system

Abstract

We provide the system. [Solution] An information processing device includes means for collecting data from a user's terminal through multiple data sources, A means for analyzing the aforementioned data and determining the priority of the user's schedule and tasks, A means of generating multiple proposals based on the analysis results and notifying the user, Means for enabling the user to approve or modify the aforementioned proposal, Based on the user's approval, means for updating the schedule and coordinating information between linked applications, 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 performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 individuals and businesses, there is a need to efficiently manage a vast amount of information and make timely decisions. However, in an environment where information fragments in daily life are dispersed, it is difficult to efficiently manage schedules and prioritize tasks, consuming a lot of time and effort. Therefore, a new system that improves users' time management and business efficiency through information integration and appropriate suggestions is desired.

Means for Solving the Problems

[0005] This invention provides a system that uses an information processing device to collect various data sources from a user's terminal and analyzes that data to determine the priority of the user's schedule and tasks. Furthermore, it has a function to notify the user of multiple suggestions generated based on the analysis results and to appropriately update the schedule by approving or modifying the suggestions. This enables the user to handle information efficiently and effectively carry out necessary tasks. In addition, security protocols are used for information communication, and machine learning algorithms are used to analyze the user's behavior patterns and reflect them in future suggestions, thereby improving the overall accuracy and usability of the system.

[0006] An "information processing device" is a device that includes hardware and software for collecting data from a user's terminal, analyzing it, and generating results.

[0007] A "data source" is an application or device that provides information related to the user's terminal, such as calendar, email, and activity history.

[0008] "Analysis" is the process of processing collected data to determine the priority of users' schedules and tasks.

[0009] A "proposal" is an actionable set of guidelines or plans generated based on the analysis results and communicated to the user.

[0010] "Schedule updating" is the process of modifying a user's schedule based on new information and suggestions, and reconstructing an appropriate timeline.

[0011] A "security protocol" is a set of technical specifications used to ensure privacy and the security of information in data communications.

[0012] A "machine learning algorithm" is an algorithm used in the process of analyzing user behavior patterns and generating future suggestions and action recommendations. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that utilizes an information processing device to collect and analyze necessary data from the user's terminal, thereby achieving efficient information management and action recommendations. A specific example is shown below.

[0035] In this system, the device automatically collects data such as the user's calendar, emails, and activity history. Users can pre-configure the scope and frequency of data collection, thus ensuring privacy and allowing for operation tailored to the user's preferences.

[0036] The collected data is transmitted from the terminal to the server. The server receives the data using secure communication methods and stores it in a database. Furthermore, it analyzes the data using algorithms for natural language processing and behavioral pattern analysis. Through this analysis, the server can understand the user's schedule and tasks and prioritize them.

[0037] Based on the analysis results, the server generates optimal action plans and schedule suggestions for the user. For example, it can suggest events suitable for hobbies or learning during a weekend when the user plans to spend time at home. In a business setting, it can also organize and present a list of preparation items for the following week's meeting.

[0038] The generated proposals are sent from the server to the device, and the device notifies the user via push notification or email. The user can review the proposals and accept or make minor modifications. Accepted proposals are immediately reflected in the schedule, and the action plan on the device is automatically updated.

[0039] Furthermore, the server coordinates with other related applications and platforms to maintain overall information integrity. This coordination allows for quick responses to changes in meetings, for example, and enables schedule adjustments without affecting stakeholders.

[0040] Thus, the present invention aims to significantly improve users' work efficiency and time management capabilities by highly automating user information management and proposing predictable actions.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device automatically collects data such as the user's calendar, emails, and activity history at specified times. This data collection is performed via APIs or local data storage, and is limited to the scope permitted by the user.

[0044] Step 2:

[0045] The device sends the collected data to the server in an end-to-end encrypted format. Security protocols are used for communication, and verification is performed immediately after the data is received.

[0046] Step 3:

[0047] The server stores the received data in a database and performs analysis. Natural language processing (NLP) and machine learning algorithms are used for the analysis to derive user behavior patterns and schedule priorities.

[0048] Step 4:

[0049] The server generates suggestions for the user based on the analysis results. These suggestions take into account the user's past preferences and habits and include guidelines for action and suggestions for improving the schedule.

[0050] Step 5:

[0051] The server sends the generated suggestions to the user's device. Suggestion notifications are sent via push notifications or email, with different methods used depending on the time and urgency.

[0052] Step 6:

[0053] Users receive notifications and review the proposals. By approving or modifying the proposals, they can select the necessary actions and reflect them in their schedules.

[0054] Step 7:

[0055] The device automatically updates calendars and task management tools based on user approvals and modifications, and integrates with other applications as needed. This ensures that all information is up-to-date, and changes are automatically notified to relevant parties.

[0056] (Example 1)

[0057] 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."

[0058] In today's information society, users need to efficiently manage a large volume of schedules and tasks. However, doing so manually is time-consuming, labor-intensive, and prone to errors. Furthermore, with multiple digital devices and information infrastructures in existence, information from these sources is often not unified and lacks coordination. This leads to decreased user productivity and scheduling inconsistencies.

[0059] 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.

[0060] In this invention, the server includes means for collecting information from the user's operating device, means for processing the information and determining priorities, and means for predicting the user's behavior and generating suggestions using a generative AI model. This enables the user to efficiently manage their schedule and tasks, achieving centralized information management and efficient schedule adjustment.

[0061] An "information processing system" is an integrated operating device that collects and processes data based on user requests, and provides efficient information management and decision support.

[0062] A "user's operating device" is a digital device that works in conjunction with an information processing system to collect data from users and receive notifications.

[0063] An "information source" is a medium that provides data to be analyzed by an information processing system, such as a user's calendar, electronic communications, and behavioral history.

[0064] "Processing" refers to the activity of extracting information from input and performing actions such as analysis, classification, and filtering to obtain useful information.

[0065] "Means of determining priorities" refers to the process of identifying the importance and urgency of schedules and tasks based on collected data, and then setting the order of execution accordingly.

[0066] A "generative AI model" is an artificial intelligence-based inference system that uses machine learning to predict user behavior and incorporate that prediction into processing.

[0067] "Methods for generating proposals" refer to the process of formulating and presenting action guidelines and plans to users based on analysis results and AI models.

[0068] "Communication methods" refer to methods of managing data transfer using encryption and security protocols to ensure secure data transmission.

[0069] Embodiments of the present invention will be described in detail below.

[0070] This system is a device configuration that utilizes an information processing system to efficiently collect and analyze user information and generate suggestions. The main components consist of a user operating device (hereinafter referred to as a terminal) and a central processing unit (hereinafter referred to as a server) that performs data processing.

[0071] The device automatically collects information from users from various sources, such as calendars, emails, and activity history. The device has the functionality to organize and extract this data by running dedicated application software in the background. Since data collection is performed based on the time and conditions set by the device, it allows for efficient information acquisition while respecting privacy.

[0072] The collected information is transmitted from the terminal to the server. The server uses security protocols to enable data communication and stores the received data in the information infrastructure while encrypting and protecting it. The server utilizes a generative AI model that applies machine learning technology to analyze behavioral patterns based on the received information. Through this process, suggestions for the next action to take are calculated according to the user's habits and preferences.

[0073] Based on the analysis results, the server generates efficient action plans and schedule proposals for the user. For example, it might suggest a "preparation list for a 3 PM meeting." These generated proposals are then communicated to the user via push notifications on their device. The user can review, accept, or modify the proposals, and any accepted changes are immediately synchronized with the device's calendar data.

[0074] In this way, the system streamlines users' daily tasks and enables integrated schedule management. An example of a prompt provided by this system is, "Please prepare the items on this list in advance for the next meeting," and specific, user-value-based suggestions are made based on the analysis results of the AI ​​model.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device collects information such as the user's calendar, emails, and activity history. As input, applications installed on the device access and collect digital data (e.g., emails, calendar events) within the user's permitted scope. Specifically, the device operates in the background, collecting information according to a set schedule. As output, the collected data is organized in JSON format and prepared for subsequent processing.

[0078] Step 2:

[0079] The device sends the collected data to the server using a secure protocol (e.g., SSL / TLS). The input is JSON data prepared in step 1. Specifically, the device is configured to transfer this data to the server whenever a Wi-Fi connection is active. The output is data securely stored on the server.

[0080] Step 3:

[0081] The server stores the received data in a database and prepares it for analysis. It receives JSON data transferred from the terminal as input. Specifically, the server checks the data's integrity upon receipt, adds classification tags, and stores it in the information infrastructure. The output is the information stored in the database in an analyzable format.

[0082] Step 4:

[0083] The server analyzes data using machine learning techniques and generative AI models to predict user behavior patterns. The input is the data stored in step 3. Specifically, the server applies algorithms such as natural language processing (NLP) to extract important information from the user's email content and schedule. The output generates a priority list of actions to take and prompt messages.

[0084] Step 5:

[0085] The server generates action plans and proposed schedules based on the analysis results and notifies the terminal. The input is based on the prompts and action plans generated in step 4. Specifically, the server creates concrete suggestions based on the user's individual needs (e.g., "Please prepare the following for next week's meeting"). The output is the notification message sent to the terminal.

[0086] Step 6:

[0087] The terminal notifies the user of received proposals, allowing the user to review, approve, and modify them. The input is a notification message from the server. Specifically, the terminal presents the proposal to the user via push notification or email. The output is the user's approval or modification, and the updated schedule is managed on the terminal.

[0088] Step 7:

[0089] The server coordinates to maintain information harmony with other related applications and platforms. Its input is user-approved schedule information. Specifically, the server synchronizes information with linked systems via APIs, for example, by automatically sending notifications to meeting participants to reflect the current status. As an output, a consistent schedule is created across the board, enabling users to work in a consistent information environment.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern society, managing personal schedules and integrating information has become extremely complex. Coordinating individual appointments, optimizing transportation, and utilizing local information are essential, but doing so manually is time-consuming, laborious, and inefficient. Furthermore, in today's highly competitive living environment, there is a need for systems that can support optimal choices in real time.

[0093] 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.

[0094] In this invention, the server includes means for collecting information from the user's communication device through multiple information sources, means for analyzing the information and determining the user's schedule and task priorities, and means for analyzing the surrounding situation through a civic life assistant application program and proposing optimal actions. This enables highly automated personal information management and the provision of efficient schedule management and optimal action guidelines.

[0095] An "information processing device" is a device that has the ability to collect and analyze data from various information sources and make suggestions based on the results.

[0096] "User's communication device" refers to a personal mobile information processing device such as a smartphone or tablet that an individual uses on a daily basis.

