refers to the process of devising and presenting recommended activities and services based on the user's emotional state and behavioral patterns.

[0170] "Feedback" refers to information that shows evaluations and reactions to suggestions received from users, and is used to improve the accuracy of future suggestions.

[0171] To implement this invention, a user terminal and a server as a data processing device are required. The terminal is equipped with a camera and microphone for capturing voice and facial expressions, and also utilizes GPS functionality for obtaining location information and a calendar application for managing schedules.

[0172] The user's device collects sensory data, encrypts it, and sends it to a server in the cloud. The commonly used AES encryption method is effective. Upon receiving various data, the server stores it in a database and analyzes it using machine learning algorithms. These algorithms utilize models built in programming languages ​​such as Python.

[0173] Server analysis reveals user behavior patterns and emotional trends. The collected data is particularly used to determine situations where users are likely to experience stress or where they need relaxation.

[0174] The generative AI model suggests the most suitable activity for the user based on the analysis results. This suggestion is customized to take into account the user's emotional state; for example, if the user needs relaxation, it will use a prompt such as "Suggest an activity suitable for relaxation."

[0175] Finally, the server notifies the user's terminal of the generated suggestions. The suggestions include emotionally-based benefits and reasons for recommendation, which the user reviews and provides further feedback. This feedback is reflected in future suggestions, contributing to the improvement of the system's accuracy. For example, if the user is feeling stressed, a suggestion might be made to book a nearby relaxation facility. In this way, this invention makes it possible to improve the user's quality of life.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The device collects the user's voice and facial expressions through its camera and microphone. Input consists of audio and image data, which are processed in real time. Specifically, the audio data is analyzed for tone and pitch using frequency analysis, and the image data is analyzed for facial features using facial recognition technology. The output is encoded data indicating the user's emotional state.

[0179] Step 2:

[0180] The device combines the acquired emotion encoding data with location information, calendar events, and browser history, and encrypts them all at once. The input consists of emotion data, location information, and schedule information. A secure communication protocol is used for encryption to protect the data. The output is an encrypted dataset, which is sent to a server in the cloud.

[0181] Step 3:

[0182] The server receives an encrypted dataset and first decrypts it to retrieve each data point. The input is encrypted data, and the output is each data point in plaintext. Specifically, the server uses machine learning algorithms, including decision trees and support vector machines, to analyze the user's long-term behavioral patterns. The output is an analysis showing the user's behavioral patterns and emotional tendencies.

[0183] Step 4:

[0184] The server uses a generative AI model based on the analysis results to generate activity suggestions best suited to the user's emotional state. The input is the analysis results, and the prompt "Suggest activities to promote user relaxation" is used. Specifically, the AI ​​model generates relaxation and active options based on the obtained data and determines the priority of the suggestions. The output is a list of suggestions.

[0185] Step 5:

[0186] The server notifies the user's device of the generated list of suggestions. The input is the list of suggestions and the notification format, and the output is the notification displayed on the user's device. Specifically, push notifications are used, allowing the user to view the suggestions and their benefits on the screen.

[0187] Step 6:

[0188] Users provide feedback on suggestions via their devices. Specifically, feedback is entered concisely as text or through selection options. The input is user feedback, and the output is feedback data sent to the server.

[0189] Step 7:

[0190] The server analyzes the collected feedback data and incorporates it into the next proposal. The input is the feedback data, and new learning occurs through the analysis. This contributes to improving the generative AI model, enabling more appropriate and refined proposals in subsequent steps. The output is the refined analysis algorithm and the updated proposal model.

[0191] (Application Example 2)

[0192] 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".

[0193] In today's commercial environment, improving the customer experience in physical stores requires providing personalized services instantly that respond to the customer's emotional state. However, traditional systems lacked the means to quickly and accurately analyze customer emotions and communicate the resulting service policies to staff. As a result, improving customer satisfaction was difficult.

[0194] 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.

[0195] In this invention, the server includes means for acquiring user data and transmitting it to the cloud, means for securely storing and analyzing the acquired data, means for proposing the most suitable activity to the user based on the analysis results, means for notifying the user of the proposal on the user's display device, means for identifying the user's emotions through data analysis and providing activities adjusted based on the identified emotional state, and means for notifying in-store sales staff of the emotional state and proposing appropriate customer service policies before presenting products. This enables highly accurate customer service based on the customer's emotions.

[0196] "User data" is a general term for information related to a user, including personal information, behavioral history, health status, voice tone, facial expressions, and other such information.

[0197] "Cloud" refers to a server environment that allows information to be stored and processed over the internet.

[0198] "Emotional state" refers to the user's psychological state and mood, and is information inferred from their voice, facial expressions, and behavior.

[0199] A "display device" is a terminal used to visually present information, and includes devices such as smart glasses and smartphones.

[0200] "Activities adjusted based on emotional state" refers to suggestions and services optimized according to the user's emotional state.

[0201] A "customer service policy" is a set of guidelines for staff on how to interact and communicate with customers.

[0202] This invention is a system that personalizes services and suggestions in physical stores based on the user's emotional state. A specific embodiment is shown below.

[0203] Hardware and software configuration

[0204] The device could be, for example, smart glasses or a smartphone. This would allow for the continuous collection of user data and transmission to the cloud. User voice tone and facial expression analysis would be performed using the device's built-in camera and microphone. The server would be built on the cloud, using AWS® Rekognition or Microsoft® Azure® Face API as APIs for emotion recognition. AES encryption technology would be used for data encryption.

[0205] Data acquisition and analysis

[0206] The device collects data in real time that reflects the user's behavior and emotional state. This includes voice patterns and facial expression data. This data is transmitted to a cloud server in a secure manner. On the cloud, machine learning algorithms are used to analyze the user's emotional state and behavioral patterns in detail.

[0207] Service provision and coordination

[0208] Based on the analysis results, the server suggests the most suitable activities and services for the user. These suggestions are adjusted according to the user's emotions; for example, a relaxed user might be offered a quiet environment, while an excited user might be matched with more energetic service. Furthermore, the server notifies the store staff of the user's emotional state and the suggested services, enabling them to provide appropriate customer service strategies.

[0209] Specific example

[0210] For example, if a customer in a bookstore appears relaxed, the server can instruct staff to provide that customer with a space to browse books at their leisure. Based on this information, staff can also offer coffee or tea, suggesting an even more relaxing environment.

[0211] Example of a prompt

[0212] "Please advise us on how to analyze customer emotional data within the department store and provide them with the best possible service. In particular, please explain in detail how to tailor suggestions to different situations, such as when customers are busy or relaxed."

[0213] In this way, the present invention realizes a system that can instantly analyze customer emotions and provide appropriate services accordingly.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The device acquires the user's voice and facial expression data. At this stage, it uses its built-in camera and microphone to collect data in real time and save it as a local dataset. The input at this time is voice and video data, and the output is in an encrypted data format.

[0217] Step 2:

[0218] The device sends the collected data to a cloud server. The data is protected using AES encryption technology and transmitted via a secure communication protocol. The input here is an encrypted dataset, and the output is a secure data transfer to the cloud.

[0219] Step 3:

[0220] The server receives data sent to the cloud and analyzes it using machine learning algorithms. It accurately identifies the user's emotional state from voice tone and facial expressions. The input for this step is decrypted data, and the output is the analysis result indicating the user's emotional state.

[0221] Step 4:

[0222] The server generates an optimal customer service policy based on the analysis results, tailored to the customer's emotional state. This policy is then communicated to the staff at the physical store. In this step, the system derives what kind of service should be provided based on the emotional analysis results and sends these instructions to the staff. The input is the emotional analysis results, and the output is the notification of the customer service policy.

[0223] Step 5:

[0224] After a user receives service at a store, staff collect their feedback and send it to a server. This user feedback is then used to improve future suggestions. The input to this step is the user's feedback on the service provided at the store, and the output is the feedback being reflected on the server.

[0225] This processing flow enables personalized services that respond to the user's emotional state.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention realizes a private concierge system to improve the user's daily life. It is implemented in the following manner to generate and notify optimal suggestions based on user information.

[0243] Data collection and transmission

[0244] Device: The user's device automatically collects location information, calendar events, search history, health-related data, and more. This data is encrypted on the device and then securely transmitted to a cloud server.

[0245] Data storage and analysis

[0246] Server: Received data is stored in a highly secure environment. Here, the data is standardized in format and prepared for analysis. The server uses machine learning algorithms to analyze user behavior patterns and identify hobbies and health status.

[0247] Proposal generation and notification

[0248] Server: Based on the analysis results, it generates activity suggestions optimized for the user's needs and lifestyle. For example, if the user needs to refresh themselves, it might recommend a moderate walking route or suggest simple exercises to improve concentration.

[0249] Device: The suggestion will be notified to the user's device. The suggestion will include details to encourage action (e.g., time required, benefits, calories burned, etc.).

[0250] As a concrete example, consider a scenario where a user has recently been searching for a lot of health-related content. Based on this information, the server recommends nearby fitness events for the user's days off. This suggestion is notified to the user's device, allowing them to review the details and make a quick decision. Therefore, the present invention is an effective system that provides individually optimized suggestions to enrich the user's life.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The device collects the user's location information, calendar events, browser search history, and health-related data. The data is encrypted in real time and sent to a cloud server in a secure state.

[0254] Step 2:

[0255] The server securely stores the received data in the database. The stored data is then organized and converted into a format suitable for analysis. Data cleaning is also performed at this stage to remove unnecessary information.

[0256] Step 3:

[0257] The server uses machine learning algorithms to analyze the user's behavior patterns, hobbies, and health status. This allows it to identify what activities the user prefers and when suggestions would be most effective.

[0258] Step 4:

[0259] Based on the analysis results, the server suggests activities that are suitable for the user's lifestyle. The suggestions are generated in a way that is optimized for the user, taking into account factors such as budget, time required, and calories burned.

[0260] Step 5:

[0261] The device notifies the user of suggestions received from the server. The notification includes detailed information about the suggested activity (such as the time required, benefits, and relevant links).

[0262] Step 6:

[0263] The user enters feedback into the device after implementing the provided suggestion. This feedback includes satisfaction levels and opinions on the suggestion itself.

[0264] Step 7:

[0265] The server collects and analyzes user feedback, accumulating data for future suggestions. This feedback is used to further improve the accuracy of the suggestions.

[0266] (Example 1)

[0267] 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."

