system

Abstract

We provide the system. [Solution] The system includes a device that receives information about the user's behavior, and means for collecting such information, A means of analyzing collected information and recognizing user behavior patterns, A means for generating specific suggestions that contribute to improving user efficiency based on the recognized behavioral patterns, A means of including schedule optimization that takes into account public transport delay information and congestion levels in the generated proposals, A means of displaying the generated suggestions to the user, The means used to collect user feedback and improve the proposed content, 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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people are required to efficiently manage their work, family, and personal time, but there is a problem that it is difficult to allocate time for the necessary considerations and analyses. In particular, it is difficult to achieve an efficient life because there is insufficient time and knowledge to understand one's own behavior patterns and find improvement points in a busy daily life. Therefore, there is a demand for the development of a system that can efficiently analyze an individual's behavior pattern and provide specific improvement proposals.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a means for recording the user's daily actions by providing a device that receives and collects data on the user's behavior. Furthermore, it provides a system that analyzes the collected data to recognize the user's behavior patterns and generates specific suggestions that contribute to improving the user's efficiency based on these patterns. This makes it possible to support an efficient lifestyle tailored to individual needs by providing the user with appropriate advice for behavioral improvement, collecting feedback based on that advice, and further improving the suggestions. In addition, this system includes functions for acquiring data from external devices with the user's permission and adjusting the content of suggestions based on user settings.

[0006] "Users" refer to individuals who use the system to receive suggestions for behavioral improvement.

[0007] "Behavioral data" refers to information related to the user's specific actions and schedule in their daily life.

[0008] "Device" refers to equipment or software used to receive and process data related to behavior.

[0009] "Means of data collection" refers to functions that acquire data through direct input from users or from external devices.

[0010] "Analysis" refers to the process of examining collected data and recognizing regularities and patterns.

[0011] "Behavioral patterns" refer to consistent tendencies and habits in a user's life and behavior.

[0012] "Suggestions that contribute to efficiency improvement" refer to specific advice that makes users' lives and actions more effective and efficient.

[0013] "Means of display" refers to interfaces or displays used to clearly communicate and convey the generated proposals to users.

[0014] "Feedback" refers to information that users return to the system regarding the results of actions taken based on suggestions and their impressions. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is an AI-powered system that provides users with specific advice to improve the efficiency of their daily lives. The system is installed as an application on the user's mobile device or computer and operates through a user-friendly interface.

[0037] Data collection

[0038] First, the device displays specific questions to the user and collects data on things like wake-up time, commute patterns, meal times, and exercise habits. In addition, with the user's permission, the device automatically retrieves additional data from external devices such as health trackers and calendar applications.

[0039] Data accumulation and analysis

[0040] The server receives data sent from the terminal and stores it for each user. The stored data is analyzed by machine learning algorithms to identify behavioral patterns in daily life. This clearly reveals inefficient use of time and areas for improvement in the user's lifestyle.

[0041] Proposal generation

[0042] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. These include action plans such as reviewing schedules and adjusting exercise times. The generated suggestions are customized to reflect the user's priorities.

[0043] Displaying proposals and collecting feedback

[0044] The terminal notifies the user of suggestions received from the server and displays the details on the application. The user reviews the suggestions and inputs feedback on the terminal regarding the results of their implementation and their impressions. This feedback is used to improve the accuracy of the suggestions.

[0045] Specific example

[0046] For example, suppose a user provides feedback stating, "I finish work late and have little free time before going to bed." Based on the data collected from the device, the server generates a specific suggestion, such as "Instead of going to bed a little later, incorporate a short relaxation time after returning home," and notifies the user of this suggestion. After the user implements this suggestion, they input feedback into the device indicating that they were able to relax, which provides feedback to the server regarding the effectiveness of the suggestion and is reflected in future suggestions.

[0047] In this way, the system continuously analyzes the user's daily life and provides individually customized improvement suggestions. This integrates the system into the user's daily life as a tool to help them live a more efficient life.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The device displays questions on the screen for users to input information about their daily activities, and the user enters their answers. For example, it provides fields for "wake-up time" and "commute time."

[0051] Step 2:

[0052] The device automatically acquires behavioral data (such as steps taken and exercise time) from external devices such as smartphones and wearable devices, based on the user's permission. This data, along with the input data, is used for subsequent analysis.

[0053] Step 3:

[0054] The device sends all collected data to the server either in a batch or sequentially. During this process, the data is processed in a way that ensures data integrity and privacy.

[0055] Step 4:

[0056] The server stores received user data in a database, identifying and accumulating it for each user. The database enables long-term data storage and smooth access.

[0057] Step 5:

[0058] The server analyzes the accumulated data using machine learning algorithms. This involves recognizing behavioral patterns and identifying inefficient activities, generating foundational data for future proposal formation.

[0059] Step 6:

[0060] Based on the analysis results, the server generates specific improvement suggestions tailored to the user's lifestyle. These suggestions are adjusted according to the user's goals and past feedback.

[0061] Step 7:

[0062] The server sends the generated suggestions to the user's device. It may also use notifications and alerts to highlight important suggestions to the user.

[0063] Step 8:

[0064] The device displays received suggestions to the user and provides detailed explanations. It also provides users with a function to easily provide feedback on the effectiveness and satisfaction level after implementing the suggestions.

[0065] Step 9:

[0066] Users input their impressions and detailed feedback on the results of trying out the suggestions into their device. This information serves as foundational data for determining how effective each individual suggestion was.

[0067] Step 10:

[0068] The server collects feedback from users and analyzes it to improve the accuracy of future suggestions. This increases the accuracy of providing customized support for each user.

[0069] (Example 1)

[0070] 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."

[0071] In modern society, it is crucial for individuals to use their time efficiently in their daily lives and improve their quality of life. However, many people find it difficult to identify the inefficiencies hidden in their own behavioral patterns and find ways to improve them. Traditional methods often fail to provide sufficiently personalized and specific improvement suggestions that can be adapted to daily life.

[0072] 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.

[0073] In this invention, the server includes means for receiving and collecting information about the user's daily life, means for automatically acquiring authorized information from an external measuring device, and means for storing the received information and identifying behavioral patterns using computational processing means. This makes it possible to generate personalized and specific suggestions for each user and improve the efficiency of their daily life.

[0074] A "user" is an individual who uses the system to provide information about their daily life and receive suggestions for improving efficiency.

[0075] An "external measurement device" is a device that automatically acquires user behavior data, health information, etc., and provides it to the terminal.

[0076] "Means of receiving and collecting information" refers to methods and devices for receiving and storing information related to users' daily lives in digital format.

[0077] "Computational processing means" refers to algorithms and software programs used to analyze received data and identify behavioral patterns.

[0078] "Means for identifying behavioral patterns" refer to methods and techniques for analyzing collected information to derive user-specific behaviors and habits.

[0079] "Means for generating proposals" refer to technologies and software that create specific proposals aimed at improving user efficiency based on the results of analysis.

[0080] This invention is a system that generates suggestions for efficiency improvement based on information about the user's daily life. The system is constructed in which a terminal and a server cooperate, and includes integration with external measuring devices.

[0081] The device displays questions about the user's daily life and allows the user to input their answers. For example, the user can answer questions such as, "What time do you wake up on weekdays?" Furthermore, with the user's permission, the device automatically acquires data from external measuring devices such as health trackers and calendar apps and sends it to a server. Smartphone apps and computer applications are used for this process.

[0082] The server receives data sent from terminals, stores it in a database, and classifies it by user. In data analysis, the server uses machine learning algorithms such as Python's scikit-learn to identify behavioral patterns. This analysis can identify inefficient time usage and lifestyle habits that can be improved.

[0083] The server then generates specific suggestions based on identified behavioral patterns. Natural language processing (NLP) techniques are also utilized at this stage to provide customized suggestions tailored to the user's lifestyle and priorities. The generated suggestions are sent from the server to the terminal and notified to the user.

[0084] The terminal allows users to view the details of a suggestion and input feedback on the results and their impressions after implementing it. The server then uses this feedback to continuously improve the suggestions.

[0085] For example, if a user inputs "I want to efficiently manage my daily exercise time," the system will consider their existing commuting pattern and exercise habits and make suggestions such as "shorten your morning exercise time and add light exercise during your lunch break." An example of a prompt might be, "Based on my current lifestyle data, please generate specific suggestions to improve my daily life."

[0086] This system aims to improve the quality and efficiency of daily life by providing personalized suggestions tailored to each user's lifestyle.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The device displays questions about the user's daily life and collects information. This input includes the user entering their "weekday wake-up time" and "means of commuting." The device temporarily stores the data entered by the user and standardizes the data format.

[0090] Step 2:

[0091] The device automatically acquires data from external measuring devices (e.g., health trackers, calendar apps) with the user's permission. This data includes heart rate and schedule information. The device acquires this data and integrates it with user input data.

[0092] Step 3:

[0093] The terminal collects all the data and sends it to the server using a secure communication protocol. The input data consists of numerical and categorical data related to the user's daily life, and is formatted in a way that is necessary for analysis on the server.

[0094] Step 4:

[0095] The server stores the received data in a database for each user and performs data preprocessing. This preprocessing includes data normalization and imputation of missing values. After the data cleansing is complete, the server uses machine learning algorithms to analyze behavioral patterns.

[0096] Step 5:

[0097] The server generates specific suggestions to improve user efficiency based on analyzed behavioral patterns. In this process, an AI model utilizing NLP constructs suggestions in natural language. The output is personalized improvement suggestions tailored to the user.

[0098] Step 6:

[0099] The server sends the generated suggestions to the device, and the device notifies the user. The device displays the suggestions as push notifications or in-app messages. The user reviews the details of the suggestions and tries to improve their lifestyle according to them.

[0100] Step 7:

[0101] Users input feedback on their device regarding the results and their impressions after implementing the suggestion. This feedback is important information for evaluating the effectiveness of the suggestion.

[0102] Step 8:

[0103] The terminal sends the acquired feedback to the server, which stores this feedback and uses it to improve future suggestions. The server uses the feedback to update its machine learning model and improve the accuracy of its suggestions.

[0104] (Application Example 1)

[0105] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0106] In modern urban life, inefficiency and wasted time in individual activities are challenges for many people. In particular, commuting and daily schedule management involve many unpredictable factors, making efficient time management difficult. Furthermore, proposed improvements often lack sufficient consideration of user priorities and external circumstances, reducing their feasibility.

[0107] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0108] This invention includes a server equipped with a device for receiving information on user behavior, a method for collecting such information, a method for including schedule optimization in the generated suggestions that takes into account delay information and congestion status of public transport, and a method for adjusting the suggestion content considering the user's priority settings for the generated suggestions and traffic information. This makes it possible to improve the efficiency of time management in the user's daily life.

[0109] A "user" is an individual who aims to improve their daily life more efficiently by using the system.

[0110] "Behavioral patterns" refer to the characteristics of how users behave in their daily lives based on certain cycles or rules.

[0111] A "proposal" refers to a set of specific action plans based on the analysis of behavioral patterns, designed to improve the efficiency of the user's daily life.

[0112] "Public transport" refers to means of transportation that are commonly used by people for travel, including buses and trains.

[0113] "Delay information" refers to data that shows deviations from the normal operating schedule of public transportation.

[0114] "Congestion level" refers to information about the density of people in public transport and other public spaces.

[0115] "Schedule optimization" is the process of reducing wasted time and improving convenience by rearranging users' planned activities in an efficient and effective manner.

[0116] "Feedback" refers to information that improves the accuracy of suggestions by transmitting to the system the results and impressions of actions taken by users in response to suggested actions.

[0117] The system for implementing this invention consists of a server and a user terminal. The server receives and processes data related to the user's daily life and recognizes specific behavioral patterns to generate suggestions for living an efficient life. The terminal has the function of displaying specific suggestions to the user, collecting feedback on those suggestions, and sending it to the server.

[0118] The program is implemented in programming languages ​​such as Python, and utilizes the machine learning library scikit-learn for data analysis. Specifically, it analyzes user behavior data using clustering techniques to recognize patterns. The server has the functionality to import real-time delay and congestion information for public transport from external traffic information APIs and reflect this in the optimization of suggestions. It also customizes the suggestions according to the user's priority settings and displays them on the terminal.

[0119] For example, if a user provides feedback to the system stating that they "want to shorten their commute time," the server will suggest an optimal start time based on past commute data and traffic information, and notify the user on their device. An example of a prompt message regarding this suggestion would be, "Please tell us the best way to shorten your commute time to make your daily life more efficient."

[0120] As described above, the server and terminal work together to provide users with a system that continuously offers concrete and practical support to streamline their daily lives.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] Users use a device to input data about their daily lives. Specifically, they input information such as their wake-up time, commute time, and meal times into the device and send it to the server as collected data. In this process, the device appropriately formats the input information and converts it into a format that the server can understand before sending it.

[0124] Step 2:

[0125] The server receives user behavior data sent from the terminal. To analyze the received data, it is stored in a database and clustering techniques are used to identify behavioral patterns. Specifically, the scikit-learn KMeans algorithm is used to classify the data into several patterns and identify inefficient use of time.

[0126] Step 3:

[0127] The server obtains real-time information on public transportation via external APIs. Based on delay information and congestion levels, it generates optimized daily schedule suggestions for users. In this process, it integrates traffic information with previously identified behavioral patterns and uses AI algorithms to identify potential areas for improvement.

[0128] Step 4:

[0129] The generated suggestions are finalized, taking into account the user's priority settings. The suggestions are customized based on the user's past feedback and settings. The customized suggestions are then output by the system's AI model, using prompts as a reference, in a format that is easy for the user to follow.

[0130] Step 5:

[0131] The terminal receives optimized suggestions from the server and presents them to the user. The user reviews the suggestions and decides whether to implement them. The suggestions are displayed on the interface in a format that is easy for the user to understand intuitively.