[0097] "Information sources" refer to the sources of data from which information can be extracted, such as the user's calendar, emails, and activity history.

[0098] "Analysis" is the process of extracting useful information from collected data and finding regularities and patterns.

[0099] Prioritization is the process of ordering tasks and appointments in order to complete them efficiently.

[0100] The "Citizen Life Assistant Application Program" is software designed to support users in their daily lives and suggest optimal actions and options.

[0101] "Surrounding conditions" refers to external environmental information such as traffic, weather, and event information related to the user's current location.

[0102] "Guidelines for Action" are suggestions and guidance designed to support users in efficiently managing their time and executing their schedules.

[0103] This invention is a system consisting of an information processing device that works in conjunction with the user's communication device, automating the user's schedule management and daily life support. Specifically, the information processing device periodically collects data such as calendar information, location information, and activity history from the user's communication device. Since the target and frequency of this data collection can be set by the user, privacy is maintained and flexible operation tailored to individual needs is possible.

[0104] The server receives and securely stores the collected data. It also performs data analysis using natural language processing algorithms and machine learning techniques. Based on this analysis, the server can understand and prioritize the user's schedule and tasks. Furthermore, it analyzes surrounding conditions such as nearby traffic, weather, and event information to suggest optimal actions for the user.

[0105] The proposed action plan and schedule are sent to the user's communication device and provided as a notification. The user can review the proposal and make modifications as needed. In this way, the proposed plan is put into action and the schedule is updated.

[0106] As a concrete example, when a user commutes to work on a Monday morning, the server can suggest the optimal commute route, taking into account traffic congestion and weather conditions. For example, it might say, "Rain is expected today, so we recommend using the subway route."

[0107] An example of a prompt message would be, "Based on the user's calendar and location information, suggest a mode of transportation for the next activity." This can improve the user's quality of life and enable efficient time management and a comfortable living environment.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects data such as calendar information, location information, and activity history from the user's communication device. This data is transmitted via a secure communication method. The input is various information data within the user's communication device, and the output is the unprocessed data sent to the server.

[0111] Step 2:

[0112] The server receives the collected raw data and securely stores it in the database. The input is the raw data sent in step 1, and the output is the stored structured data. Storing the data in the database allows for efficient subsequent data analysis.

[0113] Step 3:

[0114] The server uses natural language processing algorithms and machine learning techniques to analyze data in the database. The input is the stored structured data, which provides the basic information needed for analysis. The output is the analysis results, including the user's schedule and task priorities.

[0115] Step 4:

[0116] The server generates optimal action plans for the user based on the analysis results. This includes current traffic conditions and weather information. The input is the analysis results and surrounding environment information obtained from an external API, and the output is a list of recommended actions for the user.

[0117] Step 5:

[0118] The server notifies the user's communication device of the generated recommended actions. The input is the list of recommended actions generated in step 4, and the output is the notification message on the user's communication device. This allows the user to decide on an action based on the information received.

[0119] Step 6:

[0120] After reviewing the submitted proposal, users can make revisions as needed. The input is the submitted proposal, and the output is the action plan revised by the user. These revisions are automatically fed back into the system, leading to updates to the schedule and related information.

[0121] 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.

[0122] This invention combines an emotion engine with a system that efficiently manages the user's schedule and tasks through an information processing device using data collected from the user's terminal, and generates multiple suggestions. This system recognizes and analyzes the user's emotional state and adjusts the suggestion content based on this, thereby providing the user with more appropriate and personalized suggestions.

[0123] In implementing the system, the terminal first collects user behavioral data, voice information, and emotional data through camera footage. Emotional data is acquired in a natural way so as not to affect the user's daily usage environment and is used only with the user's permission.

[0124] Next, the device securely transmits this data to the server. The server receives the data using a secure communication protocol and stores it in a database. The received emotion data is analyzed by an emotion engine. The emotion engine uses voice tone and facial recognition technology to identify the user's emotions and quantify or categorize their state.

[0125] Based on this analysis, the server adjusts its suggestions. For example, if the emotion engine detects that the user is stressed, it can suggest a break to help them relax or reschedule tasks. In this way, suggestions reflecting the user's emotions are generated and notified to the user's device.

[0126] Furthermore, users can approve or modify the presented suggestions, providing feedback to receive new suggestions based on changes in their emotions. This allows the system to continuously collect data to improve user adaptation.

[0127] This system aims to improve not only the efficiency of schedule management but also the user experience by enabling flexible suggestions that respond to the user's emotions.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device collects user behavior data and related audio and camera footage in real time. Data collection is done only with the user's permission, and emotional data includes changes in facial expressions and tone of voice.

[0131] Step 2:

[0132] The device encrypts the collected emotional data and sends it to the server. It uses a secure communication protocol to maintain data confidentiality during transmission.

[0133] Step 3:

[0134] The server stores the received emotional data in a database, and an emotion engine performs analysis. This analysis uses facial recognition algorithms and voice analysis to identify the user's emotional state and quantify conditions such as stress and happiness.

[0135] Step 4:

[0136] The server generates suggestions based on the analysis results, tailored to the user's current emotions. For example, if it determines that the user is tired, it will generate a suggestion to add a break to their schedule.

[0137] Step 5:

[0138] The server sends tailored suggestions to the user's device. Suggestion notifications are delivered via push notifications or in-app displays.

[0139] Step 6:

[0140] The user reviews the proposal and approves or modifies it. Based on the user's feedback, the device updates the schedule and task list.

[0141] Step 7:

[0142] The device sends user feedback back to the server, providing data to be used in future suggestions. The server continuously learns and analyzes the data to improve the accuracy of the emotion engine.

[0143] (Example 2)

[0144] 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".

[0145] Modern schedule management systems utilizing information and communication technology enable task management based on user behavior, but they do not yet take into account the user's emotional state. Therefore, suggestions that consider the user's stress and fatigue are not provided, resulting in a less-than-ideal user experience. The challenge we aim to address here is to provide more comfortable and efficient schedule management by dynamically reflecting the user's emotional state and generating personalized suggestions.

[0146] 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.

[0147] In this invention, the server includes means for identifying the user's emotional state using an emotion engine, means for generating suggestions corresponding to the user's emotional state using a generative AI model, and means for collecting user feedback and incorporating it into future suggestions. This enables flexible schedule management and task suggestions that respond to the user's emotions.

[0148] An "information processing device" is an electronic device that analyzes multiple data points collected from users and generates suggestions.

[0149] An "emotion engine" is an algorithm that identifies, quantifies, or categorizes a user's emotional state based on their voice tone and facial expressions.

[0150] A "generative AI model" is an artificial intelligence model used to generate personalized suggestions based on the user's emotional state.

[0151] A "security protocol" is a means of communication designed to protect privacy and integrity during data transmission.

[0152] A "machine learning algorithm" is a statistical method that analyzes user behavior patterns and emotional patterns and uses this information to generate future suggestions.

[0153] "Feedback" refers to the information a user provides when approving, rejecting, or modifying a proposal.

[0154] This invention relates to an information processing system that manages schedules and tasks based on the user's emotional state. This system is realized by processing data collected from the user's terminal using an emotion engine and a generative AI model on a server.

[0155] First, the device uses hardware such as sensors, microphones, and cameras to collect user behavior data, audio information, and video information. This allows data related to the user's emotional state to be obtained in a natural way. For example, voice tone when using a voice assistant and facial expression monitoring via camera are performed. This data is collected with the user's permission.

[0156] Next, the terminal sends the collected data to the server using a secure communication protocol. The server stores the received data in a database and prepares to perform analysis. This analysis uses an emotion engine running in a programming environment such as Python, and the data is quantified or categorized. For example, voice emotion analysis using TENSORFLOW® or facial recognition technology using PyTorch may be utilized.

[0157] Furthermore, the server uses a generative AI model to generate suggestions that respond to the user's emotions. An example of a prompt used in this process is, "If the user is tired, what relaxation methods should be suggested?" The system uses such prompts to create specific action suggestions.

[0158] Users can receive suggestions via their devices and approve or modify them. This feedback is then used again on the server for future suggestions, improving the system's adaptability.

[0159] This system can improve the user experience by providing flexible schedule management that takes user emotions into account.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The device acquires user behavior data, voice information, and camera footage. It uses built-in sensors, microphones, and cameras to respond to user requests and perform periodic scans. Specifically, it recognizes speech, converts it to text, analyzes voice tone, and reads user facial expressions from camera footage to generate emotion data. The input for this step is user actions and environmental information, while the output is emotion data.

[0163] Step 2:

[0164] The device sends the acquired emotional data to the server using a secure communication protocol. This step protects privacy by encrypting the data before transmission. The input is the emotional data generated in the previous step, and the output is data securely stored on the server.

[0165] Step 3:

[0166] The server stores the received emotion data in a database and begins analysis by the emotion engine. The emotion engine generates and quantifies emotion labels from voice tone and facial expressions, for example, using a machine learning library. The input in this step is the emotion data sent from the terminal, and the output is the interpreted emotion labels and numerical data.

[0167] Step 4:

[0168] The server generates suggestions using a generative AI model based on data analyzed by the emotion engine. Specifically, it inputs a prompt message to the generative AI model asking, "What suggestions would be appropriate when this user is feeling stressed?" and obtains individually tailored suggestions. The input is the analyzed emotion data, and the output is the generated suggestions.

[0169] Step 5:

[0170] The server sends the generated proposal to the terminal and notifies the user. The user can review the notified proposal and approve or modify it as needed. The input is the proposal generated in step 4, and the output is the user's feedback.

[0171] Step 6:

[0172] The server collects user feedback and incorporates it into the generation of future suggestions. The feedback is analyzed to improve the quality of the suggestions and stored in a database. The input is user feedback, and the output is adaptive data used for future suggestions.

[0173] (Application Example 2)

[0174] 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".

[0175] Conventional information processing systems face the challenge of making suggestions that take into account the user's emotional state when managing their schedule and tasks. If a user is experiencing stress or discomfort, suggestions that ignore these feelings are not only unhelpful but may even worsen their experience. Therefore, there is a need to improve user satisfaction and provide more personalized suggestions.

[0176] 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.

[0177] In this invention, the server includes means for collecting information from the user's device through multiple information sources, means for analyzing the collected information and determining the user's schedule and task priorities, and means for recognizing the user's emotions and adjusting the suggested content based on those emotions. This makes it possible to provide appropriate suggestions according to the user's emotional state and improve the user experience.

[0178] An "information processing device" is a device that analyzes information collected from a user's device and generates appropriate suggestions.