[0268] In modern society, efficiently providing suggestions tailored to individual lifestyles and needs is crucial for optimizing users' time and resources. However, effectively analyzing the vast amounts of data users obtain from numerous sources and deriving appropriate suggestions is not easy. Furthermore, utilizing data without compromising privacy is another challenge. Against this backdrop, there is a need for a system that automatically generates optimal suggestions based on individual user behavioral characteristics, while ensuring user privacy and providing highly accurate suggestions.

[0269] 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.

[0270] In this invention, the server includes means for collecting user activity information, encrypting it, and transmitting it to the cloud; means for securely storing and preparing the received information in a unified format; means for analyzing the user's behavioral characteristics using machine learning techniques; means for using a generative AI model that generates optimized suggestions based on the analysis results; and means for notifying the user's device of the generated suggestions. This makes it possible to automatically generate and notify each individual user of the optimal suggestions tailored to their needs while protecting their privacy.

[0271] "User activity information" refers to data that shows an individual's activity history and status, such as location information, calendar events, search history, and health-related data.

[0272] "Encryption" is the process of ensuring the confidentiality of information by transforming data into a form that cannot be easily understood by third parties.

[0273] "Sending to the cloud" refers to transferring data to a cloud computing environment via the internet for processing and storage.

[0274] A "unified format" refers to converting different data formats into a consistent, standardized format, thereby enabling consistent analysis and processing.

[0275] "Machine learning techniques" are technologies that use algorithms to learn patterns and rules from large amounts of data to perform data analysis and prediction.

[0276] "User behavioral characteristics" refer to the unique properties of a user's behavior, such as their behavioral patterns, habits, interests, and concerns.

[0277] A "generative AI model" is a model equipped with an algorithm that uses artificial intelligence technology to analyze data and automatically generate new suggestions and content.

[0278] "Notifying a suggestion" means communicating information to the user's device in real time or in a timely manner so that the user can take action based on that information.

[0279] This invention provides a private concierge system for optimizing the user's daily life. This system automatically collects and analyzes the user's activity information and provides individually optimized suggestions to improve their daily life.

[0280] Data collection and transmission

[0281] Device: The user's device, such as a smartphone or tablet, automatically collects location information, calendar events, search history, and health-related data. This data is securely processed on the device using advanced encryption technology (e.g., AES encryption). Once encrypted, the data is transmitted to the cloud server via the internet using the SSL / TLS protocol.

[0282] Data storage and analysis

[0283] Server: The server on the cloud stores the received data in a secure database. Since the data may be sent in different formats, it is converted into a unified format. For example, time data is unified in the ISO 8601 format. The unified data is analyzed using machine learning algorithms to extract the user's behavior characteristics and preferences. Machine learning frameworks such as TensorFlow and PyTorch can be used.

[0284] Proposal Generation and Notification

[0285] Server: Based on the accumulated data analysis, a generative AI model (e.g., GPT-3 or similar generative models) is used to generate activity proposals optimized for the user. In this process, specific activity content is determined based on the user's past behavior data and current state. The generated proposals are sent as push notifications to the user's device.

[0286] Terminal: The terminal that receives the proposal displays a real-time notification to the user and enables the user to view the details of the proposal. The user can refer to the presented content and select future actions.

[0287] Specific Example

[0288] For example, consider a situation where the user has recently searched for a lot of health-related information. The server utilizes this information to propose local fitness events that the user can participate in on the weekend. This proposal is notified to the user's terminal, and the user can quickly check the event details and decide whether to participate.

[0289] Example of Prompt Sentence

[0290] Based on the user data, the following prompt sentence is input into the generative AI model. "Please propose the optimal fitness activities considering the recent search history and calendar schedule."

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] Data collection

[0294] Terminal: The user's device automatically collects location information, calendar events, search history, and health-related data. Inputs are various types of data stored on the device, which are in various formats. Specifically, location information is obtained from the smartphone's sensors, and data from calendar and health apps is accessed using an Application Programming Interface (API). Outputs are encrypted data for use in other processing steps.

[0295] Step 2:

[0296] Data transmission

[0297] Terminal: Sends encrypted user data to the cloud server. The input is the data encrypted in step 1, which is sent using a secure communication protocol (SSL / TLS). Specifically, it calls an API for data transmission, establishes a secure communication channel, and then packets the data and sends it. The output is the data that has been successfully transferred to the cloud server.

[0298] Step 3:

[0299] Data storage and preparation

[0300] Server: Receives data and stores it in a cloud-based database. The input is encrypted data sent from the terminal, which is then converted for format standardization. Specifically, a data format conversion tool is used to standardize date and time formats and perform unit conversions. The output is a database record formatted in a unified format.

[0301] Step 4:

[0302] Data analysis

[0303] Server: Analyze data using machine learning algorithms to identify user behavior patterns. The input is data in a unified format, which is input into an analysis model. The specific operation is to use a framework such as TensorFlow or PyTorch to train a neural network to learn user behavior trends. The output is information regarding user behavior patterns and characteristics of interests.

[0304] Step 5:

[0305] Proposal generation

[0306] Server: Based on user behavior patterns, use a generative AI model to generate proposals. The input is the output of data analysis, and a prompt sentence for instructing the generative AI model is created. The specific operation is to send a prompt sentence to the generative AI model to generate text with relevant information. The output is an activity proposal optimized for user needs.

[0307]

[0308] Proposal notification

[0309] Terminal: Push and notify the proposal content to the user's device. The input is the proposal sent from the server, and a notification API is used to create a notification message. The specific operation is to display the proposal information on the user interface to make it easy to check the details. The output is the notification received by the user and the user's reaction to the notification content.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] ​ A challenge faced by modern consumers is the difficulty in selecting the most suitable diet and supply methods from a vast array of options, based on their preferences and health conditions. Furthermore, there is the problem of not being able to quickly obtain dietary suggestions and supply methods that fit their lifestyle. Current systems fail to effectively utilize user information to provide individually optimized suggestions, leading to decreased user satisfaction.

[0313] 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.

[0314] In this invention, the server includes means for acquiring user information and transmitting it to an information processing device, means for securely storing and analyzing the acquired information, means for making optimal suggestions to the user based on the analysis results, means for suggesting and notifying the user of meal options based on their preferences, and means for providing supply means based on the suggestions. As a result, the user can receive meal suggestions that match their individual preferences and health needs, and obtain prompt supply means based on those suggestions.

[0315] A "user" refers to an individual or group that uses the system, and is the subject of information acquisition and proposals.

[0316] An "information processing device" refers to a computer system or server used to receive and process acquired data.

[0317] "Information" refers to all data, including user preferences, behavioral history, health status, and related data.

[0318] "Storage" refers to the secure accumulation of information in databases or storage devices, making it available for later use.

[0319] "Analysis" refers to the process of interpreting and understanding user preferences and behavioral patterns using acquired information.

[0320] A "suggestion" refers to a list of activities or options provided to the user based on the analysis results, and is intended to improve user convenience.

[0321] "Notification" refers to a message or alert function used to inform users of the content of a proposal.

[0322] "Means of supply" refers to the methods and mechanisms for quickly providing services or products to users.

[0323] To realize this invention, a private concierge system will be constructed that primarily utilizes a server and user terminals.

[0324] Program Overview

[0325] The server securely collects information from the user's device and receives various types of data by sending it to the cloud. For example, location information, meal history, and health information are data collected by the user using smartphones or wearable devices. This information is received in an encrypted form by the server, which is an information processing device, and the data is organized using pandas.

[0326] The organized data is analyzed using machine learning algorithms such as scikit-learn. The purpose of the analysis is to identify user preferences, health status, and behavioral patterns, and to form optimal suggestions for the user. The generated suggestions are sent to the user's device to prompt action, such as suggesting nearby suppliers of nutritious meals or recommending the use of delivery services.

[0327] For example, if a user has just started jogging, the system could suggest restaurants that offer high-protein meals. The server would then notify the user of this suggestion on their smartphone and provide links for making reservations or ordering delivery.

[0328] An example of a prompt message might be, "I've started jogging, so I'm looking for a restaurant nearby that serves high-protein meals. Please also let me know if there are any delivery options available."

[0329] Thus, the present invention aims to improve the quality of life by enabling the provision of services tailored to the individual needs and circumstances of users.

[0330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0331] Step 1:

[0332] The device automatically collects information such as the user's location, activity history, eating history, and health status. This information is encrypted on the device and sent to the information processing unit. The input consists of various pieces of information collected from the device, and the output is sent to the receiving server as encrypted information.

[0333] Step 2:

[0334] The server receives encrypted information and securely stores it in a database. During this process, the information is formatted using pandas and processed into a form suitable for analysis. The input is encrypted information, and the output is formatted data.

[0335] Step 3:

[0336] The server analyzes the prepared data using scikit-learn. Machine learning algorithms are employed to identify user preferences, behavioral patterns, and health status. The input is the prepared data, and the output is the analysis results.

[0337] Step 4:

[0338] The server generates optimal suggestions for the user based on the analysis results. For example, it provides meal plans tailored to the user's health condition and information about nearby suppliers. The input is the analysis results, and the output is the generated suggestions.

[0339] Step 5:

[0340] The server notifies the user's device of the generated suggestions. These suggestions include links and detailed information to encourage action. The input is the generated suggestions, and the output is the notification sent to the user's device.

[0341] Step 6:

[0342] The user acts based on the suggestions they receive. They can book a restaurant or order delivery according to the suggestions. The input is the suggestions notified to the device, and the output is the user's actions.

[0343] 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.

[0344] This invention provides a private concierge system that takes into account the user's emotional state. Specifically, it uses an emotion engine to generate and adjust optimal suggestions based on user data.

[0345] Data collection and emotion recognition

[0346] Device: The user's device collects general location information, calendar events, browser search history, and health-related data, as well as emotional data through analysis of the user's voice tone and facial expressions. This data is encrypted and sent to a highly secure cloud server.

[0347] Data storage and analysis

[0348] Server: Securely stores received data, including emotional data. Machine learning algorithms are used to analyze user behavior patterns, preferences, health status, and emotional state. This analysis provides foundational data for tailoring responses to user needs.

[0349] Proposal generation and emotion-based adjustment

[0350] Server: Based on the analysis results, it generates activity suggestions for the user. Utilizing data from the emotion engine, it adjusts the content to match the user's emotions; for example, it provides relaxation-focused suggestions when the user needs relaxation, and exercise-based suggestions when the user desires active activities.

[0351] Device: Notifies the user of the suggested content on their device. The suggested content includes detailed information, such as sentiment-based benefits and reasons for recommendation, to support action.