[0132] Step 6:

[0133] After the user executes the suggestion, they enter feedback. The terminal formats the feedback appropriately and sends it to the server. The feedback is used to improve the suggestion and train the AI ​​model. The server uses this feedback to improve the accuracy of future suggestions.

[0134] 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.

[0135] This invention relates to a system that provides individually customized efficiency improvement suggestions using user behavior data and emotional states. This system operates as an application installed on mobile devices and computers, and interacts with users through a user-friendly interface.

[0136] Data collection and emotion recognition

[0137] First, the device displays questions to the user about their daily activities and schedule, and collects their responses. With the user's permission, the device also automatically acquires motion data from their smartphone or wearable device. Furthermore, the device uses facial recognition and voice analysis technologies to collect emotional data to determine the user's emotions.

[0138] Data storage and analysis

[0139] The server receives all data (behavioral and emotional data) transmitted from the terminal and securely and efficiently stores it in user-specific profiles. Next, the server uses machine learning algorithms to analyze this data and recognize the user's behavioral and emotional patterns.

[0140] Proposal generation and emotional application

[0141] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. In this process, the emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it prioritizes suggesting ways to relax.

[0142] Proposal presentation and feedback gathering

[0143] The device notifies the user of the generated suggestions and displays the details in the application. The server collects feedback from the user on the device regarding the results of trying the suggested actions and any changes in their emotions during that process. This feedback is used to improve the accuracy of future suggestions.

[0144] Specific example

[0145] For example, when a user inputs feedback such as "I feel stressed due to fatigue after work," the device uses its emotion engine to suggest a relaxation plan to alleviate facial tension. This includes specific action plans such as "stretching after leaving work on time." The user then inputs feedback after trying this, and based on that, the server generates new suggestions that are further tailored to the user's emotions.

[0146] In this way, the system continuously analyzes users' emotional and behavioral data and provides individually optimized suggestions to support their daily lives.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] The device displays questions about the user's daily life and collects information such as wake-up time, meal times, commute patterns, and bedtime. Basic behavioral data is accumulated as the user answers these questions.

[0150] Step 2:

[0151] With the user's permission, the device automatically collects behavioral data from smartphones and wearable devices. This includes information such as steps taken, heart rate, and activity level.

[0152] Step 3:

[0153] The device uses the user's facial recognition camera and microphone to collect emotional data in real time from their facial expressions and voice. The data collected is used to understand the user's emotional state.

[0154] Step 4:

[0155] The device sends all collected data (behavioral and emotional data) to the server. The data is encrypted for privacy protection and transmitted securely.

[0156] Step 5:

[0157] The server stores the received data in a database, which is then organized for each user. The stored data forms the basis for analyzing long-term behavioral and emotional patterns.

[0158] Step 6:

[0159] The server performs machine learning based on accumulated data to analyze users' behavioral and emotional patterns. This analysis identifies what kinds of emotions users tend to experience in different situations.

[0160] Step 7:

[0161] Based on the analysis results, the server generates specific behavioral improvement suggestions tailored to the user's emotional state. For example, if it detects high levels of fatigue, it suggests a schedule that prioritizes rest.

[0162] Step 8:

[0163] The server sends the generated proposal to the terminal. The terminal notifies the application of the received proposal and displays detailed information within the application.

[0164] Step 9:

[0165] The user reviews the suggestions displayed on their device and decides whether to implement them. If they implement a suggestion, they provide feedback on the results, their feelings, and any changes in their emotions during the process.

[0166] Step 10:

[0167] The server collects feedback from users and re-analyzes the data to incorporate it into future suggestions. This feedback loop enables more personalized suggestions.

[0168] (Example 2)

[0169] 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".

[0170] In modern society, the decrease in efficiency and increase in stress experienced by individual users in their daily lives are serious problems. To address this, suggestions based solely on behavioral data are insufficient; highly accurate suggestions that also consider emotional states are required. However, conventional technologies have struggled to provide optimal suggestions based on emotions, failing to achieve efficiency improvements tailored to individual user needs.

[0171] 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.

[0172] In this invention, the server includes a device for receiving information on the user's behavior and emotions, means for collecting information, means for analyzing the collected information and recognizing patterns in the user's behavior and emotions, and means for using a generative AI model that generates suggestions that contribute to improving the user's efficiency based on the recognized patterns. This makes it possible to effectively provide efficient and individually optimized suggestions that are tailored to the user's emotional state.

[0173] "Users" refer to individuals who use this system and are the entities that provide information about their behavior and emotions.

[0174] "Behavior" refers to information related to the user's daily activities, including data on physical movements and schedules.

[0175] "Emotions" refers to information that identifies the user's psychological and emotional state, and includes data obtained through facial recognition and voice analysis.

[0176] "Information" encompasses all data related to users' behavior and emotions.

[0177] "Collection" refers to the process of obtaining data provided by users and gathering it into the system.

[0178] "Analysis" refers to the process of identifying and analyzing patterns in users' behavior and emotions based on collected data.

[0179] A "generative AI model" is an artificial intelligence technology used to create proposals, particularly one that utilizes natural language processing.

[0180] "Means" refers to the equipment, techniques, or methods that a system uses to perform a particular function.

[0181] "System" refers to a combination of devices and software operated using the above-mentioned means for the purpose of improving user efficiency.

[0182] In an embodiment of this invention, the system is configured and operates as follows:

[0183] Data collection and emotion recognition

[0184] First, the device displays a questionnaire to the user through an application, asking about their daily activities and schedule. When the user answers the questions on their smartphone, the device collects that data. Furthermore, with the user's permission, the device collects motion data such as location information, steps taken, and heart rate from the smartphone and wearable device. This uses the smartphone's sensors and the wearable device's APIs. In addition, the device utilizes an open-source facial recognition framework (e.g., OpenCV) for facial recognition technology and a speech recognition API (e.g., an API that converts speech to text) for speech analysis technology to obtain data necessary for determining the user's emotions.

[0185] Data storage and analysis

[0186] The server stores behavioral and emotional data transmitted from the device in a cloud database. Because this data is efficiently stored in each user's profile, encrypted protocols (e.g., HTTPS) are used for secure communication between the server and the device. The server uses machine learning algorithms (e.g., models using TENSORFLOW®) to analyze the data and recognize user behavioral and emotional patterns.

[0187] Proposal generation and display

[0188] The server uses the analyzed data to create suggestions for improving user efficiency, utilizing a generative AI model (e.g., an AI model using natural language generation technology). These suggestions take into account the user's emotional state, and the content is adjusted to meet the user's individual needs. The terminal notifies the user of these suggestions and displays detailed information within the application.

[0189] Gathering feedback and improving suggestions

[0190] Users try out suggestions and provide feedback on their devices about the results and changes in their feelings. This feedback is used on the server side to further optimize future suggestions.

[0191] Specific example

[0192] For example, if a user enters the prompt message, "I'm feeling stressed today. Do you have any suggestions for relaxing?", the device will suggest a relaxation plan such as, "I recommend a 10-minute stretch after work." In this way, the system uses user data to provide individually optimized suggestions, helping to improve the efficiency of the user's daily life.

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] The device displays questions about the user's daily activities and schedule through the application. It receives the user's answers to these questions as input. This input data is recorded as basic information about the user's behavioral patterns. Specifically, the device presents questions using a survey-style UI and waits until the user enters their answers.

[0196] Step 2:

[0197] The device automatically collects operational data from smartphones and wearable devices with the user's permission. This input consists of continuous data from the device, such as location, steps taken, and heart rate. This data is centrally managed and stored on the device. Specifically, the device periodically polls and retrieves data using the device API.

[0198] Step 3:

[0199] The device uses facial recognition and voice analysis technologies to determine the user's emotions. This process uses video of the face captured by the camera and audio from the microphone as input. The algorithm used (e.g., facial recognition framework and voice analysis API) generates and records emotion data. Specifically, the device analyzes the video / audio feed and labels the emotional state in real time.

[0200] Step 4:

[0201] The device sends collected behavioral and emotional data to the server. The input includes all data collected and generated to date. The output to the server ensures that the data is securely transferred and received. Specifically, the device uploads the data to the cloud service using the HTTPS protocol.

[0202] Step 5:

[0203] The server stores the received data in a cloud database. The input consists of behavioral and emotional data sent from the terminal, which is efficiently accumulated. The data stored in the database is used for subsequent analysis. Specifically, the server records the data in database software (e.g., a relational database).

[0204] Step 6:

[0205] The server analyzes stored data using machine learning algorithms. The input consists of accumulated behavioral and emotional data, and the analysis recognizes user behavioral and emotional patterns. The output is detailed insights into behavioral patterns. Specifically, the server uses analysis tools (e.g., TensorFlow) to model the data.

[0206] Step 7:

[0207] The server uses a generative AI model based on the analysis results to generate optimal suggestions for the user. Input includes recognized behavioral and emotional patterns. Output consists of specific suggestions designed to improve the user's efficiency. Specifically, natural language generation technology is used to translate the suggestions into text.

[0208] Step 8:

[0209] The device receives suggestions from the server and notifies the user. The input is the generated suggestion content received from the server. The output is the suggestion itself. Specifically, the device displays a pop-up notification or in-app display to allow the user to review the suggestion.

[0210] Step 9:

[0211] The user is given the opportunity to try out the presented suggestions and inputs their feedback into the device. The input includes the user's impressions and actions regarding the suggestions. The output is feedback data used to generate future suggestions. Specifically, the user enters their answers into a feedback form on the device.

[0212] Step 10:

[0213] The server uses user feedback as data to improve the accuracy of its suggestions. Input includes user feedback data, and output represents improvements for future suggestion generation. Specifically, the server analyzes the feedback and uses it to refine its machine learning model.

[0214] (Application Example 2)

[0215] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0216] The challenge lies in accurately understanding the daily activities and emotional states of users, including the elderly, and providing means to support their efficient lives while reducing their stress. In particular, it is necessary to provide suggestions that reflect changes in users' emotions in real time and utilize them to support caregiving.

[0217] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0218] In this invention, the server includes means for receiving and collecting data on user behavior, means for analyzing the collected data to recognize behavioral patterns, and means for recognizing the user's emotional state and adjusting the generated suggestions based on that emotional state. This makes it possible to provide individually optimized efficiency improvement suggestions that are tailored to the user's emotions.

[0219] "Behavioral data" refers to information related to the user's actions and habits in their daily life, and is used to understand the user's behavioral patterns.

[0220] "Behavioral patterns" refer to a set of data used to analyze and identify consistent behavioral and habitual tendencies of users, and serve as a foundation for generating suggestions for improving efficiency.

[0221] "Specific suggestions that contribute to improved efficiency" refer to suggestions for changes in specific behaviors or habits that users should make in their daily lives to reduce stress and increase comfort.

[0222] "Means of recognizing emotional states" refers to methods of collecting data using technologies such as facial recognition and voice analysis to determine the user's current emotions and psychological state.

[0223] "Biometric indicators" are data that shows the user's physical condition, such as heart rate and body temperature, and are used to evaluate their emotional state and health status.

[0224] "Care support" refers to support activities aimed at efficiently providing daily care and assistance to improve the quality of life for the elderly and people with disabilities.

[0225] The system realizing this invention is designed to analyze the user's daily behavior and emotions to provide personalized suggestions. The server collects and analyzes data on the user's behavior. The hardware used includes smartphones, fitness trackers, and camera-equipped devices. Receiving data from these devices, the server uses OpenCV for facial recognition and Google® Speech API for speech analysis as software for analyzing the information. In addition, it utilizes TensorFlow, a machine learning platform, to analyze behavioral and emotional data.

[0226] The device uses collected data to determine the user's behavioral patterns and emotional state. Based on the user's emotional state, the server generates specific suggestions to improve efficiency. For example, if the user is feeling stressed, relaxation exercises can be recommended. This can reduce the user's stress and improve their quality of life. This process is continuous, and the system keeps improving its suggestions based on user feedback.

[0227] A concrete example of a prompt message might be, "The user is feeling stressed; suggest a 10-minute breathing exercise to help them relax." This allows the generative AI model to generate the most appropriate action suggestion for the user, which the user can then perform.

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The device collects data about the user's daily activities. It uses location information and activity level data obtained from smartphones and fitness trackers as input. As output, it transfers this data to a server. Specifically, the device synchronizes with the fitness tracker to record daily step counts and travel route information.

[0231] Step 2:

[0232] The server analyzes the collected data to recognize user behavior patterns. It receives activity data transferred from the terminal as input and generates a profile of the recognized behavior patterns as output. Specifically, the server uses machine learning algorithms to analyze daily behavior data and model trends in movement frequency and time of day.

[0233] Step 3:

[0234] The device acquires live data from its camera and microphone to recognize the user's emotional state. Facial images and audio data are used as input. The output generates an index indicating the emotional state. Specifically, the device's camera captures the user's facial expressions, and OpenCV is used to analyze changes in microexpressions.

[0235] Step 4:

[0236] The server generates appropriate efficiency improvement suggestions based on recognized behavioral patterns and emotional states. It uses behavioral pattern profiles and emotional state indicators as input. Customized suggestions are generated as output. Specifically, the server uses a generated AI model to formulate suggestions based on the prompt statement, "The user is feeling stressed; suggest 10 minutes of relaxing breathing exercises."

[0237] Step 5:

[0238] The device presents the generated suggestions to the user. It receives suggestions generated by the server as input. As output, it communicates the content of the suggestions to the user visually or audibly. Specifically, it uses the smartphone's notification function to display a suggestion to the user: "Shall we start a 10-minute breathing exercise now?"

[0239] Step 6:

[0240] After trying out a suggestion, the user provides feedback on the results and experience to their device. The input includes the action taken and the resulting emotional changes. The output is feedback data sent to the server. Specifically, the user writes their thoughts on the suggestion's effectiveness on a smartphone app and presses the submit button.

