system

Abstract

We provide the system. [Solution] A means of obtaining activity data from the user's terminal, A means for analyzing the acquired data and extracting behavioral trends, A means for generating proposals based on the extracted behavioral trends, A means for notifying the user terminal of the generated proposal, A means of receiving user responses and automatically performing related tasks, A method for suggesting departure times and event information using the user's current location information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information society, individuals are faced with a large amount of information every day, but there are not enough means to effectively and efficiently manage and utilize them. As a result, users may waste time and make decision-making mistakes in both private and business activities. The problem to be solved by the present invention is to reduce the burden on users by integrating this information, predicting and optimizing user behavior, providing efficient time management and multi-faceted proposals.

Means for Solving the Problems

[0005] This invention provides a means for collecting activity information obtained from a user terminal, analyzing it, and extracting the user's behavioral patterns. It then includes means for generating suggestions based on the extracted behavioral patterns, and means for notifying the user terminal of the generated suggestions and automatically executing related tasks upon receiving the user's response. Furthermore, it implements a function for integrating and managing the user's private and business information, enabling the system to propose optimal time management using the user's location and schedule information. Through these means, users can accept efficient time management and unexpected, multifaceted suggestions, thereby improving the quality of their lives and work.

[0006] A "user terminal" is an electronic device used by a user, which is responsible for acquiring and notifying data.

[0007] "Activity information" refers to data related to a user's private and business life, including information such as calendar information, location information, and message history.

[0008] "Behavioral patterns" refer to data that indicates specific tendencies or habits derived from a user's past behavior.

[0009] A "proposal" is information that presents actions or options generated based on an analysis of behavioral patterns.

[0010] "Related tasks" refer to the specific actions or tasks that should be carried out based on the generated proposals.

[0011] "Private information" refers to information related to a user's personal activities, including personal schedules, hobbies, and preferences.

[0012] "Business information" refers to information related to the user's business activities, contacts, and project management.

[0013] "Time management" refers to the process of efficiently scheduling and allocating time. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying out the Invention

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

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

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

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

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an AI concierge system designed to streamline users' lives. It collects activity information from user devices such as smartphones and PCs and processes it on a server. The server analyzes the acquired information to extract user behavior patterns and generates suggestions based on the analysis results. These suggestions are then sent to the user's device, enabling the user to take corresponding actions.

[0036] In this embodiment, the terminal first acquires activity information such as calendar, email, message history, and GPS location data with the user's consent. The terminal transmits this information to a server via the internet using a secure protocol. The server stores the received information in a database and analyzes the user's behavior patterns using machine learning algorithms. For example, if a user has a habit of performing a specific activity on a specific day of the week, this tendency can be detected.

[0037] The server generates suggestions tailored to the user's needs and schedule based on behavioral pattern analysis. For example, if a user has a meeting scheduled, it will notify them of the optimal departure time to ensure they arrive on time. It also suggests new restaurants and events, providing users with new options in their lives.

[0038] The terminal displays suggestions from the server as notifications on the user's screen. The user can receive these notifications and either accept the suggestions or choose an alternative. If the user accepts the suggestions, the server automatically executes the associated tasks. This allows users to efficiently manage their personal and business activities without any hassle.

[0039] As a concrete example, let's assume a user attends a regular meeting on Friday afternoons. This system takes traffic congestion information into account and notifies the user of the optimal departure time for the meeting. Furthermore, it supports pre-meeting preparation by automatically generating reference materials and agendas on the server and providing them to the user.

[0040] Thus, the present invention is a system that provides valuable support to users by utilizing user information and assisting them in decision-making in both their personal and business lives.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device collects activity information with the user's consent. Specifically, this includes calendar information, email exchanges, and GPS location data. The device periodically scans this data to obtain the latest information.

[0044] Step 2:

[0045] The device sends the activity information it collects to the server. The transmission is secure, and the data is encrypted before being transferred to the server.

[0046] Step 3:

[0047] The server stores the activity information it receives in a database. During this process, the data is classified into private and business information.

[0048] Step 4:

[0049] The server analyzes stored data using machine learning algorithms. The purpose of the analysis is to extract user behavior patterns and detect recurring tasks performed at specific times of day or on specific days of the week.

[0050] Step 5:

[0051] The server generates suggestions for the user based on their behavioral patterns. These might include, for example, suggested meeting departure times or event suggestions based on the user's interests.

[0052] Step 6:

[0053] The server sends the generated suggestions to the terminal.

[0054] Step 7:

[0055] The device notifies the user of the suggestion. The notification appears on the user's screen and functions as an alert.

[0056] Step 8:

[0057] The user reviews the proposal and chooses to accept or reject it. Once the user makes a selection, the result is sent back to the server via the device.

[0058] Step 9:

[0059] The server executes relevant tasks based on user selections. These tasks include things like automatically generating documents and scheduling meetings. This allows users to work more efficiently.

[0060] (Example 1)

[0061] 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."

[0062] In today's digital society, users need to manage a wide range of activity information, which can make efficient time management and decision-making difficult. Furthermore, separating personal and business information can lead to overlooking important information or inconvenience. Additionally, there is a need to utilize location and itinerary information to make optimal time adjustments and suggestions, but achieving this with a single system is technically challenging.

[0063] 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.

[0064] In this invention, the server includes means for acquiring activity information from a communication device, means for storing the acquired activity information in a storage device, and means for analyzing the stored activity information using a machine learning algorithm and extracting behavioral characteristics. This enables users to comprehensively manage all activity information, allowing for efficient time management and appropriate decision-making. Furthermore, by unifying personal and business information, and utilizing location information and itinerary management information, users can receive highly accurate suggestions, leading to smoother daily operations.

[0065] "Communication equipment" refers to devices used by users to send and receive information, and includes mobile terminals and personal computers.

[0066] "Activity information" refers to data related to a user's behavior and status, including location information, schedules, message history, and other information that reflects the user's life and business activities.

[0067] A "storage device" is a system for storing digital data, such as cloud storage or database systems, which are media for continuous data management.

[0068] A "machine learning algorithm" is a computational method for recognizing data patterns and making predictions. It builds models based on large amounts of data and provides appropriate outputs for new inputs.

[0069] "Behavioral characteristics" are consistent patterns and tendencies derived from a user's past behavior, such as a habit of repeating the same activity on a specific day of the week, and are predictable features.

[0070] A "suggestion" is a recommendation or action plan provided to the user based on analyzed data, and is information that helps in making choices and decisions about actions.

[0071] A "challenge" is a problem or goal that users must solve in their daily lives or business, and it relates to efficient information management and time management.

[0072] This invention is a system that acquires activity information from a communication device used by a user, transmits that information to a server for analysis, and provides the user with the most suitable suggestions. Specific embodiments are described below.

[0073] With the user's consent, the device collects activity data such as calendar entries, emails, message history, and GPS data. This includes smartphones and personal computers. The collected data is then transmitted to a server via the internet. Security protocols such as HTTPS are used for communication.

[0074] The server stores the received activity information in a database and analyzes the data using machine learning algorithms (e.g., TENSORFLOW® and PyTorch) to extract user behavior patterns and characteristics. This analysis reveals specific user behavior patterns and past selection history.

[0075] Based on the analysis results, the server runs a generative AI model (e.g., GPT) to automatically generate suggestions tailored to the user's needs. This includes notifications of departure times based on the user's schedule, as well as recommendations for new locations and events. For example, a prompt could use the text, "Please tell me the best departure time for tomorrow's meeting."

[0076] The device notifies the user of this proposal and allows the user to review it. The user receives the proposal and can choose to accept or reject it. If accepted, the server automatically performs the relevant tasks (e.g., adding events to the calendar or setting reminders). This allows the user to efficiently manage their daily activities and make decisions more quickly.

[0077] This system is designed to make users' lives more efficient and convenient, centralizing personal information management and supporting choices in both personal and business matters.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device collects activity information with the user's consent. Specifically, it retrieves appointments from the calendar, collects communication-related information from email and message history, and records location data using GPS. It uses the user's digital data as input and compiles the activity information into a data package as output. This data package is then sent to a server.

[0081] Step 2:

[0082] The device sends the activity information it collects to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request and sends data to the server in JSON format. The input is a data package compiled by the device, and the server receives the data as output via a reliable communication channel.

[0083] Step 3:

[0084] The server records the activity information it receives into a database. Specifically, it uses a database management system (such as MySQL® or MongoDB) to structure and store the information. The input is activity information data in JSON format, and the output is information records stored in the database.

[0085] Step 4:

[0086] The server analyzes stored activity information. Specifically, it uses machine learning algorithms (e.g., TensorFlow or PyTorch) to extract user behavior patterns. The input is activity information stored in a database, and the output is a data model that represents the user's behavioral characteristics. This data model forms the basis of the proposal.

[0087] Step 5:

[0088] The server utilizes a generative AI model to create suggestions based on user needs. Specifically, user behavioral characteristic data is input into the generative AI model, and new suggestions are obtained as output. Prompt statements are used in suggestion generation. For example, using the prompt statement "Please tell me the best departure time for tomorrow's meeting," information tailored to the user's behavior is generated.

[0089] Step 6:

[0090] The device receives suggestions sent from the server and notifies the user. Specifically, it uses the OS notification service and displays the suggestions as pop-up messages on the user's screen. The input is suggestion data from the server, and the output is expressed in the form of a notification to the user.

[0091] Step 7:

[0092] The user reviews the proposal and chooses to accept or reject it. If the proposal is accepted, the server automatically executes the related tasks. Specific examples include adding an event to the calendar or setting a reminder. The input is the user's selection (accept or reject), and the output is the automatically completed tasks.

[0093] (Application Example 1)

[0094] 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."

[0095] In modern urban life, the sheer volume of information makes efficient time management difficult. Furthermore, the lack of optimal action suggestions based on location information in daily life often leads users to make inefficient decisions. To solve this problem, a system is needed that deeply understands user behavior patterns and provides appropriate suggestions based on real-time information.

[0096] 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.

[0097] In this invention, the server includes means for acquiring activity data from a user terminal, means for analyzing the acquired data to extract behavioral trends, and means for using the user's current location information to suggest departure times and event information. This enables users to efficiently manage their activities and optimize their daily decision-making.

[0098] A "user terminal" is a type of computing device used by a user, specifically a device for data input and communication.

[0099] "Activity data" refers to information about a user's behavior and schedule, and is data obtained from the user's device.

[0100] "Behavioral tendencies" refer to patterns and habits of behavior obtained by analyzing a user's past activity data.

[0101] A "suggestion" is information or advice generated by a system to encourage users to take action or to support their decision-making.

[0102] "User response" refers to the results of the user's choices or instructions in response to a proposal.

[0103] "Work" refers to specific tasks related to the user's duties and daily activities.

[0104] "Current location information" refers to data about the user's current location, including geographical coordinate information.

[0105] "Departure time" refers to the time a user should begin in order to travel to a specific activity or destination.

[0106] "Event information" refers to information about activities and events held on specific dates and times, and is intended to provide users with new options.

[0107] This invention is an AI concierge system designed to streamline users' lives. The system acquires and analyzes user activity data to extract user behavioral trends. The hardware used in this process includes a user terminal for collecting user activity data, such as a smartphone or personal computer. The software includes a machine learning algorithm for analysis. This algorithm runs on a server. The server stores and analyzes the collected data, gains insights, and then makes appropriate suggestions to the user.

[0108] This system helps users avoid traffic congestion by collecting their current location information and suggesting the optimal departure time based on the analysis results. It also provides new event information based on the user's interests, thereby opening up new experiences and options for the user. For example, if a user lives in Tokyo, the system can check traffic conditions and notify them of the optimal departure time. It can also provide information about new movies showing at nearby theaters.

[0109] By using generative AI models, more personalized suggestions tailored to user preferences become possible. An example of a prompt is, "Analyze the user's behavior patterns and suggest the optimal departure time and event information based on their schedule and location for the next day." This prompt is used to instruct the system on what kind of output it should generate.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] With the user's consent, the device acquires activity data such as calendar events, message history, and GPS location data. This data is input data to be sent to the server.

[0113] Step 2:

[0114] The server stores activity data received from terminals in a database using a secure protocol. This data forms the basis for analyzing behavioral trends.

[0115] Step 3:

[0116] The server analyzes the stored data using machine learning algorithms to extract user behavioral trends. Here, the input is past behavioral data, and the output is the analyzed behavioral patterns. In this step, the server identifies whether a user performs a specific action on a particular day.

[0117] Step 4:

[0118] The server generates suggestions regarding departure times and available events based on behavioral patterns and current location information. External data such as traffic information and weather forecasts are utilized in generating these suggestions. The input consists of analyzed behavioral patterns and external data, and the output is a list of suggestions.