[0352] As a concrete example, consider a case where the emotion engine determines that a user is experiencing stress in their busy daily life. In this case, the server suggests to the user that they book a nearby spa or relaxation room. Furthermore, by collecting the user's emotional feedback and incorporating it into future suggestions, more accurate and personalized suggestions can be provided. Thus, this invention is a system that enables the provision of sophisticated services tailored to the user's emotional state, thereby improving their quality of life.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The device collects the user's location information, calendar events, search history, and emotional data (voice tone, emotional state based on facial expression analysis). This data is encrypted and sent to a cloud server.

[0356] Step 2:

[0357] The server stores received data in a highly secure environment and standardizes the data format. The stored data includes both behavioral and emotional data.

[0358] Step 3:

[0359] The server utilizes machine learning algorithms to analyze the user's behavioral tendencies, hobbies, health status, and emotional state. The emotion engine evaluates the user's emotional changes in real time, forming the basis for ideal suggestions.

[0360] Step 4:

[0361] The server generates activity suggestions based on the analysis results. Depending on the emotional data, the suggestions flexibly respond to the user's immediate needs, such as prioritizing relaxation or activity.

[0362] Step 5:

[0363] The device notifies the user of the generated suggestions. The notification includes details of the suggestion (estimated time, reason for recommendation, relevant links, etc.) and sentiment-based benefits.

[0364] Step 6:

[0365] The user selects and performs a suggested activity and enters feedback about the results into the device. This feedback includes changes in emotions and satisfaction levels.

[0366] Step 7:

[0367] The server collects and analyzes user feedback, further optimizing the system for future suggestions. This feedback loop continuously improves the quality of the services provided.

[0368] (Example 2)

[0369] 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".

[0370] In modern society, accurately understanding the stress and emotional fluctuations experienced by individual users and providing personalized suggestions based on that understanding is not easy. Furthermore, conventional systems have faced the challenge of not being able to accurately understand users' emotions and provide the most suitable suggestions in response to them.

[0371] 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.

[0372] In this invention, the server includes means for collecting multiple sensory data, including voice and facial expression analysis, to identify the user's emotional state; means for encrypting the collected data and transmitting it to a data processing device; and means for the data processing device to analyze the user's behavior and emotional patterns using a machine learning algorithm. This enables sophisticated suggestions based on the user's emotional state.

[0373] "Emotional state" refers to the degree of stress or relaxation a user experiences, as well as their psychological and mental state, including emotions such as joy, anger, sadness, and happiness.

[0374] "Voice analysis" refers to technology that analyzes the tone, pitch, and speed of a user's voice to infer their emotions and physical condition.

[0375] "Facial expression analysis" refers to a technology that reads a user's facial expressions from image data and estimates their emotions based on the changes in those expressions.

[0376] "Sensory data" refers to a variety of data acquired to understand the user's state and situation, such as voice, facial expressions, location information, schedule, and browser history.

[0377] "Encryption" refers to the technology of transforming information using specific algorithms in order to protect data from unauthorized access and tampering from external sources.

[0378] A "data processing system" refers to the entire system that analyzes, stores, and processes received data, and cloud servers are the primary example of this.

[0379] "Machine learning algorithms" refer to computer technologies that recognize patterns based on historical data and predict future changes.

[0380] "Generating suggestions" refers to the process of devising and presenting recommended activities and services based on the user's emotional state and behavioral patterns.

[0381] "Feedback" refers to information that shows evaluations and reactions to suggestions received from users, and is used to improve the accuracy of future suggestions.

[0382] To implement this invention, a user terminal and a server as a data processing device are required. The terminal is equipped with a camera and microphone for capturing voice and facial expressions, and also utilizes GPS functionality for obtaining location information and a calendar application for managing schedules.

[0383] The user's device collects sensory data, encrypts it, and sends it to a server in the cloud. The commonly used AES encryption method is effective. Upon receiving various data, the server stores it in a database and analyzes it using machine learning algorithms. These algorithms utilize models built in programming languages ​​such as Python.

[0384] Server analysis reveals user behavior patterns and emotional trends. The collected data is particularly used to determine situations where users are likely to experience stress or where they need relaxation.

[0385] The generative AI model suggests the most suitable activity for the user based on the analysis results. This suggestion is customized to take into account the user's emotional state; for example, if the user needs relaxation, it will use a prompt such as "Suggest an activity suitable for relaxation."

[0386] Finally, the server notifies the user's terminal of the generated suggestions. The suggestions include emotionally-based benefits and reasons for recommendation, which the user reviews and provides further feedback. This feedback is reflected in future suggestions, contributing to the improvement of the system's accuracy. For example, if the user is feeling stressed, a suggestion might be made to book a nearby relaxation facility. In this way, this invention makes it possible to improve the user's quality of life.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The device collects the user's voice and facial expressions through its camera and microphone. Input consists of audio and image data, which are processed in real time. Specifically, the audio data is analyzed for tone and pitch using frequency analysis, and the image data is analyzed for facial features using facial recognition technology. The output is encoded data indicating the user's emotional state.

[0390] Step 2:

[0391] The device combines the acquired emotion encoding data with location information, calendar events, and browser history, and encrypts them all at once. The input consists of emotion data, location information, and schedule information. A secure communication protocol is used for encryption to protect the data. The output is an encrypted dataset, which is sent to a server in the cloud.

[0392] Step 3:

[0393] The server receives an encrypted dataset and first decrypts it to retrieve each data point. The input is encrypted data, and the output is each data point in plaintext. Specifically, the server uses machine learning algorithms, including decision trees and support vector machines, to analyze the user's long-term behavioral patterns. The output is an analysis showing the user's behavioral patterns and emotional tendencies.

[0394] Step 4:

[0395] The server uses a generative AI model based on the analysis results to generate activity suggestions best suited to the user's emotional state. The input is the analysis results, and the prompt "Suggest activities to promote user relaxation" is used. Specifically, the AI ​​model generates relaxation and active options based on the obtained data and determines the priority of the suggestions. The output is a list of suggestions.

[0396] Step 5:

[0397] The server notifies the user's device of the generated list of suggestions. The input is the list of suggestions and the notification format, and the output is the notification displayed on the user's device. Specifically, push notifications are used, allowing the user to view the suggestions and their benefits on the screen.

[0398] Step 6:

[0399] Users provide feedback on suggestions via their devices. Specifically, feedback is entered concisely as text or through selection options. The input is user feedback, and the output is feedback data sent to the server.

[0400] Step 7:

[0401] The server analyzes the collected feedback data and incorporates it into the next proposal. The input is the feedback data, and new learning occurs through the analysis. This contributes to improving the generative AI model, enabling more appropriate and refined proposals in subsequent steps. The output is the refined analysis algorithm and the updated proposal model.

[0402] (Application Example 2)

[0403] 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."

[0404] In today's commercial environment, improving the customer experience in physical stores requires providing personalized services instantly that respond to the customer's emotional state. However, traditional systems lacked the means to quickly and accurately analyze customer emotions and communicate the resulting service policies to staff. As a result, improving customer satisfaction was difficult.

[0405] 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.

[0406] In this invention, the server includes means for acquiring user data and transmitting it to the cloud, means for securely storing and analyzing the acquired data, means for proposing the most suitable activity to the user based on the analysis results, means for notifying the user of the proposal on the user's display device, means for identifying the user's emotions through data analysis and providing activities adjusted based on the identified emotional state, and means for notifying in-store sales staff of the emotional state and proposing appropriate customer service policies before presenting products. This enables highly accurate customer service based on the customer's emotions.

[0407] "User data" is a general term for information related to a user, including personal information, behavioral history, health status, voice tone, facial expressions, and other such information.

[0408] "Cloud" refers to a server environment that allows information to be stored and processed over the internet.

[0409] "Emotional state" refers to the user's psychological state and mood, and is information inferred from their voice, facial expressions, and behavior.

[0410] A "display device" is a terminal used to visually present information, and includes devices such as smart glasses and smartphones.

[0411] "Activities adjusted based on emotional state" refers to suggestions and services optimized according to the user's emotional state.

[0412] A "customer service policy" is a set of guidelines for staff on how to interact and communicate with customers.

[0413] This invention is a system that personalizes services and suggestions in physical stores based on the user's emotional state. A specific embodiment is shown below.

[0414] Hardware and software configuration

[0415] The device could be, for example, smart glasses or a smartphone. This would allow for the continuous collection of user data, which would then be sent to the cloud. User voice tone and facial expression analysis would be performed using the device's built-in camera and microphone. The server would be built on the cloud, using AWS Rekognition or Microsoft Azure Face API as APIs for emotion recognition. AES encryption technology would be used for data encryption.

[0416] Data acquisition and analysis

[0417] The device collects data in real time that reflects the user's behavior and emotional state. This includes voice patterns and facial expression data. This data is transmitted to a cloud server in a secure manner. On the cloud, machine learning algorithms are used to analyze the user's emotional state and behavioral patterns in detail.

[0418] Service provision and coordination

[0419] Based on the analysis results, the server suggests the most suitable activities and services for the user. These suggestions are adjusted according to the user's emotions; for example, a relaxed user might be offered a quiet environment, while an excited user might be matched with more energetic service. Furthermore, the server notifies the store staff of the user's emotional state and the suggested services, enabling them to provide appropriate customer service strategies.

[0420] Specific example

[0421] For example, if a customer in a bookstore appears relaxed, the server can instruct staff to provide that customer with a space to browse books at their leisure. Based on this information, staff can also offer coffee or tea, suggesting an even more relaxing environment.

[0422] Example of a prompt

[0423] "Please advise us on how to analyze customer emotional data within the department store and provide them with the best possible service. In particular, please explain in detail how to tailor suggestions to different situations, such as when customers are busy or relaxed."

[0424] In this way, the present invention realizes a system that can instantly analyze customer emotions and provide appropriate services accordingly.

[0425] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0426] Step 1:

[0427] The device acquires the user's voice and facial expression data. At this stage, it uses its built-in camera and microphone to collect data in real time and save it as a local dataset. The input at this time is voice and video data, and the output is in an encrypted data format.

[0428] Step 2:

[0429] The device sends the collected data to a cloud server. The data is protected using AES encryption technology and transmitted via a secure communication protocol. The input here is an encrypted dataset, and the output is a secure data transfer to the cloud.

[0430] Step 3:

[0431] The server receives data sent to the cloud and analyzes it using machine learning algorithms. It accurately identifies the user's emotional state from voice tone and facial expressions. The input for this step is decrypted data, and the output is the analysis result indicating the user's emotional state.

[0432] Step 4:

[0433] The server generates an optimal customer service policy based on the analysis results, tailored to the customer's emotional state. This policy is then communicated to the staff at the physical store. In this step, the system derives what kind of service should be provided based on the emotional analysis results and sends these instructions to the staff. The input is the emotional analysis results, and the output is the notification of the customer service policy.