[0179] "User's devices" refers to terminals and devices that users use on a daily basis, and these are the targets of information collection.

[0180] "Information source" refers to the source of various forms of information collected by the user's device.

[0181] "Information" refers to data about users, which is the subject of collection and analysis.

[0182] "Analysis" is a method of processing collected information to understand the user's state and behavior.

[0183] "Schedule" refers to actions or events that the user plans to perform.

[0184] "Task prioritization" refers to the order in which tasks within a schedule are organized based on their importance and urgency.

[0185] "Proposed content" refers to recommendations and action plans that should be presented to users.

[0186] "Emotional recognition" is the process of identifying an emotional state from a user's facial expressions and voice.

[0187] "Adjusting the proposal" means optimizing the proposal based on the emotions that have been perceived.

[0188] "User experience" refers to the overall feeling and impression gained when using a system.

[0189] The system for carrying out this invention includes an advanced information processing device for providing personalized suggestions that take into account the user's emotions. The server receives facial expression data and voice data collected from the user's terminal via camera and microphone, and securely stores this data. In particular, information is transmitted and received using security protocols to ensure data protection.

[0190] The server uses machine learning algorithms and emotion recognition engines to analyze the received data. For example, it uses OpenCV and Dlib to analyze facial features from camera footage, and Google® Cloud Speech-to-Text to analyze emotions from audio data, quantifying or categorizing them. Based on the analysis results, the server generates suggestions appropriate to the user's emotional state.

[0191] Furthermore, the generated suggestions are used with a generation AI model (e.g., a GPT-based model) to create detailed responses tailored to the user's individual circumstances. In operating this AI model, pre-collected and analyzed data is provided as prompts to improve the accuracy of the responses.

[0192] As a concrete example, when a user returns home from an outing such as shopping, the robot might suggest, "You look tired. I'll play some relaxing music. Is there a genre you'd like to listen to?" Furthermore, examples of prompts provided to the AI ​​model include sentences like, "The user seems stressed; I will continue to offer suggestions to help them relax."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The device uses a camera and microphone to collect the user's facial expressions and voice data. The input is real-time video and audio, and the output is this raw data. This data is temporarily stored locally for subsequent processing.

[0196] Step 2:

[0197] The terminal transmits the collected raw data to the server using a secure communication protocol. The input is raw data, and the output is encrypted data packets. This communication uses the latest security technologies to prevent data eavesdropping and tampering.

[0198] Step 3:

[0199] The server activates an emotion recognition engine to analyze the received data. Specifically, it uses OpenCV and Dlib to analyze facial features from video data and Google Cloud Speech-to-Text to analyze speech tone from audio data. The input is the transmitted video and audio data, and the output is a numerical or categorized emotion index.

[0200] Step 4:

[0201] The server generates suggestions based on sentiment data. It applies a generative AI model (GPT series) and provides prompts to generate responses. The input consists of sentiment indicators and the user's past behavioral patterns, while the output is a customized suggestion message. In this operation, the AI ​​model constructs appropriate responses related to the emotional state.

[0202] Step 5:

[0203] The server sends the generated suggestions to the terminal. The terminal notifies the user of the suggestions via voice or display. The input is the suggestion message from the server, and the output is a notification message to the user. In this step, the terminal provides direct feedback to the user using its speaker or display.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention is a system that utilizes an information processing device to collect and analyze necessary data from the user's terminal, thereby achieving efficient information management and action recommendations. A specific example is shown below.

[0221] In this system, the device automatically collects data such as the user's calendar, emails, and activity history. Users can pre-configure the scope and frequency of data collection, thus ensuring privacy and allowing for operation tailored to the user's preferences.

[0222] The collected data is transmitted from the terminal to the server. The server receives the data using secure communication methods and stores it in a database. Furthermore, it analyzes the data using algorithms for natural language processing and behavioral pattern analysis. Through this analysis, the server can understand the user's schedule and tasks and prioritize them.

[0223] Based on the analysis results, the server generates optimal action plans and schedule suggestions for the user. For example, it can suggest events suitable for hobbies or learning during a weekend when the user plans to spend time at home. In a business setting, it can also organize and present a list of preparation items for the following week's meeting.

[0224] The generated proposals are sent from the server to the device, and the device notifies the user via push notification or email. The user can review the proposals and accept or make minor modifications. Accepted proposals are immediately reflected in the schedule, and the action plan on the device is automatically updated.

[0225] Furthermore, the server coordinates with other related applications and platforms to maintain overall information integrity. This coordination allows for quick responses to changes in meetings, for example, and enables schedule adjustments without affecting stakeholders.

[0226] Thus, the present invention aims to significantly improve users' work efficiency and time management capabilities by highly automating user information management and proposing predictable actions.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The device automatically collects data such as the user's calendar, emails, and activity history at specified times. This data collection is performed via APIs or local data storage, and is limited to the scope permitted by the user.

[0230] Step 2:

[0231] The device sends the collected data to the server in an end-to-end encrypted format. Security protocols are used for communication, and verification is performed immediately after the data is received.

[0232] Step 3:

[0233] The server stores the received data in a database and performs analysis. Natural language processing (NLP) and machine learning algorithms are used for the analysis to derive user behavior patterns and schedule priorities.

[0234] Step 4:

[0235] The server generates suggestions for the user based on the analysis results. These suggestions take into account the user's past preferences and habits and include guidelines for action and suggestions for improving the schedule.

[0236] Step 5:

[0237] The server sends the generated suggestions to the user's device. Suggestion notifications are sent via push notifications or email, with different methods used depending on the time and urgency.

[0238] Step 6:

[0239] Users receive notifications and review the proposals. By approving or modifying the proposals, they can select the necessary actions and reflect them in their schedules.

[0240] Step 7:

[0241] The device automatically updates calendars and task management tools based on user approvals and modifications, and integrates with other applications as needed. This ensures that all information is up-to-date, and changes are automatically notified to relevant parties.

[0242] (Example 1)

[0243] 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."

[0244] In today's information society, users need to efficiently manage a large volume of schedules and tasks. However, doing so manually is time-consuming, labor-intensive, and prone to errors. Furthermore, with multiple digital devices and information infrastructures in existence, information from these sources is often not unified and lacks coordination. This leads to decreased user productivity and scheduling inconsistencies.

[0245] 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.

[0246] In this invention, the server includes means for collecting information from the user's operating device, means for processing the information and determining priorities, and means for predicting the user's behavior and generating suggestions using a generative AI model. This enables the user to efficiently manage their schedule and tasks, achieving centralized information management and efficient schedule adjustment.

[0247] An "information processing system" is an integrated operating device that collects and processes data based on user requests, and provides efficient information management and decision support.

[0248] A "user's operating device" is a digital device that works in conjunction with an information processing system to collect data from users and receive notifications.

[0249] An "information source" is a medium that provides data to be analyzed by an information processing system, such as a user's calendar, electronic communications, and behavioral history.

[0250] "Processing" refers to the activity of extracting information from input and performing actions such as analysis, classification, and filtering to obtain useful information.

[0251] "Means of determining priorities" refers to the process of identifying the importance and urgency of schedules and tasks based on collected data, and then setting the order of execution accordingly.

[0252] A "generative AI model" is an artificial intelligence-based inference system that uses machine learning to predict user behavior and incorporate that prediction into processing.

[0253] "Methods for generating proposals" refer to the process of formulating and presenting action guidelines and plans to users based on analysis results and AI models.

[0254] "Communication methods" refer to methods of managing data transfer using encryption and security protocols to ensure secure data transmission.

[0255] Embodiments of the present invention will be described in detail below.

[0256] This system is a device configuration that utilizes an information processing system to efficiently collect and analyze user information and generate suggestions. The main components consist of a user operating device (hereinafter referred to as a terminal) and a central processing unit (hereinafter referred to as a server) that performs data processing.

[0257] The device automatically collects information from users from various sources, such as calendars, emails, and activity history. The device has the functionality to organize and extract this data by running dedicated application software in the background. Since data collection is performed based on the time and conditions set by the device, it allows for efficient information acquisition while respecting privacy.

[0258] The collected information is transmitted from the terminal to the server. The server uses security protocols to enable data communication and stores the received data in the information infrastructure while encrypting and protecting it. The server utilizes a generative AI model that applies machine learning technology to analyze behavioral patterns based on the received information. Through this process, suggestions for the next action to take are calculated according to the user's habits and preferences.

[0259] Based on the analysis results, the server generates efficient action plans and schedule proposals for the user. For example, it might suggest a "preparation list for a 3 PM meeting." These generated proposals are then communicated to the user via push notifications on their device. The user can review, accept, or modify the proposals, and any accepted changes are immediately synchronized with the device's calendar data.

[0260] In this way, the system streamlines users' daily tasks and enables integrated schedule management. An example of a prompt provided by this system is, "Please prepare the items on this list in advance for the next meeting," and specific, user-value-based suggestions are made based on the analysis results of the AI ​​model.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The device collects information such as the user's calendar, emails, and activity history. As input, applications installed on the device access and collect digital data (e.g., emails, calendar events) within the user's permitted scope. Specifically, the device operates in the background, collecting information according to a set schedule. As output, the collected data is organized in JSON format and prepared for subsequent processing.

[0264] Step 2:

[0265] The device sends the collected data to the server using a secure protocol (e.g., SSL / TLS). The input is JSON data prepared in step 1. Specifically, the device is configured to transfer this data to the server whenever a Wi-Fi connection is active. The output is data securely stored on the server.

[0266] Step 3:

[0267] The server stores the received data in a database and prepares it for analysis. It receives JSON data transferred from the terminal as input. Specifically, the server checks the data's integrity upon receipt, adds classification tags, and stores it in the information infrastructure. The output is the information stored in the database in an analyzable format.

[0268] Step 4:

[0269] The server analyzes data using machine learning techniques and generative AI models to predict user behavior patterns. The input is the data stored in step 3. Specifically, the server applies algorithms such as natural language processing (NLP) to extract important information from the user's email content and schedule. The output generates a priority list of actions to take and prompt messages.

[0270] Step 5:

[0271] The server generates action plans and proposed schedules based on the analysis results and notifies the terminal. The input is based on the prompts and action plans generated in step 4. Specifically, the server creates concrete suggestions based on the user's individual needs (e.g., "Please prepare the following for next week's meeting"). The output is the notification message sent to the terminal.

[0272] Step 6:

[0273] The terminal notifies the user of received proposals, allowing the user to review, approve, and modify them. The input is a notification message from the server. Specifically, the terminal presents the proposal to the user via push notification or email. The output is the user's approval or modification, and the updated schedule is managed on the terminal.