[0119] Step 5:

[0120] The server notifies the terminal of the generated proposal. This notification becomes the user's input, and the user reviews the proposal and decides whether to accept it or not.

[0121] Step 6:

[0122] If the user accepts the proposal, the server automatically performs the associated tasks, including updating the schedule and sending navigation data. The input is the user's response, and the output is the result of the specific task performed.

[0123] These processes are continuously improved through the generative AI model, enabling the provision of optimal information to the user based on the prompt text.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is an AI concierge system that combines an emotion engine to recognize user emotions, collects and analyzes user activity information, and provides suggestions based on behavior and emotions.

[0126] First, the device collects activity information and emotion-related data with the user's consent. This emotion data is extracted from the user's voice tone, facial expressions, and keywords in text communications. The collected data is sent to a server via a secure protocol.

[0127] The server stores and analyzes received activity and sentiment data in a database. Machine learning algorithms and sentiment analysis techniques are used to extract user behavior and emotional patterns. This analysis allows for an understanding of how users tend to behave in different emotional states.

[0128] The server generates optimal suggestions for the user based on extracted behavioral and emotional patterns. These suggestions are presented in a way that is most relatable to the user's current emotional state. For example, if the user is feeling stressed, it will notify them of relaxation suggestions or schedule adjustments.

[0129] The device notifies the user of suggestions from the server. The notification method is adjusted according to the user's mood. For less urgent matters, a soft-toned chat notification is sent, while for more urgent matters, an alert sound is used. Once the user reviews the suggestion and chooses to accept or modify it, that information is sent back to the server.

[0130] The server automatically performs relevant tasks according to the user's selections. Emotion-based responses include playing music to promote relaxation and delivering information to alleviate tension.

[0131] For example, if a user is feeling nervous before an important presentation, this system uses an emotion engine to detect this and suggests ways to relax or take a break. It also assists the user by automatically generating necessary presentation materials and adjusting the timetable.

[0132] Thus, the present invention is a system that supports users' daily lives and business activities by utilizing user emotional and behavioral information to provide more personalized suggestions.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device collects activity information and emotion-related data with the user's consent. Emotion data is obtained through the user's voice recordings, facial recognition camera data, and emotion keywords in text messages.

[0136] Step 2:

[0137] The device sends activity and sentiment data it collects to the server. This transmission is encrypted and uses a privacy-protecting protocol.

[0138] Step 3:

[0139] The server saves the transmitted information to a database. The saved data will be used for future analysis and proposal generation.

[0140] Step 4:

[0141] The server uses machine learning algorithms and sentiment analysis engines to analyze the user's behavioral and emotional patterns. This helps understand the user's typical behavior and the emotional states that result from it.

[0142] Step 5:

[0143] The server generates optimal suggestions for the user based on their behavioral and emotional patterns. These suggestions are adjusted to take into account the user's current emotional state.

[0144] Step 6:

[0145] The server sends the generated proposal to the terminal. The terminal then prepares to notify the user of this proposal.

[0146] Step 7:

[0147] The device notifies the user of suggestions. The notification method is flexibly selected according to the user's mood. For example, a soft notification is sent when relaxation is needed, and an immediate alert is sent when there is an emergency.

[0148] Step 8:

[0149] The user checks the notification on their device and chooses to accept or modify the suggestion. The user's selection is sent back to the server via the device.

[0150] Step 9:

[0151] The server automatically performs relevant tasks based on user selections. This may include playing relaxation music or rescheduling tasks. As a result, users can carry out their daily routines and work more comfortably.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In modern society, there is a demand for personalized services that respond to users' emotions and behaviors. However, existing systems struggle to integrate user activity data and emotional data, making it difficult to generate optimal suggestions in real time. Furthermore, there is a lack of mechanisms to improve the quality of suggestions based on user feedback. In this situation, there is a challenge in increasing user satisfaction.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for acquiring and analyzing activity data and emotional data from the user terminal to extract behavioral patterns and emotional patterns, means for generating optimal suggestions for the user based on the extracted patterns, and means for optimizing the suggestion content using generational AI technology. This enables accurate suggestions that correspond to the user's emotional state and behavior, and further allows for continuous improvement of the quality of the suggestions.

[0157] "User terminal" refers to an electronic device used by a user to collect or receive information, and includes smartphones and tablets.

[0158] "Activity data" refers to information related to a user's daily actions and operations, including location information and logs of applications used.

[0159] "Emotional data" refers to information related to the user's emotional state, including voice tone, facial expression changes, and keywords in text communications.

[0160] "Behavioral patterns" refer to information that reveals certain tendencies or characteristics, extracted by analyzing a user's past behavioral history.

[0161] "Emotional patterns" are information that indicates the trends and states of specific emotions, obtained by analyzing a user's emotional state.

[0162] "Generative AI technology" refers to the technology of generating suggestions and responses that meet specific purposes using artificial intelligence technology, and includes the use of machine learning algorithms.

[0163] "Optimizing proposals" refers to the process of adjusting the proposals offered to users so that they are best suited to their current situation and needs.

[0164] This invention is an AI concierge system that recognizes user emotions and makes appropriate suggestions based on that information. This system primarily utilizes user activity data and emotional data to analyze and generate behavioral and emotional patterns, and then notifies the user of suggestions.

[0165] First, the device collects activity and emotional data from the user. This process uses electronic devices such as smartphones and tablets, utilizing the microphones, cameras, and various sensors built into the device. The emotional data collected includes voice tone, changes in facial expressions, and keywords contained in messages.

[0166] Next, the data sent from the terminal to the server is stored in a database via a secure communication protocol. The server uses machine learning algorithms and sentiment analysis engines to analyze this data and extract the user's behavioral and emotional patterns. This analysis identifies the user's past behavioral tendencies and generates optimized suggestions tailored to the user's needs.

[0167] The generated suggestions are optimized using a generative AI model and then provided to the user. This AI model continuously learns from past feedback to provide suggestions in a format best suited to the user's current emotional state. It also incorporates a system that improves the content of the suggestions based on the user's responses.

[0168] For example, if a user is experiencing stress, the system will suggest relaxation techniques or a short break. By inputting a prompt such as, "Please suggest relaxation techniques for when the user is experiencing stress," into the model, the system can generate the most appropriate suggestions.

[0169] In this way, the present invention can be implemented as a system that incorporates user emotions and behavioral information to provide better services.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device collects user activity and emotional data. Specifically, it obtains user consent and tracks changes in voice tone and facial expressions using its built-in microphone and camera. It also records application usage history and keywords contained in text communications. Inputs include user voice, video, and text, which are captured by sensors and stored in the device. Outputs are sets of raw activity and emotional data.

[0173] Step 2:

[0174] The terminal transmits the collected data to the server using a secure protocol. Specifically, it encrypts the data and uploads it to the server via a secure communication channel. In this process, the terminal formats and transforms the data to prepare it for the server to receive. The input is the data obtained in step 1, and the output is the dataset transferred to the server.

[0175] Step 3:

[0176] The server stores the received data in a database. Specifically, it inserts the data into the appropriate table in the database, adds indexes, and makes it accessible quickly. It also verifies the validity of the data during storage, checking for missing or outliers. The input is a dataset transferred from the terminal, and the output is structured data recorded in the database.

[0177] Step 4:

[0178] The server analyzes stored data using machine learning algorithms. Specifically, an emotion analysis engine extracts emotional characteristics from speech and text, and a behavior pattern recognition algorithm identifies user behavioral tendencies from past data. The input is structured data stored in a database, and the output is a set of behavioral and emotional patterns.

[0179] Step 5:

[0180] The server generates suggestions for the user based on the analysis results. Using a generative AI model, it creates suggestions tailored to the user's current situation. Specifically, it leverages past feedback to generate prompts and optimizes suggestions based on them. For example, it might use the prompt, "Suggest relaxation methods for when the user is feeling stressed." The input is a set of behavioral and emotional patterns, and the output is the generated suggestions.

[0181] Step 6:

[0182] The device receives suggestions generated from the server and notifies the user. Specifically, it adjusts the intensity and method of the notification according to the user's emotions. Voice notifications, vibrations, or screen pop-ups may be used. The input is the suggestion content from the server, and the output is the notification to the user.

[0183] Step 7:

[0184] The user reviews the proposal and chooses to accept or modify it. This choice is sent back to the server via the terminal. The input is the proposal the user received, and the output is the user's response.

[0185] Step 8:

[0186] The server receives user responses and automatically executes related tasks, such as playing specified relaxation music. This process utilizes automated scripts. The input is the user's response, and the output is the result of the executed task.

[0187] (Application Example 2)

[0188] 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".

[0189] In modern society, accurately understanding the emotional state of individual users and proposing optimal actions and environmental settings accordingly is crucial for improving their quality of life. However, conventional systems have faced many challenges in analyzing user emotions in detail and personalizing the environment. In this context, there is a need for the development of systems that enable automatic environmental adjustments adapted to user emotions.

[0190] 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.

[0191] In this invention, the server includes means for acquiring activity attributes from a user terminal, means for analyzing the acquired attributes to extract behavioral tendencies, and means for generating suggestions based on the extracted behavioral tendencies. This makes it possible to recommend appropriate environmental settings (such as lighting and music) according to the user's emotional state and improve the user's quality of life.

[0192] A "user terminal" is an electronic device used to acquire user activity attributes and generate and notify suggestions based on those attributes.

[0193] "Activity attributes" refer to data that contains information necessary to identify a user's behavior and emotional state.

[0194] "Behavioral tendencies" refer to certain patterns or trends extracted to analyze and predict users' behavior patterns over a specific period of time.

[0195] A "suggestion" is a set of instructions or advice regarding optimal actions or environmental settings, generated based on the user's behavioral tendencies and emotional state.

[0196] "Environment settings" refer to settings that adjust the living environment, such as lighting, music, and temperature, according to the user's emotional state.

[0197] "Related tasks" refer to specific tasks or processes that are automatically executed based on user responses.

[0198] This invention is a system that understands the user's emotional state and makes appropriate suggestions based on that understanding. The system is configured to work in conjunction with a server and a user terminal.

[0199] The server analyzes behavioral tendencies and emotional states based on activity attributes received from the user's terminal using machine learning algorithms. This analysis utilizes machine learning libraries such as TensorFlow, and uses OpenCV and Dlib to analyze images and facial expressions, thereby gaining a detailed understanding of the user's current emotional state. It also analyzes speech data using the Google® Speech-to-Text API.

[0200] The user's device uses hardware such as a microphone and camera to acquire user voice tone and facial expression data in real time and transmit it to the server. This data is transmitted reliably using a secure protocol, and privacy is protected.

[0201] Based on the analysis results, the server suggests environmental settings tailored to the user's emotional state and notifies the user's device of these suggestions. For example, if the server detects that the user is stressed, it might suggest softening the lighting and playing relaxing music. These suggestions are also personalized by considering the user's past response history and preferences.

[0202] For example, if a user is feeling stressed after a long day at work, this system can sense their emotions from their tone of voice and facial expressions, and then provide calming music or suggest relaxing lighting settings. It can also activate an aroma diffuser if scent control is available. In this way, the system helps improve the user's quality of life.

[0203] Examples of prompts used in generative AI models include the following:

[0204] "Please come up with suggestions for the best relaxation methods for users who are experiencing stress."

[0205] "Please provide specific examples of how household assistance robots can reduce user stress."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user terminal uses a microphone and camera to acquire the user's voice and video data. This data includes the user's voice tone and facial expressions and is captured in real time. Input is audio files and image data, and output is the transmission of this data to the server.

[0209] Step 2:

[0210] The user terminal transmits the acquired audio and video data to the server via a secure protocol. This ensures that the data remains confidential and intact during analysis. The input consists of audio and video data files, and the output is a secure data transfer to the server.

[0211] Step 3:

[0212] The server processes the received audio data using the Google Speech-to-Text API to convert it from speech to text. Furthermore, it analyzes faces and expressions using OpenCV and Dlib on the image data. This allows for the analysis of the user's emotional state as numerical data. The input consists of audio files and image data, while the output consists of text data and emotion analysis data.

[0213] Step 4:

[0214] The server uses the acquired analysis data to run a machine learning model that predicts user behavioral tendencies and emotional patterns. It continuously improves the model by comparing past and current data using tools like TensorFlow. Inputs are text data and sentiment analysis data, while outputs are predicted emotional states and behavioral tendencies.

[0215] Step 5:

[0216] The server generates suggestions for optimal environmental settings for the user based on their predicted emotional state. These suggestions include the selection of lighting and music, aiming to create a relaxing environment for the user. The input is the predicted emotional state, and the output is the suggested environmental settings information.

[0217] Step 6:

[0218] The user terminal notifies the user of the suggestions received from the server. The user reviews these suggestions, accepts or modifies them, and sends that information back to the server. The input is the proposed environment configuration information, and the output is the acceptance or modification information of the suggestions.