[0434] Step 5:

[0435] After a user receives service at a store, staff collect their feedback and send it to a server. This user feedback is then used to improve future suggestions. The input to this step is the user's feedback on the service provided at the store, and the output is the feedback being reflected on the server.

[0436] This processing flow enables personalized services that respond to the user's emotional state.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] This invention realizes a private concierge system to improve the user's daily life. It is implemented in the following manner to generate and notify optimal suggestions based on user information.

[0454] Data collection and transmission

[0455] Device: The user's device automatically collects location information, calendar events, search history, health-related data, and more. This data is encrypted on the device and then securely transmitted to a cloud server.

[0456] Data storage and analysis

[0457] Server: Received data is stored in a highly secure environment. Here, the data is standardized in format and prepared for analysis. The server uses machine learning algorithms to analyze user behavior patterns and identify hobbies and health status.

[0458] Proposal generation and notification

[0459] Server: Based on the analysis results, it generates activity suggestions optimized for the user's needs and lifestyle. For example, if the user needs to refresh themselves, it might recommend a moderate walking route or suggest simple exercises to improve concentration.

[0460] Device: The suggestion will be notified to the user's device. The suggestion will include details to encourage action (e.g., time required, benefits, calories burned, etc.).

[0461] As a concrete example, consider a scenario where a user has recently been searching for a lot of health-related content. Based on this information, the server recommends nearby fitness events for the user's days off. This suggestion is notified to the user's device, allowing them to review the details and make a quick decision. Therefore, the present invention is an effective system that provides individually optimized suggestions to enrich the user's life.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The device collects the user's location information, calendar events, browser search history, and health-related data. The data is encrypted in real time and sent to a cloud server in a secure state.

[0465] Step 2:

[0466] The server securely stores the received data in the database. The stored data is then organized and converted into a format suitable for analysis. Data cleaning is also performed at this stage to remove unnecessary information.

[0467] Step 3:

[0468] The server uses machine learning algorithms to analyze the user's behavior patterns, hobbies, and health status. This allows it to identify what activities the user prefers and when suggestions would be most effective.

[0469] Step 4:

[0470] Based on the analysis results, the server suggests activities that are suitable for the user's lifestyle. The suggestions are generated in a way that is optimized for the user, taking into account factors such as budget, time required, and calories burned.

[0471] Step 5:

[0472] The device notifies the user of suggestions received from the server. The notification includes detailed information about the suggested activity (such as the time required, benefits, and relevant links).

[0473] Step 6:

[0474] The user enters feedback into the device after implementing the provided suggestion. This feedback includes satisfaction levels and opinions on the suggestion itself.

[0475] Step 7:

[0476] The server collects and analyzes user feedback, accumulating data for future suggestions. This feedback is used to further improve the accuracy of the suggestions.

[0477] (Example 1)

[0478] 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."

[0479] In modern society, efficiently providing suggestions tailored to individual lifestyles and needs is crucial for optimizing users' time and resources. However, effectively analyzing the vast amounts of data users obtain from numerous sources and deriving appropriate suggestions is not easy. Furthermore, utilizing data without compromising privacy is another challenge. Against this backdrop, there is a need for a system that automatically generates optimal suggestions based on individual user behavioral characteristics, while ensuring user privacy and providing highly accurate suggestions.

[0480] 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.

[0481] In this invention, the server includes means for collecting user activity information, encrypting it, and transmitting it to the cloud; means for securely storing and preparing the received information in a unified format; means for analyzing the user's behavioral characteristics using machine learning techniques; means for using a generative AI model that generates optimized suggestions based on the analysis results; and means for notifying the user's device of the generated suggestions. This makes it possible to automatically generate and notify each individual user of the optimal suggestions tailored to their needs while protecting their privacy.

[0482] "User activity information" refers to data that shows an individual's activity history and status, such as location information, calendar events, search history, and health-related data.

[0483] "Encryption" is the process of ensuring the confidentiality of information by transforming data into a form that cannot be easily understood by third parties.

[0484] "Sending to the cloud" refers to transferring data to a cloud computing environment via the internet for processing and storage.

[0485] A "unified format" refers to converting different data formats into a consistent, standardized format, thereby enabling consistent analysis and processing.

[0486] "Machine learning techniques" are technologies that use algorithms to learn patterns and rules from large amounts of data to perform data analysis and prediction.

[0487] "User behavioral characteristics" refer to the unique properties of a user's behavior, such as their behavioral patterns, habits, interests, and concerns.

[0488] A "generative AI model" is a model equipped with an algorithm that uses artificial intelligence technology to analyze data and automatically generate new suggestions and content.

[0489] "Notifying a suggestion" means communicating information to the user's device in real time or in a timely manner so that the user can take action based on that information.

[0490] This invention provides a private concierge system for optimizing the user's daily life. This system automatically collects and analyzes the user's activity information and provides individually optimized suggestions to improve their daily life.

[0491] Data collection and transmission

[0492] Device: The user's device, such as a smartphone or tablet, automatically collects location information, calendar events, search history, and health-related data. This data is securely processed on the device using advanced encryption technology (e.g., AES encryption). Once encrypted, the data is transmitted to the cloud server via the internet using the SSL / TLS protocol.

[0493] Data storage and analysis

[0494] Server: Servers in the cloud store received data in a secure database. Since data may be transmitted in different formats, it is converted to a unified format. For example, time data is standardized to ISO 8601 format. The unified data is then analyzed using machine learning algorithms to extract user behavioral characteristics and preferences. Machine learning frameworks used include TensorFlow and PyTorch.

[0495] Proposal generation and notification

[0496] Server: Based on accumulated data analysis, it uses generative AI models (e.g., GPT-3 or similar generative models) to generate activity suggestions optimized for the user. This process determines specific activities based on the user's past behavior data and current state. The generated suggestions are sent as push notifications to the user's device.

[0497] Device: The device receiving the suggestion will display a real-time notification to the user, allowing them to view the details of the suggestion. The user can then review the presented information and choose their next course of action.

[0498] Specific example

[0499] For example, consider a situation where a user has recently been searching for a lot of health-related information. The server uses this information to suggest local fitness events that the user can attend this weekend. This suggestion is notified to the user's device, allowing the user to quickly review the event details and decide whether to participate.

[0500] Example of a prompt

[0501] Based on user data, the following prompt is input into the AI ​​model: "Consider recent search history and calendar appointments, and suggest the most suitable fitness activities."

[0502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0503] Step 1:

[0504] Data collection

[0505] Terminal: The user's device automatically collects location information, calendar events, search history, and health-related data. Inputs are various types of data stored on the device, which are in various formats. Specifically, location information is obtained from the smartphone's sensors, and data from calendar and health apps is accessed using an Application Programming Interface (API). Outputs are encrypted data for use in other processing steps.

[0506] Step 2:

[0507] Data transmission

[0508] Terminal: Sends encrypted user data to the cloud server. The input is the data encrypted in step 1, which is sent using a secure communication protocol (SSL / TLS). Specifically, it calls an API for data transmission, establishes a secure communication channel, and then packets the data and sends it. The output is the data that has been successfully transferred to the cloud server.

[0509] Step 3:

[0510] Data storage and preparation

[0511] Server: Receives data and stores it in a cloud-based database. The input is encrypted data sent from the terminal, which is then converted for format standardization. Specifically, a data format conversion tool is used to standardize date and time formats and perform unit conversions. The output is a database record formatted in a unified format.

[0512] Step 4:

[0513] Data Analysis

[0514] Server: Analyzes data using machine learning algorithms to identify user behavior patterns. Input is data in a unified format, which is then fed into the analysis model. Specifically, it uses frameworks such as TensorFlow or PyTorch to train a neural network and learn user behavior tendencies. Output is information about user behavior patterns and interest characteristics.

[0515] Step 5:

[0516] Proposal generation

[0517] Server: Automatically generates suggestions using a generative AI model based on user behavior patterns. The input is the output of data analysis, which creates prompt sentences that instruct the generative AI model. Specifically, it prompts the generative AI model with these sentences and generates text containing relevant information. The output is activity suggestions optimized for user needs.

[0518] Step 6:

[0519] proposal notification

[0520] Terminal: The system delivers suggestions to the user's device via push notifications. The input is suggestions sent from the server, and a notification message is created using the notification API. Specifically, the system displays the suggestion information in the user interface, making it easy to view the details. The output consists of the notification received by the user and the user's reaction to the notification content.

[0521] (Application Example 1)

[0522] 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."

[0523] A challenge faced by modern consumers is the difficulty in selecting the most suitable diet and supply methods from a vast array of options, based on their preferences and health conditions. Furthermore, there is the problem of not being able to quickly obtain dietary suggestions and supply methods that fit their lifestyle. Current systems fail to effectively utilize user information to provide individually optimized suggestions, leading to decreased user satisfaction.

[0524] 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.

[0525] In this invention, the server includes means for acquiring user information and transmitting it to an information processing device, means for securely storing and analyzing the acquired information, means for making optimal suggestions to the user based on the analysis results, means for suggesting and notifying the user of meal options based on their preferences, and means for providing supply means based on the suggestions. As a result, the user can receive meal suggestions that match their individual preferences and health needs, and obtain prompt supply means based on those suggestions.

[0526] A "user" refers to an individual or group that uses the system, and is the subject of information acquisition and proposals.

[0527] An "information processing device" refers to a computer system or server used to receive and process acquired data.

[0528] "Information" refers to all data, including user preferences, behavioral history, health status, and related data.

[0529] "Storage" refers to the secure accumulation of information in databases or storage devices, making it available for later use.

[0530] "Analysis" refers to the process of interpreting and understanding user preferences and behavioral patterns using acquired information.

[0531] A "suggestion" refers to a list of activities or options provided to the user based on the analysis results, and is intended to improve user convenience.

[0532] "Notification" refers to a message or alert function used to inform users of the content of a proposal.

[0533] "Means of supply" refers to the methods and mechanisms for quickly providing services or products to users.

[0534] To realize this invention, a private concierge system will be constructed that primarily utilizes a server and user terminals.

[0535] Program Overview

[0536] The server securely collects information from the user's device and receives various types of data by sending it to the cloud. For example, location information, meal history, and health information are data collected by the user using smartphones or wearable devices. This information is received in an encrypted form by the server, which is an information processing device, and the data is organized using pandas.

[0537] The organized data is analyzed using machine learning algorithms such as scikit-learn. The purpose of the analysis is to identify user preferences, health status, and behavioral patterns, and to form optimal suggestions for the user. The generated suggestions are sent to the user's device to prompt action, such as suggesting nearby suppliers of nutritious meals or recommending the use of delivery services.