[0274] Step 7:

[0275] The server coordinates to maintain information harmony with other related applications and platforms. Its input is user-approved schedule information. Specifically, the server synchronizes information with linked systems via APIs, for example, by automatically sending notifications to meeting participants to reflect the current status. As an output, a consistent schedule is created across the board, enabling users to work in a consistent information environment.

[0276] (Application Example 1)

[0277] 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."

[0278] In modern society, managing personal schedules and integrating information has become extremely complex. Coordinating individual appointments, optimizing transportation, and utilizing local information are essential, but doing so manually is time-consuming, laborious, and inefficient. Furthermore, in today's highly competitive living environment, there is a need for systems that can support optimal choices in real time.

[0279] 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.

[0280] In this invention, the server includes means for collecting information from a plurality of information sources through the user's communication device, means for analyzing the information to determine the user's schedule and work priorities, and means for analyzing the surrounding situation through a citizen life assistant application program and proposing optimal actions. This enables highly automated personal information management, efficient schedule management, and the provision of optimal action guidelines.

[0281] An "information processing device" is a device that has the ability to collect and analyze data from various information sources and make proposals based on the results.

[0282] The "user's communication device" refers to a portable information processing device such as a smartphone or tablet that an individual uses daily.

[0283] An "information source" is the origin of data for extracting information, such as the user's calendar, emails, and activity history.

[0284] "Analysis" is the process of extracting useful information based on the collected data and finding regularities and patterns.

[0285] "Priority" means ordering tasks and schedules for efficient completion.

[0286] A "citizen life assistant application program" is software that supports users in daily life and proposes optimal actions and options.

[0287] "Surrounding situation" refers to external environmental information such as traffic, weather, and event information related to the user's current location.

[0288] "Action guidelines" are proposals and guidance for supporting the user's efficient time management and schedule execution.

[0289] This invention is a system consisting of an information processing device that works in conjunction with the user's communication device, automating the user's schedule management and daily life support. Specifically, the information processing device periodically collects data such as calendar information, location information, and activity history from the user's communication device. Since the target and frequency of this data collection can be set by the user, privacy is maintained and flexible operation tailored to individual needs is possible.

[0290] The server receives and securely stores the collected data. It also performs data analysis using natural language processing algorithms and machine learning techniques. Based on this analysis, the server can understand and prioritize the user's schedule and tasks. Furthermore, it analyzes surrounding conditions such as nearby traffic, weather, and event information to suggest optimal actions for the user.

[0291] The proposed action plan and schedule are sent to the user's communication device and provided as a notification. The user can review the proposal and make modifications as needed. In this way, the proposed plan is put into action and the schedule is updated.

[0292] As a concrete example, when a user commutes to work on a Monday morning, the server can suggest the optimal commute route, taking into account traffic congestion and weather conditions. For example, it might say, "Rain is expected today, so we recommend using the subway route."

[0293] An example of a prompt message would be, "Based on the user's calendar and location information, suggest a mode of transportation for the next activity." This can improve the user's quality of life and enable efficient time management and a comfortable living environment.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server collects data such as calendar information, location information, and activity history from the user's communication device. This data is transmitted via a secure communication method. The input is various information data within the user's communication device, and the output is the unprocessed data sent to the server.

[0297] Step 2:

[0298] The server receives the collected raw data and securely stores it in the database. The input is the raw data sent in step 1, and the output is the stored structured data. Storing the data in the database allows for efficient subsequent data analysis.

[0299] Step 3:

[0300] The server uses natural language processing algorithms and machine learning techniques to analyze data in the database. The input is the stored structured data, which provides the basic information needed for analysis. The output is the analysis results, including the user's schedule and task priorities.

[0301] Step 4:

[0302] The server generates optimal action plans for the user based on the analysis results. This includes current traffic conditions and weather information. The input is the analysis results and surrounding environment information obtained from an external API, and the output is a list of recommended actions for the user.

[0303] Step 5:

[0304] The server notifies the user's communication device of the generated recommended actions. The input is the list of recommended actions generated in step 4, and the output is the notification message on the user's communication device. This allows the user to decide on an action based on the information received.

[0305] Step 6:

[0306] After the user checks the sent proposal, they can make corrections if necessary. The input is the content of the notified proposal, and the output is the action plan modified by the user. This correction result is automatically fed back to the system, leading to updates of the schedule and related information.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0308] The present invention combines an emotion engine with a system that uses data collected from the user's terminal, efficiently manages the user's schedule and tasks through an information processing device, and generates multiple proposals. This system can recognize and analyze the user's emotional state, and based on this, adjust the content of the proposal, thereby providing a more appropriate and personalized proposal to the user.

[0309] When implementing the system, first, the terminal collects emotion data through the user's action data, voice information, and further camera images. The emotion data is acquired in a natural way so as not to affect the user's daily usage environment and is used based on the user's permission.

[0310] Next, the terminal securely transmits this data to the server. The server receives the data using a secure communication protocol and stores it in the database. The received emotion data is analyzed by the emotion engine. The emotion engine uses voice tone and facial expression recognition technologies to identify the user's emotions and quantify or categorize their state.

[0311] Based on this analysis result, the server adjusts the proposal. For example, if the emotion engine detects that the user is feeling stressed, it can propose a break time to promote relaxation or reschedule the task. In this way, a proposal reflecting the user's emotions is generated and notified to the user's terminal.

[0312] Furthermore, users can approve or modify the presented suggestions, providing feedback to receive new suggestions based on changes in their emotions. This allows the system to continuously collect data to improve user adaptation.

[0313] This system aims to improve not only the efficiency of schedule management but also the user experience by enabling flexible suggestions that respond to the user's emotions.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The device collects user behavior data and related audio and camera footage in real time. Data collection is done only with the user's permission, and emotional data includes changes in facial expressions and tone of voice.

[0317] Step 2:

[0318] The device encrypts the collected emotional data and sends it to the server. It uses a secure communication protocol to maintain data confidentiality during transmission.

[0319] Step 3:

[0320] The server stores the received emotional data in a database, and an emotion engine performs analysis. This analysis uses facial recognition algorithms and voice analysis to identify the user's emotional state and quantify conditions such as stress and happiness.

[0321] Step 4:

[0322] The server generates suggestions based on the analysis results, tailored to the user's current emotions. For example, if it determines that the user is tired, it will generate a suggestion to add a break to their schedule.

[0323] Step 5:

[0324] The server sends tailored suggestions to the user's device. Suggestion notifications are delivered via push notifications or in-app displays.

[0325] Step 6:

[0326] The user reviews the proposal and approves or modifies it. Based on the user's feedback, the device updates the schedule and task list.

[0327] Step 7:

[0328] The device sends user feedback back to the server, providing data to be used in future suggestions. The server continuously learns and analyzes the data to improve the accuracy of the emotion engine.

[0329] (Example 2)

[0330] 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".

[0331] Modern schedule management systems utilizing information and communication technology enable task management based on user behavior, but they do not yet take into account the user's emotional state. Therefore, suggestions that consider the user's stress and fatigue are not provided, resulting in a less-than-ideal user experience. The challenge we aim to address here is to provide more comfortable and efficient schedule management by dynamically reflecting the user's emotional state and generating personalized suggestions.

[0332] 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.

[0333] In this invention, the server includes means for identifying the user's emotional state using an emotion engine, means for generating suggestions corresponding to the user's emotional state using a generative AI model, and means for collecting user feedback and incorporating it into future suggestions. This enables flexible schedule management and task suggestions that respond to the user's emotions.

[0334] An "information processing device" is an electronic device that analyzes multiple data points collected from users and generates suggestions.

[0335] An "emotion engine" is an algorithm that identifies, quantifies, or categorizes a user's emotional state based on their voice tone and facial expressions.

[0336] A "generative AI model" is an artificial intelligence model used to generate personalized suggestions based on the user's emotional state.

[0337] A "security protocol" is a means of communication designed to protect privacy and integrity during data transmission.

[0338] A "machine learning algorithm" is a statistical method that analyzes user behavior patterns and emotional patterns and uses this information to generate future suggestions.

[0339] "Feedback" refers to the information a user provides when approving, rejecting, or modifying a proposal.

[0340] This invention relates to an information processing system that manages schedules and tasks based on the user's emotional state. This system is realized by processing data collected from the user's terminal using an emotion engine and a generative AI model on a server.

[0341] First, the device uses hardware such as sensors, microphones, and cameras to collect user behavior data, audio information, and video information. This allows data related to the user's emotional state to be obtained in a natural way. For example, voice tone when using a voice assistant and facial expression monitoring via camera are performed. This data is collected with the user's permission.

[0342] Next, the device sends the collected data to the server using a secure communication protocol. The server stores the received data in a database and prepares it for analysis. This analysis uses an emotion engine running in a programming environment such as Python, and the data is quantified or categorized. For example, voice emotion analysis using TensorFlow or facial recognition technology using PyTorch may be utilized.

[0343] Furthermore, the server uses a generative AI model to generate suggestions that respond to the user's emotions. An example of a prompt used in this process is, "If the user is tired, what relaxation methods should be suggested?" The system uses such prompts to create specific action suggestions.

[0344] Users can receive suggestions via their devices and approve or modify them. This feedback is then used again on the server for future suggestions, improving the system's adaptability.

[0345] This system can improve the user experience by providing flexible schedule management that takes user emotions into account.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The device acquires user behavior data, voice information, and camera footage. It uses built-in sensors, microphones, and cameras to respond to user requests and perform periodic scans. Specifically, it recognizes speech, converts it to text, analyzes voice tone, and reads user facial expressions from camera footage to generate emotion data. The input for this step is user actions and environmental information, while the output is emotion data.

[0349] Step 2:

[0350] The device sends the acquired emotional data to the server using a secure communication protocol. This step protects privacy by encrypting the data before transmission. The input is the emotional data generated in the previous step, and the output is data securely stored on the server.

[0351] Step 3:

[0352] The server stores the received emotion data in a database and begins analysis by the emotion engine. The emotion engine generates and quantifies emotion labels from voice tone and facial expressions, for example, using a machine learning library. The input in this step is the emotion data sent from the terminal, and the output is the interpreted emotion labels and numerical data.

[0353] Step 4:

[0354] The server generates suggestions using a generative AI model based on data analyzed by the emotion engine. Specifically, it inputs a prompt message to the generative AI model asking, "What suggestions would be appropriate when this user is feeling stressed?" and obtains individually tailored suggestions. The input is the analyzed emotion data, and the output is the generated suggestions.