[0219] Step 7:

[0220] The server processes user responses and automatically performs related environmental settings, such as adjusting lighting, playing music, or activating a fragrance diffuser. Input is user consent or modification information, and output is the result of the performed environmental settings.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is an AI concierge system designed to streamline users' lives. It collects activity information from user devices such as smartphones and PCs and processes it on a server. The server analyzes the acquired information to extract user behavior patterns and generates suggestions based on the analysis results. These suggestions are then sent to the user's device, enabling the user to take corresponding actions.

[0238] In this embodiment, the terminal first acquires activity information such as calendar, email, message history, and GPS location data with the user's consent. The terminal transmits this information to a server via the internet using a secure protocol. The server stores the received information in a database and analyzes the user's behavior patterns using machine learning algorithms. For example, if a user has a habit of performing a specific activity on a specific day of the week, this tendency can be detected.

[0239] The server generates suggestions tailored to the user's needs and schedule based on behavioral pattern analysis. For example, if a user has a meeting scheduled, it will notify them of the optimal departure time to ensure they arrive on time. It also suggests new restaurants and events, providing users with new options in their lives.

[0240] The terminal displays suggestions from the server as notifications on the user's screen. The user can receive these notifications and either accept the suggestions or choose an alternative. If the user accepts the suggestions, the server automatically executes the associated tasks. This allows users to efficiently manage their personal and business activities without any hassle.

[0241] As a concrete example, let's assume a user attends a regular meeting on Friday afternoons. This system takes traffic congestion information into account and notifies the user of the optimal departure time for the meeting. Furthermore, it supports pre-meeting preparation by automatically generating reference materials and agendas on the server and providing them to the user.

[0242] Thus, the present invention is a system that provides valuable support to users by utilizing user information and assisting them in decision-making in both their personal and business lives.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The device collects activity information with the user's consent. Specifically, this includes calendar information, email exchanges, and GPS location data. The device periodically scans this data to obtain the latest information.

[0246] Step 2:

[0247] The device sends the activity information it collects to the server. The transmission is secure, and the data is encrypted before being transferred to the server.

[0248] Step 3:

[0249] The server stores the activity information it receives in a database. During this process, the data is classified into private and business information.

[0250] Step 4:

[0251] The server analyzes stored data using machine learning algorithms. The purpose of the analysis is to extract user behavior patterns and detect recurring tasks performed at specific times of day or on specific days of the week.

[0252] Step 5:

[0253] The server generates suggestions for the user based on their behavioral patterns. These might include, for example, suggested meeting departure times or event suggestions based on the user's interests.

[0254] Step 6:

[0255] The server sends the generated suggestions to the terminal.

[0256] Step 7:

[0257] The device notifies the user of the suggestion. The notification appears on the user's screen and functions as an alert.

[0258] Step 8:

[0259] The user reviews the proposal and chooses to accept or reject it. Once the user makes a selection, the result is sent back to the server via the device.

[0260] Step 9:

[0261] The server executes relevant tasks based on user selections. These tasks include things like automatically generating documents and scheduling meetings. This allows users to work more efficiently.

[0262] (Example 1)

[0263] 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."

[0264] In today's digital society, users need to manage a wide range of activity information, which can make efficient time management and decision-making difficult. Furthermore, separating personal and business information can lead to overlooking important information or inconvenience. Additionally, there is a need to utilize location and itinerary information to make optimal time adjustments and suggestions, but achieving this with a single system is technically challenging.

[0265] 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.

[0266] In this invention, the server includes means for acquiring activity information from a communication device, means for storing the acquired activity information in a storage device, and means for analyzing the stored activity information using a machine learning algorithm and extracting behavioral characteristics. This enables users to comprehensively manage all activity information, allowing for efficient time management and appropriate decision-making. Furthermore, by unifying personal and business information, and utilizing location information and itinerary management information, users can receive highly accurate suggestions, leading to smoother daily operations.

[0267] "Communication equipment" refers to devices used by users to send and receive information, and includes mobile terminals and personal computers.

[0268] "Activity information" refers to data related to a user's behavior and status, including location information, schedules, message history, and other information that reflects the user's life and business activities.

[0269] A "storage device" is a system for storing digital data, such as cloud storage or database systems, which are media for continuous data management.

[0270] A "machine learning algorithm" is a computational method for recognizing data patterns and making predictions. It builds models based on large amounts of data and provides appropriate outputs for new inputs.

[0271] "Behavioral characteristics" are consistent patterns and tendencies derived from a user's past behavior, such as a habit of repeating the same activity on a specific day of the week, and are predictable features.

[0272] A "suggestion" is a set of recommendations or action plans provided to the user based on analyzed data, and is information that helps in making choices and decisions about actions.

[0273] A "challenge" is a problem or goal that users must solve in their daily lives or business, and it relates to efficient information management and time management.

[0274] This invention is a system that acquires activity information from a communication device used by a user, transmits that information to a server for analysis, and provides the user with the most suitable suggestions. Specific embodiments are described below.

[0275] With the user's consent, the device collects activity data such as calendar entries, emails, message history, and GPS data. This includes smartphones and personal computers. The collected data is then transmitted to a server via the internet. Security protocols such as HTTPS are used for communication.

[0276] The server stores the received activity information in a database and analyzes the data using machine learning algorithms (e.g., TensorFlow or PyTorch) to extract user behavior patterns and characteristics. This analysis reveals specific user behavior patterns and past selection history.

[0277] Based on the analysis results, the server runs a generative AI model (e.g., GPT) to automatically generate suggestions tailored to the user's needs. This includes notifications of departure times based on the user's schedule, as well as recommendations for new locations and events. For example, a prompt could use the text, "Please tell me the best departure time for tomorrow's meeting."

[0278] The device notifies the user of this proposal and allows the user to review it. The user receives the proposal and can choose to accept or reject it. If accepted, the server automatically performs the relevant tasks (e.g., adding events to the calendar or setting reminders). This allows the user to efficiently manage their daily activities and make decisions more quickly.

[0279] This system is designed to make users' lives more efficient and convenient, centralizing personal information management and supporting choices in both personal and business matters.

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

[0281] Step 1:

[0282] The terminal collects activity information after obtaining the user's consent. Specifically, it retrieves schedules from the calendar, collects information related to communication from email and message histories, and records location data using the GPS function. It utilizes the user's digital data as input and aggregates the activity information into a data package as output. This data package is then sent to the server.

[0283] Step 2:

[0284] The terminal sends the activity information it has collected to the server via a secure protocol (e.g., HTTPS). Specifically, the terminal creates an HTTP POST request and sends the data in JSON format to the server. The input is the data package assembled by the terminal, and as output, the server receives the data through a reliable communication channel.

[0285] Step 3:

[0286] The server records the activity information it has received in a database. As a specific operation, it uses a database management system (e.g., MySQL or MongoDB) to structure and store the information. The input is the activity information data in JSON format, and the output is the information record stored in the database.

[0287] Step 4:

[0288] The server analyzes the activity information stored. Specifically, it uses machine learning algorithms (e.g., TensorFlow or PyTorch) to extract the user's behavior patterns. The input is the activity information stored in the database, and as output, it generates a data model indicating the user's behavior characteristics. This data model serves as the basis for the proposal.

[0289] Step 5:

[0290] The server utilizes a generative AI model to create suggestions based on user needs. Specifically, user behavioral characteristic data is input into the generative AI model, and new suggestions are obtained as output. Prompt statements are used in suggestion generation. For example, using the prompt statement "Please tell me the best departure time for tomorrow's meeting," information tailored to the user's behavior is generated.

[0291] Step 6:

[0292] The device receives suggestions sent from the server and notifies the user. Specifically, it uses the OS notification service and displays the suggestions as pop-up messages on the user's screen. The input is suggestion data from the server, and the output is expressed in the form of a notification to the user.

[0293] Step 7:

[0294] The user reviews the proposal and chooses to accept or reject it. If the proposal is accepted, the server automatically executes the related tasks. Specific examples include adding an event to the calendar or setting a reminder. The input is the user's selection (accept or reject), and the output is the automatically completed tasks.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] In modern urban life, the sheer volume of information makes efficient time management difficult. Furthermore, the lack of optimal action suggestions based on location information in daily life often leads users to make inefficient decisions. To solve this problem, a system is needed that deeply understands user behavior patterns and provides appropriate suggestions based on real-time information.

[0298] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes means for acquiring activity data from a user terminal, means for analyzing the acquired data to extract behavioral tendencies, and means for proposing departure time and event information using the current location information of the user. As a result, the user can efficiently manage activities and optimize daily decision-making.

[0300] A "user terminal" is one of the computing devices used by a user and is a device for data input and communication.

[0301] "Activity data" is information related to the behavior and schedule of a user and is data acquired from the user's terminal.

[0302] "Behavioral tendency" refers to the patterns and habits of behavior obtained by analyzing the past activity data of a user.

[0303] "Proposal" refers to the information and advice generated by the system to prompt actions or support decision-making for the user.

[0304] "User response" refers to the result of the user's selection or instruction in response to a proposal.

[0305] "Task" refers to the specific tasks related to the work or daily activities that the user should perform.

[0306] "Current location information" is data related to the location where the user is currently located and includes geographical coordinate information.

[0307] "Departure time" is the time when the user should start to head towards a specific activity or destination.

[0308] "Event information" refers to information about activities and events held on specific dates and times, and is intended to provide users with new options.

[0309] This invention is an AI concierge system designed to streamline users' lives. The system acquires and analyzes user activity data to extract user behavioral trends. The hardware used in this process includes a user terminal for collecting user activity data, such as a smartphone or personal computer. The software includes a machine learning algorithm for analysis. This algorithm runs on a server. The server stores and analyzes the collected data, gains insights, and then makes appropriate suggestions to the user.

[0310] This system helps users avoid traffic congestion by collecting their current location information and suggesting the optimal departure time based on the analysis results. It also provides new event information based on the user's interests, thereby opening up new experiences and options for the user. For example, if a user lives in Tokyo, the system can check traffic conditions and notify them of the optimal departure time. It can also provide information about new movies showing at nearby theaters.

[0311] By using generative AI models, more personalized suggestions tailored to user preferences become possible. An example of a prompt is, "Analyze the user's behavior patterns and suggest the optimal departure time and event information based on their schedule and location for the next day." This prompt is used to instruct the system on what kind of output it should generate.

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

[0313] Step 1:

[0314] With the user's consent, the device acquires activity data such as calendar events, message history, and GPS location data. This data is input data to be sent to the server.

[0315] Step 2:

[0316] The server stores activity data received from terminals in a database using a secure protocol. This data forms the basis for analyzing behavioral trends.

[0317] Step 3:

[0318] The server analyzes the stored data using machine learning algorithms to extract user behavioral trends. Here, the input is past behavioral data, and the output is the analyzed behavioral patterns. In this step, the server identifies whether a user performs a specific action on a particular day.

[0319] Step 4:

[0320] The server generates suggestions regarding departure times and available events based on behavioral patterns and current location information. External data such as traffic information and weather forecasts are utilized in generating these suggestions. The input consists of analyzed behavioral patterns and external data, and the output is a list of suggestions.

[0321] Step 5:

[0322] The server notifies the terminal of the generated proposal. This notification becomes the user's input, and the user reviews the proposal and decides whether to accept it or not.

[0323] Step 6:

[0324] If the user accepts the proposal, the server automatically performs the associated tasks, including updating the schedule and sending navigation data. The input is the user's response, and the output is the result of the specific task performed.

[0325] These processes are continuously improved through the generative AI model, enabling the provision of optimal information to the user based on the prompt text.

[0326] 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.

[0327] This invention is an AI concierge system that combines an emotion engine to recognize user emotions, collects and analyzes user activity information, and provides suggestions based on behavior and emotions.

[0328] First, the device collects activity information and emotion-related data with the user's consent. This emotion data is extracted from the user's voice tone, facial expressions, and keywords in text communications. The collected data is sent to a server via a secure protocol.

[0329] The server stores and analyzes received activity and sentiment data in a database. Machine learning algorithms and sentiment analysis techniques are used to extract user behavior and emotional patterns. This analysis allows for an understanding of how users tend to behave in different emotional states.

[0330] The server generates optimal suggestions for the user based on extracted behavioral and emotional patterns. These suggestions are presented in a way that is most relatable to the user's current emotional state. For example, if the user is feeling stressed, it will notify them of relaxation suggestions or schedule adjustments.

[0331] The device notifies the user of suggestions from the server. The notification method is adjusted according to the user's mood. For less urgent matters, a soft-toned chat notification is sent, while for more urgent matters, an alert sound is used. Once the user reviews the suggestion and chooses to accept or modify it, that information is sent back to the server.