[0241] Step 7:

[0242] The server receives feedback from users and stores and analyzes data to improve the accuracy of future suggestions. User feedback data is used as input. The output is an updated, improved suggestion algorithm. Specifically, the server retrains the machine learning model with the new data to improve the algorithm's performance.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention is an AI-powered system that provides users with specific advice to improve the efficiency of their daily lives. The system is installed as an application on the user's mobile device or computer and operates through a user-friendly interface.

[0260] Data collection

[0261] First, the device displays specific questions to the user and collects data on things like wake-up time, commute patterns, meal times, and exercise habits. In addition, with the user's permission, the device automatically retrieves additional data from external devices such as health trackers and calendar applications.

[0262] Data accumulation and analysis

[0263] The server receives data sent from the terminal and stores it for each user. The stored data is analyzed by machine learning algorithms to identify behavioral patterns in daily life. This clearly reveals inefficient use of time and areas for improvement in the user's lifestyle.

[0264] Proposal generation

[0265] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. These include action plans such as reviewing schedules and adjusting exercise times. The generated suggestions are customized to reflect the user's priorities.

[0266] Displaying proposals and collecting feedback

[0267] The terminal notifies the user of suggestions received from the server and displays the details on the application. The user reviews the suggestions and inputs feedback on the terminal regarding the results of their implementation and their impressions. This feedback is used to improve the accuracy of the suggestions.

[0268] Specific example

[0269] For example, suppose a user provides feedback stating, "I finish work late and have little free time before going to bed." Based on the data collected from the device, the server generates a specific suggestion, such as "Instead of going to bed a little later, incorporate a short relaxation time after returning home," and notifies the user of this suggestion. After the user implements this suggestion, they input feedback into the device indicating that they were able to relax, which provides feedback to the server regarding the effectiveness of the suggestion and is reflected in future suggestions.

[0270] In this way, the system continuously analyzes the user's daily life and provides individually customized improvement suggestions. This integrates the system into the user's daily life as a tool to help them live a more efficient life.

[0271] The following describes the processing flow.

[0272] Step 1:

[0273] The device displays questions on the screen for users to input information about their daily activities, and the user enters their answers. For example, it provides fields for "wake-up time" and "commute time."

[0274] Step 2:

[0275] The device automatically acquires behavioral data (such as steps taken and exercise time) from external devices such as smartphones and wearable devices, based on the user's permission. This data, along with the input data, is used for subsequent analysis.

[0276] Step 3:

[0277] The device sends all collected data to the server either in a batch or sequentially. During this process, the data is processed in a way that ensures data integrity and privacy.

[0278] Step 4:

[0279] The server stores the received user data in a database, identifies and accumulates it for each user. The database enables long-term data accumulation and smooth access.

[0280] Step 5:

[0281] The server analyzes the accumulated data using a machine learning algorithm. Here, it recognizes behavior patterns and identifies inefficient activities, generating the basic data for the next proposal formation.

[0282] Step 6:

[0283] Based on the analysis results, the server generates specific improvement proposals tailored to the user's lifestyle. These proposals are adjusted according to the user's goals and past feedback.

[0284] Step 7:

[0285] The server sends the generated proposals to the user's terminal. For the user, notifications or alerts may be used to make important proposals stand out.

[0286] Step 8:

[0287] The terminal displays the received proposals to the user and provides detailed explanations. Also, after the execution of the proposals, it provides the user with a function to easily give feedback on the effects and satisfaction levels.

[0288] Step 9:

[0289] The user inputs their feelings and detailed feedback on the results of trying out the proposals into the terminal. This information becomes the basic data for judging how effective each individual proposal was.

[0290] Step 10:

[0291] The server collects feedback from users and analyzes it to improve the accuracy of future suggestions. This increases the accuracy of providing customized support for each user.

[0292] (Example 1)

[0293] 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."

[0294] In modern society, it is crucial for individuals to use their time efficiently in their daily lives and improve their quality of life. However, many people find it difficult to identify the inefficiencies hidden in their own behavioral patterns and find ways to improve them. Traditional methods often fail to provide sufficiently personalized and specific improvement suggestions that can be adapted to daily life.

[0295] 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.

[0296] In this invention, the server includes means for receiving and collecting information about the user's daily life, means for automatically acquiring authorized information from an external measuring device, and means for storing the received information and identifying behavioral patterns using computational processing means. This makes it possible to generate personalized and specific suggestions for each user and improve the efficiency of their daily life.

[0297] A "user" is an individual who uses the system to provide information about their daily life and receive suggestions for improving efficiency.

[0298] An "external measurement device" is a device that automatically acquires user behavior data, health information, etc., and provides it to the terminal.

[0299] "Means of receiving and collecting information" refers to methods and devices for receiving and storing information related to users' daily lives in digital format.

[0300] "Computational processing means" refers to algorithms and software programs used to analyze received data and identify behavioral patterns.

[0301] "Means for identifying behavioral patterns" refer to methods and techniques for analyzing collected information to derive user-specific behaviors and habits.

[0302] "Means for generating proposals" refer to technologies and software that create specific proposals aimed at improving user efficiency based on the results of analysis.

[0303] This invention is a system that generates suggestions for efficiency improvement based on information about the user's daily life. The system is constructed in which a terminal and a server cooperate, and includes integration with external measuring devices.

[0304] The device displays questions about the user's daily life and allows the user to input their answers. For example, the user can answer questions such as, "What time do you wake up on weekdays?" Furthermore, with the user's permission, the device automatically acquires data from external measuring devices such as health trackers and calendar apps and sends it to a server. Smartphone apps and computer applications are used for this process.

[0305] The server receives data sent from terminals, stores it in a database, and classifies it by user. In data analysis, the server uses machine learning algorithms such as Python's scikit-learn to identify behavioral patterns. This analysis can identify inefficient time usage and lifestyle habits that can be improved.

[0306] The server further generates specific proposals based on the identified behavior patterns. At this stage, natural language processing technology (NLP) is also utilized to make customized proposals that match the user's lifestyle and priorities. The generated proposals are sent from the server to the terminal and notified to the user.

[0307] The terminal enables the user to view the details of the proposal and allows the user to input the results and feelings after executing it as feedback. Thereby, the server continuously improves the proposal content using this feedback.

[0308] As a specific example, when the user inputs "want to manage daily exercise time efficiently", the system makes proposals such as "shorten the morning exercise time and add light exercise during the lunch break" considering the existing commuting patterns and exercise habits. As an example of the prompt sentence, a form such as "Please generate specific proposals to improve my daily life based on the current lifestyle data" can be considered.

[0309] This system aims to improve the quality and efficiency of daily life by making proposals tailored to the user's individual lifestyle.

[0310] The flow of the specific process in Example 1 will be described using FIG. 11.

[0311] Step 1:

[0312] The terminal displays questions regarding the user's daily life to the user and collects information. This input includes the user inputting "weekday wake-up time" and "commuting means". The terminal temporarily stores the data input by the user and unifies the data format.

[0313] Step 2:

[0314] The device automatically acquires data from external measuring devices (e.g., health trackers, calendar apps) with the user's permission. This data includes heart rate and schedule information. The device acquires this data and integrates it with user input data.

[0315] Step 3:

[0316] The terminal collects all the data and sends it to the server using a secure communication protocol. The input data consists of numerical and categorical data related to the user's daily life, and is formatted in a way that is necessary for analysis on the server.

[0317] Step 4:

[0318] The server stores the received data in a database for each user and performs data preprocessing. This preprocessing includes data normalization and imputation of missing values. After the data cleansing is complete, the server uses machine learning algorithms to analyze behavioral patterns.

[0319] Step 5:

[0320] The server generates specific suggestions to improve user efficiency based on analyzed behavioral patterns. In this process, an AI model utilizing NLP constructs suggestions in natural language. The output is personalized improvement suggestions tailored to the user.

[0321] Step 6:

[0322] The server sends the generated suggestions to the device, and the device notifies the user. The device displays the suggestions as push notifications or in-app messages. The user reviews the details of the suggestions and tries to improve their lifestyle according to them.

[0323] Step 7:

[0324] Users input feedback on their device regarding the results and their impressions after implementing the suggestion. This feedback is important information for evaluating the effectiveness of the suggestion.

[0325] Step 8:

[0326] The terminal sends the acquired feedback to the server, which stores this feedback and uses it to improve future suggestions. The server uses the feedback to update its machine learning model and improve the accuracy of its suggestions.

[0327] (Application Example 1)

[0328] 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."

[0329] In modern urban life, inefficiency and wasted time in individual activities are challenges for many people. In particular, commuting and daily schedule management involve many unpredictable factors, making efficient time management difficult. Furthermore, proposed improvements often lack sufficient consideration of user priorities and external circumstances, reducing their feasibility.

[0330] 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.

[0331] This invention includes a server equipped with a device for receiving information on user behavior, a method for collecting such information, a method for including schedule optimization in the generated suggestions that takes into account delay information and congestion status of public transport, and a method for adjusting the suggestion content considering the user's priority settings for the generated suggestions and traffic information. This makes it possible to improve the efficiency of time management in the user's daily life.

[0332] A "user" is an individual who aims to improve their daily life more efficiently by using the system.

[0333] "Behavioral patterns" refer to the characteristics of how users behave in their daily lives based on certain cycles or rules.

[0334] A "proposal" refers to a set of specific action plans based on the analysis of behavioral patterns, designed to improve the efficiency of the user's daily life.

[0335] "Public transport" refers to means of transportation that are commonly used by people for travel, including buses and trains.

[0336] "Delay information" refers to data that shows deviations from the normal operating schedule of public transportation.

[0337] "Congestion level" refers to information about the density of people in public transport and other public spaces.

[0338] "Schedule optimization" is the process of reducing wasted time and improving convenience by rearranging users' planned activities in an efficient and effective manner.

[0339] "Feedback" refers to information that improves the accuracy of suggestions by transmitting to the system the results and impressions of actions taken by users in response to suggested actions.

[0340] The system for implementing this invention consists of a server and a user terminal. The server receives and processes data related to the user's daily life and recognizes specific behavioral patterns to generate suggestions for living an efficient life. The terminal has the function of displaying specific suggestions to the user, collecting feedback on those suggestions, and sending it to the server.

[0341] The program is implemented in programming languages ​​such as Python, and utilizes the machine learning library scikit-learn for data analysis. Specifically, it analyzes user behavior data using clustering techniques to recognize patterns. The server has the functionality to import real-time delay and congestion information for public transport from external traffic information APIs and reflect this in the optimization of suggestions. It also customizes the suggestions according to the user's priority settings and displays them on the terminal.

[0342] For example, if a user provides feedback to the system stating that they "want to shorten their commute time," the server will suggest an optimal start time based on past commute data and traffic information, and notify the user on their device. An example of a prompt message regarding this suggestion would be, "Please tell us the best way to shorten your commute time to make your daily life more efficient."

[0343] As described above, the server and terminal work together to provide users with a system that continuously offers concrete and practical support to streamline their daily lives.

[0344] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0345] Step 1:

[0346] Users use a device to input data about their daily lives. Specifically, they input information such as their wake-up time, commute time, and meal times into the device and send it to the server as collected data. In this process, the device appropriately formats the input information and converts it into a format that the server can understand before sending it.

[0347] Step 2:

[0348] The server receives user behavior data sent from the terminal. To analyze the received data, it is stored in a database and clustering techniques are used to identify behavioral patterns. Specifically, the scikit-learn KMeans algorithm is used to classify the data into several patterns and identify inefficient use of time.

[0349] Step 3:

[0350] The server obtains real-time information on public transportation via external APIs. Based on delay information and congestion levels, it generates optimized daily schedule suggestions for users. In this process, it integrates traffic information with previously identified behavioral patterns and uses AI algorithms to identify potential areas for improvement.

[0351] Step 4:

[0352] The generated suggestions are finalized, taking into account the user's priority settings. The suggestions are customized based on the user's past feedback and settings. The customized suggestions are then output by the system's AI model, using prompts as a reference, in a format that is easy for the user to follow.

[0353] Step 5:

[0354] The terminal receives optimized suggestions from the server and presents them to the user. The user reviews the suggestions and decides whether to implement them. The suggestions are displayed on the interface in a format that is easy for the user to understand intuitively.

[0355] Step 6:

[0356] After the user executes the suggestion, they enter feedback. The terminal formats the feedback appropriately and sends it to the server. The feedback is used to improve the suggestion and train the AI ​​model. The server uses this feedback to improve the accuracy of future suggestions.

[0357] 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.

[0358] This invention relates to a system that provides individually customized efficiency improvement suggestions using user behavior data and emotional states. This system operates as an application installed on mobile devices and computers, and interacts with users through a user-friendly interface.

[0359] Data collection and emotion recognition

[0360] First, the device displays questions to the user about their daily activities and schedule, and collects their responses. With the user's permission, the device also automatically acquires motion data from their smartphone or wearable device. Furthermore, the device uses facial recognition and voice analysis technologies to collect emotional data to determine the user's emotions.

[0361] Data storage and analysis

[0362] The server receives all data (behavioral and emotional data) transmitted from the terminal and securely and efficiently stores it in user-specific profiles. Next, the server uses machine learning algorithms to analyze this data and recognize the user's behavioral and emotional patterns.

[0363] Proposal generation and emotional application

[0364] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. In this process, the emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it prioritizes suggesting ways to relax.

[0365] Proposal presentation and feedback gathering

[0366] The device notifies the user of the generated suggestions and displays the details in the application. The server collects feedback from the user on the device regarding the results of trying the suggested actions and any changes in their emotions during that process. This feedback is used to improve the accuracy of future suggestions.

[0367] Specific example

[0368] For example, when a user inputs feedback such as "I feel stressed due to fatigue after work," the device uses its emotion engine to suggest a relaxation plan to alleviate facial tension. This includes specific action plans such as "stretching after leaving work on time." The user then inputs feedback after trying this, and based on that, the server generates new suggestions that are further tailored to the user's emotions.