[0355] Step 5:

[0356] The server sends the generated proposal to the terminal and notifies the user. The user can review the notified proposal and approve or modify it as needed. The input is the proposal generated in step 4, and the output is the user's feedback.

[0357] Step 6:

[0358] The server collects user feedback and incorporates it into the generation of future suggestions. The feedback is analyzed to improve the quality of the suggestions and stored in a database. The input is user feedback, and the output is adaptive data used for future suggestions.

[0359] (Application Example 2)

[0360] 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."

[0361] Conventional information processing systems face the challenge of making suggestions that take into account the user's emotional state when managing their schedule and tasks. If a user is experiencing stress or discomfort, suggestions that ignore these feelings are not only unhelpful but may even worsen their experience. Therefore, there is a need to improve user satisfaction and provide more personalized suggestions.

[0362] 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.

[0363] In this invention, the server includes means for collecting information from the user's device through multiple information sources, means for analyzing the collected information and determining the user's schedule and task priorities, and means for recognizing the user's emotions and adjusting the suggested content based on those emotions. This makes it possible to provide appropriate suggestions according to the user's emotional state and improve the user experience.

[0364] An "information processing device" is a device that analyzes information collected from a user's device and generates appropriate suggestions.

[0365] "User's devices" refers to terminals and devices that users use on a daily basis, and these are the targets of information collection.

[0366] "Information source" refers to the source of various forms of information collected by the user's device.

[0367] "Information" refers to data about users, which is the subject of collection and analysis.

[0368] "Analysis" is a method of processing collected information to understand the user's state and behavior.

[0369] "Schedule" refers to actions or events that the user plans to perform.

[0370] "Task prioritization" refers to the order in which tasks within a schedule are organized based on their importance and urgency.

[0371] "Proposed content" refers to recommendations and action plans that should be presented to users.

[0372] "Emotional recognition" is the process of identifying an emotional state from a user's facial expressions and voice.

[0373] "Adjusting the proposal" means optimizing the proposal based on the emotions that have been perceived.

[0374] "User experience" refers to the overall feeling and impression gained when using a system.

[0375] The system for carrying out this invention includes an advanced information processing device for providing personalized suggestions that take into account the user's emotions. The server receives facial expression data and voice data collected from the user's terminal via camera and microphone, and securely stores this data. In particular, information is transmitted and received using security protocols to ensure data protection.

[0376] The server uses machine learning algorithms and emotion recognition engines to analyze the received data. For example, it uses OpenCV and Dlib to analyze facial features from camera footage, and Google Cloud Speech-to-Text to analyze emotions from audio data, quantifying or categorizing them. Based on the analysis results, the server generates suggestions appropriate to the user's emotional state.

[0377] Furthermore, the generated suggestions are used with a generation AI model (e.g., a GPT-based model) to create detailed responses tailored to the user's individual circumstances. In operating this AI model, pre-collected and analyzed data is provided as prompts to improve the accuracy of the responses.

[0378] As a concrete example, when a user returns home from an outing such as shopping, the robot might suggest, "You look tired. I'll play some relaxing music. Is there a genre you'd like to listen to?" Furthermore, examples of prompts provided to the AI ​​model include sentences like, "The user seems stressed; I will continue to offer suggestions to help them relax."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The device uses a camera and microphone to collect the user's facial expressions and voice data. The input is real-time video and audio, and the output is this raw data. This data is temporarily stored locally for subsequent processing.

[0382] Step 2:

[0383] The terminal transmits the collected raw data to the server using a secure communication protocol. The input is raw data, and the output is encrypted data packets. This communication uses the latest security technologies to prevent data eavesdropping and tampering.

[0384] Step 3:

[0385] The server activates an emotion recognition engine to analyze the received data. Specifically, it uses OpenCV and Dlib to analyze facial features from video data and Google Cloud Speech-to-Text to analyze speech tone from audio data. The input is the transmitted video and audio data, and the output is a numerical or categorized emotion index.

[0386] Step 4:

[0387] The server generates suggestions based on sentiment data. It applies a generative AI model (GPT series) and provides prompts to generate responses. The input consists of sentiment indicators and the user's past behavioral patterns, while the output is a customized suggestion message. In this operation, the AI ​​model constructs appropriate responses related to the emotional state.

[0388] Step 5:

[0389] The server sends the generated suggestions to the terminal. The terminal notifies the user of the suggestions via voice or display. The input is the suggestion message from the server, and the output is a notification message to the user. In this step, the terminal provides direct feedback to the user using its speaker or display.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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".

[0406] This invention is a system that utilizes an information processing device to collect and analyze necessary data from the user's terminal, thereby achieving efficient information management and action recommendations. A specific example is shown below.

[0407] In this system, the device automatically collects data such as the user's calendar, emails, and activity history. Users can pre-configure the scope and frequency of data collection, thus ensuring privacy and allowing for operation tailored to the user's preferences.

[0408] The collected data is transmitted from the terminal to the server. The server receives the data using secure communication methods and stores it in a database. Furthermore, it analyzes the data using algorithms for natural language processing and behavioral pattern analysis. Through this analysis, the server can understand the user's schedule and tasks and prioritize them.

[0409] Based on the analysis results, the server generates optimal action plans and schedule suggestions for the user. For example, it can suggest events suitable for hobbies or learning during a weekend when the user plans to spend time at home. In a business setting, it can also organize and present a list of preparation items for the following week's meeting.

[0410] The generated proposals are sent from the server to the device, and the device notifies the user via push notification or email. The user can review the proposals and accept or make minor modifications. Accepted proposals are immediately reflected in the schedule, and the action plan on the device is automatically updated.

[0411] Furthermore, the server coordinates with other related applications and platforms to maintain overall information integrity. This coordination allows for quick responses to changes in meetings, for example, and enables schedule adjustments without affecting stakeholders.

[0412] Thus, the present invention aims to significantly improve users' work efficiency and time management capabilities by highly automating user information management and proposing predictable actions.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The device automatically collects data such as the user's calendar, emails, and activity history at specified times. This data collection is performed via APIs or local data storage, and is limited to the scope permitted by the user.

[0416] Step 2:

[0417] The device sends the collected data to the server in an end-to-end encrypted format. Security protocols are used for communication, and verification is performed immediately after the data is received.

[0418] Step 3:

[0419] The server stores the received data in a database and performs analysis. Natural language processing (NLP) and machine learning algorithms are used for the analysis to derive user behavior patterns and schedule priorities.

[0420] Step 4:

[0421] The server generates suggestions for the user based on the analysis results. These suggestions take into account the user's past preferences and habits and include guidelines for action and suggestions for improving the schedule.

[0422] Step 5:

[0423] The server sends the generated suggestions to the user's device. Suggestion notifications are sent via push notifications or email, with different methods used depending on the time and urgency.

[0424] Step 6:

[0425] Users receive notifications and review the proposals. By approving or modifying the proposals, they can select the necessary actions and reflect them in their schedules.

[0426] Step 7:

[0427] The device automatically updates calendars and task management tools based on user approvals and modifications, and integrates with other applications as needed. This ensures that all information is up-to-date, and changes are automatically notified to relevant parties.

[0428] (Example 1)

[0429] 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."

[0430] In today's information society, users need to efficiently manage a large volume of schedules and tasks. However, doing so manually is time-consuming, labor-intensive, and prone to errors. Furthermore, with multiple digital devices and information infrastructures in existence, information from these sources is often not unified and lacks coordination. This leads to decreased user productivity and scheduling inconsistencies.

[0431] 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.

[0432] In this invention, the server includes means for collecting information from the user's operating device, means for processing the information and determining priorities, and means for predicting the user's behavior and generating suggestions using a generative AI model. This enables the user to efficiently manage their schedule and tasks, achieving centralized information management and efficient schedule adjustment.

[0433] An "information processing system" is an integrated operating device that collects and processes data based on user requests, and provides efficient information management and decision support.

[0434] A "user's operating device" is a digital device that works in conjunction with an information processing system to collect data from users and receive notifications.

[0435] An "information source" is a medium that provides data to be analyzed by an information processing system, such as a user's calendar, electronic communications, and behavioral history.

[0436] "Processing" refers to the activity of extracting information from input and performing actions such as analysis, classification, and filtering to obtain useful information.

[0437] "Means of determining priorities" refers to the process of identifying the importance and urgency of schedules and tasks based on collected data, and then setting the order of execution accordingly.

[0438] A "generative AI model" is an artificial intelligence-based inference system that uses machine learning to predict user behavior and incorporate that prediction into processing.

[0439] "Methods for generating proposals" refer to the process of formulating and presenting action guidelines and plans to users based on analysis results and AI models.

[0440] "Communication methods" refer to methods of managing data transfer using encryption and security protocols to ensure secure data transmission.

[0441] Embodiments of the present invention will be described in detail below.

[0442] This system is a device configuration that utilizes an information processing system to efficiently collect and analyze user information and generate suggestions. The main components consist of a user operating device (hereinafter referred to as a terminal) and a central processing unit (hereinafter referred to as a server) that performs data processing.

[0443] The device automatically collects information from users from various sources, such as calendars, emails, and activity history. The device has the functionality to organize and extract this data by running dedicated application software in the background. Since data collection is performed based on the time and conditions set by the device, it allows for efficient information acquisition while respecting privacy.

[0444] The collected information is transmitted from the terminal to the server. The server uses security protocols to enable data communication and stores the received data in the information infrastructure while encrypting and protecting it. The server utilizes a generative AI model that applies machine learning technology to analyze behavioral patterns based on the received information. Through this process, suggestions for the next action to take are calculated according to the user's habits and preferences.

[0445] Based on the analysis results, the server generates efficient action plans and schedule proposals for the user. For example, it might suggest a "preparation list for a 3 PM meeting." These generated proposals are then communicated to the user via push notifications on their device. The user can review, accept, or modify the proposals, and any accepted changes are immediately synchronized with the device's calendar data.

[0446] In this way, the system streamlines users' daily tasks and enables integrated schedule management. An example of a prompt provided by this system is, "Please prepare the items on this list in advance for the next meeting," and specific, user-value-based suggestions are made based on the analysis results of the AI ​​model.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The device collects information such as the user's calendar, emails, and activity history. As input, applications installed on the device access and collect digital data (e.g., emails, calendar events) within the user's permitted scope. Specifically, the device operates in the background, collecting information according to a set schedule. As output, the collected data is organized in JSON format and prepared for subsequent processing.

[0450] Step 2:

[0451] The device sends the collected data to the server using a secure protocol (e.g., SSL / TLS). The input is JSON data prepared in step 1. Specifically, the device is configured to transfer this data to the server whenever a Wi-Fi connection is active. The output is data securely stored on the server.