[0538] For example, if a user has just started jogging, the system could suggest restaurants that offer high-protein meals. The server would then notify the user of this suggestion on their smartphone and provide links for making reservations or ordering delivery.

[0539] An example of a prompt message might be, "I've started jogging, so I'm looking for a restaurant nearby that serves high-protein meals. Please also let me know if there are any delivery options available."

[0540] Thus, the present invention aims to improve the quality of life by enabling the provision of services tailored to the individual needs and circumstances of users.

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] The device automatically collects information such as the user's location, activity history, eating history, and health status. This information is encrypted on the device and sent to the information processing unit. The input consists of various pieces of information collected from the device, and the output is sent to the receiving server as encrypted information.

[0544] Step 2:

[0545] The server receives encrypted information and securely stores it in a database. During this process, the information is formatted using pandas and processed into a form suitable for analysis. The input is encrypted information, and the output is formatted data.

[0546] Step 3:

[0547] The server analyzes the prepared data using scikit-learn. Machine learning algorithms are employed to identify user preferences, behavioral patterns, and health status. The input is the prepared data, and the output is the analysis results.

[0548] Step 4:

[0549] The server generates optimal suggestions for the user based on the analysis results. For example, it provides meal plans tailored to the user's health condition and information about nearby suppliers. The input is the analysis results, and the output is the generated suggestions.

[0550] Step 5:

[0551] The server notifies the user's device of the generated suggestions. These suggestions include links and detailed information to encourage action. The input is the generated suggestions, and the output is the notification sent to the user's device.

[0552] Step 6:

[0553] The user acts based on the suggestions they receive. They can book a restaurant or order delivery according to the suggestions. The input is the suggestions notified to the device, and the output is the user's actions.

[0554] 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.

[0555] This invention provides a private concierge system that takes into account the user's emotional state. Specifically, it uses an emotion engine to generate and adjust optimal suggestions based on user data.

[0556] Data collection and emotion recognition

[0557] Device: The user's device collects general location information, calendar events, browser search history, and health-related data, as well as emotional data through analysis of the user's voice tone and facial expressions. This data is encrypted and sent to a highly secure cloud server.

[0558] Data storage and analysis

[0559] Server: Securely stores received data, including emotional data. Machine learning algorithms are used to analyze user behavior patterns, preferences, health status, and emotional state. This analysis provides foundational data for tailoring responses to user needs.

[0560] Proposal generation and emotion-based adjustment

[0561] Server: Based on the analysis results, it generates activity suggestions for the user. Utilizing data from the emotion engine, it adjusts the content to match the user's emotions; for example, it provides relaxation-focused suggestions when the user needs relaxation, and exercise-based suggestions when the user desires active activities.

[0562] Device: Notifies the user of the suggested content on their device. The suggested content includes detailed information, such as sentiment-based benefits and reasons for recommendation, to support action.

[0563] As a concrete example, consider a case where the emotion engine determines that a user is experiencing stress in their busy daily life. In this case, the server suggests to the user that they book a nearby spa or relaxation room. Furthermore, by collecting the user's emotional feedback and incorporating it into future suggestions, more accurate and personalized suggestions can be provided. Thus, this invention is a system that enables the provision of sophisticated services tailored to the user's emotional state, thereby improving their quality of life.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] The device collects the user's location information, calendar events, search history, and emotional data (voice tone, emotional state based on facial expression analysis). This data is encrypted and sent to a cloud server.

[0567] Step 2:

[0568] The server stores received data in a highly secure environment and standardizes the data format. The stored data includes both behavioral and emotional data.

[0569] Step 3:

[0570] The server utilizes machine learning algorithms to analyze the user's behavioral tendencies, hobbies, health status, and emotional state. The emotion engine evaluates the user's emotional changes in real time, forming the basis for ideal suggestions.

[0571] Step 4:

[0572] The server generates activity suggestions based on the analysis results. Depending on the emotional data, the suggestions flexibly respond to the user's immediate needs, such as prioritizing relaxation or activity.

[0573] Step 5:

[0574] The device notifies the user of the generated suggestions. The notification includes details of the suggestion (estimated time, reason for recommendation, relevant links, etc.) and sentiment-based benefits.

[0575] Step 6:

[0576] The user selects and performs a suggested activity and enters feedback about the results into the device. This feedback includes changes in emotions and satisfaction levels.

[0577] Step 7:

[0578] The server collects and analyzes user feedback, further optimizing the system for future suggestions. This feedback loop continuously improves the quality of the services provided.

[0579] (Example 2)

[0580] 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."

[0581] In modern society, accurately understanding the stress and emotional fluctuations experienced by individual users and providing personalized suggestions based on that understanding is not easy. Furthermore, conventional systems have faced the challenge of not being able to accurately understand users' emotions and provide the most suitable suggestions in response to them.

[0582] 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.

[0583] In this invention, the server includes means for collecting multiple sensory data, including voice and facial expression analysis, to identify the user's emotional state; means for encrypting the collected data and transmitting it to a data processing device; and means for the data processing device to analyze the user's behavior and emotional patterns using a machine learning algorithm. This enables sophisticated suggestions based on the user's emotional state.

[0584] "Emotional state" refers to the degree of stress or relaxation a user experiences, as well as their psychological and mental state, including emotions such as joy, anger, sadness, and happiness.

[0585] "Voice analysis" refers to technology that analyzes the tone, pitch, and speed of a user's voice to infer their emotions and physical condition.

[0586] "Facial expression analysis" refers to a technology that reads a user's facial expressions from image data and estimates their emotions based on the changes in those expressions.

[0587] "Sensory data" refers to a variety of data acquired to understand the user's state and situation, such as voice, facial expressions, location information, schedule, and browser history.

[0588] "Encryption" refers to the technology of transforming information using specific algorithms in order to protect data from unauthorized access and tampering from external sources.

[0589] A "data processing system" refers to the entire system that analyzes, stores, and processes received data, and cloud servers are the primary example of this.

[0590] "Machine learning algorithms" refer to computer technologies that recognize patterns based on historical data and predict future changes.

[0591] "Generating suggestions" refers to the process of devising and presenting recommended activities and services based on the user's emotional state and behavioral patterns.

[0592] "Feedback" refers to information that shows evaluations and reactions to suggestions received from users, and is used to improve the accuracy of future suggestions.

[0593] To implement this invention, a user terminal and a server as a data processing device are required. The terminal is equipped with a camera and microphone for capturing voice and facial expressions, and also utilizes GPS functionality for obtaining location information and a calendar application for managing schedules.

[0594] The user's device collects sensory data, encrypts it, and sends it to a server in the cloud. The commonly used AES encryption method is effective. Upon receiving various data, the server stores it in a database and analyzes it using machine learning algorithms. These algorithms utilize models built in programming languages ​​such as Python.

[0595] Server analysis reveals user behavior patterns and emotional trends. The collected data is particularly used to determine situations where users are likely to experience stress or where they need relaxation.

[0596] The generative AI model suggests the most suitable activity for the user based on the analysis results. This suggestion is customized to take into account the user's emotional state; for example, if the user needs relaxation, it will use a prompt such as "Suggest an activity suitable for relaxation."

[0597] Finally, the server notifies the user's terminal of the generated suggestions. The suggestions include emotionally-based benefits and reasons for recommendation, which the user reviews and provides further feedback. This feedback is reflected in future suggestions, contributing to the improvement of the system's accuracy. For example, if the user is feeling stressed, a suggestion might be made to book a nearby relaxation facility. In this way, this invention makes it possible to improve the user's quality of life.

[0598] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0599] Step 1:

[0600] The device collects the user's voice and facial expressions through its camera and microphone. Input consists of audio and image data, which are processed in real time. Specifically, the audio data is analyzed for tone and pitch using frequency analysis, and the image data is analyzed for facial features using facial recognition technology. The output is encoded data indicating the user's emotional state.

[0601] Step 2:

[0602] The device combines the acquired emotion encoding data with location information, calendar events, and browser history, and encrypts them all at once. The input consists of emotion data, location information, and schedule information. A secure communication protocol is used for encryption to protect the data. The output is an encrypted dataset, which is sent to a server in the cloud.

[0603] Step 3:

[0604] The server receives an encrypted dataset and first decrypts it to retrieve each data point. The input is encrypted data, and the output is each data point in plaintext. Specifically, the server uses machine learning algorithms, including decision trees and support vector machines, to analyze the user's long-term behavioral patterns. The output is an analysis showing the user's behavioral patterns and emotional tendencies.

[0605] Step 4:

[0606] The server uses a generative AI model based on the analysis results to generate activity suggestions best suited to the user's emotional state. The input is the analysis results, and the prompt "Suggest activities to promote user relaxation" is used. Specifically, the AI ​​model generates relaxation and active options based on the obtained data and determines the priority of the suggestions. The output is a list of suggestions.

[0607] Step 5:

[0608] The server notifies the user's device of the generated list of suggestions. The input is the list of suggestions and the notification format, and the output is the notification displayed on the user's device. Specifically, push notifications are used, allowing the user to view the suggestions and their benefits on the screen.

[0609] Step 6:

[0610] Users provide feedback on suggestions via their devices. Specifically, feedback is entered concisely as text or through selection options. The input is user feedback, and the output is feedback data sent to the server.

[0611] Step 7:

[0612] The server analyzes the collected feedback data and incorporates it into the next proposal. The input is the feedback data, and new learning occurs through the analysis. This contributes to improving the generative AI model, enabling more appropriate and refined proposals in subsequent steps. The output is the refined analysis algorithm and the updated proposal model.

[0613] (Application Example 2)

[0614] 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."

[0615] In today's commercial environment, improving the customer experience in physical stores requires providing personalized services instantly that respond to the customer's emotional state. However, traditional systems lacked the means to quickly and accurately analyze customer emotions and communicate the resulting service policies to staff. As a result, improving customer satisfaction was difficult.

[0616] 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.

[0617] In this invention, the server includes means for acquiring user data and transmitting it to the cloud, means for securely storing and analyzing the acquired data, means for proposing the most suitable activity to the user based on the analysis results, means for notifying the user of the proposal on the user's display device, means for identifying the user's emotions through data analysis and providing activities adjusted based on the identified emotional state, and means for notifying in-store sales staff of the emotional state and proposing appropriate customer service policies before presenting products. This enables highly accurate customer service based on the customer's emotions.

[0618] "User data" is a general term for information related to a user, including personal information, behavioral history, health status, voice tone, facial expressions, and other such information.