[0369] In this way, the system continuously analyzes users' emotional and behavioral data and provides individually optimized suggestions to support their daily lives.

[0370] The following describes the processing flow.

[0371] Step 1:

[0372] The device displays questions about the user's daily life and collects information such as wake-up time, meal times, commute patterns, and bedtime. Basic behavioral data is accumulated as the user answers these questions.

[0373] Step 2:

[0374] With the user's permission, the device automatically collects behavioral data from smartphones and wearable devices. This includes information such as steps taken, heart rate, and activity level.

[0375] Step 3:

[0376] The device uses the user's facial recognition camera and microphone to collect emotional data in real time from their facial expressions and voice. The data collected is used to understand the user's emotional state.

[0377] Step 4:

[0378] The device sends all collected data (behavioral and emotional data) to the server. The data is encrypted for privacy protection and transmitted securely.

[0379] Step 5:

[0380] The server stores the received data in a database, which is then organized for each user. The stored data forms the basis for analyzing long-term behavioral and emotional patterns.

[0381] Step 6:

[0382] The server performs machine learning based on accumulated data to analyze users' behavioral and emotional patterns. This analysis identifies what kinds of emotions users tend to experience in different situations.

[0383] Step 7:

[0384] Based on the analysis results, the server generates specific behavioral improvement suggestions tailored to the user's emotional state. For example, if it detects high levels of fatigue, it suggests a schedule that prioritizes rest.

[0385] Step 8:

[0386] The server sends the generated proposal to the terminal. The terminal notifies the application of the received proposal and displays detailed information within the application.

[0387] Step 9:

[0388] The user reviews the suggestions displayed on their device and decides whether to implement them. If they implement a suggestion, they provide feedback on the results, their feelings, and any changes in their emotions during the process.

[0389] Step 10:

[0390] The server collects feedback from users and re-analyzes the data to incorporate it into future suggestions. This feedback loop enables more personalized suggestions.

[0391] (Example 2)

[0392] 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".

[0393] In modern society, the decrease in efficiency and increase in stress experienced by individual users in their daily lives are serious problems. To address this, suggestions based solely on behavioral data are insufficient; highly accurate suggestions that also consider emotional states are required. However, conventional technologies have struggled to provide optimal suggestions based on emotions, failing to achieve efficiency improvements tailored to individual user needs.

[0394] 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.

[0395] In this invention, the server includes a device for receiving information on the user's behavior and emotions, means for collecting information, means for analyzing the collected information and recognizing patterns in the user's behavior and emotions, and means for using a generative AI model that generates suggestions that contribute to improving the user's efficiency based on the recognized patterns. This makes it possible to effectively provide efficient and individually optimized suggestions that are tailored to the user's emotional state.

[0396] "Users" refer to individuals who use this system and are the entities that provide information about their behavior and emotions.

[0397] "Behavior" refers to information related to the user's daily activities, including data on physical movements and schedules.

[0398] "Emotions" refers to information that identifies the user's psychological and emotional state, and includes data obtained through facial recognition and voice analysis.

[0399] "Information" encompasses all data related to users' behavior and emotions.

[0400] "Collection" refers to the process of obtaining data provided by users and gathering it into the system.

[0401] "Analysis" refers to the process of identifying and analyzing patterns in users' behavior and emotions based on collected data.

[0402] A "generative AI model" is an artificial intelligence technology used to create proposals, particularly one that utilizes natural language processing.

[0403] "Means" refers to the equipment, techniques, or methods that a system uses to perform a particular function.

[0404] "System" refers to a combination of devices and software operated using the above-mentioned means for the purpose of improving user efficiency.

[0405] In an embodiment of this invention, the system is configured and operates as follows:

[0406] Data collection and emotion recognition

[0407] First, the device displays a questionnaire to the user through an application, asking about their daily activities and schedule. When the user answers the questions on their smartphone, the device collects that data. Furthermore, with the user's permission, the device collects motion data such as location information, steps taken, and heart rate from the smartphone and wearable device. This uses the smartphone's sensors and the wearable device's APIs. In addition, the device utilizes an open-source facial recognition framework (e.g., OpenCV) for facial recognition technology and a speech recognition API (e.g., an API that converts speech to text) for speech analysis technology to obtain data necessary for determining the user's emotions.

[0408] Data storage and analysis

[0409] The server stores behavioral and emotional data transmitted from the device in a cloud database. Because this data is efficiently stored in each user's profile, encrypted protocols (e.g., HTTPS) are used for secure communication between the server and the device. The server uses machine learning algorithms (e.g., models using TensorFlow) to analyze the data and recognize user behavioral and emotional patterns.

[0410] Proposal generation and display

[0411] The server uses the analyzed data to create suggestions for improving user efficiency, utilizing a generative AI model (e.g., an AI model using natural language generation technology). These suggestions take into account the user's emotional state, and the content is adjusted to meet the user's individual needs. The terminal notifies the user of these suggestions and displays detailed information within the application.

[0412] Gathering feedback and improving suggestions

[0413] Users try out suggestions and provide feedback on their devices about the results and changes in their feelings. This feedback is used on the server side to further optimize future suggestions.

[0414] Specific example

[0415] For example, if a user enters the prompt message, "I'm feeling stressed today. Do you have any suggestions for relaxing?", the device will suggest a relaxation plan such as, "I recommend a 10-minute stretch after work." In this way, the system uses user data to provide individually optimized suggestions, helping to improve the efficiency of the user's daily life.

[0416] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0417] Step 1:

[0418] The device displays questions about the user's daily activities and schedule through the application. It receives the user's answers to these questions as input. This input data is recorded as basic information about the user's behavioral patterns. Specifically, the device presents questions using a survey-style UI and waits until the user enters their answers.

[0419] Step 2:

[0420] The device automatically collects operational data from smartphones and wearable devices with the user's permission. This input consists of continuous data from the device, such as location, steps taken, and heart rate. This data is centrally managed and stored on the device. Specifically, the device periodically polls and retrieves data using the device API.

[0421] Step 3:

[0422] The device uses facial recognition and voice analysis technologies to determine the user's emotions. This process uses video of the face captured by the camera and audio from the microphone as input. The algorithm used (e.g., facial recognition framework and voice analysis API) generates and records emotion data. Specifically, the device analyzes the video / audio feed and labels the emotional state in real time.

[0423] Step 4:

[0424] The device sends collected behavioral and emotional data to the server. The input includes all data collected and generated to date. The output to the server ensures that the data is securely transferred and received. Specifically, the device uploads the data to the cloud service using the HTTPS protocol.

[0425] Step 5:

[0426] The server stores the received data in a cloud database. The input consists of behavioral and emotional data sent from the terminal, which is efficiently accumulated. The data stored in the database is used for subsequent analysis. Specifically, the server records the data in database software (e.g., a relational database).

[0427] Step 6:

[0428] The server analyzes stored data using machine learning algorithms. The input consists of accumulated behavioral and emotional data, and the analysis recognizes user behavioral and emotional patterns. The output is detailed insights into behavioral patterns. Specifically, the server uses analysis tools (e.g., TensorFlow) to model the data.

[0429] Step 7:

[0430] The server uses a generative AI model based on the analysis results to generate optimal suggestions for the user. Input includes recognized behavioral and emotional patterns. Output consists of specific suggestions designed to improve the user's efficiency. Specifically, natural language generation technology is used to translate the suggestions into text.

[0431] Step 8:

[0432] The device receives suggestions from the server and notifies the user. The input is the generated suggestion content received from the server. The output is the suggestion itself. Specifically, the device displays a pop-up notification or in-app display to allow the user to review the suggestion.

[0433] Step 9:

[0434] The user is given the opportunity to try out the presented suggestions and inputs their feedback into the device. The input includes the user's impressions and actions regarding the suggestions. The output is feedback data used to generate future suggestions. Specifically, the user enters their answers into a feedback form on the device.

[0435] Step 10:

[0436] The server uses user feedback as data to improve the accuracy of its suggestions. Input includes user feedback data, and output represents improvements for future suggestion generation. Specifically, the server analyzes the feedback and uses it to refine its machine learning model.

[0437] (Application Example 2)

[0438] 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."

[0439] The challenge lies in accurately understanding the daily activities and emotional states of users, including the elderly, and providing means to support their efficient lives while reducing their stress. In particular, it is necessary to provide suggestions that reflect changes in users' emotions in real time and utilize them to support caregiving.

[0440] 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.

[0441] In this invention, the server includes means for receiving and collecting data on user behavior, means for analyzing the collected data to recognize behavioral patterns, and means for recognizing the user's emotional state and adjusting the generated suggestions based on that emotional state. This makes it possible to provide individually optimized efficiency improvement suggestions that are tailored to the user's emotions.

[0442] "Behavioral data" refers to information related to the user's actions and habits in their daily life, and is used to understand the user's behavioral patterns.

[0443] "Behavioral patterns" refer to a set of data used to analyze and identify consistent behavioral and habitual tendencies of users, and serve as a foundation for generating suggestions for improving efficiency.

[0444] "Specific suggestions that contribute to improved efficiency" refer to suggestions for changes in specific behaviors or habits that users should make in their daily lives to reduce stress and increase comfort.

[0445] "Means of recognizing emotional states" refers to methods of collecting data using technologies such as facial recognition and voice analysis to determine the user's current emotions and psychological state.

[0446] "Biometric indicators" are data that shows the user's physical condition, such as heart rate and body temperature, and are used to evaluate their emotional state and health status.

[0447] "Care support" refers to support activities aimed at efficiently providing daily care and assistance to improve the quality of life for the elderly and people with disabilities.

[0448] The system that realizes this invention is designed to analyze the user's daily behavior and emotions to provide personalized suggestions. The server collects and analyzes data on the user's behavior. The hardware used includes smartphones, fitness trackers, and camera-equipped devices. Receiving data from these devices, the server uses OpenCV for facial recognition and the Google Speech API for speech analysis as software for analyzing the information. In addition, it utilizes TensorFlow, a machine learning platform, to analyze behavioral and emotional data.

[0449] The device uses collected data to determine the user's behavioral patterns and emotional state. Based on the user's emotional state, the server generates specific suggestions to improve efficiency. For example, if the user is feeling stressed, relaxation exercises can be recommended. This can reduce the user's stress and improve their quality of life. This process is continuous, and the system keeps improving its suggestions based on user feedback.

[0450] A concrete example of a prompt message might be, "The user is feeling stressed; suggest a 10-minute breathing exercise to help them relax." This allows the generative AI model to generate the most appropriate action suggestion for the user, which the user can then perform.

[0451] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0452] Step 1:

[0453] The device collects data about the user's daily activities. It uses location information and activity level data obtained from smartphones and fitness trackers as input. As output, it transfers this data to a server. Specifically, the device synchronizes with the fitness tracker to record daily step counts and travel route information.

[0454] Step 2:

[0455] The server analyzes the collected data to recognize user behavior patterns. It receives activity data transferred from the terminal as input and generates a profile of the recognized behavior patterns as output. Specifically, the server uses machine learning algorithms to analyze daily behavior data and model trends in movement frequency and time of day.

[0456] Step 3:

[0457] The device acquires live data from its camera and microphone to recognize the user's emotional state. Facial images and audio data are used as input. The output generates an index indicating the emotional state. Specifically, the device's camera captures the user's facial expressions, and OpenCV is used to analyze changes in microexpressions.

[0458] Step 4:

[0459] The server generates appropriate efficiency improvement suggestions based on recognized behavioral patterns and emotional states. It uses behavioral pattern profiles and emotional state indicators as input. Customized suggestions are generated as output. Specifically, the server uses a generated AI model to formulate suggestions based on the prompt statement, "The user is feeling stressed; suggest 10 minutes of relaxing breathing exercises."

[0460] Step 5:

[0461] The device presents the generated suggestions to the user. It receives suggestions generated by the server as input. As output, it communicates the content of the suggestions to the user visually or audibly. Specifically, it uses the smartphone's notification function to display a suggestion to the user: "Shall we start a 10-minute breathing exercise now?"

[0462] Step 6:

[0463] After trying out a suggestion, the user provides feedback on the results and experience to their device. The input includes the action taken and the resulting emotional changes. The output is feedback data sent to the server. Specifically, the user writes their thoughts on the suggestion's effectiveness on a smartphone app and presses the submit button.

[0464] Step 7:

[0465] The server receives feedback from users and stores and analyzes data to improve the accuracy of future suggestions. User feedback data is used as input. The output is an updated, improved suggestion algorithm. Specifically, the server retrains the machine learning model with the new data to improve the algorithm's performance.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] [Third Embodiment]

[0470] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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".

[0482] This invention is an AI-powered system that provides users with specific advice to improve the efficiency of their daily lives. The system is installed as an application on the user's mobile device or computer and operates through a user-friendly interface.

[0483] Data collection

[0484] First, the device displays specific questions to the user and collects data on things like wake-up time, commute patterns, meal times, and exercise habits. In addition, with the user's permission, the device automatically retrieves additional data from external devices such as health trackers and calendar applications.

[0485] Data accumulation and analysis

[0486] The server receives data sent from the terminal and stores it for each user. The stored data is analyzed by machine learning algorithms to identify behavioral patterns in daily life. This clearly reveals inefficient use of time and areas for improvement in the user's lifestyle.

[0487] Proposal generation

[0488] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. These include action plans such as reviewing schedules and adjusting exercise times. The generated suggestions are customized to reflect the user's priorities.

[0489] Displaying proposals and collecting feedback

[0490] The terminal notifies the user of suggestions received from the server and displays the details on the application. The user reviews the suggestions and inputs feedback on the terminal regarding the results of their implementation and their impressions. This feedback is used to improve the accuracy of the suggestions.