[0332] The server automatically performs relevant tasks according to the user's selections. Emotion-based responses include playing music to promote relaxation and delivering information to alleviate tension.

[0333] For example, if a user is feeling nervous before an important presentation, this system uses an emotion engine to detect this and suggests ways to relax or take a break. It also assists the user by automatically generating necessary presentation materials and adjusting the timetable.

[0334] Thus, the present invention is a system that supports users' daily lives and business activities by utilizing user emotional and behavioral information to provide more personalized suggestions.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The device collects activity information and emotion-related data with the user's consent. Emotion data is obtained through the user's voice recordings, facial recognition camera data, and emotion keywords in text messages.

[0338] Step 2:

[0339] The device sends activity and sentiment data it collects to the server. This transmission is encrypted and uses a privacy-protecting protocol.

[0340] Step 3:

[0341] The server saves the transmitted information to a database. The saved data will be used for future analysis and proposal generation.

[0342] Step 4:

[0343] The server uses machine learning algorithms and sentiment analysis engines to analyze the user's behavioral and emotional patterns. This helps understand the user's typical behavior and the emotional states that result from it.

[0344] Step 5:

[0345] The server generates optimal suggestions for the user based on their behavioral and emotional patterns. These suggestions are adjusted to take into account the user's current emotional state.

[0346] Step 6:

[0347] The server sends the generated proposal to the terminal. The terminal then prepares to notify the user of this proposal.

[0348] Step 7:

[0349] The device notifies the user of suggestions. The notification method is flexibly selected according to the user's mood. For example, a soft notification is sent when relaxation is needed, and an immediate alert is sent when there is an emergency.

[0350] Step 8:

[0351] The user checks the notification on their device and chooses to accept or modify the suggestion. The user's selection is sent back to the server via the device.

[0352] Step 9:

[0353] The server automatically performs relevant tasks based on user selections. This may include playing relaxation music or rescheduling tasks. As a result, users can carry out their daily routines and work more comfortably.

[0354] (Example 2)

[0355] 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".

[0356] In modern society, there is a demand for personalized services that respond to users' emotions and behaviors. However, existing systems struggle to integrate user activity data and emotional data, making it difficult to generate optimal suggestions in real time. Furthermore, there is a lack of mechanisms to improve the quality of suggestions based on user feedback. In this situation, there is a challenge in increasing user satisfaction.

[0357] 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.

[0358] In this invention, the server includes means for acquiring and analyzing activity data and emotional data from the user terminal to extract behavioral patterns and emotional patterns, means for generating optimal suggestions for the user based on the extracted patterns, and means for optimizing the suggestion content using generational AI technology. This enables accurate suggestions that correspond to the user's emotional state and behavior, and further allows for continuous improvement of the quality of the suggestions.

[0359] "User terminal" refers to an electronic device used by a user to collect or receive information, and includes smartphones and tablets.

[0360] "Activity data" refers to information related to a user's daily actions and operations, including location information and logs of applications used.

[0361] "Emotional data" refers to information related to the user's emotional state, including voice tone, facial expression changes, and keywords in text communications.

[0362] "Behavioral patterns" refer to information that reveals certain tendencies and characteristics, extracted by analyzing a user's past behavioral history.

[0363] "Emotional patterns" are information that indicates the trends and states of specific emotions, obtained by analyzing a user's emotional state.

[0364] "Generative AI technology" refers to the technology of generating suggestions and responses that meet specific purposes using artificial intelligence technology, and includes the use of machine learning algorithms.

[0365] "Optimizing proposals" refers to the process of adjusting the proposals offered to users so that they are best suited to their current situation and needs.

[0366] This invention is an AI concierge system that recognizes user emotions and makes appropriate suggestions based on that information. This system primarily utilizes user activity data and emotional data to analyze and generate behavioral and emotional patterns, and then notifies the user of suggestions.

[0367] First, the device collects activity and emotional data from the user. This process uses electronic devices such as smartphones and tablets, utilizing the microphones, cameras, and various sensors built into the device. The emotional data collected includes voice tone, changes in facial expressions, and keywords contained in messages.

[0368] Next, the data sent from the terminal to the server is stored in a database via a secure communication protocol. The server uses machine learning algorithms and sentiment analysis engines to analyze this data and extract the user's behavioral and emotional patterns. This analysis identifies the user's past behavioral tendencies and generates optimized suggestions tailored to the user's needs.

[0369] The generated suggestions are optimized using a generative AI model and then provided to the user. This AI model continuously learns from past feedback to provide suggestions in a format best suited to the user's current emotional state. It also incorporates a system that improves the content of the suggestions based on the user's responses.

[0370] For example, if a user is experiencing stress, the system will suggest relaxation techniques or a short break. By inputting a prompt such as, "Please suggest relaxation techniques for when the user is experiencing stress," into the model, the system can generate the most appropriate suggestions.

[0371] In this way, the present invention can be implemented as a system that incorporates user emotions and behavioral information to provide better services.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The device collects user activity and emotional data. Specifically, it obtains user consent and tracks changes in voice tone and facial expressions using its built-in microphone and camera. It also records application usage history and keywords contained in text communications. Inputs include user voice, video, and text, which are captured by sensors and stored in the device. Outputs are sets of raw activity and emotional data.

[0375] Step 2:

[0376] The terminal transmits the collected data to the server using a secure protocol. Specifically, it encrypts the data and uploads it to the server via a secure communication channel. In this process, the terminal formats and transforms the data to prepare it for the server to receive. The input is the data obtained in step 1, and the output is the dataset transferred to the server.

[0377] Step 3:

[0378] The server stores the received data in a database. Specifically, it inserts the data into the appropriate table in the database, adds indexes, and makes it accessible quickly. It also verifies the validity of the data during storage, checking for missing or outliers. The input is a dataset transferred from the terminal, and the output is structured data recorded in the database.

[0379] Step 4:

[0380] The server analyzes stored data using machine learning algorithms. Specifically, an emotion analysis engine extracts emotional characteristics from speech and text, and a behavior pattern recognition algorithm identifies user behavioral tendencies from past data. The input is structured data stored in a database, and the output is a set of behavioral and emotional patterns.

[0381] Step 5:

[0382] The server generates suggestions for the user based on the analysis results. Using a generative AI model, it creates suggestions tailored to the user's current situation. Specifically, it leverages past feedback to generate prompts and optimizes suggestions based on them. For example, it might use the prompt, "Suggest relaxation methods for when the user is feeling stressed." The input is a set of behavioral and emotional patterns, and the output is the generated suggestions.

[0383] Step 6:

[0384] The device receives suggestions generated from the server and notifies the user. Specifically, it adjusts the intensity and method of the notification according to the user's emotions. Voice notifications, vibrations, or screen pop-ups may be used. The input is the suggestion content from the server, and the output is the notification to the user.

[0385] Step 7:

[0386] The user reviews the proposal and chooses to accept or modify it. This choice is sent back to the server via the terminal. The input is the proposal the user received, and the output is the user's response.

[0387] Step 8:

[0388] The server receives user responses and automatically executes related tasks, such as playing specified relaxation music. This process utilizes automated scripts. The input is the user's response, and the output is the result of the executed task.

[0389] (Application Example 2)

[0390] 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."

[0391] In modern society, accurately understanding the emotional state of individual users and proposing optimal actions and environmental settings accordingly is crucial for improving their quality of life. However, conventional systems have faced many challenges in analyzing user emotions in detail and personalizing the environment. In this context, there is a need for the development of systems that enable automatic environmental adjustments adapted to user emotions.

[0392] 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.

[0393] In this invention, the server includes means for acquiring activity attributes from a user terminal, means for analyzing the acquired attributes to extract behavioral tendencies, and means for generating suggestions based on the extracted behavioral tendencies. This makes it possible to recommend appropriate environmental settings (such as lighting and music) according to the user's emotional state and improve the user's quality of life.

[0394] A "user terminal" is an electronic device used to acquire user activity attributes and generate and notify suggestions based on those attributes.

[0395] "Activity attributes" refer to data that contains information necessary to identify a user's behavior and emotional state.

[0396] "Behavioral tendencies" refer to certain patterns or trends extracted to analyze and predict users' behavior patterns over a specific period of time.

[0397] A "suggestion" is a set of instructions or advice regarding optimal actions or environmental settings, generated based on the user's behavioral tendencies and emotional state.

[0398] "Environment settings" refer to settings that adjust the living environment, such as lighting, music, and temperature, according to the user's emotional state.

[0399] "Related tasks" refer to specific tasks or processes that are automatically executed based on user responses.

[0400] This invention is a system that understands the user's emotional state and makes appropriate suggestions based on that understanding. The system is configured to work in conjunction with a server and a user terminal.

[0401] The server analyzes behavioral tendencies and emotional states based on activity attributes received from the user's terminal using machine learning algorithms. This analysis utilizes machine learning libraries such as TensorFlow, and uses OpenCV and Dlib to analyze images and facial expressions, thereby gaining a detailed understanding of the user's current emotional state. It also analyzes audio data using the Google Speech-to-Text API.

[0402] The user's device uses hardware such as a microphone and camera to acquire user voice tone and facial expression data in real time and transmit it to the server. This data is transmitted reliably using a secure protocol, and privacy is protected.

[0403] Based on the analysis results, the server suggests environmental settings tailored to the user's emotional state and notifies the user's device of these suggestions. For example, if the server detects that the user is stressed, it might suggest softening the lighting and playing relaxing music. These suggestions are also personalized by considering the user's past response history and preferences.

[0404] For example, if a user is feeling stressed after a long day at work, this system can sense their emotions from their tone of voice and facial expressions, and then provide calming music or suggest relaxing lighting settings. It can also activate an aroma diffuser if scent control is available. In this way, the system helps improve the user's quality of life.

[0405] Examples of prompts used in generative AI models include the following:

[0406] "Please suggest the most suitable relaxation methods for users who are experiencing stress."

[0407] "Please provide specific examples of how household assistance robots can reduce user stress."

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The user terminal uses a microphone and camera to acquire the user's voice and video data. This data includes the user's voice tone and facial expressions and is captured in real time. Input is audio files and image data, and output is the transmission of this data to the server.

[0411] Step 2:

[0412] The user terminal transmits the acquired audio and video data to the server via a secure protocol. This ensures that the data remains confidential and intact during analysis. The input consists of audio and video data files, and the output is a secure data transfer to the server.

[0413] Step 3:

[0414] The server processes the received audio data using the Google Speech-to-Text API to convert it from speech to text. Furthermore, it analyzes faces and expressions using OpenCV and Dlib on the image data. This allows for the analysis of the user's emotional state as numerical data. The input consists of audio files and image data, while the output consists of text data and emotion analysis data.

[0415] Step 4:

[0416] The server uses the acquired analysis data to run a machine learning model that predicts user behavioral tendencies and emotional patterns. It continuously improves the model by comparing past and current data using tools like TensorFlow. Inputs are text data and sentiment analysis data, while outputs are predicted emotional states and behavioral tendencies.

[0417] Step 5:

[0418] The server generates suggestions for optimal environmental settings for the user based on their predicted emotional state. These suggestions include the selection of lighting and music, aiming to create a relaxing environment for the user. The input is the predicted emotional state, and the output is the suggested environmental settings information.

[0419] Step 6:

[0420] The user terminal notifies the user of the suggestions received from the server. The user reviews these suggestions, accepts or modifies them, and sends that information back to the server. The input is the proposed environment configuration information, and the output is the acceptance or modification information of the suggestions.

[0421] Step 7:

[0422] The server processes user responses and automatically performs related environmental settings, such as adjusting lighting, playing music, or activating a fragrance diffuser. Input is user consent or modification information, and output is the result of the performed environmental settings.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] This invention is an AI concierge system designed to streamline users' lives. It collects activity information from user devices such as smartphones and PCs and processes it on a server. The server analyzes the acquired information to extract user behavior patterns and generates suggestions based on the analysis results. These suggestions are then sent to the user's device, enabling the user to take corresponding actions.

[0440] In this embodiment, the terminal first acquires activity information such as calendar, email, message history, and GPS location data with the user's consent. The terminal transmits this information to a server via the internet using a secure protocol. The server stores the received information in a database and analyzes the user's behavior patterns using machine learning algorithms. For example, if a user has a habit of performing a specific activity on a specific day of the week, this tendency can be detected.

[0441] The server generates suggestions tailored to the user's needs and schedule based on behavioral pattern analysis. For example, if a user has a meeting scheduled, it will notify them of the optimal departure time to ensure they arrive on time. It also suggests new restaurants and events, providing users with new options in their lives.

[0442] The terminal displays suggestions from the server as notifications on the user's screen. The user can receive these notifications and either accept the suggestions or choose an alternative. If the user accepts the suggestions, the server automatically executes the associated tasks. This allows users to efficiently manage their personal and business activities without any hassle.