[0619] "Cloud" refers to a server environment that allows information to be stored and processed over the internet.

[0620] "Emotional state" refers to the user's psychological state and mood, and is information inferred from their voice, facial expressions, and behavior.

[0621] A "display device" is a terminal used to visually present information, and includes devices such as smart glasses and smartphones.

[0622] "Activities adjusted based on emotional state" refers to suggestions and services optimized according to the user's emotional state.

[0623] A "customer service policy" is a set of guidelines for staff on how to interact and communicate with customers.

[0624] This invention is a system that personalizes services and suggestions in physical stores based on the user's emotional state. A specific embodiment is shown below.

[0625] Hardware and software configuration

[0626] The device could be, for example, smart glasses or a smartphone. This would allow for the continuous collection of user data, which would then be sent to the cloud. User voice tone and facial expression analysis would be performed using the device's built-in camera and microphone. The server would be built on the cloud, using AWS Rekognition or Microsoft Azure Face API as APIs for emotion recognition. AES encryption technology would be used for data encryption.

[0627] Data acquisition and analysis

[0628] The device collects data in real time that reflects the user's behavior and emotional state. This includes voice patterns and facial expression data. This data is transmitted to a cloud server in a secure manner. On the cloud, machine learning algorithms are used to analyze the user's emotional state and behavioral patterns in detail.

[0629] Service provision and coordination

[0630] Based on the analysis results, the server suggests the most suitable activities and services for the user. These suggestions are adjusted according to the user's emotions; for example, a relaxed user might be offered a quiet environment, while an excited user might be matched with more energetic service. Furthermore, the server notifies the store staff of the user's emotional state and the suggested services, enabling them to provide appropriate customer service strategies.

[0631] Specific example

[0632] For example, if a customer in a bookstore appears relaxed, the server can instruct staff to provide that customer with a space to browse books at their leisure. Based on this information, staff can also offer coffee or tea, suggesting an even more relaxing environment.

[0633] Example of a prompt

[0634] "Please advise us on how to analyze customer emotional data within the department store and provide them with the best possible service. In particular, please explain in detail how to tailor suggestions to different situations, such as when customers are busy or relaxed."

[0635] In this way, the present invention realizes a system that can instantly analyze customer emotions and provide appropriate services accordingly.

[0636] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0637] Step 1:

[0638] The device acquires the user's voice and facial expression data. At this stage, it uses its built-in camera and microphone to collect data in real time and save it as a local dataset. The input at this time is voice and video data, and the output is in an encrypted data format.

[0639] Step 2:

[0640] The device sends the collected data to a cloud server. The data is protected using AES encryption technology and transmitted via a secure communication protocol. The input here is an encrypted dataset, and the output is a secure data transfer to the cloud.

[0641] Step 3:

[0642] The server receives data sent to the cloud and analyzes it using machine learning algorithms. It accurately identifies the user's emotional state from voice tone and facial expressions. The input for this step is decrypted data, and the output is the analysis result indicating the user's emotional state.

[0643] Step 4:

[0644] The server generates an optimal customer service policy based on the analysis results, tailored to the customer's emotional state. This policy is then communicated to the staff at the physical store. In this step, the system derives what kind of service should be provided based on the emotional analysis results and sends these instructions to the staff. The input is the emotional analysis results, and the output is the notification of the customer service policy.

[0645] Step 5:

[0646] After a user receives service at a store, staff collect their feedback and send it to a server. This user feedback is then used to improve future suggestions. The input to this step is the user's feedback on the service provided at the store, and the output is the feedback being reflected on the server.

[0647] This processing flow enables personalized services that respond to the user's emotional state.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] [Fourth Embodiment]

[0652] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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".

[0665] This invention realizes a private concierge system to improve the user's daily life. It is implemented in the following manner to generate and notify optimal suggestions based on user information.

[0666] Data collection and transmission

[0667] Device: The user's device automatically collects location information, calendar events, search history, health-related data, and more. This data is encrypted on the device and then securely transmitted to a cloud server.

[0668] Data storage and analysis

[0669] Server: Received data is stored in a highly secure environment. Here, the data is standardized in format and prepared for analysis. The server uses machine learning algorithms to analyze user behavior patterns and identify hobbies and health status.

[0670] Proposal generation and notification

[0671] Server: Based on the analysis results, it generates activity suggestions optimized for the user's needs and lifestyle. For example, if the user needs to refresh themselves, it might recommend a moderate walking route or suggest simple exercises to improve concentration.

[0672] Device: The suggestion will be notified to the user's device. The suggestion will include details to encourage action (e.g., time required, benefits, calories burned, etc.).

[0673] As a concrete example, consider a scenario where a user has recently been searching for a lot of health-related content. Based on this information, the server recommends nearby fitness events for the user's days off. This suggestion is notified to the user's device, allowing them to review the details and make a quick decision. Therefore, the present invention is an effective system that provides individually optimized suggestions to enrich the user's life.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The device collects the user's location information, calendar events, browser search history, and health-related data. The data is encrypted in real time and sent to a cloud server in a secure state.

[0677] Step 2:

[0678] The server securely stores the received data in the database. The stored data is then organized and converted into a format suitable for analysis. Data cleaning is also performed at this stage to remove unnecessary information.

[0679] Step 3:

[0680] The server uses machine learning algorithms to analyze the user's behavior patterns, hobbies, and health status. This allows it to identify what activities the user prefers and when suggestions would be most effective.

[0681] Step 4:

[0682] Based on the analysis results, the server suggests activities that are suitable for the user's lifestyle. The suggestions are generated in a way that is optimized for the user, taking into account factors such as budget, time required, and calories burned.

[0683] Step 5:

[0684] The device notifies the user of suggestions received from the server. The notification includes detailed information about the suggested activity (such as the time required, benefits, and relevant links).

[0685] Step 6:

[0686] The user enters feedback into the device after implementing the provided suggestion. This feedback includes satisfaction levels and opinions on the suggestion itself.

[0687] Step 7:

[0688] The server collects and analyzes user feedback, accumulating data for future suggestions. This feedback is used to further improve the accuracy of the suggestions.

[0689] (Example 1)

[0690] 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".

[0691] In modern society, efficiently providing suggestions tailored to individual lifestyles and needs is crucial for optimizing users' time and resources. However, effectively analyzing the vast amounts of data users obtain from numerous sources and deriving appropriate suggestions is not easy. Furthermore, utilizing data without compromising privacy is another challenge. Against this backdrop, there is a need for a system that automatically generates optimal suggestions based on individual user behavioral characteristics, while ensuring user privacy and providing highly accurate suggestions.

[0692] 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.

[0693] In this invention, the server includes means for collecting user activity information, encrypting it, and transmitting it to the cloud; means for securely storing and preparing the received information in a unified format; means for analyzing the user's behavioral characteristics using machine learning techniques; means for using a generative AI model that generates optimized suggestions based on the analysis results; and means for notifying the user's device of the generated suggestions. This makes it possible to automatically generate and notify each individual user of the optimal suggestions tailored to their needs while protecting their privacy.

[0694] "User activity information" refers to data that shows an individual's activity history and status, such as location information, calendar events, search history, and health-related data.

[0695] "Encryption" is the process of ensuring the confidentiality of information by transforming data into a form that cannot be easily understood by third parties.

[0696] "Sending to the cloud" refers to transferring data to a cloud computing environment via the internet for processing and storage.

[0697] A "unified format" refers to converting different data formats into a consistent, standardized format, thereby enabling consistent analysis and processing.

[0698] "Machine learning techniques" are technologies that use algorithms to learn patterns and rules from large amounts of data to perform data analysis and prediction.

[0699] "User behavioral characteristics" refer to the unique properties of a user's behavior, such as their behavioral patterns, habits, interests, and concerns.

[0700] A "generative AI model" is a model equipped with an algorithm that uses artificial intelligence technology to analyze data and automatically generate new suggestions and content.

[0701] "Notifying a suggestion" means communicating information to the user's device in real time or in a timely manner so that the user can take action based on that information.

[0702] This invention provides a private concierge system for optimizing the user's daily life. This system automatically collects and analyzes the user's activity information and provides individually optimized suggestions to improve their daily life.

[0703] Data collection and transmission

[0704] Device: The user's device, such as a smartphone or tablet, automatically collects location information, calendar events, search history, and health-related data. This data is securely processed on the device using advanced encryption technology (e.g., AES encryption). Once encrypted, the data is transmitted to the cloud server via the internet using the SSL / TLS protocol.

[0705] Data storage and analysis

[0706] Server: Servers in the cloud store received data in a secure database. Since data may be transmitted in different formats, it is converted to a unified format. For example, time data is standardized to ISO 8601 format. The unified data is then analyzed using machine learning algorithms to extract user behavioral characteristics and preferences. Machine learning frameworks used include TensorFlow and PyTorch.

[0707] Proposal generation and notification

[0708] Server: Based on accumulated data analysis, it uses generative AI models (e.g., GPT-3 or similar generative models) to generate activity suggestions optimized for the user. This process determines specific activities based on the user's past behavior data and current state. The generated suggestions are sent as push notifications to the user's device.

[0709] Device: The device receiving the suggestion will display a real-time notification to the user, allowing them to view the details of the suggestion. The user can then review the presented information and choose their next course of action.

[0710] Specific example

[0711] For example, consider a situation where a user has recently been searching for a lot of health-related information. The server uses this information to suggest local fitness events that the user can attend this weekend. This suggestion is notified to the user's device, allowing the user to quickly review the event details and decide whether to participate.

[0712] Example of a prompt

[0713] Based on user data, the following prompt is input into the AI ​​model: "Consider recent search history and calendar appointments, and suggest the most suitable fitness activities."

[0714] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0715] Step 1:

[0716] Data collection

[0717] Terminal: The user's device automatically collects location information, calendar events, search history, and health-related data. Inputs are various types of data stored on the device, which are in various formats. Specifically, location information is obtained from the smartphone's sensors, and data from calendar and health apps is accessed using an Application Programming Interface (API). Outputs are encrypted data for use in other processing steps.

[0718] Step 2:

[0719] Data transmission

[0720] Terminal: Sends encrypted user data to the cloud server. The input is the data encrypted in step 1, which is sent using a secure communication protocol (SSL / TLS). Specifically, it calls an API for data transmission, establishes a secure communication channel, and then packets the data and sends it. The output is the data that has been successfully transferred to the cloud server.

[0721] Step 3:

[0722] Data storage and preparation

[0723] Server: Receives data and stores it in a cloud-based database. The input is encrypted data sent from the terminal, which is then converted for format standardization. Specifically, a data format conversion tool is used to standardize date and time formats and perform unit conversions. The output is a database record formatted in a unified format.