[0491] Specific example

[0492] For example, suppose a user provides feedback stating, "I finish work late and have little free time before going to bed." Based on the data collected from the device, the server generates a specific suggestion, such as "Instead of going to bed a little later, incorporate a short relaxation time after returning home," and notifies the user of this suggestion. After the user implements this suggestion, they input feedback into the device indicating that they were able to relax, which provides feedback to the server regarding the effectiveness of the suggestion and is reflected in future suggestions.

[0493] In this way, the system continuously analyzes the user's daily life and provides individually customized improvement suggestions. This integrates the system into the user's daily life as a tool to help them live a more efficient life.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The device displays questions on the screen for users to input information about their daily activities, and the user enters their answers. For example, it provides fields for "wake-up time" and "commute time."

[0497] Step 2:

[0498] The device automatically acquires behavioral data (such as steps taken and exercise time) from external devices such as smartphones and wearable devices, based on the user's permission. This data, along with the input data, is used for subsequent analysis.

[0499] Step 3:

[0500] The device sends all collected data to the server either in a batch or sequentially. During this process, the data is processed in a way that ensures data integrity and privacy.

[0501] Step 4:

[0502] The server stores received user data in a database, identifying and accumulating it for each user. The database enables long-term data storage and smooth access.

[0503] Step 5:

[0504] The server analyzes the accumulated data using machine learning algorithms. This involves recognizing behavioral patterns and identifying inefficient activities, generating foundational data for future proposal formation.

[0505] Step 6:

[0506] Based on the analysis results, the server generates specific improvement suggestions tailored to the user's lifestyle. These suggestions are adjusted according to the user's goals and past feedback.

[0507] Step 7:

[0508] The server sends the generated suggestions to the user's device. It may also use notifications and alerts to highlight important suggestions to the user.

[0509] Step 8:

[0510] The device displays received suggestions to the user and provides detailed explanations. It also provides users with a function to easily provide feedback on the effectiveness and satisfaction level after implementing the suggestions.

[0511] Step 9:

[0512] Users input their impressions and detailed feedback on the results of trying out the suggestions into their device. This information serves as foundational data for determining how effective each individual suggestion was.

[0513] Step 10:

[0514] The server collects feedback from users and analyzes it to improve the accuracy of future suggestions. This increases the accuracy of providing customized support for each user.

[0515] (Example 1)

[0516] 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."

[0517] In modern society, it is crucial for individuals to use their time efficiently in their daily lives and improve their quality of life. However, many people find it difficult to identify the inefficiencies hidden in their own behavioral patterns and find ways to improve them. Traditional methods often fail to provide sufficiently personalized and specific improvement suggestions that can be adapted to daily life.

[0518] 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.

[0519] In this invention, the server includes means for receiving and collecting information about the user's daily life, means for automatically acquiring authorized information from an external measuring device, and means for storing the received information and identifying behavioral patterns using computational processing means. This makes it possible to generate personalized and specific suggestions for each user and improve the efficiency of their daily life.

[0520] A "user" is an individual who uses the system to provide information about their daily life and receive suggestions for improving efficiency.

[0521] An "external measurement device" is a device that automatically acquires user behavior data, health information, etc., and provides it to the terminal.

[0522] "Means of receiving and collecting information" refers to methods and devices for receiving and storing information related to users' daily lives in digital format.

[0523] "Computational processing means" refers to algorithms and software programs used to analyze received data and identify behavioral patterns.

[0524] "Means for identifying behavioral patterns" refer to methods and techniques for analyzing collected information to derive user-specific behaviors and habits.

[0525] "Means for generating proposals" refer to technologies and software that create specific proposals aimed at improving user efficiency based on the results of analysis.

[0526] This invention is a system that generates suggestions for efficiency improvement based on information about the user's daily life. The system is constructed in which a terminal and a server cooperate, and includes integration with external measuring devices.

[0527] The device displays questions about the user's daily life and allows the user to input their answers. For example, the user can answer questions such as, "What time do you wake up on weekdays?" Furthermore, with the user's permission, the device automatically acquires data from external measuring devices such as health trackers and calendar apps and sends it to a server. Smartphone apps and computer applications are used for this process.

[0528] The server receives data sent from terminals, stores it in a database, and classifies it by user. In data analysis, the server uses machine learning algorithms such as Python's scikit-learn to identify behavioral patterns. This analysis can identify inefficient time usage and lifestyle habits that can be improved.

[0529] The server then generates specific suggestions based on identified behavioral patterns. Natural language processing (NLP) techniques are also utilized at this stage to provide customized suggestions tailored to the user's lifestyle and priorities. The generated suggestions are sent from the server to the terminal and notified to the user.

[0530] The terminal allows users to view the details of a suggestion and input feedback on the results and their impressions after implementing it. The server then uses this feedback to continuously improve the suggestions.

[0531] For example, if a user inputs "I want to efficiently manage my daily exercise time," the system will consider their existing commuting pattern and exercise habits and make suggestions such as "shorten your morning exercise time and add light exercise during your lunch break." An example of a prompt might be, "Based on my current lifestyle data, please generate specific suggestions to improve my daily life."

[0532] This system aims to improve the quality and efficiency of daily life by providing personalized suggestions tailored to each user's lifestyle.

[0533] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0534] Step 1:

[0535] The device displays questions about the user's daily life and collects information. This input includes the user entering their "weekday wake-up time" and "means of commuting." The device temporarily stores the data entered by the user and standardizes the data format.

[0536] Step 2:

[0537] The device automatically acquires data from external measuring devices (e.g., health trackers, calendar apps) with the user's permission. This data includes heart rate and schedule information. The device acquires this data and integrates it with user input data.

[0538] Step 3:

[0539] The terminal collects all the data and sends it to the server using a secure communication protocol. The input data consists of numerical and categorical data related to the user's daily life, and is formatted in a way that is necessary for analysis on the server.

[0540] Step 4:

[0541] The server stores the received data in a database for each user and performs data preprocessing. This preprocessing includes data normalization and imputation of missing values. After the data cleansing is complete, the server uses machine learning algorithms to analyze behavioral patterns.

[0542] Step 5:

[0543] The server generates specific suggestions to improve user efficiency based on analyzed behavioral patterns. In this process, an AI model utilizing NLP constructs suggestions in natural language. The output is personalized improvement suggestions tailored to the user.

[0544] Step 6:

[0545] The server sends the generated suggestions to the device, and the device notifies the user. The device displays the suggestions as push notifications or in-app messages. The user reviews the details of the suggestions and tries to improve their lifestyle according to them.

[0546] Step 7:

[0547] Users input feedback on their device regarding the results and their impressions after implementing the suggestion. This feedback is important information for evaluating the effectiveness of the suggestion.

[0548] Step 8:

[0549] The terminal sends the acquired feedback to the server, which stores this feedback and uses it to improve future suggestions. The server uses the feedback to update its machine learning model and improve the accuracy of its suggestions.

[0550] (Application Example 1)

[0551] 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."

[0552] In modern urban life, inefficiency and wasted time in individual activities are challenges for many people. In particular, commuting and daily schedule management involve many unpredictable factors, making efficient time management difficult. Furthermore, proposed improvements often lack sufficient consideration of user priorities and external circumstances, reducing their feasibility.

[0553] 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.

[0554] This invention includes a server equipped with a device for receiving information on user behavior, a method for collecting such information, a method for including schedule optimization in the generated suggestions that takes into account delay information and congestion status of public transport, and a method for adjusting the suggestion content considering the user's priority settings for the generated suggestions and traffic information. This makes it possible to improve the efficiency of time management in the user's daily life.

[0555] A "user" is an individual who aims to improve their daily life more efficiently by using the system.

[0556] "Behavioral patterns" refer to the characteristics of how users behave in their daily lives based on certain cycles or rules.

[0557] A "proposal" refers to a set of specific action plans based on the analysis of behavioral patterns, designed to improve the efficiency of the user's daily life.

[0558] "Public transport" refers to means of transportation that are commonly used by people for travel, including buses and trains.

[0559] "Delay information" refers to data that shows deviations from the normal operating schedule of public transportation.

[0560] "Congestion level" refers to information about the density of people in public transport and other public spaces.

[0561] "Schedule optimization" is the process of reducing wasted time and improving convenience by rearranging users' planned activities in an efficient and effective manner.

[0562] "Feedback" refers to information that improves the accuracy of suggestions by transmitting to the system the results and impressions of actions taken by users in response to suggested actions.

[0563] The system for implementing this invention consists of a server and a user terminal. The server receives and processes data related to the user's daily life and recognizes specific behavioral patterns to generate suggestions for living an efficient life. The terminal has the function of displaying specific suggestions to the user, collecting feedback on those suggestions, and sending it to the server.

[0564] The program is implemented in programming languages ​​such as Python, and utilizes the machine learning library scikit-learn for data analysis. Specifically, it analyzes user behavior data using clustering techniques to recognize patterns. The server has the functionality to import real-time delay and congestion information for public transport from external traffic information APIs and reflect this in the optimization of suggestions. It also customizes the suggestions according to the user's priority settings and displays them on the terminal.

[0565] For example, if a user provides feedback to the system stating that they "want to shorten their commute time," the server will suggest an optimal start time based on past commute data and traffic information, and notify the user on their device. An example of a prompt message regarding this suggestion would be, "Please tell us the best way to shorten your commute time to make your daily life more efficient."

[0566] As described above, the server and terminal work together to provide users with a system that continuously offers concrete and practical support to streamline their daily lives.

[0567] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0568] Step 1:

[0569] Users use a device to input data about their daily lives. Specifically, they input information such as their wake-up time, commute time, and meal times into the device and send it to the server as collected data. In this process, the device appropriately formats the input information and converts it into a format that the server can understand before sending it.

[0570] Step 2:

[0571] The server receives user behavior data sent from the terminal. To analyze the received data, it is stored in a database and clustering techniques are used to identify behavioral patterns. Specifically, the scikit-learn KMeans algorithm is used to classify the data into several patterns and identify inefficient use of time.

[0572] Step 3:

[0573] The server obtains real-time information on public transportation via external APIs. Based on delay information and congestion levels, it generates optimized daily schedule suggestions for users. In this process, it integrates traffic information with previously identified behavioral patterns and uses AI algorithms to identify potential areas for improvement.

[0574] Step 4:

[0575] The generated suggestions are finalized, taking into account the user's priority settings. The suggestions are customized based on the user's past feedback and settings. The customized suggestions are then output by the system's AI model, using prompts as a reference, in a format that is easy for the user to follow.

[0576] Step 5:

[0577] The terminal receives optimized suggestions from the server and presents them to the user. The user reviews the suggestions and decides whether to implement them. The suggestions are displayed on the interface in a format that is easy for the user to understand intuitively.

[0578] Step 6:

[0579] After the user executes the suggestion, they enter feedback. The terminal formats the feedback appropriately and sends it to the server. The feedback is used to improve the suggestion and train the AI ​​model. The server uses this feedback to improve the accuracy of future suggestions.

[0580] 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.

[0581] This invention relates to a system that provides individually customized efficiency improvement suggestions using user behavior data and emotional states. This system operates as an application installed on mobile devices and computers, and interacts with users through a user-friendly interface.

[0582] Data collection and emotion recognition

[0583] First, the device displays questions to the user about their daily activities and schedule, and collects their responses. With the user's permission, the device also automatically acquires motion data from their smartphone or wearable device. Furthermore, the device uses facial recognition and voice analysis technologies to collect emotional data to determine the user's emotions.

[0584] Data storage and analysis

[0585] The server receives all data (behavioral and emotional data) transmitted from the terminal and securely and efficiently stores it in user-specific profiles. Next, the server uses machine learning algorithms to analyze this data and recognize the user's behavioral and emotional patterns.

[0586] Proposal generation and emotional application

[0587] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. In this process, the emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it prioritizes suggesting ways to relax.

[0588] Proposal presentation and feedback gathering

[0589] The device notifies the user of the generated suggestions and displays the details in the application. The server collects feedback from the user on the device regarding the results of trying the suggested actions and any changes in their emotions during that process. This feedback is used to improve the accuracy of future suggestions.

[0590] Specific example

[0591] For example, when a user inputs feedback such as "I feel stressed due to fatigue after work," the device uses its emotion engine to suggest a relaxation plan to alleviate facial tension. This includes specific action plans such as "stretching after leaving work on time." The user then inputs feedback after trying this, and based on that, the server generates new suggestions that are further tailored to the user's emotions.

[0592] In this way, the system continuously analyzes users' emotional and behavioral data and provides individually optimized suggestions to support their daily lives.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The device displays questions about the user's daily life and collects information such as wake-up time, meal times, commute patterns, and bedtime. Basic behavioral data is accumulated as the user answers these questions.

[0596] Step 2:

[0597] With the user's permission, the device automatically collects behavioral data from smartphones and wearable devices. This includes information such as steps taken, heart rate, and activity level.

[0598] Step 3:

[0599] The device uses the user's facial recognition camera and microphone to collect emotional data in real time from their facial expressions and voice. The data collected is used to understand the user's emotional state.

[0600] Step 4:

[0601] The device sends all collected data (behavioral and emotional data) to the server. The data is encrypted for privacy protection and transmitted securely.

[0602] Step 5:

[0603] The server stores the received data in a database, which is then organized for each user. The stored data forms the basis for analyzing long-term behavioral and emotional patterns.

[0604] Step 6:

[0605] The server performs machine learning based on accumulated data to analyze users' behavioral and emotional patterns. This analysis identifies what kinds of emotions users tend to experience in different situations.

[0606] Step 7:

[0607] Based on the analysis results, the server generates specific behavioral improvement suggestions tailored to the user's emotional state. For example, if it detects high levels of fatigue, it suggests a schedule that prioritizes rest.

[0608] Step 8:

[0609] The server sends the generated proposal to the terminal. The terminal notifies the application of the received proposal and displays detailed information within the application.

[0610] Step 9:

[0611] The user reviews the suggestions displayed on their device and decides whether to implement them. If they implement a suggestion, they provide feedback on the results, their feelings, and any changes in their emotions during the process.