[0443] As a concrete example, let's assume a user attends a regular meeting on Friday afternoons. This system takes traffic congestion information into account and notifies the user of the optimal departure time for the meeting. Furthermore, it supports pre-meeting preparation by automatically generating reference materials and agendas on the server and providing them to the user.

[0444] Thus, the present invention is a system that provides valuable support to users by utilizing user information and assisting them in decision-making in both their personal and business lives.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The device collects activity information with the user's consent. Specifically, this includes calendar information, email exchanges, and GPS location data. The device periodically scans this data to obtain the latest information.

[0448] Step 2:

[0449] The device sends the activity information it collects to the server. The transmission is secure, and the data is encrypted before being transferred to the server.

[0450] Step 3:

[0451] The server stores the activity information it receives in a database. During this process, the data is classified into private and business information.

[0452] Step 4:

[0453] The server analyzes stored data using machine learning algorithms. The purpose of the analysis is to extract user behavior patterns and detect recurring tasks performed at specific times of day or on specific days of the week.

[0454] Step 5:

[0455] The server generates suggestions for the user based on their behavioral patterns. These might include, for example, suggested meeting departure times or event suggestions based on the user's interests.

[0456] Step 6:

[0457] The server sends the generated suggestions to the terminal.

[0458] Step 7:

[0459] The device notifies the user of the suggestion. The notification appears on the user's screen and functions as an alert.

[0460] Step 8:

[0461] The user reviews the proposal and chooses to accept or reject it. Once the user makes a selection, the result is sent back to the server via the device.

[0462] Step 9:

[0463] The server executes relevant tasks based on user selections. These tasks include things like automatically generating documents and scheduling meetings. This allows users to work more efficiently.

[0464] (Example 1)

[0465] 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."

[0466] In today's digital society, users need to manage a wide range of activity information, which can make efficient time management and decision-making difficult. Furthermore, separating personal and business information can lead to overlooking important information or inconvenience. Additionally, there is a need to utilize location and itinerary information to make optimal time adjustments and suggestions, but achieving this with a single system is technically challenging.

[0467] 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.

[0468] In this invention, the server includes means for acquiring activity information from a communication device, means for storing the acquired activity information in a storage device, and means for analyzing the stored activity information using a machine learning algorithm and extracting behavioral characteristics. This enables users to comprehensively manage all activity information, allowing for efficient time management and appropriate decision-making. Furthermore, by unifying personal and business information, and utilizing location information and itinerary management information, users can receive highly accurate suggestions, leading to smoother daily operations.

[0469] "Communication equipment" refers to devices used by users to send and receive information, and includes mobile terminals and personal computers.

[0470] "Activity information" refers to data related to a user's behavior and status, including location information, schedules, message history, and other information that reflects the user's life and business activities.

[0471] A "storage device" is a system for storing digital data, such as cloud storage or database systems, which are media for continuous data management.

[0472] A "machine learning algorithm" is a computational method for recognizing data patterns and making predictions. It builds models based on large amounts of data and provides appropriate outputs for new inputs.

[0473] "Behavioral characteristics" are consistent patterns and tendencies derived from a user's past behavior, such as a habit of repeating the same activity on a specific day of the week, and are predictable features.

[0474] A "suggestion" is a set of recommendations or action plans provided to the user based on analyzed data, and is information that helps in making choices and decisions about actions.

[0475] A "challenge" is a problem or goal that users must solve in their daily lives or business, and it relates to efficient information management and time management.

[0476] This invention is a system that acquires activity information from a communication device used by a user, transmits that information to a server for analysis, and provides the user with the most suitable suggestions. Specific embodiments are described below.

[0477] With the user's consent, the device collects activity data such as calendar entries, emails, message history, and GPS data. This includes smartphones and personal computers. The collected data is then transmitted to a server via the internet. Security protocols such as HTTPS are used for communication.

[0478] The server stores the received activity information in a database and analyzes the data using machine learning algorithms (e.g., TensorFlow or PyTorch) to extract user behavior patterns and characteristics. This analysis reveals specific user behavior patterns and past selection history.

[0479] Based on the analysis results, the server runs a generative AI model (e.g., GPT) to automatically generate suggestions tailored to the user's needs. This includes notifications of departure times based on the user's schedule, as well as recommendations for new locations and events. For example, a prompt could use the text, "Please tell me the best departure time for tomorrow's meeting."

[0480] The device notifies the user of this proposal and allows the user to review it. The user receives the proposal and can choose to accept or reject it. If accepted, the server automatically performs the relevant tasks (e.g., adding events to the calendar or setting reminders). This allows the user to efficiently manage their daily activities and make decisions more quickly.

[0481] This system is designed to make users' lives more efficient and convenient, centralizing personal information management and supporting choices in both personal and business matters.

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1:

[0484] The device collects activity information with the user's consent. Specifically, it retrieves appointments from the calendar, collects communication-related information from email and message history, and records location data using GPS. It uses the user's digital data as input and compiles the activity information into a data package as output. This data package is then sent to a server.

[0485] Step 2:

[0486] The device sends the activity information it collects to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request and sends data to the server in JSON format. The input is a data package compiled by the device, and the server receives the data as output via a reliable communication channel.

[0487] Step 3:

[0488] The server records the activity information it receives in a database. Specifically, it uses a database management system (such as MySQL or MongoDB) to structure and store the information. The input is activity information data in JSON format, and the output is the information record stored in the database.

[0489] Step 4:

[0490] The server analyzes stored activity information. Specifically, it uses machine learning algorithms (e.g., TensorFlow or PyTorch) to extract user behavior patterns. The input is activity information stored in a database, and the output is a data model that represents the user's behavioral characteristics. This data model forms the basis of the proposal.

[0491] Step 5:

[0492] The server utilizes a generative AI model to create suggestions based on user needs. Specifically, user behavioral characteristic data is input into the generative AI model, and new suggestions are obtained as output. Prompt statements are used in suggestion generation. For example, using the prompt statement "Please tell me the best departure time for tomorrow's meeting," information tailored to the user's behavior is generated.

[0493] Step 6:

[0494] The device receives suggestions sent from the server and notifies the user. Specifically, it uses the OS notification service and displays the suggestions as pop-up messages on the user's screen. The input is suggestion data from the server, and the output is expressed in the form of a notification to the user.

[0495] Step 7:

[0496] The user reviews the proposal and chooses to accept or reject it. If the proposal is accepted, the server automatically executes the related tasks. Specific examples include adding an event to the calendar or setting a reminder. The input is the user's selection (accept or reject), and the output is the automatically completed tasks.

[0497] (Application Example 1)

[0498] 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."

[0499] In modern urban life, the sheer volume of information makes efficient time management difficult. Furthermore, the lack of optimal action suggestions based on location information in daily life often leads users to make inefficient decisions. To solve this problem, a system is needed that deeply understands user behavior patterns and provides appropriate suggestions based on real-time information.

[0500] 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.

[0501] In this invention, the server includes means for acquiring activity data from a user terminal, means for analyzing the acquired data to extract behavioral trends, and means for using the user's current location information to suggest departure times and event information. This enables users to efficiently manage their activities and optimize their daily decision-making.

[0502] A "user terminal" is a type of computing device used by a user, specifically a device for data input and communication.

[0503] "Activity data" refers to information about a user's behavior and schedule, and is data obtained from the user's device.

[0504] "Behavioral tendencies" refer to patterns and habits of behavior obtained by analyzing a user's past activity data.

[0505] A "suggestion" is information or advice generated by a system to encourage users to take action or to support their decision-making.

[0506] "User response" refers to the results of the user's choices or instructions in response to a proposal.

[0507] "Work" refers to specific tasks related to the user's duties and daily activities.

[0508] "Current location information" refers to data about the user's current location, including geographical coordinate information.

[0509] "Departure time" refers to the time a user should begin in order to travel to a specific activity or destination.

[0510] "Event information" refers to information about activities and events held on specific dates and times, and is intended to provide users with new options.

[0511] This invention is an AI concierge system designed to streamline users' lives. The system acquires and analyzes user activity data to extract user behavioral trends. The hardware used in this process includes a user terminal for collecting user activity data, such as a smartphone or personal computer. The software includes a machine learning algorithm for analysis. This algorithm runs on a server. The server stores and analyzes the collected data, gains insights, and then makes appropriate suggestions to the user.

[0512] This system helps users avoid traffic congestion by collecting their current location information and suggesting the optimal departure time based on the analysis results. It also provides new event information based on the user's interests, thereby opening up new experiences and options for the user. For example, if a user lives in Tokyo, the system can check traffic conditions and notify them of the optimal departure time. It can also provide information about new movies showing at nearby theaters.

[0513] By using generative AI models, more personalized suggestions tailored to user preferences become possible. An example of a prompt is, "Analyze the user's behavior patterns and suggest the optimal departure time and event information based on their schedule and location for the next day." This prompt is used to instruct the system on what kind of output it should generate.

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] With the user's consent, the device acquires activity data such as calendar events, message history, and GPS location data. This data is input data to be sent to the server.

[0517] Step 2:

[0518] The server stores activity data received from terminals in a database using a secure protocol. This data forms the basis for analyzing behavioral trends.

[0519] Step 3:

[0520] The server analyzes the stored data using machine learning algorithms to extract user behavioral trends. Here, the input is past behavioral data, and the output is the analyzed behavioral patterns. In this step, the server identifies whether a user performs a specific action on a particular day.

[0521] Step 4:

[0522] The server generates suggestions regarding departure times and available events based on behavioral patterns and current location information. External data such as traffic information and weather forecasts are utilized in generating these suggestions. The input consists of analyzed behavioral patterns and external data, and the output is a list of suggestions.

[0523] Step 5:

[0524] The server notifies the terminal of the generated proposal. This notification becomes the user's input, and the user reviews the proposal and decides whether to accept it or not.

[0525] Step 6:

[0526] If the user accepts the proposal, the server automatically performs the associated tasks, including updating the schedule and sending navigation data. The input is the user's response, and the output is the result of the specific task performed.

[0527] These processes are continuously improved through the generative AI model, enabling the provision of optimal information to the user based on the prompt text.

[0528] 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.

[0529] This invention is an AI concierge system that combines an emotion engine to recognize user emotions, collects and analyzes user activity information, and provides suggestions based on behavior and emotions.

[0530] First, the device collects activity information and emotion-related data with the user's consent. This emotion data is extracted from the user's voice tone, facial expressions, and keywords in text communications. The collected data is sent to a server via a secure protocol.

[0531] The server stores and analyzes received activity and sentiment data in a database. Machine learning algorithms and sentiment analysis techniques are used to extract user behavior and emotional patterns. This analysis allows for an understanding of how users tend to behave in different emotional states.

[0532] The server generates optimal suggestions for the user based on extracted behavioral and emotional patterns. These suggestions are presented in a way that is most relatable to the user's current emotional state. For example, if the user is feeling stressed, it will notify them of relaxation suggestions or schedule adjustments.

[0533] The device notifies the user of suggestions from the server. The notification method is adjusted according to the user's mood. For less urgent matters, a soft-toned chat notification is sent, while for more urgent matters, an alert sound is used. Once the user reviews the suggestion and chooses to accept or modify it, that information is sent back to the server.

[0534] The server automatically performs relevant tasks according to the user's selections. Emotion-based responses include playing music to promote relaxation and delivering information to alleviate tension.

[0535] For example, if a user is feeling nervous before an important presentation, this system uses an emotion engine to detect this and suggests ways to relax or take a break. It also assists the user by automatically generating necessary presentation materials and adjusting the timetable.

[0536] Thus, the present invention is a system that supports users' daily lives and business activities by utilizing user emotional and behavioral information to provide more personalized suggestions.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The device collects activity information and emotion-related data with the user's consent. Emotion data is obtained through the user's voice recordings, facial recognition camera data, and emotion keywords in text messages.

[0540] Step 2:

[0541] The device sends activity and sentiment data it collects to the server. This transmission is encrypted and uses a privacy-protecting protocol.

[0542] Step 3:

[0543] The server saves the transmitted information to a database. The saved data will be used for future analysis and proposal generation.

[0544] Step 4:

[0545] The server uses machine learning algorithms and sentiment analysis engines to analyze the user's behavioral and emotional patterns. This helps understand the user's typical behavior and the emotional states that result from it.

[0546] Step 5:

[0547] The server generates optimal suggestions for the user based on their behavioral and emotional patterns. These suggestions are adjusted to take into account the user's current emotional state.

[0548] Step 6:

[0549] The server sends the generated proposal to the terminal. The terminal then prepares to notify the user of this proposal.