[0452] Step 3:

[0453] The server stores the received data in a database and prepares it for analysis. It receives JSON data transferred from the terminal as input. Specifically, the server checks the data's integrity upon receipt, adds classification tags, and stores it in the information infrastructure. The output is the information stored in the database in an analyzable format.

[0454] Step 4:

[0455] The server analyzes data using machine learning techniques and generative AI models to predict user behavior patterns. The input is the data stored in step 3. Specifically, the server applies algorithms such as natural language processing (NLP) to extract important information from the user's email content and schedule. The output generates a priority list of actions to take and prompt messages.

[0456] Step 5:

[0457] The server generates action plans and proposed schedules based on the analysis results and notifies the terminal. The input is based on the prompts and action plans generated in step 4. Specifically, the server creates concrete suggestions based on the user's individual needs (e.g., "Please prepare the following for next week's meeting"). The output is the notification message sent to the terminal.

[0458] Step 6:

[0459] The terminal notifies the user of received proposals, allowing the user to review, approve, and modify them. The input is a notification message from the server. Specifically, the terminal presents the proposal to the user via push notification or email. The output is the user's approval or modification, and the updated schedule is managed on the terminal.

[0460] Step 7:

[0461] The server coordinates to maintain information harmony with other related applications and platforms. Its input is user-approved schedule information. Specifically, the server synchronizes information with linked systems via APIs, for example, by automatically sending notifications to meeting participants to reflect the current status. As an output, a consistent schedule is created across the board, enabling users to work in a consistent information environment.

[0462] (Application Example 1)

[0463] 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."

[0464] In modern society, managing personal schedules and integrating information has become extremely complex. Coordinating individual appointments, optimizing transportation, and utilizing local information are essential, but doing so manually is time-consuming, laborious, and inefficient. Furthermore, in today's highly competitive living environment, there is a need for systems that can support optimal choices in real time.

[0465] 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.

[0466] In this invention, the server includes means for collecting information from the user's communication device through multiple information sources, means for analyzing the information and determining the user's schedule and task priorities, and means for analyzing the surrounding situation through a civic life assistant application program and proposing optimal actions. This enables highly automated personal information management and the provision of efficient schedule management and optimal action guidelines.

[0467] An "information processing device" is a device that has the ability to collect and analyze data from various information sources and make suggestions based on the results.

[0468] "User's communication device" refers to a personal mobile information processing device such as a smartphone or tablet that an individual uses on a daily basis.

[0469] "Information sources" refer to the sources of data from which information can be extracted, such as the user's calendar, emails, and activity history.

[0470] "Analysis" is the process of extracting useful information from collected data and finding regularities and patterns.

[0471] Prioritization is the process of ordering tasks and appointments in order to complete them efficiently.

[0472] The "Citizen Life Assistant Application Program" is software designed to support users in their daily lives and suggest optimal actions and options.

[0473] "Surrounding conditions" refers to external environmental information such as traffic, weather, and event information related to the user's current location.

[0474] "Guidelines for Action" are suggestions and guidance designed to support users in efficiently managing their time and executing their schedules.

[0475] This invention is a system consisting of an information processing device that works in conjunction with the user's communication device, automating the user's schedule management and daily life support. Specifically, the information processing device periodically collects data such as calendar information, location information, and activity history from the user's communication device. Since the target and frequency of this data collection can be set by the user, privacy is maintained and flexible operation tailored to individual needs is possible.

[0476] The server receives and securely stores the collected data. It also performs data analysis using natural language processing algorithms and machine learning techniques. Based on this analysis, the server can understand and prioritize the user's schedule and tasks. Furthermore, it analyzes surrounding conditions such as nearby traffic, weather, and event information to suggest optimal actions for the user.

[0477] The proposed action plan and schedule are sent to the user's communication device and provided as a notification. The user can review the proposal and make modifications as needed. In this way, the proposed plan is put into action and the schedule is updated.

[0478] As a concrete example, when a user commutes to work on a Monday morning, the server can suggest the optimal commute route, taking into account traffic congestion and weather conditions. For example, it might say, "Rain is expected today, so we recommend using the subway route."

[0479] An example of a prompt message would be, "Based on the user's calendar and location information, suggest a mode of transportation for the next activity." This can improve the user's quality of life and enable efficient time management and a comfortable living environment.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The server collects data such as calendar information, location information, and activity history from the user's communication device. This data is transmitted via a secure communication method. The input is various information data within the user's communication device, and the output is the unprocessed data sent to the server.

[0483] Step 2:

[0484] The server receives the collected raw data and securely stores it in the database. The input is the raw data sent in step 1, and the output is the stored structured data. Storing the data in the database allows for efficient subsequent data analysis.

[0485] Step 3:

[0486] The server uses natural language processing algorithms and machine learning techniques to analyze data in the database. The input is the stored structured data, which provides the basic information needed for analysis. The output is the analysis results, including the user's schedule and task priorities.

[0487] Step 4:

[0488] The server generates optimal action plans for the user based on the analysis results. This includes current traffic conditions and weather information. The input is the analysis results and surrounding environment information obtained from an external API, and the output is a list of recommended actions for the user.

[0489] Step 5:

[0490] The server notifies the user's communication device of the generated recommended actions. The input is the list of recommended actions generated in step 4, and the output is the notification message on the user's communication device. This allows the user to decide on an action based on the information received.

[0491] Step 6:

[0492] After reviewing the submitted proposal, users can make revisions as needed. The input is the submitted proposal, and the output is the action plan revised by the user. These revisions are automatically fed back into the system, leading to updates to the schedule and related information.

[0493] 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.

[0494] This invention combines an emotion engine with a system that efficiently manages the user's schedule and tasks through an information processing device using data collected from the user's terminal, and generates multiple suggestions. This system recognizes and analyzes the user's emotional state and adjusts the suggestion content based on this, thereby providing the user with more appropriate and personalized suggestions.

[0495] In implementing the system, the terminal first collects user behavioral data, voice information, and emotional data through camera footage. Emotional data is acquired in a natural way so as not to affect the user's daily usage environment and is used only with the user's permission.

[0496] Next, the device securely transmits this data to the server. The server receives the data using a secure communication protocol and stores it in a database. The received emotion data is analyzed by an emotion engine. The emotion engine uses voice tone and facial recognition technology to identify the user's emotions and quantify or categorize their state.

[0497] Based on this analysis, the server adjusts its suggestions. For example, if the emotion engine detects that the user is stressed, it can suggest a break to help them relax or reschedule tasks. In this way, suggestions reflecting the user's emotions are generated and notified to the user's device.

[0498] Furthermore, users can approve or modify the presented suggestions, providing feedback to receive new suggestions based on changes in their emotions. This allows the system to continuously collect data to improve user adaptation.

[0499] This system aims to improve not only the efficiency of schedule management but also the user experience by enabling flexible suggestions that respond to the user's emotions.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The device collects user behavior data and related audio and camera footage in real time. Data collection is done only with the user's permission, and emotional data includes changes in facial expressions and tone of voice.

[0503] Step 2:

[0504] The device encrypts the collected emotional data and sends it to the server. It uses a secure communication protocol to maintain data confidentiality during transmission.

[0505] Step 3:

[0506] The server stores the received emotional data in a database, and an emotion engine performs analysis. This analysis uses facial recognition algorithms and voice analysis to identify the user's emotional state and quantify conditions such as stress and happiness.

[0507] Step 4:

[0508] The server generates suggestions based on the analysis results, tailored to the user's current emotions. For example, if it determines that the user is tired, it will generate a suggestion to add a break to their schedule.

[0509] Step 5:

[0510] The server sends tailored suggestions to the user's device. Suggestion notifications are delivered via push notifications or in-app displays.

[0511] Step 6:

[0512] The user reviews the proposal and approves or modifies it. Based on the user's feedback, the device updates the schedule and task list.

[0513] Step 7:

[0514] The device sends user feedback back to the server, providing data to be used in future suggestions. The server continuously learns and analyzes the data to improve the accuracy of the emotion engine.

[0515] (Example 2)

[0516] 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."

[0517] Modern schedule management systems utilizing information and communication technology enable task management based on user behavior, but they do not yet take into account the user's emotional state. Therefore, suggestions that consider the user's stress and fatigue are not provided, resulting in a less-than-ideal user experience. The challenge we aim to address here is to provide more comfortable and efficient schedule management by dynamically reflecting the user's emotional state and generating personalized suggestions.

[0518] 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.

[0519] In this invention, the server includes means for identifying the user's emotional state using an emotion engine, means for generating suggestions corresponding to the user's emotional state using a generative AI model, and means for collecting user feedback and incorporating it into future suggestions. This enables flexible schedule management and task suggestions that respond to the user's emotions.

[0520] An "information processing device" is an electronic device that analyzes multiple data points collected from users and generates suggestions.

[0521] An "emotion engine" is an algorithm that identifies, quantifies, or categorizes a user's emotional state based on their voice tone and facial expressions.

[0522] A "generative AI model" is an artificial intelligence model used to generate personalized suggestions based on the user's emotional state.

[0523] A "security protocol" is a means of communication designed to protect privacy and integrity during data transmission.

[0524] A "machine learning algorithm" is a statistical method that analyzes user behavior patterns and emotional patterns and uses this information to generate future suggestions.

[0525] "Feedback" refers to the information a user provides when approving, rejecting, or modifying a proposal.

[0526] This invention relates to an information processing system that manages schedules and tasks based on the user's emotional state. This system is realized by processing data collected from the user's terminal using an emotion engine and a generative AI model on a server.

[0527] First, the device uses hardware such as sensors, microphones, and cameras to collect user behavior data, audio information, and video information. This allows data related to the user's emotional state to be obtained in a natural way. For example, voice tone when using a voice assistant and facial expression monitoring via camera are performed. This data is collected with the user's permission.

[0528] Next, the device sends the collected data to the server using a secure communication protocol. The server stores the received data in a database and prepares it for analysis. This analysis uses an emotion engine running in a programming environment such as Python, and the data is quantified or categorized. For example, voice emotion analysis using TensorFlow or facial recognition technology using PyTorch may be utilized.

[0529] Furthermore, the server uses a generative AI model to generate suggestions that respond to the user's emotions. An example of a prompt used in this process is, "If the user is tired, what relaxation methods should be suggested?" The system uses such prompts to create specific action suggestions.

[0530] Users can receive suggestions via their devices and approve or modify them. This feedback is then used again on the server for future suggestions, improving the system's adaptability.