[0724] Step 4:

[0725] Data Analysis

[0726] Server: Analyzes data using machine learning algorithms to identify user behavior patterns. Input is data in a unified format, which is then fed into the analysis model. Specifically, it uses frameworks such as TensorFlow or PyTorch to train a neural network and learn user behavior tendencies. Output is information about user behavior patterns and interest characteristics.

[0727] Step 5:

[0728] Proposal generation

[0729] Server: Automatically generates suggestions using a generative AI model based on user behavior patterns. The input is the output of data analysis, which creates prompt sentences that instruct the generative AI model. Specifically, it prompts the generative AI model with these sentences and generates text containing relevant information. The output is activity suggestions optimized for user needs.

[0730] Step 6:

[0731] proposal notification

[0732] Terminal: The system delivers suggestions to the user's device via push notifications. The input is suggestions sent from the server, and a notification message is created using the notification API. Specifically, the system displays the suggestion information in the user interface, making it easy to view the details. The output consists of the notification received by the user and the user's reaction to the notification content.

[0733] (Application Example 1)

[0734] 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".

[0735] A challenge faced by modern consumers is the difficulty in selecting the most suitable diet and supply methods from a vast array of options, based on their preferences and health conditions. Furthermore, there is the problem of not being able to quickly obtain dietary suggestions and supply methods that fit their lifestyle. Current systems fail to effectively utilize user information to provide individually optimized suggestions, leading to decreased user satisfaction.

[0736] 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.

[0737] In this invention, the server includes means for acquiring user information and transmitting it to an information processing device, means for securely storing and analyzing the acquired information, means for making optimal suggestions to the user based on the analysis results, means for suggesting and notifying the user of meal options based on their preferences, and means for providing supply means based on the suggestions. As a result, the user can receive meal suggestions that match their individual preferences and health needs, and obtain prompt supply means based on those suggestions.

[0738] A "user" refers to an individual or group that uses the system, and is the subject of information acquisition and proposals.

[0739] An "information processing device" refers to a computer system or server used to receive and process acquired data.

[0740] "Information" refers to all data, including user preferences, behavioral history, health status, and related data.

[0741] "Storage" refers to the secure accumulation of information in databases or storage devices, making it available for later use.

[0742] "Analysis" refers to the process of interpreting and understanding user preferences and behavioral patterns using acquired information.

[0743] A "suggestion" refers to a list of activities or options provided to the user based on the analysis results, and is intended to improve user convenience.

[0744] "Notification" refers to a message or alert function used to inform users of the content of a proposal.

[0745] "Means of supply" refers to the methods and mechanisms for quickly providing services or products to users.

[0746] To realize this invention, a private concierge system will be constructed that primarily utilizes a server and user terminals.

[0747] Program Overview

[0748] The server securely collects information from the user's device and receives various types of data by sending it to the cloud. For example, location information, meal history, and health information are data collected by the user using smartphones or wearable devices. This information is received in an encrypted form by the server, which is an information processing device, and the data is organized using pandas.

[0749] The organized data is analyzed using machine learning algorithms such as scikit-learn. The purpose of the analysis is to identify user preferences, health status, and behavioral patterns, and to form optimal suggestions for the user. The generated suggestions are sent to the user's device to prompt action, such as suggesting nearby suppliers of nutritious meals or recommending the use of delivery services.

[0750] For example, if a user has just started jogging, the system could suggest restaurants that offer high-protein meals. The server would then notify the user of this suggestion on their smartphone and provide links for making reservations or ordering delivery.

[0751] An example of a prompt message might be, "I've started jogging, so I'm looking for a restaurant nearby that serves high-protein meals. Please also let me know if there are any delivery options available."

[0752] Thus, the present invention aims to improve the quality of life by enabling the provision of services tailored to the individual needs and circumstances of users.

[0753] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0754] Step 1:

[0755] The device automatically collects information such as the user's location, activity history, eating history, and health status. This information is encrypted on the device and sent to the information processing unit. The input consists of various pieces of information collected from the device, and the output is sent to the receiving server as encrypted information.

[0756] Step 2:

[0757] The server receives encrypted information and securely stores it in a database. During this process, the information is formatted using pandas and processed into a form suitable for analysis. The input is encrypted information, and the output is formatted data.

[0758] Step 3:

[0759] The server analyzes the prepared data using scikit-learn. Machine learning algorithms are employed to identify user preferences, behavioral patterns, and health status. The input is the prepared data, and the output is the analysis results.

[0760] Step 4:

[0761] The server generates optimal suggestions for the user based on the analysis results. For example, it provides meal plans tailored to the user's health condition and information about nearby suppliers. The input is the analysis results, and the output is the generated suggestions.

[0762] Step 5:

[0763] The server notifies the user's device of the generated suggestions. These suggestions include links and detailed information to encourage action. The input is the generated suggestions, and the output is the notification sent to the user's device.

[0764] Step 6:

[0765] The user acts based on the suggestions they receive. They can book a restaurant or order delivery according to the suggestions. The input is the suggestions notified to the device, and the output is the user's actions.

[0766] 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.

[0767] This invention provides a private concierge system that takes into account the user's emotional state. Specifically, it uses an emotion engine to generate and adjust optimal suggestions based on user data.

[0768] Data collection and emotion recognition

[0769] Device: The user's device collects general location information, calendar events, browser search history, and health-related data, as well as emotional data through analysis of the user's voice tone and facial expressions. This data is encrypted and sent to a highly secure cloud server.

[0770] Data storage and analysis

[0771] Server: Securely stores received data, including emotional data. Machine learning algorithms are used to analyze user behavior patterns, preferences, health status, and emotional state. This analysis provides foundational data for tailoring responses to user needs.

[0772] Proposal generation and emotion-based adjustment

[0773] Server: Based on the analysis results, it generates activity suggestions for the user. Utilizing data from the emotion engine, it adjusts the content to match the user's emotions; for example, it provides relaxation-focused suggestions when the user needs relaxation, and exercise-based suggestions when the user desires active activities.

[0774] Device: Notifies the user of the suggested content on their device. The suggested content includes detailed information, such as sentiment-based benefits and reasons for recommendation, to support action.

[0775] As a concrete example, consider a case where the emotion engine determines that a user is experiencing stress in their busy daily life. In this case, the server suggests to the user that they book a nearby spa or relaxation room. Furthermore, by collecting the user's emotional feedback and incorporating it into future suggestions, more accurate and personalized suggestions can be provided. Thus, this invention is a system that enables the provision of sophisticated services tailored to the user's emotional state, thereby improving their quality of life.

[0776] The following describes the processing flow.

[0777] Step 1:

[0778] The device collects the user's location information, calendar events, search history, and emotional data (voice tone, emotional state based on facial expression analysis). This data is encrypted and sent to a cloud server.

[0779] Step 2:

[0780] The server stores received data in a highly secure environment and standardizes the data format. The stored data includes both behavioral and emotional data.

[0781] Step 3:

[0782] The server utilizes machine learning algorithms to analyze the user's behavioral tendencies, hobbies, health status, and emotional state. The emotion engine evaluates the user's emotional changes in real time, forming the basis for ideal suggestions.

[0783] Step 4:

[0784] The server generates activity suggestions based on the analysis results. Depending on the emotional data, the suggestions flexibly respond to the user's immediate needs, such as prioritizing relaxation or activity.

[0785] Step 5:

[0786] The device notifies the user of the generated suggestions. The notification includes details of the suggestion (estimated time, reason for recommendation, relevant links, etc.) and sentiment-based benefits.

[0787] Step 6:

[0788] The user selects and performs a suggested activity and enters feedback about the results into the device. This feedback includes changes in emotions and satisfaction levels.

[0789] Step 7:

[0790] The server collects and analyzes user feedback, further optimizing the system for future suggestions. This feedback loop continuously improves the quality of the services provided.

[0791] (Example 2)

[0792] 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".

[0793] In modern society, accurately understanding the stress and emotional fluctuations experienced by individual users and providing personalized suggestions based on that understanding is not easy. Furthermore, conventional systems have faced the challenge of not being able to accurately understand users' emotions and provide the most suitable suggestions in response to them.

[0794] 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.

[0795] In this invention, the server includes means for collecting multiple sensory data, including voice and facial expression analysis, to identify the user's emotional state; means for encrypting the collected data and transmitting it to a data processing device; and means for the data processing device to analyze the user's behavior and emotional patterns using a machine learning algorithm. This enables sophisticated suggestions based on the user's emotional state.

[0796] "Emotional state" refers to the degree of stress or relaxation a user experiences, as well as their psychological and mental state, including emotions such as joy, anger, sadness, and happiness.

[0797] "Voice analysis" refers to technology that analyzes the tone, pitch, and speed of a user's voice to infer their emotions and physical condition.

[0798] "Facial expression analysis" refers to a technology that reads a user's facial expressions from image data and estimates their emotions based on the changes in those expressions.

[0799] "Sensory data" refers to a variety of data acquired to understand the user's state and situation, such as voice, facial expressions, location information, schedule, and browser history.

[0800] "Encryption" refers to the technology of transforming information using specific algorithms in order to protect data from unauthorized access and tampering from external sources.

[0801] A "data processing system" refers to the entire system that analyzes, stores, and processes received data, and cloud servers are the primary example of this.

[0802] "Machine learning algorithms" refer to computer technologies that recognize patterns based on historical data and predict future changes.

[0803] "Generating suggestions" refers to the process of devising and presenting recommended activities and services based on the user's emotional state and behavioral patterns.

[0804] "Feedback" refers to information that shows evaluations and reactions to suggestions received from users, and is used to improve the accuracy of future suggestions.

[0805] To implement this invention, a user terminal and a server as a data processing device are required. The terminal is equipped with a camera and microphone for capturing voice and facial expressions, and also utilizes GPS functionality for obtaining location information and a calendar application for managing schedules.

[0806] The user's device collects sensory data, encrypts it, and sends it to a server in the cloud. The commonly used AES encryption method is effective. Upon receiving various data, the server stores it in a database and analyzes it using machine learning algorithms. These algorithms utilize models built in programming languages ​​such as Python.

[0807] Server analysis reveals user behavior patterns and emotional trends. The collected data is particularly used to determine situations where users are likely to experience stress or where they need relaxation.

[0808] The generative AI model suggests the most suitable activity for the user based on the analysis results. This suggestion is customized to take into account the user's emotional state; for example, if the user needs relaxation, it will use a prompt such as "Suggest an activity suitable for relaxation."