[0550] Step 7:

[0551] The device notifies the user of suggestions. The notification method is flexibly selected according to the user's mood. For example, a soft notification is sent when relaxation is needed, and an immediate alert is sent when there is an emergency.

[0552] Step 8:

[0553] The user checks the notification on their device and chooses to accept or modify the suggestion. The user's selection is sent back to the server via the device.

[0554] Step 9:

[0555] The server automatically performs relevant tasks based on user selections. This may include playing relaxation music or rescheduling tasks. As a result, users can carry out their daily routines and work more comfortably.

[0556] (Example 2)

[0557] 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."

[0558] In modern society, there is a demand for personalized services that respond to users' emotions and behaviors. However, existing systems struggle to integrate user activity data and emotional data, making it difficult to generate optimal suggestions in real time. Furthermore, there is a lack of mechanisms to improve the quality of suggestions based on user feedback. In this situation, there is a challenge in increasing user satisfaction.

[0559] 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.

[0560] In this invention, the server includes means for acquiring and analyzing activity data and emotional data from the user terminal to extract behavioral patterns and emotional patterns, means for generating optimal suggestions for the user based on the extracted patterns, and means for optimizing the suggestion content using generational AI technology. This enables accurate suggestions that correspond to the user's emotional state and behavior, and further allows for continuous improvement of the quality of the suggestions.

[0561] "User terminal" refers to an electronic device used by a user to collect or receive information, and includes smartphones and tablets.

[0562] "Activity data" refers to information related to a user's daily actions and operations, including location information and logs of applications used.

[0563] "Emotional data" refers to information related to the user's emotional state, including voice tone, facial expression changes, and keywords in text communications.

[0564] "Behavioral patterns" refer to information that reveals certain tendencies and characteristics, extracted by analyzing a user's past behavioral history.

[0565] "Emotional patterns" are information that indicates the trends and states of specific emotions, obtained by analyzing a user's emotional state.

[0566] "Generative AI technology" refers to the technology of generating suggestions and responses that meet specific purposes using artificial intelligence technology, and includes the use of machine learning algorithms.

[0567] "Optimizing proposals" refers to the process of adjusting the proposals offered to users so that they are best suited to their current situation and needs.

[0568] This invention is an AI concierge system that recognizes user emotions and makes appropriate suggestions based on that information. This system primarily utilizes user activity data and emotional data to analyze and generate behavioral and emotional patterns, and then notifies the user of suggestions.

[0569] First, the device collects activity and emotional data from the user. This process uses electronic devices such as smartphones and tablets, utilizing the microphones, cameras, and various sensors built into the device. The emotional data collected includes voice tone, changes in facial expressions, and keywords contained in messages.

[0570] Next, the data sent from the terminal to the server is stored in a database via a secure communication protocol. The server uses machine learning algorithms and sentiment analysis engines to analyze this data and extract the user's behavioral and emotional patterns. This analysis identifies the user's past behavioral tendencies and generates optimized suggestions tailored to the user's needs.

[0571] The generated suggestions are optimized using a generative AI model and then provided to the user. This AI model continuously learns from past feedback to provide suggestions in a format best suited to the user's current emotional state. It also incorporates a system that improves the content of the suggestions based on the user's responses.

[0572] For example, if a user is experiencing stress, the system will suggest relaxation techniques or a short break. By inputting a prompt such as, "Please suggest relaxation techniques for when the user is experiencing stress," into the model, the system can generate the most appropriate suggestions.

[0573] In this way, the present invention can be implemented as a system that incorporates user emotions and behavioral information to provide better services.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The device collects user activity and emotional data. Specifically, it obtains user consent and tracks changes in voice tone and facial expressions using its built-in microphone and camera. It also records application usage history and keywords contained in text communications. Inputs include user voice, video, and text, which are captured by sensors and stored in the device. Outputs are sets of raw activity and emotional data.

[0577] Step 2:

[0578] The terminal transmits the collected data to the server using a secure protocol. Specifically, it encrypts the data and uploads it to the server via a secure communication channel. In this process, the terminal formats and transforms the data to prepare it for the server to receive. The input is the data obtained in step 1, and the output is the dataset transferred to the server.

[0579] Step 3:

[0580] The server stores the received data in a database. Specifically, it inserts the data into the appropriate table in the database, adds indexes, and makes it accessible quickly. It also verifies the validity of the data during storage, checking for missing or outliers. The input is a dataset transferred from the terminal, and the output is structured data recorded in the database.

[0581] Step 4:

[0582] The server analyzes stored data using machine learning algorithms. Specifically, an emotion analysis engine extracts emotional characteristics from speech and text, and a behavior pattern recognition algorithm identifies user behavioral tendencies from past data. The input is structured data stored in a database, and the output is a set of behavioral and emotional patterns.

[0583] Step 5:

[0584] The server generates suggestions for the user based on the analysis results. Using a generative AI model, it creates suggestions tailored to the user's current situation. Specifically, it leverages past feedback to generate prompts and optimizes suggestions based on them. For example, it might use the prompt, "Suggest relaxation methods for when the user is feeling stressed." The input is a set of behavioral and emotional patterns, and the output is the generated suggestions.

[0585] Step 6:

[0586] The device receives suggestions generated from the server and notifies the user. Specifically, it adjusts the intensity and method of the notification according to the user's emotions. Voice notifications, vibrations, or screen pop-ups may be used. The input is the suggestion content from the server, and the output is the notification to the user.

[0587] Step 7:

[0588] The user reviews the proposal and chooses to accept or modify it. This choice is sent back to the server via the terminal. The input is the proposal the user received, and the output is the user's response.

[0589] Step 8:

[0590] The server receives user responses and automatically executes related tasks, such as playing specified relaxation music. This process utilizes automated scripts. The input is the user's response, and the output is the result of the executed task.

[0591] (Application Example 2)

[0592] 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."

[0593] In modern society, accurately understanding the emotional state of individual users and proposing optimal actions and environmental settings accordingly is crucial for improving their quality of life. However, conventional systems have faced many challenges in analyzing user emotions in detail and personalizing the environment. In this context, there is a need for the development of systems that enable automatic environmental adjustments adapted to user emotions.

[0594] 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.

[0595] In this invention, the server includes means for acquiring activity attributes from a user terminal, means for analyzing the acquired attributes to extract behavioral tendencies, and means for generating suggestions based on the extracted behavioral tendencies. This makes it possible to recommend appropriate environmental settings (such as lighting and music) according to the user's emotional state and improve the user's quality of life.

[0596] A "user terminal" is an electronic device used to acquire user activity attributes and generate and notify suggestions based on those attributes.

[0597] "Activity attributes" refer to data that contains information necessary to identify a user's behavior and emotional state.

[0598] "Behavioral tendencies" refer to certain patterns or trends extracted to analyze and predict users' behavior patterns over a specific period of time.

[0599] A "suggestion" is a set of instructions or advice regarding optimal actions or environmental settings, generated based on the user's behavioral tendencies and emotional state.

[0600] "Environment settings" refer to settings that adjust the living environment, such as lighting, music, and temperature, according to the user's emotional state.

[0601] "Related tasks" refer to specific tasks or processes that are automatically executed based on user responses.

[0602] This invention is a system that understands the user's emotional state and makes appropriate suggestions based on that understanding. The system is configured to work in conjunction with a server and a user terminal.

[0603] The server analyzes behavioral tendencies and emotional states based on activity attributes received from the user's terminal using machine learning algorithms. This analysis utilizes machine learning libraries such as TensorFlow, and uses OpenCV and Dlib to analyze images and facial expressions, thereby gaining a detailed understanding of the user's current emotional state. It also analyzes audio data using the Google Speech-to-Text API.

[0604] The user's device uses hardware such as a microphone and camera to acquire user voice tone and facial expression data in real time and transmit it to the server. This data is transmitted reliably using a secure protocol, and privacy is protected.

[0605] Based on the analysis results, the server suggests environmental settings tailored to the user's emotional state and notifies the user's device of these suggestions. For example, if the server detects that the user is stressed, it might suggest softening the lighting and playing relaxing music. These suggestions are also personalized by considering the user's past response history and preferences.

[0606] For example, if a user is feeling stressed after a long day at work, this system can sense their emotions from their tone of voice and facial expressions, and then provide calming music or suggest relaxing lighting settings. It can also activate an aroma diffuser if scent control is available. In this way, the system helps improve the user's quality of life.

[0607] Examples of prompts used in generative AI models include the following:

[0608] "Please suggest the most suitable relaxation methods for users who are experiencing stress."

[0609] "Please provide specific examples of how household assistance robots can reduce user stress."

[0610] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0611] Step 1:

[0612] The user terminal uses a microphone and camera to acquire the user's voice and video data. This data includes the user's voice tone and facial expressions and is captured in real time. Input is audio files and image data, and output is the transmission of this data to the server.

[0613] Step 2:

[0614] The user terminal transmits the acquired audio and video data to the server via a secure protocol. This ensures that the data remains confidential and intact during analysis. The input consists of audio and video data files, and the output is a secure data transfer to the server.

[0615] Step 3:

[0616] The server processes the received audio data using the Google Speech-to-Text API to convert it from speech to text. Furthermore, it analyzes faces and expressions using OpenCV and Dlib on the image data. This allows for the analysis of the user's emotional state as numerical data. The input consists of audio files and image data, while the output consists of text data and emotion analysis data.

[0617] Step 4:

[0618] The server uses the acquired analysis data to run a machine learning model that predicts user behavioral tendencies and emotional patterns. It continuously improves the model by comparing past and current data using tools like TensorFlow. Inputs are text data and sentiment analysis data, while outputs are predicted emotional states and behavioral tendencies.

[0619] Step 5:

[0620] The server generates suggestions for optimal environmental settings for the user based on their predicted emotional state. These suggestions include the selection of lighting and music, aiming to create a relaxing environment for the user. The input is the predicted emotional state, and the output is the suggested environmental settings information.

[0621] Step 6:

[0622] The user terminal notifies the user of the suggestions received from the server. The user reviews these suggestions, accepts or modifies them, and sends that information back to the server. The input is the proposed environment configuration information, and the output is the acceptance or modification information of the suggestions.

[0623] Step 7:

[0624] The server processes user responses and automatically performs related environmental settings, such as adjusting lighting, playing music, or activating a fragrance diffuser. Input is user consent or modification information, and output is the result of the performed environmental settings.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] [Fourth Embodiment]

[0629] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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".

[0642] This invention is an AI concierge system designed to streamline users' lives. It collects activity information from user devices such as smartphones and PCs and processes it on a server. The server analyzes the acquired information to extract user behavior patterns and generates suggestions based on the analysis results. These suggestions are then sent to the user's device, enabling the user to take corresponding actions.

[0643] In this embodiment, the terminal first acquires activity information such as calendar, email, message history, and GPS location data with the user's consent. The terminal transmits this information to a server via the internet using a secure protocol. The server stores the received information in a database and analyzes the user's behavior patterns using machine learning algorithms. For example, if a user has a habit of performing a specific activity on a specific day of the week, this tendency can be detected.

[0644] The server generates suggestions tailored to the user's needs and schedule based on behavioral pattern analysis. For example, if a user has a meeting scheduled, it will notify them of the optimal departure time to ensure they arrive on time. It also suggests new restaurants and events, providing users with new options in their lives.

[0645] The terminal displays suggestions from the server as notifications on the user's screen. The user can receive these notifications and either accept the suggestions or choose an alternative. If the user accepts the suggestions, the server automatically executes the associated tasks. This allows users to efficiently manage their personal and business activities without any hassle.

[0646] As a concrete example, let's assume a user attends a regular meeting on Friday afternoons. This system takes traffic congestion information into account and notifies the user of the optimal departure time for the meeting. Furthermore, it supports pre-meeting preparation by automatically generating reference materials and agendas on the server and providing them to the user.

[0647] Thus, the present invention is a system that provides valuable support to users by utilizing user information and assisting them in decision-making in both their personal and business lives.

[0648] The following describes the processing flow.

[0649] Step 1:

[0650] The device collects activity information with the user's consent. Specifically, this includes calendar information, email exchanges, and GPS location data. The device periodically scans this data to obtain the latest information.

[0651] Step 2:

[0652] The device sends the activity information it collects to the server. The transmission is secure, and the data is encrypted before being transferred to the server.

[0653] Step 3:

[0654] The server stores the activity information it receives in a database. During this process, the data is classified into private and business information.

[0655] Step 4:

[0656] The server analyzes stored data using machine learning algorithms. The purpose of the analysis is to extract user behavior patterns and detect recurring tasks performed at specific times of day or on specific days of the week.

[0657] Step 5:

[0658] The server generates suggestions for the user based on their behavioral patterns. These might include, for example, suggested meeting departure times or event suggestions based on the user's interests.

[0659] Step 6:

[0660] The server sends the generated suggestions to the terminal.