[0612] Step 10:

[0613] The server collects feedback from users and re-analyzes the data to incorporate it into future suggestions. This feedback loop enables more personalized suggestions.

[0614] (Example 2)

[0615] 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."

[0616] In modern society, the decrease in efficiency and increase in stress experienced by individual users in their daily lives are serious problems. To address this, suggestions based solely on behavioral data are insufficient; highly accurate suggestions that also consider emotional states are required. However, conventional technologies have struggled to provide optimal suggestions based on emotions, failing to achieve efficiency improvements tailored to individual user needs.

[0617] 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.

[0618] In this invention, the server includes a device for receiving information on the user's behavior and emotions, means for collecting information, means for analyzing the collected information and recognizing patterns in the user's behavior and emotions, and means for using a generative AI model that generates suggestions that contribute to improving the user's efficiency based on the recognized patterns. This makes it possible to effectively provide efficient and individually optimized suggestions that are tailored to the user's emotional state.

[0619] "Users" refer to individuals who use this system and are the entities that provide information about their behavior and emotions.

[0620] "Behavior" refers to information related to the user's daily activities, including data on physical movements and schedules.

[0621] "Emotions" refers to information that identifies the user's psychological and emotional state, and includes data obtained through facial recognition and voice analysis.

[0622] "Information" encompasses all data related to users' behavior and emotions.

[0623] "Collection" refers to the process of obtaining data provided by users and gathering it into the system.

[0624] "Analysis" refers to the process of identifying and analyzing patterns in users' behavior and emotions based on collected data.

[0625] A "generative AI model" is an artificial intelligence technology used to create proposals, particularly one that utilizes natural language processing.

[0626] "Means" refers to the equipment, techniques, or methods that a system uses to perform a particular function.

[0627] "System" refers to a combination of devices and software operated using the above-mentioned means for the purpose of improving user efficiency.

[0628] In an embodiment of this invention, the system is configured and operates as follows:

[0629] Data collection and emotion recognition

[0630] First, the device displays a questionnaire to the user through an application, asking about their daily activities and schedule. When the user answers the questions on their smartphone, the device collects that data. Furthermore, with the user's permission, the device collects motion data such as location information, steps taken, and heart rate from the smartphone and wearable device. This uses the smartphone's sensors and the wearable device's APIs. In addition, the device utilizes an open-source facial recognition framework (e.g., OpenCV) for facial recognition technology and a speech recognition API (e.g., an API that converts speech to text) for speech analysis technology to obtain data necessary for determining the user's emotions.

[0631] Data storage and analysis

[0632] The server stores behavioral and emotional data transmitted from the device in a cloud database. Because this data is efficiently stored in each user's profile, encrypted protocols (e.g., HTTPS) are used for secure communication between the server and the device. The server uses machine learning algorithms (e.g., models using TensorFlow) to analyze the data and recognize user behavioral and emotional patterns.

[0633] Proposal generation and display

[0634] The server uses the analyzed data to create suggestions for improving user efficiency, utilizing a generative AI model (e.g., an AI model using natural language generation technology). These suggestions take into account the user's emotional state, and the content is adjusted to meet the user's individual needs. The terminal notifies the user of these suggestions and displays detailed information within the application.

[0635] Gathering feedback and improving suggestions

[0636] Users try out suggestions and provide feedback on their devices about the results and changes in their feelings. This feedback is used on the server side to further optimize future suggestions.

[0637] Specific example

[0638] For example, if a user enters the prompt message, "I'm feeling stressed today. Do you have any suggestions for relaxing?", the device will suggest a relaxation plan such as, "I recommend a 10-minute stretch after work." In this way, the system uses user data to provide individually optimized suggestions, helping to improve the efficiency of the user's daily life.

[0639] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0640] Step 1:

[0641] The device displays questions about the user's daily activities and schedule through the application. It receives the user's answers to these questions as input. This input data is recorded as basic information about the user's behavioral patterns. Specifically, the device presents questions using a survey-style UI and waits until the user enters their answers.

[0642] Step 2:

[0643] The device automatically collects operational data from smartphones and wearable devices with the user's permission. This input consists of continuous data from the device, such as location, steps taken, and heart rate. This data is centrally managed and stored on the device. Specifically, the device periodically polls and retrieves data using the device API.

[0644] Step 3:

[0645] The device uses facial recognition and voice analysis technologies to determine the user's emotions. This process uses video of the face captured by the camera and audio from the microphone as input. The algorithm used (e.g., facial recognition framework and voice analysis API) generates and records emotion data. Specifically, the device analyzes the video / audio feed and labels the emotional state in real time.

[0646] Step 4:

[0647] The device sends collected behavioral and emotional data to the server. The input includes all data collected and generated to date. The output to the server ensures that the data is securely transferred and received. Specifically, the device uploads the data to the cloud service using the HTTPS protocol.

[0648] Step 5:

[0649] The server stores the received data in a cloud database. The input consists of behavioral and emotional data sent from the terminal, which is efficiently accumulated. The data stored in the database is used for subsequent analysis. Specifically, the server records the data in database software (e.g., a relational database).

[0650] Step 6:

[0651] The server analyzes stored data using machine learning algorithms. The input consists of accumulated behavioral and emotional data, and the analysis recognizes user behavioral and emotional patterns. The output is detailed insights into behavioral patterns. Specifically, the server uses analysis tools (e.g., TensorFlow) to model the data.

[0652] Step 7:

[0653] The server uses a generative AI model based on the analysis results to generate optimal suggestions for the user. Input includes recognized behavioral and emotional patterns. Output consists of specific suggestions designed to improve the user's efficiency. Specifically, natural language generation technology is used to translate the suggestions into text.

[0654] Step 8:

[0655] The device receives suggestions from the server and notifies the user. The input is the generated suggestion content received from the server. The output is the suggestion itself. Specifically, the device displays a pop-up notification or in-app display to allow the user to review the suggestion.

[0656] Step 9:

[0657] The user is given the opportunity to try out the presented suggestions and inputs their feedback into the device. The input includes the user's impressions and actions regarding the suggestions. The output is feedback data used to generate future suggestions. Specifically, the user enters their answers into a feedback form on the device.

[0658] Step 10:

[0659] The server uses user feedback as data to improve the accuracy of its suggestions. Input includes user feedback data, and output represents improvements for future suggestion generation. Specifically, the server analyzes the feedback and uses it to refine its machine learning model.

[0660] (Application Example 2)

[0661] 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."

[0662] The challenge lies in accurately understanding the daily activities and emotional states of users, including the elderly, and providing means to support their efficient lives while reducing their stress. In particular, it is necessary to provide suggestions that reflect changes in users' emotions in real time and utilize them to support caregiving.

[0663] 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.

[0664] In this invention, the server includes means for receiving and collecting data on user behavior, means for analyzing the collected data to recognize behavioral patterns, and means for recognizing the user's emotional state and adjusting the generated suggestions based on that emotional state. This makes it possible to provide individually optimized efficiency improvement suggestions that are tailored to the user's emotions.

[0665] "Behavioral data" refers to information related to the user's actions and habits in their daily life, and is used to understand the user's behavioral patterns.

[0666] "Behavioral patterns" refer to a set of data used to analyze and identify consistent behavioral and habitual tendencies of users, and serve as a foundation for generating suggestions for improving efficiency.

[0667] "Specific suggestions that contribute to improved efficiency" refer to suggestions for changes in specific behaviors or habits that users should make in their daily lives to reduce stress and increase comfort.

[0668] "Means of recognizing emotional states" refers to methods of collecting data using technologies such as facial recognition and voice analysis to determine the user's current emotions and psychological state.

[0669] "Biometric indicators" are data that shows the user's physical condition, such as heart rate and body temperature, and are used to evaluate their emotional state and health status.

[0670] "Care support" refers to support activities aimed at efficiently providing daily care and assistance to improve the quality of life for the elderly and people with disabilities.

[0671] The system that realizes this invention is designed to analyze the user's daily behavior and emotions to provide personalized suggestions. The server collects and analyzes data on the user's behavior. The hardware used includes smartphones, fitness trackers, and camera-equipped devices. Receiving data from these devices, the server uses OpenCV for facial recognition and the Google Speech API for speech analysis as software for analyzing the information. In addition, it utilizes TensorFlow, a machine learning platform, to analyze behavioral and emotional data.

[0672] The device uses collected data to determine the user's behavioral patterns and emotional state. Based on the user's emotional state, the server generates specific suggestions to improve efficiency. For example, if the user is feeling stressed, relaxation exercises can be recommended. This can reduce the user's stress and improve their quality of life. This process is continuous, and the system keeps improving its suggestions based on user feedback.

[0673] A concrete example of a prompt message might be, "The user is feeling stressed; suggest a 10-minute breathing exercise to help them relax." This allows the generative AI model to generate the most appropriate action suggestion for the user, which the user can then perform.

[0674] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0675] Step 1:

[0676] The device collects data about the user's daily activities. It uses location information and activity level data obtained from smartphones and fitness trackers as input. As output, it transfers this data to a server. Specifically, the device synchronizes with the fitness tracker to record daily step counts and travel route information.

[0677] Step 2:

[0678] The server analyzes the collected data to recognize user behavior patterns. It receives activity data transferred from the terminal as input and generates a profile of the recognized behavior patterns as output. Specifically, the server uses machine learning algorithms to analyze daily behavior data and model trends in movement frequency and time of day.

[0679] Step 3:

[0680] The device acquires live data from its camera and microphone to recognize the user's emotional state. Facial images and audio data are used as input. The output generates an index indicating the emotional state. Specifically, the device's camera captures the user's facial expressions, and OpenCV is used to analyze changes in microexpressions.

[0681] Step 4:

[0682] The server generates appropriate efficiency improvement suggestions based on recognized behavioral patterns and emotional states. It uses behavioral pattern profiles and emotional state indicators as input. Customized suggestions are generated as output. Specifically, the server uses a generated AI model to formulate suggestions based on the prompt statement, "The user is feeling stressed; suggest 10 minutes of relaxing breathing exercises."

[0683] Step 5:

[0684] The device presents the generated suggestions to the user. It receives suggestions generated by the server as input. As output, it communicates the content of the suggestions to the user visually or audibly. Specifically, it uses the smartphone's notification function to display a suggestion to the user: "Shall we start a 10-minute breathing exercise now?"

[0685] Step 6:

[0686] After trying out a suggestion, the user provides feedback on the results and experience to their device. The input includes the action taken and the resulting emotional changes. The output is feedback data sent to the server. Specifically, the user writes their thoughts on the suggestion's effectiveness on a smartphone app and presses the submit button.

[0687] Step 7:

[0688] The server receives feedback from users and stores and analyzes data to improve the accuracy of future suggestions. User feedback data is used as input. The output is an updated, improved suggestion algorithm. Specifically, the server retrains the machine learning model with the new data to improve the algorithm's performance.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] [Fourth Embodiment]

[0693] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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".

[0706] This invention is an AI-powered system that provides users with specific advice to improve the efficiency of their daily lives. The system is installed as an application on the user's mobile device or computer and operates through a user-friendly interface.

[0707] Data collection

[0708] First, the device displays specific questions to the user and collects data on things like wake-up time, commute patterns, meal times, and exercise habits. In addition, with the user's permission, the device automatically retrieves additional data from external devices such as health trackers and calendar applications.

[0709] Data accumulation and analysis

[0710] The server receives data sent from the terminal and stores it for each user. The stored data is analyzed by machine learning algorithms to identify behavioral patterns in daily life. This clearly reveals inefficient use of time and areas for improvement in the user's lifestyle.

[0711] Proposal generation

[0712] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. These include action plans such as reviewing schedules and adjusting exercise times. The generated suggestions are customized to reflect the user's priorities.

[0713] Displaying proposals and collecting feedback

[0714] The terminal notifies the user of suggestions received from the server and displays the details on the application. The user reviews the suggestions and inputs feedback on the terminal regarding the results of their implementation and their impressions. This feedback is used to improve the accuracy of the suggestions.

[0715] Specific example

[0716] For example, suppose a user provides feedback stating, "I finish work late and have little free time before going to bed." Based on the data collected from the device, the server generates a specific suggestion, such as "Instead of going to bed a little later, incorporate a short relaxation time after returning home," and notifies the user of this suggestion. After the user implements this suggestion, they input feedback into the device indicating that they were able to relax, which provides feedback to the server regarding the effectiveness of the suggestion and is reflected in future suggestions.

[0717] In this way, the system continuously analyzes the user's daily life and provides individually customized improvement suggestions. This integrates the system into the user's daily life as a tool to help them live a more efficient life.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The device displays questions on the screen for users to input information about their daily activities, and the user enters their answers. For example, it provides fields for "wake-up time" and "commute time."

[0721] Step 2:

[0722] The device automatically acquires behavioral data (such as steps taken and exercise time) from external devices such as smartphones and wearable devices, based on the user's permission. This data, along with the input data, is used for subsequent analysis.

[0723] Step 3:

[0724] The device sends all collected data to the server either in a batch or sequentially. During this process, the data is processed in a way that ensures data integrity and privacy.

[0725] Step 4:

[0726] The server stores received user data in a database, identifying and accumulating it for each user. The database enables long-term data storage and smooth access.

[0727] Step 5:

[0728] The server analyzes the accumulated data using machine learning algorithms. This involves recognizing behavioral patterns and identifying inefficient activities, generating foundational data for future proposal formation.

[0729] Step 6:

[0730] Based on the analysis results, the server generates specific improvement suggestions tailored to the user's lifestyle. These suggestions are adjusted according to the user's goals and past feedback.

[0731] Step 7:

[0732] The server sends the generated suggestions to the user's device. It may also use notifications and alerts to highlight important suggestions to the user.

[0733] Step 8:

[0734] The device displays received suggestions to the user and provides detailed explanations. It also provides users with a function to easily provide feedback on the effectiveness and satisfaction level after implementing the suggestions.