[0809] Finally, the server notifies the user's terminal of the generated suggestions. The suggestions include emotionally-based benefits and reasons for recommendation, which the user reviews and provides further feedback. This feedback is reflected in future suggestions, contributing to the improvement of the system's accuracy. For example, if the user is feeling stressed, a suggestion might be made to book a nearby relaxation facility. In this way, this invention makes it possible to improve the user's quality of life.

[0810] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0811] Step 1:

[0812] The device collects the user's voice and facial expressions through its camera and microphone. Input consists of audio and image data, which are processed in real time. Specifically, the audio data is analyzed for tone and pitch using frequency analysis, and the image data is analyzed for facial features using facial recognition technology. The output is encoded data indicating the user's emotional state.

[0813] Step 2:

[0814] The device combines the acquired emotion encoding data with location information, calendar events, and browser history, and encrypts them all at once. The input consists of emotion data, location information, and schedule information. A secure communication protocol is used for encryption to protect the data. The output is an encrypted dataset, which is sent to a server in the cloud.

[0815] Step 3:

[0816] The server receives an encrypted dataset and first decrypts it to retrieve each data point. The input is encrypted data, and the output is each data point in plaintext. Specifically, the server uses machine learning algorithms, including decision trees and support vector machines, to analyze the user's long-term behavioral patterns. The output is an analysis showing the user's behavioral patterns and emotional tendencies.

[0817] Step 4:

[0818] The server uses a generative AI model based on the analysis results to generate activity suggestions best suited to the user's emotional state. The input is the analysis results, and the prompt "Suggest activities to promote user relaxation" is used. Specifically, the AI ​​model generates relaxation and active options based on the obtained data and determines the priority of the suggestions. The output is a list of suggestions.

[0819] Step 5:

[0820] The server notifies the user's device of the generated list of suggestions. The input is the list of suggestions and the notification format, and the output is the notification displayed on the user's device. Specifically, push notifications are used, allowing the user to view the suggestions and their benefits on the screen.

[0821] Step 6:

[0822] Users provide feedback on suggestions via their devices. Specifically, feedback is entered concisely as text or through selection options. The input is user feedback, and the output is feedback data sent to the server.

[0823] Step 7:

[0824] The server analyzes the collected feedback data and incorporates it into the next proposal. The input is the feedback data, and new learning occurs through the analysis. This contributes to improving the generative AI model, enabling more appropriate and refined proposals in subsequent steps. The output is the refined analysis algorithm and the updated proposal model.

[0825] (Application Example 2)

[0826] 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".

[0827] In today's commercial environment, improving the customer experience in physical stores requires providing personalized services instantly that respond to the customer's emotional state. However, traditional systems lacked the means to quickly and accurately analyze customer emotions and communicate the resulting service policies to staff. As a result, improving customer satisfaction was difficult.

[0828] 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.

[0829] In this invention, the server includes means for acquiring user data and transmitting it to the cloud, means for securely storing and analyzing the acquired data, means for proposing the most suitable activity to the user based on the analysis results, means for notifying the user of the proposal on the user's display device, means for identifying the user's emotions through data analysis and providing activities adjusted based on the identified emotional state, and means for notifying in-store sales staff of the emotional state and proposing appropriate customer service policies before presenting products. This enables highly accurate customer service based on the customer's emotions.

[0830] "User data" is a general term for information related to a user, including personal information, behavioral history, health status, voice tone, facial expressions, and other such information.

[0831] "Cloud" refers to a server environment that allows information to be stored and processed over the internet.

[0832] "Emotional state" refers to the user's psychological state and mood, and is information inferred from their voice, facial expressions, and behavior.

[0833] A "display device" is a terminal used to visually present information, and includes devices such as smart glasses and smartphones.

[0834] "Activities adjusted based on emotional state" refers to suggestions and services optimized according to the user's emotional state.

[0835] A "customer service policy" is a set of guidelines for staff on how to interact and communicate with customers.

[0836] This invention is a system that personalizes services and suggestions in physical stores based on the user's emotional state. A specific embodiment is shown below.

[0837] Hardware and software configuration

[0838] The device could be, for example, smart glasses or a smartphone. This would allow for the continuous collection of user data, which would then be sent to the cloud. User voice tone and facial expression analysis would be performed using the device's built-in camera and microphone. The server would be built on the cloud, using AWS Rekognition or Microsoft Azure Face API as APIs for emotion recognition. AES encryption technology would be used for data encryption.

[0839] Data acquisition and analysis

[0840] The device collects data in real time that reflects the user's behavior and emotional state. This includes voice patterns and facial expression data. This data is transmitted to a cloud server in a secure manner. On the cloud, machine learning algorithms are used to analyze the user's emotional state and behavioral patterns in detail.

[0841] Service provision and coordination

[0842] Based on the analysis results, the server suggests the most suitable activities and services for the user. These suggestions are adjusted according to the user's emotions; for example, a relaxed user might be offered a quiet environment, while an excited user might be matched with more energetic service. Furthermore, the server notifies the store staff of the user's emotional state and the suggested services, enabling them to provide appropriate customer service strategies.

[0843] Specific example

[0844] For example, if a customer in a bookstore appears relaxed, the server can instruct staff to provide that customer with a space to browse books at their leisure. Based on this information, staff can also offer coffee or tea, suggesting an even more relaxing environment.

[0845] Example of a prompt

[0846] "Please advise us on how to analyze customer emotional data within the department store and provide them with the best possible service. In particular, please explain in detail how to tailor suggestions to different situations, such as when customers are busy or relaxed."

[0847] In this way, the present invention realizes a system that can instantly analyze customer emotions and provide appropriate services accordingly.

[0848] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0849] Step 1:

[0850] The device acquires the user's voice and facial expression data. At this stage, it uses its built-in camera and microphone to collect data in real time and save it as a local dataset. The input at this time is voice and video data, and the output is in an encrypted data format.

[0851] Step 2:

[0852] The device sends the collected data to a cloud server. The data is protected using AES encryption technology and transmitted via a secure communication protocol. The input here is an encrypted dataset, and the output is a secure data transfer to the cloud.

[0853] Step 3:

[0854] The server receives data sent to the cloud and analyzes it using machine learning algorithms. It accurately identifies the user's emotional state from voice tone and facial expressions. The input for this step is decrypted data, and the output is the analysis result indicating the user's emotional state.

[0855] Step 4:

[0856] The server generates an optimal customer service policy based on the analysis results, tailored to the customer's emotional state. This policy is then communicated to the staff at the physical store. In this step, the system derives what kind of service should be provided based on the emotional analysis results and sends these instructions to the staff. The input is the emotional analysis results, and the output is the notification of the customer service policy.

[0857] Step 5:

[0858] After a user receives service at a store, staff collect their feedback and send it to a server. This user feedback is then used to improve future suggestions. The input to this step is the user's feedback on the service provided at the store, and the output is the feedback being reflected on the server.

[0859] This processing flow enables personalized services that respond to the user's emotional state.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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."

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] The following is further disclosed regarding the embodiments described above.

[0882] (Claim 1)

[0883] A means of acquiring user data and sending it to the cloud,

[0884] Means for securely storing and analyzing acquired data,

[0885] A means of proposing the most suitable activities to the user based on the analysis results,

[0886] A means of notifying the user of the proposal on their device,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, comprising means of using a machine learning algorithm to identify user behavior patterns and preferences.

[0890] (Claim 3)

[0891] The system according to claim 1, comprising means for collecting user feedback on a proposal and optimizing the next proposal.

[0892] "Example 1"

[0893] (Claim 1)

[0894] A means of collecting user activity information, encrypting it, and sending it to the cloud,

[0895] A means of securely storing and preparing received information in a unified format,

[0896] A means of analyzing user behavioral characteristics using machine learning techniques,

[0897] A method using a generative AI model that generates optimized proposals based on analysis results,

[0898] A means of notifying the user's device of the generated proposal,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which includes means for predicting trends based on the user's past activity history and individually optimizing the suggested content.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for collecting user responses regarding a proposal and incorporating them into subsequent proposal improvements.

[0904] "Application Example 1"

[0905] (Claim 1)

[0906] A means for acquiring user information and transmitting it to an information processing device,

[0907] Means for securely storing and analyzing the acquired information,

[0908] A means of providing the best possible suggestions to users based on the analysis results,

[0909] A means of suggesting and notifying users of meal options based on their preferences,

[0910] Means of providing supply means based on the proposal,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, comprising means of using a machine learning algorithm to identify user behavior patterns and preferences.

[0914] (Claim 3)

[0915] The system according to claim 1, comprising means for collecting user feedback on a proposal and optimizing the next proposal.

[0916] "Example 2 of combining an emotion engine"

[0917] (Claim 1)

[0918] A means for collecting multiple sensory data, including voice and facial expression analysis, in order to identify the user's emotional state,

[0919] A means for encrypting the collected data and transmitting it to a data processing device,

[0920] A means for analyzing user behavior and emotional patterns using machine learning algorithms in a data processing device,

[0921] A means for generating suggestions that take into account the user's emotional state based on the analysis results, and for adjusting the content of those suggestions,

[0922] A means of notifying the user's device of the generated suggestions,

[0923] A means of collecting user feedback and using it for future proposals,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, comprising means for customizing suggestions using a generated AI model based on the user's emotional state in a data processing device.

[0927] (Claim 3)

[0928] The system according to claim 1, comprising means for adjusting prompt statements and improving the accuracy of suggested content using feedback obtained from users.

[0929] "Application example 2 when combining with an emotional engine"

[0930] (Claim 1)

[0931] A means of acquiring user data and sending it to the cloud,

[0932] Means for securely storing and analyzing acquired data,

[0933] A means of proposing the most suitable activities to the user based on the analysis results,

[0934] A means of notifying the user of the proposal on their display device,

[0935] A means of identifying users' emotions through data analysis and providing activities adjusted based on the identified emotional state,

[0936] Before making recommendations, a means of informing in-store sales staff of the customer's emotional state and suggesting appropriate customer service strategies,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, comprising means of using a machine learning algorithm to identify user behavioral trends.

[0940] (Claim 3)

[0941] The system according to claim 1, comprising means for obtaining user responses to a proposal and optimizing the next proposal. [Explanation of Symbols]

[0942] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring user data and sending it to the cloud, Means for securely storing and analyzing acquired data, A means of proposing the most suitable activities to the user based on the analysis results, A means of notifying the user of the proposal on their device, A system that includes this.

2. The system according to claim 1, comprising means of using a machine learning algorithm to identify user behavior patterns and preferences.

3. The system according to claim 1, comprising means for collecting user feedback on a proposal and optimizing the next proposal.

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