[0661] Step 7:

[0662] The device notifies the user of the suggestion. The notification appears on the user's screen and functions as an alert.

[0663] Step 8:

[0664] The user reviews the proposal and chooses to accept or reject it. Once the user makes a selection, the result is sent back to the server via the device.

[0665] Step 9:

[0666] The server executes relevant tasks based on user selections. These tasks include things like automatically generating documents and scheduling meetings. This allows users to work more efficiently.

[0667] (Example 1)

[0668] 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".

[0669] In today's digital society, users need to manage a wide range of activity information, which can make efficient time management and decision-making difficult. Furthermore, separating personal and business information can lead to overlooking important information or inconvenience. Additionally, there is a need to utilize location and itinerary information to make optimal time adjustments and suggestions, but achieving this with a single system is technically challenging.

[0670] 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.

[0671] In this invention, the server includes means for acquiring activity information from a communication device, means for storing the acquired activity information in a storage device, and means for analyzing the stored activity information using a machine learning algorithm and extracting behavioral characteristics. This enables users to comprehensively manage all activity information, allowing for efficient time management and appropriate decision-making. Furthermore, by unifying personal and business information, and utilizing location information and itinerary management information, users can receive highly accurate suggestions, leading to smoother daily operations.

[0672] "Communication equipment" refers to devices used by users to send and receive information, and includes mobile terminals and personal computers.

[0673] "Activity information" refers to data related to a user's behavior and status, including location information, schedules, message history, and other information that reflects the user's life and business activities.

[0674] A "storage device" is a system for storing digital data, such as cloud storage or database systems, which are media for continuous data management.

[0675] A "machine learning algorithm" is a computational method for recognizing data patterns and making predictions. It builds models based on large amounts of data and provides appropriate outputs for new inputs.

[0676] "Behavioral characteristics" are consistent patterns and tendencies derived from a user's past behavior, such as a habit of repeating the same activity on a specific day of the week, and are predictable features.

[0677] A "suggestion" is a set of recommendations or action plans provided to the user based on analyzed data, and is information that helps in making choices and decisions about actions.

[0678] A "challenge" is a problem or goal that users must solve in their daily lives or business, and it relates to efficient information management and time management.

[0679] This invention is a system that acquires activity information from a communication device used by a user, transmits that information to a server for analysis, and provides the user with the most suitable suggestions. Specific embodiments are described below.

[0680] With the user's consent, the device collects activity data such as calendar entries, emails, message history, and GPS data. This includes smartphones and personal computers. The collected data is then transmitted to a server via the internet. Security protocols such as HTTPS are used for communication.

[0681] The server stores the received activity information in a database and analyzes the data using machine learning algorithms (e.g., TensorFlow or PyTorch) to extract user behavior patterns and characteristics. This analysis reveals specific user behavior patterns and past selection history.

[0682] Based on the analysis results, the server runs a generative AI model (e.g., GPT) to automatically generate suggestions tailored to the user's needs. This includes notifications of departure times based on the user's schedule, as well as recommendations for new locations and events. For example, a prompt could use the text, "Please tell me the best departure time for tomorrow's meeting."

[0683] The device notifies the user of this proposal and allows the user to review it. The user receives the proposal and can choose to accept or reject it. If accepted, the server automatically performs the relevant tasks (e.g., adding events to the calendar or setting reminders). This allows the user to efficiently manage their daily activities and make decisions more quickly.

[0684] This system is designed to make users' lives more efficient and convenient, centralizing personal information management and supporting choices in both personal and business matters.

[0685] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0686] Step 1:

[0687] The device collects activity information with the user's consent. Specifically, it retrieves appointments from the calendar, collects communication-related information from email and message history, and records location data using GPS. It uses the user's digital data as input and compiles the activity information into a data package as output. This data package is then sent to a server.

[0688] Step 2:

[0689] The device sends the activity information it collects to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request and sends data to the server in JSON format. The input is a data package compiled by the device, and the server receives the data as output via a reliable communication channel.

[0690] Step 3:

[0691] The server records the activity information it receives in a database. Specifically, it uses a database management system (such as MySQL or MongoDB) to structure and store the information. The input is activity information data in JSON format, and the output is the information record stored in the database.

[0692] Step 4:

[0693] The server analyzes stored activity information. Specifically, it uses machine learning algorithms (e.g., TensorFlow or PyTorch) to extract user behavior patterns. The input is activity information stored in a database, and the output is a data model that represents the user's behavioral characteristics. This data model forms the basis of the proposal.

[0694] Step 5:

[0695] The server utilizes a generative AI model to create suggestions based on user needs. Specifically, user behavioral characteristic data is input into the generative AI model, and new suggestions are obtained as output. Prompt statements are used in suggestion generation. For example, using the prompt statement "Please tell me the best departure time for tomorrow's meeting," information tailored to the user's behavior is generated.

[0696] Step 6:

[0697] The device receives suggestions sent from the server and notifies the user. Specifically, it uses the OS notification service and displays the suggestions as pop-up messages on the user's screen. The input is suggestion data from the server, and the output is expressed in the form of a notification to the user.

[0698] Step 7:

[0699] The user reviews the proposal and chooses to accept or reject it. If the proposal is accepted, the server automatically executes the related tasks. Specific examples include adding an event to the calendar or setting a reminder. The input is the user's selection (accept or reject), and the output is the automatically completed tasks.

[0700] (Application Example 1)

[0701] 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".

[0702] In modern urban life, the sheer volume of information makes efficient time management difficult. Furthermore, the lack of optimal action suggestions based on location information in daily life often leads users to make inefficient decisions. To solve this problem, a system is needed that deeply understands user behavior patterns and provides appropriate suggestions based on real-time information.

[0703] 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.

[0704] In this invention, the server includes means for acquiring activity data from a user terminal, means for analyzing the acquired data to extract behavioral trends, and means for using the user's current location information to suggest departure times and event information. This enables users to efficiently manage their activities and optimize their daily decision-making.

[0705] A "user terminal" is a type of computing device used by a user, specifically a device for data input and communication.

[0706] "Activity data" refers to information about a user's behavior and schedule, and is data obtained from the user's device.

[0707] "Behavioral tendencies" refer to patterns and habits of behavior obtained by analyzing a user's past activity data.

[0708] A "suggestion" is information or advice generated by a system to encourage users to take action or to support their decision-making.

[0709] "User response" refers to the results of the user's choices or instructions in response to a proposal.

[0710] "Work" refers to specific tasks related to the user's duties and daily activities.

[0711] "Current location information" refers to data about the user's current location, including geographical coordinate information.

[0712] "Departure time" refers to the time a user should begin in order to travel to a specific activity or destination.

[0713] "Event information" refers to information about activities and events held on specific dates and times, and is intended to provide users with new options.

[0714] This invention is an AI concierge system designed to streamline users' lives. The system acquires and analyzes user activity data to extract user behavioral trends. The hardware used in this process includes a user terminal for collecting user activity data, such as a smartphone or personal computer. The software includes a machine learning algorithm for analysis. This algorithm runs on a server. The server stores and analyzes the collected data, gains insights, and then makes appropriate suggestions to the user.

[0715] This system helps users avoid traffic congestion by collecting their current location information and suggesting the optimal departure time based on the analysis results. It also provides new event information based on the user's interests, thereby opening up new experiences and options for the user. For example, if a user lives in Tokyo, the system can check traffic conditions and notify them of the optimal departure time. It can also provide information about new movies showing at nearby theaters.

[0716] By using generative AI models, more personalized suggestions tailored to user preferences become possible. An example of a prompt is, "Analyze the user's behavior patterns and suggest the optimal departure time and event information based on their schedule and location for the next day." This prompt is used to instruct the system on what kind of output it should generate.

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] With the user's consent, the device acquires activity data such as calendar events, message history, and GPS location data. This data is input data to be sent to the server.

[0720] Step 2:

[0721] The server stores activity data received from terminals in a database using a secure protocol. This data forms the basis for analyzing behavioral trends.

[0722] Step 3:

[0723] The server analyzes the stored data using machine learning algorithms to extract user behavioral trends. Here, the input is past behavioral data, and the output is the analyzed behavioral patterns. In this step, the server identifies whether a user performs a specific action on a particular day.

[0724] Step 4:

[0725] The server generates suggestions regarding departure times and available events based on behavioral patterns and current location information. External data such as traffic information and weather forecasts are utilized in generating these suggestions. The input consists of analyzed behavioral patterns and external data, and the output is a list of suggestions.

[0726] Step 5:

[0727] The server notifies the terminal of the generated proposal. This notification becomes the user's input, and the user reviews the proposal and decides whether to accept it or not.

[0728] Step 6:

[0729] If the user accepts the proposal, the server automatically performs the associated tasks, including updating the schedule and sending navigation data. The input is the user's response, and the output is the result of the specific task performed.

[0730] These processes are continuously improved through the generative AI model, enabling the provision of optimal information to the user based on the prompt text.

[0731] 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.

[0732] This invention is an AI concierge system that combines an emotion engine to recognize user emotions, collects and analyzes user activity information, and provides suggestions based on behavior and emotions.

[0733] First, the device collects activity information and emotion-related data with the user's consent. This emotion data is extracted from the user's voice tone, facial expressions, and keywords in text communications. The collected data is sent to a server via a secure protocol.

[0734] The server stores and analyzes received activity and sentiment data in a database. Machine learning algorithms and sentiment analysis techniques are used to extract user behavior and emotional patterns. This analysis allows for an understanding of how users tend to behave in different emotional states.

[0735] The server generates optimal suggestions for the user based on extracted behavioral and emotional patterns. These suggestions are presented in a way that is most relatable to the user's current emotional state. For example, if the user is feeling stressed, it will notify them of relaxation suggestions or schedule adjustments.

[0736] The device notifies the user of suggestions from the server. The notification method is adjusted according to the user's mood. For less urgent matters, a soft-toned chat notification is sent, while for more urgent matters, an alert sound is used. Once the user reviews the suggestion and chooses to accept or modify it, that information is sent back to the server.

[0737] The server automatically performs relevant tasks according to the user's selections. Emotion-based responses include playing music to promote relaxation and delivering information to alleviate tension.

[0738] For example, if a user is feeling nervous before an important presentation, this system uses an emotion engine to detect this and suggests ways to relax or take a break. It also assists the user by automatically generating necessary presentation materials and adjusting the timetable.

[0739] Thus, the present invention is a system that supports users' daily lives and business activities by utilizing user emotional and behavioral information to provide more personalized suggestions.

[0740] The following describes the processing flow.

[0741] Step 1:

[0742] The device collects activity information and emotion-related data with the user's consent. Emotion data is obtained through the user's voice recordings, facial recognition camera data, and emotion keywords in text messages.

[0743] Step 2:

[0744] The device sends activity and sentiment data it collects to the server. This transmission is encrypted and uses a privacy-protecting protocol.

[0745] Step 3:

[0746] The server saves the transmitted information to a database. The saved data will be used for future analysis and proposal generation.

[0747] Step 4:

[0748] The server uses machine learning algorithms and sentiment analysis engines to analyze the user's behavioral and emotional patterns. This helps understand the user's typical behavior and the emotional states that result from it.

[0749] Step 5:

[0750] The server generates optimal suggestions for the user based on their behavioral and emotional patterns. These suggestions are adjusted to take into account the user's current emotional state.

[0751] Step 6:

[0752] The server sends the generated proposal to the terminal. The terminal then prepares to notify the user of this proposal.

[0753] Step 7:

[0754] The device notifies the user of suggestions. The notification method is flexibly selected according to the user's mood. For example, a soft notification is sent when relaxation is needed, and an immediate alert is sent when there is an emergency.

[0755] Step 8:

[0756] The user checks the notification on their device and chooses to accept or modify the suggestion. The user's selection is sent back to the server via the device.

[0757] Step 9:

[0758] The server automatically performs relevant tasks based on user selections. This may include playing relaxation music or rescheduling tasks. As a result, users can carry out their daily routines and work more comfortably.

[0759] (Example 2)

[0760] 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".

[0761] In modern society, there is a demand for personalized services that respond to users' emotions and behaviors. However, existing systems struggle to integrate user activity data and emotional data, making it difficult to generate optimal suggestions in real time. Furthermore, there is a lack of mechanisms to improve the quality of suggestions based on user feedback. In this situation, there is a challenge in increasing user satisfaction.

[0762] 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.

[0763] In this invention, the server includes means for acquiring and analyzing activity data and emotional data from the user terminal to extract behavioral patterns and emotional patterns, means for generating optimal suggestions for the user based on the extracted patterns, and means for optimizing the suggestion content using generational AI technology. This enables accurate suggestions that correspond to the user's emotional state and behavior, and further allows for continuous improvement of the quality of the suggestions.