[0735] Step 9:

[0736] Users input their impressions and detailed feedback on the results of trying out the suggestions into their device. This information serves as foundational data for determining how effective each individual suggestion was.

[0737] Step 10:

[0738] The server collects feedback from users and analyzes it to improve the accuracy of future suggestions. This increases the accuracy of providing customized support for each user.

[0739] (Example 1)

[0740] 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".

[0741] In modern society, it is crucial for individuals to use their time efficiently in their daily lives and improve their quality of life. However, many people find it difficult to identify the inefficiencies hidden in their own behavioral patterns and find ways to improve them. Traditional methods often fail to provide sufficiently personalized and specific improvement suggestions that can be adapted to daily life.

[0742] 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.

[0743] In this invention, the server includes means for receiving and collecting information about the user's daily life, means for automatically acquiring authorized information from an external measuring device, and means for storing the received information and identifying behavioral patterns using computational processing means. This makes it possible to generate personalized and specific suggestions for each user and improve the efficiency of their daily life.

[0744] A "user" is an individual who uses the system to provide information about their daily life and receive suggestions for improving efficiency.

[0745] An "external measurement device" is a device that automatically acquires user behavior data, health information, etc., and provides it to the terminal.

[0746] "Means of receiving and collecting information" refers to methods and devices for receiving and storing information related to users' daily lives in digital format.

[0747] "Computational processing means" refers to algorithms and software programs used to analyze received data and identify behavioral patterns.

[0748] "Means for identifying behavioral patterns" refer to methods and techniques for analyzing collected information to derive user-specific behaviors and habits.

[0749] "Means for generating proposals" refer to technologies and software that create specific proposals aimed at improving user efficiency based on the results of analysis.

[0750] This invention is a system that generates suggestions for efficiency improvement based on information about the user's daily life. The system is constructed in which a terminal and a server cooperate, and includes integration with external measuring devices.

[0751] The device displays questions about the user's daily life and allows the user to input their answers. For example, the user can answer questions such as, "What time do you wake up on weekdays?" Furthermore, with the user's permission, the device automatically acquires data from external measuring devices such as health trackers and calendar apps and sends it to a server. Smartphone apps and computer applications are used for this process.

[0752] The server receives data sent from terminals, stores it in a database, and classifies it by user. In data analysis, the server uses machine learning algorithms such as Python's scikit-learn to identify behavioral patterns. This analysis can identify inefficient time usage and lifestyle habits that can be improved.

[0753] The server then generates specific suggestions based on identified behavioral patterns. Natural language processing (NLP) techniques are also utilized at this stage to provide customized suggestions tailored to the user's lifestyle and priorities. The generated suggestions are sent from the server to the terminal and notified to the user.

[0754] The terminal allows users to view the details of a suggestion and input feedback on the results and their impressions after implementing it. The server then uses this feedback to continuously improve the suggestions.

[0755] For example, if a user inputs "I want to efficiently manage my daily exercise time," the system will consider their existing commuting pattern and exercise habits and make suggestions such as "shorten your morning exercise time and add light exercise during your lunch break." An example of a prompt might be, "Based on my current lifestyle data, please generate specific suggestions to improve my daily life."

[0756] This system aims to improve the quality and efficiency of daily life by providing personalized suggestions tailored to each user's lifestyle.

[0757] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0758] Step 1:

[0759] The device displays questions about the user's daily life and collects information. This input includes the user entering their "weekday wake-up time" and "means of commuting." The device temporarily stores the data entered by the user and standardizes the data format.

[0760] Step 2:

[0761] The device automatically acquires data from external measuring devices (e.g., health trackers, calendar apps) with the user's permission. This data includes heart rate and schedule information. The device acquires this data and integrates it with user input data.

[0762] Step 3:

[0763] The terminal collects all the data and sends it to the server using a secure communication protocol. The input data consists of numerical and categorical data related to the user's daily life, and is formatted in a way that is necessary for analysis on the server.

[0764] Step 4:

[0765] The server stores the received data in a database for each user and performs data preprocessing. This preprocessing includes data normalization and imputation of missing values. After the data cleansing is complete, the server uses machine learning algorithms to analyze behavioral patterns.

[0766] Step 5:

[0767] The server generates specific suggestions to improve user efficiency based on analyzed behavioral patterns. In this process, an AI model utilizing NLP constructs suggestions in natural language. The output is personalized improvement suggestions tailored to the user.

[0768] Step 6:

[0769] The server sends the generated suggestions to the device, and the device notifies the user. The device displays the suggestions as push notifications or in-app messages. The user reviews the details of the suggestions and tries to improve their lifestyle according to them.

[0770] Step 7:

[0771] Users input feedback on their device regarding the results and their impressions after implementing the suggestion. This feedback is important information for evaluating the effectiveness of the suggestion.

[0772] Step 8:

[0773] The terminal sends the acquired feedback to the server, which stores this feedback and uses it to improve future suggestions. The server uses the feedback to update its machine learning model and improve the accuracy of its suggestions.

[0774] (Application Example 1)

[0775] 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".

[0776] In modern urban life, inefficiency and wasted time in individual activities are challenges for many people. In particular, commuting and daily schedule management involve many unpredictable factors, making efficient time management difficult. Furthermore, proposed improvements often lack sufficient consideration of user priorities and external circumstances, reducing their feasibility.

[0777] 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.

[0778] This invention includes a server equipped with a device for receiving information on user behavior, a method for collecting such information, a method for including schedule optimization in the generated suggestions that takes into account delay information and congestion status of public transport, and a method for adjusting the suggestion content considering the user's priority settings for the generated suggestions and traffic information. This makes it possible to improve the efficiency of time management in the user's daily life.

[0779] A "user" is an individual who aims to improve their daily life more efficiently by using the system.

[0780] "Behavioral patterns" refer to the characteristics of how users behave in their daily lives based on certain cycles or rules.

[0781] A "proposal" refers to a set of specific action plans based on the analysis of behavioral patterns, designed to improve the efficiency of the user's daily life.

[0782] "Public transport" refers to means of transportation that are commonly used by people for travel, including buses and trains.

[0783] "Delay information" refers to data that shows deviations from the normal operating schedule of public transportation.

[0784] "Congestion level" refers to information about the density of people in public transport and other public spaces.

[0785] "Schedule optimization" is the process of reducing wasted time and improving convenience by rearranging users' planned activities in an efficient and effective manner.

[0786] "Feedback" refers to information that improves the accuracy of suggestions by transmitting to the system the results and impressions of actions taken by users in response to suggested actions.

[0787] The system for implementing this invention consists of a server and a user terminal. The server receives and processes data related to the user's daily life and recognizes specific behavioral patterns to generate suggestions for living an efficient life. The terminal has the function of displaying specific suggestions to the user, collecting feedback on those suggestions, and sending it to the server.

[0788] The program is implemented in programming languages ​​such as Python, and utilizes the machine learning library scikit-learn for data analysis. Specifically, it analyzes user behavior data using clustering techniques to recognize patterns. The server has the functionality to import real-time delay and congestion information for public transport from external traffic information APIs and reflect this in the optimization of suggestions. It also customizes the suggestions according to the user's priority settings and displays them on the terminal.

[0789] For example, if a user provides feedback to the system stating that they "want to shorten their commute time," the server will suggest an optimal start time based on past commute data and traffic information, and notify the user on their device. An example of a prompt message regarding this suggestion would be, "Please tell us the best way to shorten your commute time to make your daily life more efficient."

[0790] As described above, the server and terminal work together to provide users with a system that continuously offers concrete and practical support to streamline their daily lives.

[0791] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0792] Step 1:

[0793] Users use a device to input data about their daily lives. Specifically, they input information such as their wake-up time, commute time, and meal times into the device and send it to the server as collected data. In this process, the device appropriately formats the input information and converts it into a format that the server can understand before sending it.

[0794] Step 2:

[0795] The server receives user behavior data sent from the terminal. To analyze the received data, it is stored in a database and clustering techniques are used to identify behavioral patterns. Specifically, the scikit-learn KMeans algorithm is used to classify the data into several patterns and identify inefficient use of time.

[0796] Step 3:

[0797] The server obtains real-time information on public transportation via external APIs. Based on delay information and congestion levels, it generates optimized daily schedule suggestions for users. In this process, it integrates traffic information with previously identified behavioral patterns and uses AI algorithms to identify potential areas for improvement.

[0798] Step 4:

[0799] The generated suggestions are finalized, taking into account the user's priority settings. The suggestions are customized based on the user's past feedback and settings. The customized suggestions are then output by the system's AI model, using prompts as a reference, in a format that is easy for the user to follow.

[0800] Step 5:

[0801] The terminal receives optimized suggestions from the server and presents them to the user. The user reviews the suggestions and decides whether to implement them. The suggestions are displayed on the interface in a format that is easy for the user to understand intuitively.

[0802] Step 6:

[0803] After the user executes the suggestion, they enter feedback. The terminal formats the feedback appropriately and sends it to the server. The feedback is used to improve the suggestion and train the AI ​​model. The server uses this feedback to improve the accuracy of future suggestions.

[0804] 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.

[0805] This invention relates to a system that provides individually customized efficiency improvement suggestions using user behavior data and emotional states. This system operates as an application installed on mobile devices and computers, and interacts with users through a user-friendly interface.

[0806] Data collection and emotion recognition

[0807] First, the device displays questions to the user about their daily activities and schedule, and collects their responses. With the user's permission, the device also automatically acquires motion data from their smartphone or wearable device. Furthermore, the device uses facial recognition and voice analysis technologies to collect emotional data to determine the user's emotions.

[0808] Data storage and analysis

[0809] The server receives all data (behavioral and emotional data) transmitted from the terminal and securely and efficiently stores it in user-specific profiles. Next, the server uses machine learning algorithms to analyze this data and recognize the user's behavioral and emotional patterns.

[0810] Proposal generation and emotional application

[0811] Based on the analysis results, the server generates specific suggestions to help users live their daily lives more efficiently. In this process, the emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is feeling stressed, it prioritizes suggesting ways to relax.

[0812] Proposal presentation and feedback gathering

[0813] The device notifies the user of the generated suggestions and displays the details in the application. The server collects feedback from the user on the device regarding the results of trying the suggested actions and any changes in their emotions during that process. This feedback is used to improve the accuracy of future suggestions.

[0814] Specific example

[0815] For example, when a user inputs feedback such as "I feel stressed due to fatigue after work," the device uses its emotion engine to suggest a relaxation plan to alleviate facial tension. This includes specific action plans such as "stretching after leaving work on time." The user then inputs feedback after trying this, and based on that, the server generates new suggestions that are further tailored to the user's emotions.

[0816] In this way, the system continuously analyzes users' emotional and behavioral data and provides individually optimized suggestions to support their daily lives.

[0817] The following describes the processing flow.

[0818] Step 1:

[0819] The device displays questions about the user's daily life and collects information such as wake-up time, meal times, commute patterns, and bedtime. Basic behavioral data is accumulated as the user answers these questions.

[0820] Step 2:

[0821] With the user's permission, the device automatically collects behavioral data from smartphones and wearable devices. This includes information such as steps taken, heart rate, and activity level.

[0822] Step 3:

[0823] The device uses the user's facial recognition camera and microphone to collect emotional data in real time from their facial expressions and voice. The data collected is used to understand the user's emotional state.

[0824] Step 4:

[0825] The device sends all collected data (behavioral and emotional data) to the server. The data is encrypted for privacy protection and transmitted securely.

[0826] Step 5:

[0827] The server stores the received data in a database, which is then organized for each user. The stored data forms the basis for analyzing long-term behavioral and emotional patterns.

[0828] Step 6:

[0829] The server performs machine learning based on accumulated data to analyze users' behavioral and emotional patterns. This analysis identifies what kinds of emotions users tend to experience in different situations.

[0830] Step 7:

[0831] Based on the analysis results, the server generates specific behavioral improvement suggestions tailored to the user's emotional state. For example, if it detects high levels of fatigue, it suggests a schedule that prioritizes rest.

[0832] Step 8:

[0833] The server sends the generated proposal to the terminal. The terminal notifies the application of the received proposal and displays detailed information within the application.

[0834] Step 9:

[0835] The user reviews the suggestions displayed on their device and decides whether to implement them. If they implement a suggestion, they provide feedback on the results, their feelings, and any changes in their emotions during the process.

[0836] Step 10:

[0837] The server collects feedback from users and re-analyzes the data to incorporate it into future suggestions. This feedback loop enables more personalized suggestions.

[0838] (Example 2)

[0839] 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".

[0840] In modern society, the decrease in efficiency and increase in stress experienced by individual users in their daily lives are serious problems. To address this, suggestions based solely on behavioral data are insufficient; highly accurate suggestions that also consider emotional states are required. However, conventional technologies have struggled to provide optimal suggestions based on emotions, failing to achieve efficiency improvements tailored to individual user needs.

[0841] 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.

[0842] In this invention, the server includes a device for receiving information on the user's behavior and emotions, means for collecting information, means for analyzing the collected information and recognizing patterns in the user's behavior and emotions, and means for using a generative AI model that generates suggestions that contribute to improving the user's efficiency based on the recognized patterns. This makes it possible to effectively provide efficient and individually optimized suggestions that are tailored to the user's emotional state.

[0843] "Users" refer to individuals who use this system and are the entities that provide information about their behavior and emotions.

[0844] "Behavior" refers to information related to the user's daily activities, including data on physical movements and schedules.

[0845] "Emotions" refers to information that identifies the user's psychological and emotional state, and includes data obtained through facial recognition and voice analysis.

[0846] "Information" encompasses all data related to users' behavior and emotions.

[0847] "Collection" refers to the process of obtaining data provided by users and gathering it into the system.

[0848] "Analysis" refers to the process of identifying and analyzing patterns in users' behavior and emotions based on collected data.