[0531] This system can improve the user experience by providing flexible schedule management that takes user emotions into account.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The device acquires user behavior data, voice information, and camera footage. It uses built-in sensors, microphones, and cameras to respond to user requests and perform periodic scans. Specifically, it recognizes speech, converts it to text, analyzes voice tone, and reads user facial expressions from camera footage to generate emotion data. The input for this step is user actions and environmental information, while the output is emotion data.

[0535] Step 2:

[0536] The device sends the acquired emotional data to the server using a secure communication protocol. This step protects privacy by encrypting the data before transmission. The input is the emotional data generated in the previous step, and the output is data securely stored on the server.

[0537] Step 3:

[0538] The server stores the received emotion data in a database and begins analysis by the emotion engine. The emotion engine generates and quantifies emotion labels from voice tone and facial expressions, for example, using a machine learning library. The input in this step is the emotion data sent from the terminal, and the output is the interpreted emotion labels and numerical data.

[0539] Step 4:

[0540] The server generates suggestions using a generative AI model based on data analyzed by the emotion engine. Specifically, it inputs a prompt message to the generative AI model asking, "What suggestions would be appropriate when this user is feeling stressed?" and obtains individually tailored suggestions. The input is the analyzed emotion data, and the output is the generated suggestions.

[0541] Step 5:

[0542] The server sends the generated proposal to the terminal and notifies the user. The user can review the notified proposal and approve or modify it as needed. The input is the proposal generated in step 4, and the output is the user's feedback.

[0543] Step 6:

[0544] The server collects user feedback and incorporates it into the generation of future suggestions. The feedback is analyzed to improve the quality of the suggestions and stored in a database. The input is user feedback, and the output is adaptive data used for future suggestions.

[0545] (Application Example 2)

[0546] 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."

[0547] Conventional information processing systems face the challenge of making suggestions that take into account the user's emotional state when managing their schedule and tasks. If a user is experiencing stress or discomfort, suggestions that ignore these feelings are not only unhelpful but may even worsen their experience. Therefore, there is a need to improve user satisfaction and provide more personalized suggestions.

[0548] 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.

[0549] In this invention, the server includes means for collecting information from the user's device through multiple information sources, means for analyzing the collected information and determining the user's schedule and task priorities, and means for recognizing the user's emotions and adjusting the suggested content based on those emotions. This makes it possible to provide appropriate suggestions according to the user's emotional state and improve the user experience.

[0550] An "information processing device" is a device that analyzes information collected from a user's device and generates appropriate suggestions.

[0551] "User's devices" refers to terminals and devices that users use on a daily basis, and these are the targets of information collection.

[0552] "Information source" refers to the source of various forms of information collected by the user's device.

[0553] "Information" refers to data about users, which is the subject of collection and analysis.

[0554] "Analysis" is a method of processing collected information to understand the user's state and behavior.

[0555] "Schedule" refers to actions or events that the user plans to perform.

[0556] "Task prioritization" refers to the order in which tasks within a schedule are organized based on their importance and urgency.

[0557] "Proposed content" refers to recommendations and action plans that should be presented to users.

[0558] "Emotional recognition" is the process of identifying an emotional state from a user's facial expressions and voice.

[0559] "Adjusting the proposal" means optimizing the proposal based on the emotions that have been perceived.

[0560] "User experience" refers to the overall feeling and impression gained when using a system.

[0561] The system for carrying out this invention includes an advanced information processing device for providing personalized suggestions that take into account the user's emotions. The server receives facial expression data and voice data collected from the user's terminal via camera and microphone, and securely stores this data. In particular, information is transmitted and received using security protocols to ensure data protection.

[0562] The server uses machine learning algorithms and emotion recognition engines to analyze the received data. For example, it uses OpenCV and Dlib to analyze facial features from camera footage, and Google Cloud Speech-to-Text to analyze emotions from audio data, quantifying or categorizing them. Based on the analysis results, the server generates suggestions appropriate to the user's emotional state.

[0563] Furthermore, the generated suggestions are used with a generation AI model (e.g., a GPT-based model) to create detailed responses tailored to the user's individual circumstances. In operating this AI model, pre-collected and analyzed data is provided as prompts to improve the accuracy of the responses.

[0564] As a concrete example, when a user returns home from an outing such as shopping, the robot might suggest, "You look tired. I'll play some relaxing music. Is there a genre you'd like to listen to?" Furthermore, examples of prompts provided to the AI ​​model include sentences like, "The user seems stressed; I will continue to offer suggestions to help them relax."

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The device uses a camera and microphone to collect the user's facial expressions and voice data. The input is real-time video and audio, and the output is this raw data. This data is temporarily stored locally for subsequent processing.

[0568] Step 2:

[0569] The terminal transmits the collected raw data to the server using a secure communication protocol. The input is raw data, and the output is encrypted data packets. This communication uses the latest security technologies to prevent data eavesdropping and tampering.

[0570] Step 3:

[0571] The server activates an emotion recognition engine to analyze the received data. Specifically, it uses OpenCV and Dlib to analyze facial features from video data and Google Cloud Speech-to-Text to analyze speech tone from audio data. The input is the transmitted video and audio data, and the output is a numerical or categorized emotion index.

[0572] Step 4:

[0573] The server generates suggestions based on sentiment data. It applies a generative AI model (GPT series) and provides prompts to generate responses. The input consists of sentiment indicators and the user's past behavioral patterns, while the output is a customized suggestion message. In this operation, the AI ​​model constructs appropriate responses related to the emotional state.

[0574] Step 5:

[0575] The server sends the generated suggestions to the terminal. The terminal notifies the user of the suggestions via voice or display. The input is the suggestion message from the server, and the output is a notification message to the user. In this step, the terminal provides direct feedback to the user using its speaker or display.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] [Fourth Embodiment]

[0580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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".

[0593] This invention is a system that utilizes an information processing device to collect and analyze necessary data from the user's terminal, thereby achieving efficient information management and action recommendations. A specific example is shown below.

[0594] In this system, the device automatically collects data such as the user's calendar, emails, and activity history. Users can pre-configure the scope and frequency of data collection, thus ensuring privacy and allowing for operation tailored to the user's preferences.

[0595] The collected data is transmitted from the terminal to the server. The server receives the data using secure communication methods and stores it in a database. Furthermore, it analyzes the data using algorithms for natural language processing and behavioral pattern analysis. Through this analysis, the server can understand the user's schedule and tasks and prioritize them.

[0596] Based on the analysis results, the server generates optimal action plans and schedule suggestions for the user. For example, it can suggest events suitable for hobbies or learning during a weekend when the user plans to spend time at home. In a business setting, it can also organize and present a list of preparation items for the following week's meeting.

[0597] The generated proposals are sent from the server to the device, and the device notifies the user via push notification or email. The user can review the proposals and accept or make minor modifications. Accepted proposals are immediately reflected in the schedule, and the action plan on the device is automatically updated.

[0598] Furthermore, the server coordinates with other related applications and platforms to maintain overall information integrity. This coordination allows for quick responses to changes in meetings, for example, and enables schedule adjustments without affecting stakeholders.

[0599] Thus, the present invention aims to significantly improve users' work efficiency and time management capabilities by highly automating user information management and proposing predictable actions.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The device automatically collects data such as the user's calendar, emails, and activity history at specified times. This data collection is performed via APIs or local data storage, and is limited to the scope permitted by the user.

[0603] Step 2:

[0604] The device sends the collected data to the server in an end-to-end encrypted format. Security protocols are used for communication, and verification is performed immediately after the data is received.

[0605] Step 3:

[0606] The server stores the received data in a database and performs analysis. Natural language processing (NLP) and machine learning algorithms are used for the analysis to derive user behavior patterns and schedule priorities.

[0607] Step 4:

[0608] The server generates suggestions for the user based on the analysis results. These suggestions take into account the user's past preferences and habits and include guidelines for action and suggestions for improving the schedule.

[0609] Step 5:

[0610] The server sends the generated suggestions to the user's device. Suggestion notifications are sent via push notifications or email, with different methods used depending on the time and urgency.

[0611] Step 6:

[0612] Users receive notifications and review the proposals. By approving or modifying the proposals, they can select the necessary actions and reflect them in their schedules.

[0613] Step 7:

[0614] The device automatically updates calendars and task management tools based on user approvals and modifications, and integrates with other applications as needed. This ensures that all information is up-to-date, and changes are automatically notified to relevant parties.

[0615] (Example 1)

[0616] 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".

[0617] In today's information society, users need to efficiently manage a large volume of schedules and tasks. However, doing so manually is time-consuming, labor-intensive, and prone to errors. Furthermore, with multiple digital devices and information infrastructures in existence, information from these sources is often not unified and lacks coordination. This leads to decreased user productivity and scheduling inconsistencies.

[0618] 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.

[0619] In this invention, the server includes means for collecting information from the user's operating device, means for processing the information and determining priorities, and means for predicting the user's behavior and generating suggestions using a generative AI model. This enables the user to efficiently manage their schedule and tasks, achieving centralized information management and efficient schedule adjustment.

[0620] An "information processing system" is an integrated operating device that collects and processes data based on user requests, and provides efficient information management and decision support.

[0621] A "user's operating device" is a digital device that works in conjunction with an information processing system to collect data from users and receive notifications.

[0622] An "information source" is a medium that provides data to be analyzed by an information processing system, such as a user's calendar, electronic communications, and behavioral history.

[0623] "Processing" refers to the activity of extracting information from input and performing actions such as analysis, classification, and filtering to obtain useful information.

[0624] "Means of determining priorities" refers to the process of identifying the importance and urgency of schedules and tasks based on collected data, and then setting the order of execution accordingly.

[0625] A "generative AI model" is an artificial intelligence-based inference system that uses machine learning to predict user behavior and incorporate that prediction into processing.

[0626] "Methods for generating proposals" refer to the process of formulating and presenting action guidelines and plans to users based on analysis results and AI models.

[0627] "Communication methods" refer to methods of managing data transfer using encryption and security protocols to ensure secure data transmission.

[0628] Embodiments of the present invention will be described in detail below.

[0629] This system is a device configuration that utilizes an information processing system to efficiently collect and analyze user information and generate suggestions. The main components consist of a user operating device (hereinafter referred to as a terminal) and a central processing unit (hereinafter referred to as a server) that performs data processing.

[0630] The device automatically collects information from users from various sources, such as calendars, emails, and activity history. The device has the functionality to organize and extract this data by running dedicated application software in the background. Since data collection is performed based on the time and conditions set by the device, it allows for efficient information acquisition while respecting privacy.