[0764] "User terminal" refers to an electronic device used by a user to collect or receive information, and includes smartphones and tablets.

[0765] "Activity data" refers to information related to a user's daily actions and operations, including location information and logs of applications used.

[0766] "Emotional data" refers to information related to the user's emotional state, including voice tone, facial expression changes, and keywords in text communications.

[0767] "Behavioral patterns" refer to information that reveals certain tendencies and characteristics, extracted by analyzing a user's past behavioral history.

[0768] "Emotional patterns" are information that indicates the trends and states of specific emotions, obtained by analyzing a user's emotional state.

[0769] "Generative AI technology" refers to the technology of generating suggestions and responses that meet specific purposes using artificial intelligence technology, and includes the use of machine learning algorithms.

[0770] "Optimizing proposals" refers to the process of adjusting the proposals offered to users so that they are best suited to their current situation and needs.

[0771] This invention is an AI concierge system that recognizes user emotions and makes appropriate suggestions based on that information. This system primarily utilizes user activity data and emotional data to analyze and generate behavioral and emotional patterns, and then notifies the user of suggestions.

[0772] First, the device collects activity and emotional data from the user. This process uses electronic devices such as smartphones and tablets, utilizing the microphones, cameras, and various sensors built into the device. The emotional data collected includes voice tone, changes in facial expressions, and keywords contained in messages.

[0773] Next, the data sent from the terminal to the server is stored in a database via a secure communication protocol. The server uses machine learning algorithms and sentiment analysis engines to analyze this data and extract the user's behavioral and emotional patterns. This analysis identifies the user's past behavioral tendencies and generates optimized suggestions tailored to the user's needs.

[0774] The generated suggestions are optimized using a generative AI model and then provided to the user. This AI model continuously learns from past feedback to provide suggestions in a format best suited to the user's current emotional state. It also incorporates a system that improves the content of the suggestions based on the user's responses.

[0775] For example, if a user is experiencing stress, the system will suggest relaxation techniques or a short break. By inputting a prompt such as, "Please suggest relaxation techniques for when the user is experiencing stress," into the model, the system can generate the most appropriate suggestions.

[0776] In this way, the present invention can be implemented as a system that incorporates user emotions and behavioral information to provide better services.

[0777] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0778] Step 1:

[0779] The device collects user activity and emotional data. Specifically, it obtains user consent and tracks changes in voice tone and facial expressions using its built-in microphone and camera. It also records application usage history and keywords contained in text communications. Inputs include user voice, video, and text, which are captured by sensors and stored in the device. Outputs are sets of raw activity and emotional data.

[0780] Step 2:

[0781] The terminal transmits the collected data to the server using a secure protocol. Specifically, it encrypts the data and uploads it to the server via a secure communication channel. In this process, the terminal formats and transforms the data to prepare it for the server to receive. The input is the data obtained in step 1, and the output is the dataset transferred to the server.

[0782] Step 3:

[0783] The server stores the received data in a database. Specifically, it inserts the data into the appropriate table in the database, adds indexes, and makes it accessible quickly. It also verifies the validity of the data during storage, checking for missing or outliers. The input is a dataset transferred from the terminal, and the output is structured data recorded in the database.

[0784] Step 4:

[0785] The server analyzes stored data using machine learning algorithms. Specifically, an emotion analysis engine extracts emotional characteristics from speech and text, and a behavior pattern recognition algorithm identifies user behavioral tendencies from past data. The input is structured data stored in a database, and the output is a set of behavioral and emotional patterns.

[0786] Step 5:

[0787] The server generates suggestions for the user based on the analysis results. Using a generative AI model, it creates suggestions tailored to the user's current situation. Specifically, it leverages past feedback to generate prompts and optimizes suggestions based on them. For example, it might use the prompt, "Suggest relaxation methods for when the user is feeling stressed." The input is a set of behavioral and emotional patterns, and the output is the generated suggestions.

[0788] Step 6:

[0789] The device receives suggestions generated from the server and notifies the user. Specifically, it adjusts the intensity and method of the notification according to the user's emotions. Voice notifications, vibrations, or screen pop-ups may be used. The input is the suggestion content from the server, and the output is the notification to the user.

[0790] Step 7:

[0791] The user reviews the proposal and chooses to accept or modify it. This choice is sent back to the server via the terminal. The input is the proposal the user received, and the output is the user's response.

[0792] Step 8:

[0793] The server receives user responses and automatically executes related tasks, such as playing specified relaxation music. This process utilizes automated scripts. The input is the user's response, and the output is the result of the executed task.

[0794] (Application Example 2)

[0795] 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".

[0796] In modern society, accurately understanding the emotional state of individual users and proposing optimal actions and environmental settings accordingly is crucial for improving their quality of life. However, conventional systems have faced many challenges in analyzing user emotions in detail and personalizing the environment. In this context, there is a need for the development of systems that enable automatic environmental adjustments adapted to user emotions.

[0797] 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.

[0798] In this invention, the server includes means for acquiring activity attributes from a user terminal, means for analyzing the acquired attributes to extract behavioral tendencies, and means for generating suggestions based on the extracted behavioral tendencies. This makes it possible to recommend appropriate environmental settings (such as lighting and music) according to the user's emotional state and improve the user's quality of life.

[0799] A "user terminal" is an electronic device used to acquire user activity attributes and generate and notify suggestions based on those attributes.

[0800] "Activity attributes" refer to data that contains information necessary to identify a user's behavior and emotional state.

[0801] "Behavioral tendencies" refer to certain patterns or trends extracted to analyze and predict users' behavior patterns over a specific period of time.

[0802] A "suggestion" is a set of instructions or advice regarding optimal actions or environmental settings, generated based on the user's behavioral tendencies and emotional state.

[0803] "Environment settings" refer to settings that adjust the living environment, such as lighting, music, and temperature, according to the user's emotional state.

[0804] "Related tasks" refer to specific tasks or processes that are automatically executed based on user responses.

[0805] This invention is a system that understands the user's emotional state and makes appropriate suggestions based on that understanding. The system is configured to work in conjunction with a server and a user terminal.

[0806] The server analyzes behavioral tendencies and emotional states based on activity attributes received from the user's terminal using machine learning algorithms. This analysis utilizes machine learning libraries such as TensorFlow, and uses OpenCV and Dlib to analyze images and facial expressions, thereby gaining a detailed understanding of the user's current emotional state. It also analyzes audio data using the Google Speech-to-Text API.

[0807] The user's device uses hardware such as a microphone and camera to acquire user voice tone and facial expression data in real time and transmit it to the server. This data is transmitted reliably using a secure protocol, and privacy is protected.

[0808] Based on the analysis results, the server suggests environmental settings tailored to the user's emotional state and notifies the user's device of these suggestions. For example, if the server detects that the user is stressed, it might suggest softening the lighting and playing relaxing music. These suggestions are also personalized by considering the user's past response history and preferences.

[0809] For example, if a user is feeling stressed after a long day at work, this system can sense their emotions from their tone of voice and facial expressions, and then provide calming music or suggest relaxing lighting settings. It can also activate an aroma diffuser if scent control is available. In this way, the system helps improve the user's quality of life.

[0810] Examples of prompts used in generative AI models include the following:

[0811] "Please suggest the most suitable relaxation methods for users who are experiencing stress."

[0812] "Please provide specific examples of how household assistance robots can reduce user stress."

[0813] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0814] Step 1:

[0815] The user terminal uses a microphone and camera to acquire the user's voice and video data. This data includes the user's voice tone and facial expressions and is captured in real time. Input is audio files and image data, and output is the transmission of this data to the server.

[0816] Step 2:

[0817] The user terminal transmits the acquired audio and video data to the server via a secure protocol. This ensures that the data remains confidential and intact during analysis. The input consists of audio and video data files, and the output is a secure data transfer to the server.

[0818] Step 3:

[0819] The server processes the received audio data using the Google Speech-to-Text API to convert it from speech to text. Furthermore, it analyzes faces and expressions using OpenCV and Dlib on the image data. This allows for the analysis of the user's emotional state as numerical data. The input consists of audio files and image data, while the output consists of text data and emotion analysis data.

[0820] Step 4:

[0821] The server uses the acquired analysis data to run a machine learning model that predicts user behavioral tendencies and emotional patterns. It continuously improves the model by comparing past and current data using tools like TensorFlow. Inputs are text data and sentiment analysis data, while outputs are predicted emotional states and behavioral tendencies.

[0822] Step 5:

[0823] The server generates suggestions for optimal environmental settings for the user based on their predicted emotional state. These suggestions include the selection of lighting and music, aiming to create a relaxing environment for the user. The input is the predicted emotional state, and the output is the suggested environmental settings information.

[0824] Step 6:

[0825] The user terminal notifies the user of the suggestions received from the server. The user reviews these suggestions, accepts or modifies them, and sends that information back to the server. The input is the proposed environment configuration information, and the output is the acceptance or modification information of the suggestions.

[0826] Step 7:

[0827] The server processes user responses and automatically performs related environmental settings, such as adjusting lighting, playing music, or activating a fragrance diffuser. Input is user consent or modification information, and output is the result of the performed environmental settings.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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."

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] The following is further disclosed regarding the embodiments described above.

[0850] (Claim 1)

[0851] A means of obtaining activity information from the user's terminal,

[0852] A means for analyzing the acquired information and extracting behavioral patterns,

[0853] A means for generating proposals based on the extracted behavioral patterns,

[0854] A means for notifying the user terminal of the generated proposal,

[0855] A means of receiving user responses and automatically executing related tasks,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for integrating and managing users' private and business information.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for suggesting optimal time management using the user's location information and schedule information.

[0861] "Example 1"

[0862] (Claim 1)

[0863] A means of obtaining activity information from a communication device,

[0864] Means for storing the acquired activity information in a storage device,

[0865] A means for analyzing the stored activity information using a machine learning algorithm and extracting behavioral characteristics,

[0866] A means for generating proposals that match the user's needs based on the extracted behavioral characteristics,

[0867] Means for notifying the generated proposal to a communication device,

[0868] A means of receiving user responses and automatically executing related tasks,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, further comprising means for integrating and managing user personal information and business information.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising means for suggesting optimal time adjustments using the user's location information and itinerary management information.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] A means of obtaining activity data from the user's terminal,

[0877] A means for analyzing the acquired data and extracting behavioral trends,

[0878] A means for generating proposals based on the extracted behavioral trends,

[0879] A means for notifying the user terminal of the generated proposal,

[0880] A means of receiving user responses and automatically performing related tasks,

[0881] A method for suggesting departure times and event information using the user's current location information,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, further comprising means for integrating and managing users' personal information and business information.

[0885] (Claim 3)

[0886] The system according to claim 1, further comprising means for suggesting optimal time management using the user's location information and schedule information.

[0887] "Example 2 of combining an emotion engine"

[0888] (Claim 1)

[0889] A means of obtaining activity data and emotional data from the user's device,

[0890] A means for analyzing the acquired data to extract behavioral patterns and emotional patterns,

[0891] A means for generating optimal suggestions based on the user's emotional state and behavior, based on extracted behavioral and emotional patterns.

[0892] A means of adaptively notifying the user of the generated suggestions according to their emotional state,

[0893] A means of receiving user responses to proposals and automatically executing related tasks,

[0894] A means of optimizing the proposed content using generative AI technology,

[0895] A means of continuously improving the quality of suggestions based on user feedback,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for integrating and managing user privacy information and business data and ensuring data security.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for utilizing user location data and time information to propose efficient time management based on emotional state.

[0901] "Application example 2 when combining with an emotional engine"

[0902] (Claim 1)

[0903] A means of obtaining activity attributes from the user terminal,

[0904] A means for analyzing the acquired attributes and extracting behavioral tendencies,

[0905] A means for generating proposals based on the extracted behavioral trends,

[0906] A means for notifying the user terminal of the generated proposal,

[0907] A means of recommending appropriate environmental settings (lighting, music, etc.) that correspond to the user's emotional state,

[0908] A means of receiving user responses and automatically performing related tasks,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, further comprising means for integrating and managing users' private and professional information.

[0912] (Claim 3)

[0913] The system according to claim 1, further comprising means for suggesting optimal time management using the user's location information and schedule information. [Explanation of Symbols]

[0914] 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 obtaining activity data from the user's terminal, A means for analyzing the acquired data and extracting behavioral trends, A means for generating proposals based on the extracted behavioral trends, A means for notifying the user terminal of the generated proposal, A means of receiving user responses and automatically performing related tasks, A method for suggesting departure times and event information using the user's current location information, A system that includes this.

2. The system according to claim 1, further comprising means for integrating and managing users' personal information and business information.

3. The system according to claim 1, further comprising means for suggesting optimal time management using the user's location information and schedule information.

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