[0849] A "generative AI model" is an artificial intelligence technology used to create proposals, particularly one that utilizes natural language processing.

[0850] "Means" refers to the equipment, techniques, or methods that a system uses to perform a particular function.

[0851] "System" refers to a combination of devices and software operated using the above-mentioned means for the purpose of improving user efficiency.

[0852] In an embodiment of this invention, the system is configured and operates as follows:

[0853] Data collection and emotion recognition

[0854] First, the device displays a questionnaire to the user through an application, asking about their daily activities and schedule. When the user answers the questions on their smartphone, the device collects that data. Furthermore, with the user's permission, the device collects motion data such as location information, steps taken, and heart rate from the smartphone and wearable device. This uses the smartphone's sensors and the wearable device's APIs. In addition, the device utilizes an open-source facial recognition framework (e.g., OpenCV) for facial recognition technology and a speech recognition API (e.g., an API that converts speech to text) for speech analysis technology to obtain data necessary for determining the user's emotions.

[0855] Data storage and analysis

[0856] The server stores behavioral and emotional data transmitted from the device in a cloud database. Because this data is efficiently stored in each user's profile, encrypted protocols (e.g., HTTPS) are used for secure communication between the server and the device. The server uses machine learning algorithms (e.g., models using TensorFlow) to analyze the data and recognize user behavioral and emotional patterns.

[0857] Proposal generation and display

[0858] The server uses the analyzed data to create suggestions for improving user efficiency, utilizing a generative AI model (e.g., an AI model using natural language generation technology). These suggestions take into account the user's emotional state, and the content is adjusted to meet the user's individual needs. The terminal notifies the user of these suggestions and displays detailed information within the application.

[0859] Gathering feedback and improving suggestions

[0860] Users try out suggestions and provide feedback on their devices about the results and changes in their feelings. This feedback is used on the server side to further optimize future suggestions.

[0861] Specific example

[0862] For example, if a user enters the prompt message, "I'm feeling stressed today. Do you have any suggestions for relaxing?", the device will suggest a relaxation plan such as, "I recommend a 10-minute stretch after work." In this way, the system uses user data to provide individually optimized suggestions, helping to improve the efficiency of the user's daily life.

[0863] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0864] Step 1:

[0865] The device displays questions about the user's daily activities and schedule through the application. It receives the user's answers to these questions as input. This input data is recorded as basic information about the user's behavioral patterns. Specifically, the device presents questions using a survey-style UI and waits until the user enters their answers.

[0866] Step 2:

[0867] The device automatically collects operational data from smartphones and wearable devices with the user's permission. This input consists of continuous data from the device, such as location, steps taken, and heart rate. This data is centrally managed and stored on the device. Specifically, the device periodically polls and retrieves data using the device API.

[0868] Step 3:

[0869] The device uses facial recognition and voice analysis technologies to determine the user's emotions. This process uses video of the face captured by the camera and audio from the microphone as input. The algorithm used (e.g., facial recognition framework and voice analysis API) generates and records emotion data. Specifically, the device analyzes the video / audio feed and labels the emotional state in real time.

[0870] Step 4:

[0871] The device sends collected behavioral and emotional data to the server. The input includes all data collected and generated to date. The output to the server ensures that the data is securely transferred and received. Specifically, the device uploads the data to the cloud service using the HTTPS protocol.

[0872] Step 5:

[0873] The server stores the received data in a cloud database. The input consists of behavioral and emotional data sent from the terminal, which is efficiently accumulated. The data stored in the database is used for subsequent analysis. Specifically, the server records the data in database software (e.g., a relational database).

[0874] Step 6:

[0875] The server analyzes stored data using machine learning algorithms. The input consists of accumulated behavioral and emotional data, and the analysis recognizes user behavioral and emotional patterns. The output is detailed insights into behavioral patterns. Specifically, the server uses analysis tools (e.g., TensorFlow) to model the data.

[0876] Step 7:

[0877] The server uses a generative AI model based on the analysis results to generate optimal suggestions for the user. Input includes recognized behavioral and emotional patterns. Output consists of specific suggestions designed to improve the user's efficiency. Specifically, natural language generation technology is used to translate the suggestions into text.

[0878] Step 8:

[0879] The device receives suggestions from the server and notifies the user. The input is the generated suggestion content received from the server. The output is the suggestion itself. Specifically, the device displays a pop-up notification or in-app display to allow the user to review the suggestion.

[0880] Step 9:

[0881] The user is given the opportunity to try out the presented suggestions and inputs their feedback into the device. The input includes the user's impressions and actions regarding the suggestions. The output is feedback data used to generate future suggestions. Specifically, the user enters their answers into a feedback form on the device.

[0882] Step 10:

[0883] The server uses user feedback as data to improve the accuracy of its suggestions. Input includes user feedback data, and output represents improvements for future suggestion generation. Specifically, the server analyzes the feedback and uses it to refine its machine learning model.

[0884] (Application Example 2)

[0885] 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".

[0886] The challenge lies in accurately understanding the daily activities and emotional states of users, including the elderly, and providing means to support their efficient lives while reducing their stress. In particular, it is necessary to provide suggestions that reflect changes in users' emotions in real time and utilize them to support caregiving.

[0887] 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.

[0888] In this invention, the server includes means for receiving and collecting data on user behavior, means for analyzing the collected data to recognize behavioral patterns, and means for recognizing the user's emotional state and adjusting the generated suggestions based on that emotional state. This makes it possible to provide individually optimized efficiency improvement suggestions that are tailored to the user's emotions.

[0889] "Behavioral data" refers to information related to the user's actions and habits in their daily life, and is used to understand the user's behavioral patterns.

[0890] "Behavioral patterns" refer to a set of data used to analyze and identify consistent behavioral and habitual tendencies of users, and serve as a foundation for generating suggestions for improving efficiency.

[0891] "Specific suggestions that contribute to improved efficiency" refer to suggestions for changes in specific behaviors or habits that users should make in their daily lives to reduce stress and increase comfort.

[0892] "Means of recognizing emotional states" refers to methods of collecting data using technologies such as facial recognition and voice analysis to determine the user's current emotions and psychological state.

[0893] "Biometric indicators" are data that shows the user's physical condition, such as heart rate and body temperature, and are used to evaluate their emotional state and health status.

[0894] "Care support" refers to support activities aimed at efficiently providing daily care and assistance to improve the quality of life for the elderly and people with disabilities.

[0895] The system that realizes this invention is designed to analyze the user's daily behavior and emotions to provide personalized suggestions. The server collects and analyzes data on the user's behavior. The hardware used includes smartphones, fitness trackers, and camera-equipped devices. Receiving data from these devices, the server uses OpenCV for facial recognition and the Google Speech API for speech analysis as software for analyzing the information. In addition, it utilizes TensorFlow, a machine learning platform, to analyze behavioral and emotional data.

[0896] The device uses collected data to determine the user's behavioral patterns and emotional state. Based on the user's emotional state, the server generates specific suggestions to improve efficiency. For example, if the user is feeling stressed, relaxation exercises can be recommended. This can reduce the user's stress and improve their quality of life. This process is continuous, and the system keeps improving its suggestions based on user feedback.

[0897] A concrete example of a prompt message might be, "The user is feeling stressed; suggest a 10-minute breathing exercise to help them relax." This allows the generative AI model to generate the most appropriate action suggestion for the user, which the user can then perform.

[0898] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0899] Step 1:

[0900] The device collects data about the user's daily activities. It uses location information and activity level data obtained from smartphones and fitness trackers as input. As output, it transfers this data to a server. Specifically, the device synchronizes with the fitness tracker to record daily step counts and travel route information.

[0901] Step 2:

[0902] The server analyzes the collected data to recognize user behavior patterns. It receives activity data transferred from the terminal as input and generates a profile of the recognized behavior patterns as output. Specifically, the server uses machine learning algorithms to analyze daily behavior data and model trends in movement frequency and time of day.

[0903] Step 3:

[0904] The device acquires live data from its camera and microphone to recognize the user's emotional state. Facial images and audio data are used as input. The output generates an index indicating the emotional state. Specifically, the device's camera captures the user's facial expressions, and OpenCV is used to analyze changes in microexpressions.

[0905] Step 4:

[0906] The server generates appropriate efficiency improvement suggestions based on recognized behavioral patterns and emotional states. It uses behavioral pattern profiles and emotional state indicators as input. Customized suggestions are generated as output. Specifically, the server uses a generated AI model to formulate suggestions based on the prompt statement, "The user is feeling stressed; suggest 10 minutes of relaxing breathing exercises."

[0907] Step 5:

[0908] The device presents the generated suggestions to the user. It receives suggestions generated by the server as input. As output, it communicates the content of the suggestions to the user visually or audibly. Specifically, it uses the smartphone's notification function to display a suggestion to the user: "Shall we start a 10-minute breathing exercise now?"

[0909] Step 6:

[0910] After trying out a suggestion, the user provides feedback on the results and experience to their device. The input includes the action taken and the resulting emotional changes. The output is feedback data sent to the server. Specifically, the user writes their thoughts on the suggestion's effectiveness on a smartphone app and presses the submit button.

[0911] Step 7:

[0912] The server receives feedback from users and stores and analyzes data to improve the accuracy of future suggestions. User feedback data is used as input. The output is an updated, improved suggestion algorithm. Specifically, the server retrains the machine learning model with the new data to improve the algorithm's performance.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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."

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0934] The following is further disclosed regarding the embodiments described above.

[0935] (Claim 1)

[0936] The system includes a device for receiving data relating to user behavior, and means for collecting said data,

[0937] A means of analyzing collected data and recognizing user behavior patterns,

[0938] A means for generating specific suggestions that contribute to improving user efficiency based on the recognized behavioral patterns,

[0939] A means of displaying the generated suggestions to the user,

[0940] The means used to collect user feedback and improve the proposed content,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, comprising means for automatically acquiring behavioral data from an external device with the user's permission.

[0944] (Claim 3)

[0945] The system according to claim 1, comprising means for adjusting the content of the generated suggestions, taking into account the user's priority settings for those suggestions.

[0946] "Example 1"

[0947] (Claim 1)

[0948] Means for receiving and collecting information about daily life from users,

[0949] A means for automatically acquiring authorized information from an external measuring device,

[0950] A means for storing received information and identifying behavioral patterns using computational processing means,

[0951] A means to generate concrete proposals for improving living efficiency based on the identification results,

[0952] A means of notifying and presenting the generated proposals to the user,

[0953] A means of collecting user feedback and using it to improve the proposed content,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, comprising means for automatically acquiring information from an external measuring device.

[0957] (Claim 3)

[0958] The system according to claim 1, comprising means for optimizing the content of the generated suggestions by reflecting the user's priority settings for those suggestions.

[0959] "Application Example 1"

[0960] (Claim 1)

[0961] A device that receives information about user behavior, and a method for collecting such information,

[0962] A method for analyzing collected information and recognizing user behavior patterns,

[0963] A method for generating specific suggestions that contribute to improving user efficiency based on the aforementioned recognized behavioral patterns,

[0964] A method for including schedule optimization that takes into account public transport delay information and congestion levels in the generated proposals,

[0965] How to display the generated suggestions to the user,

[0966] Methods for collecting user feedback and using it to improve suggestions,

[0967] A system that includes this.

[0968] (Claim 2)

[0969] The system according to claim 1, comprising a method for automatically acquiring behavioral information from an external device with the user's permission.

[0970] (Claim 3)

[0971] The system according to claim 1, comprising a method for adjusting the content of the generated suggestions, taking into account the user's priority settings for the suggestions and traffic information.

[0972] "Example 2 of combining an emotion engine"

[0973] (Claim 1)

[0974] The system includes a device for receiving information about the user's behavior and emotions, and means for collecting such information,

[0975] A means of analyzing collected information and recognizing patterns in user behavior and emotions,

[0976] A means of using a generative AI model that generates suggestions that contribute to improving user efficiency based on the recognized patterns,

[0977] A means of presenting the generated suggestions to users and collecting feedback,

[0978] A means of improving the proposal using the collected feedback,

[0979] A system that includes this.

[0980] (Claim 2)

[0981] The system according to claim 1, comprising means for automatically acquiring behavioral and emotional information from an external computing device with the user's permission.

[0982] (Claim 3)

[0983] The system according to claim 1, further comprising means for adjusting the generated suggestion content in consideration of the user's emotional state.

[0984] "Application example 2 when combining with an emotional engine"

[0985] (Claim 1)

[0986] The system includes a device for receiving data relating to user behavior, and means for collecting said data,

[0987] A means of analyzing collected data and recognizing user behavior patterns,

[0988] A means for generating specific suggestions that contribute to improving user efficiency based on the recognized behavioral patterns,

[0989] A means for recognizing the user's emotional state and adjusting the generated suggestions based on that emotional state,

[0990] A means of displaying the generated suggestions to the user,

[0991] The means used to collect user feedback and improve the proposed content,

[0992] A system that includes this.

[0993] (Claim 2)

[0994] The system according to claim 1, comprising means for automatically acquiring behavioral data and biometric indicators from an external device with the user's permission.

[0995] (Claim 3)

[0996] The system according to claim 1, comprising means for adjusting the content of the generated suggestions, taking into account the user's priority setting for those suggestions, and applying them to care support. [Explanation of Symbols]

[0997] 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. The system includes a device that receives information about the user's behavior, and means for collecting such information, A means of analyzing collected information and recognizing user behavior patterns, A means for generating specific suggestions that contribute to improving user efficiency based on the recognized behavioral patterns, A means of including schedule optimization that takes into account public transport delay information and congestion levels in the generated proposals, A means of displaying the generated suggestions to the user, The means used to collect user feedback and improve the proposed content, A system that includes this.

2. The system according to claim 1, comprising means for automatically acquiring behavioral information from an external device with the user's permission.

3. The system according to claim 1, comprising means for adjusting the content of the generated suggestions, taking into account the user's priority setting for the suggestions and traffic information.

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