[0631] The collected information is transmitted from the terminal to the server. The server uses security protocols to enable data communication and stores the received data in the information infrastructure while encrypting and protecting it. The server utilizes a generative AI model that applies machine learning technology to analyze behavioral patterns based on the received information. Through this process, suggestions for the next action to take are calculated according to the user's habits and preferences.

[0632] Based on the analysis results, the server generates efficient action plans and schedule proposals for the user. For example, it might suggest a "preparation list for a 3 PM meeting." These generated proposals are then communicated to the user via push notifications on their device. The user can review, accept, or modify the proposals, and any accepted changes are immediately synchronized with the device's calendar data.

[0633] In this way, the system streamlines users' daily tasks and enables integrated schedule management. An example of a prompt provided by this system is, "Please prepare the items on this list in advance for the next meeting," and specific, user-value-based suggestions are made based on the analysis results of the AI ​​model.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The device collects information such as the user's calendar, emails, and activity history. As input, applications installed on the device access and collect digital data (e.g., emails, calendar events) within the user's permitted scope. Specifically, the device operates in the background, collecting information according to a set schedule. As output, the collected data is organized in JSON format and prepared for subsequent processing.

[0637] Step 2:

[0638] The device sends the collected data to the server using a secure protocol (e.g., SSL / TLS). The input is JSON data prepared in step 1. Specifically, the device is configured to transfer this data to the server whenever a Wi-Fi connection is active. The output is data securely stored on the server.

[0639] Step 3:

[0640] The server stores the received data in a database and prepares it for analysis. It receives JSON data transferred from the terminal as input. Specifically, the server checks the data's integrity upon receipt, adds classification tags, and stores it in the information infrastructure. The output is the information stored in the database in an analyzable format.

[0641] Step 4:

[0642] The server analyzes data using machine learning techniques and generative AI models to predict user behavior patterns. The input is the data stored in step 3. Specifically, the server applies algorithms such as natural language processing (NLP) to extract important information from the user's email content and schedule. The output generates a priority list of actions to take and prompt messages.

[0643] Step 5:

[0644] The server generates action plans and proposed schedules based on the analysis results and notifies the terminal. The input is based on the prompts and action plans generated in step 4. Specifically, the server creates concrete suggestions based on the user's individual needs (e.g., "Please prepare the following for next week's meeting"). The output is the notification message sent to the terminal.

[0645] Step 6:

[0646] The terminal notifies the user of received proposals, allowing the user to review, approve, and modify them. The input is a notification message from the server. Specifically, the terminal presents the proposal to the user via push notification or email. The output is the user's approval or modification, and the updated schedule is managed on the terminal.

[0647] Step 7:

[0648] The server coordinates to maintain information harmony with other related applications and platforms. Its input is user-approved schedule information. Specifically, the server synchronizes information with linked systems via APIs, for example, by automatically sending notifications to meeting participants to reflect the current status. As an output, a consistent schedule is created across the board, enabling users to work in a consistent information environment.

[0649] (Application Example 1)

[0650] 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".

[0651] In modern society, managing personal schedules and integrating information has become extremely complex. Coordinating individual appointments, optimizing transportation, and utilizing local information are essential, but doing so manually is time-consuming, laborious, and inefficient. Furthermore, in today's highly competitive living environment, there is a need for systems that can support optimal choices in real time.

[0652] 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.

[0653] In this invention, the server includes means for collecting information from the user's communication device through multiple information sources, means for analyzing the information and determining the user's schedule and task priorities, and means for analyzing the surrounding situation through a civic life assistant application program and proposing optimal actions. This enables highly automated personal information management and the provision of efficient schedule management and optimal action guidelines.

[0654] An "information processing device" is a device that has the ability to collect and analyze data from various information sources and make suggestions based on the results.

[0655] "User's communication device" refers to a personal mobile information processing device such as a smartphone or tablet that an individual uses on a daily basis.

[0656] "Information sources" refer to the sources of data from which information can be extracted, such as the user's calendar, emails, and activity history.

[0657] "Analysis" is the process of extracting useful information from collected data and finding regularities and patterns.

[0658] Prioritization is the process of ordering tasks and appointments in order to complete them efficiently.

[0659] The "Citizen Life Assistant Application Program" is software designed to support users in their daily lives and suggest optimal actions and options.

[0660] "Surrounding conditions" refers to external environmental information such as traffic, weather, and event information related to the user's current location.

[0661] "Guidelines for Action" are suggestions and guidance designed to support users in efficiently managing their time and executing their schedules.

[0662] This invention is a system consisting of an information processing device that works in conjunction with the user's communication device, automating the user's schedule management and daily life support. Specifically, the information processing device periodically collects data such as calendar information, location information, and activity history from the user's communication device. Since the target and frequency of this data collection can be set by the user, privacy is maintained and flexible operation tailored to individual needs is possible.

[0663] The server receives and securely stores the collected data. It also performs data analysis using natural language processing algorithms and machine learning techniques. Based on this analysis, the server can understand and prioritize the user's schedule and tasks. Furthermore, it analyzes surrounding conditions such as nearby traffic, weather, and event information to suggest optimal actions for the user.

[0664] The proposed action plan and schedule are sent to the user's communication device and provided as a notification. The user can review the proposal and make modifications as needed. In this way, the proposed plan is put into action and the schedule is updated.

[0665] As a concrete example, when a user commutes to work on a Monday morning, the server can suggest the optimal commute route, taking into account traffic congestion and weather conditions. For example, it might say, "Rain is expected today, so we recommend using the subway route."

[0666] An example of a prompt message would be, "Based on the user's calendar and location information, suggest a mode of transportation for the next activity." This can improve the user's quality of life and enable efficient time management and a comfortable living environment.

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] The server collects data such as calendar information, location information, and activity history from the user's communication device. This data is transmitted via a secure communication method. The input is various information data within the user's communication device, and the output is the unprocessed data sent to the server.

[0670] Step 2:

[0671] The server receives the collected raw data and securely stores it in the database. The input is the raw data sent in step 1, and the output is the stored structured data. Storing the data in the database allows for efficient subsequent data analysis.

[0672] Step 3:

[0673] The server uses natural language processing algorithms and machine learning techniques to analyze data in the database. The input is the stored structured data, which provides the basic information needed for analysis. The output is the analysis results, including the user's schedule and task priorities.

[0674] Step 4:

[0675] The server generates optimal action plans for the user based on the analysis results. This includes current traffic conditions and weather information. The input is the analysis results and surrounding environment information obtained from an external API, and the output is a list of recommended actions for the user.

[0676] Step 5:

[0677] The server notifies the user's communication device of the generated recommended actions. The input is the list of recommended actions generated in step 4, and the output is the notification message on the user's communication device. This allows the user to decide on an action based on the information received.

[0678] Step 6:

[0679] After reviewing the submitted proposal, users can make revisions as needed. The input is the submitted proposal, and the output is the action plan revised by the user. These revisions are automatically fed back into the system, leading to updates to the schedule and related information.

[0680] 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.

[0681] This invention combines an emotion engine with a system that efficiently manages the user's schedule and tasks through an information processing device using data collected from the user's terminal, and generates multiple suggestions. This system recognizes and analyzes the user's emotional state and adjusts the suggestion content based on this, thereby providing the user with more appropriate and personalized suggestions.

[0682] In implementing the system, the terminal first collects user behavioral data, voice information, and emotional data through camera footage. Emotional data is acquired in a natural way so as not to affect the user's daily usage environment and is used only with the user's permission.

[0683] Next, the device securely transmits this data to the server. The server receives the data using a secure communication protocol and stores it in a database. The received emotion data is analyzed by an emotion engine. The emotion engine uses voice tone and facial recognition technology to identify the user's emotions and quantify or categorize their state.

[0684] Based on this analysis, the server adjusts its suggestions. For example, if the emotion engine detects that the user is stressed, it can suggest a break to help them relax or reschedule tasks. In this way, suggestions reflecting the user's emotions are generated and notified to the user's device.

[0685] Furthermore, users can approve or modify the presented suggestions, providing feedback to receive new suggestions based on changes in their emotions. This allows the system to continuously collect data to improve user adaptation.

[0686] This system aims to improve not only the efficiency of schedule management but also the user experience by enabling flexible suggestions that respond to the user's emotions.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The device collects user behavior data and related audio and camera footage in real time. Data collection is done only with the user's permission, and emotional data includes changes in facial expressions and tone of voice.

[0690] Step 2:

[0691] The device encrypts the collected emotional data and sends it to the server. It uses a secure communication protocol to maintain data confidentiality during transmission.

[0692] Step 3:

[0693] The server stores the received emotional data in a database, and an emotion engine performs analysis. This analysis uses facial recognition algorithms and voice analysis to identify the user's emotional state and quantify conditions such as stress and happiness.

[0694] Step 4:

[0695] The server generates suggestions based on the analysis results, tailored to the user's current emotions. For example, if it determines that the user is tired, it will generate a suggestion to add a break to their schedule.

[0696] Step 5:

[0697] The server sends tailored suggestions to the user's device. Suggestion notifications are delivered via push notifications or in-app displays.

[0698] Step 6:

[0699] The user reviews the proposal and approves or modifies it. Based on the user's feedback, the device updates the schedule and task list.

[0700] Step 7:

[0701] The device sends user feedback back to the server, providing data to be used in future suggestions. The server continuously learns and analyzes the data to improve the accuracy of the emotion engine.

[0702] (Example 2)

[0703] 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".

[0704] Modern schedule management systems utilizing information and communication technology enable task management based on user behavior, but they do not yet take into account the user's emotional state. Therefore, suggestions that consider the user's stress and fatigue are not provided, resulting in a less-than-ideal user experience. The challenge we aim to address here is to provide more comfortable and efficient schedule management by dynamically reflecting the user's emotional state and generating personalized suggestions.

[0705] 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.

[0706] In this invention, the server includes means for identifying the user's emotional state using an emotion engine, means for generating suggestions corresponding to the user's emotional state using a generative AI model, and means for collecting user feedback and incorporating it into future suggestions. This enables flexible schedule management and task suggestions that respond to the user's emotions.

[0707] An "information processing device" is an electronic device that analyzes multiple data points collected from users and generates suggestions.

[0708] An "emotion engine" is an algorithm that identifies, quantifies, or categorizes a user's emotional state based on their voice tone and facial expressions.

[0709] A "generative AI model" is an artificial intelligence model used to generate personalized suggestions based on the user's emotional state.

[0710] A "security protocol" is a means of communication designed to protect privacy and integrity during data transmission.

[0711] A "machine learning algorithm" is a statistical method that analyzes user behavior patterns and emotional patterns and uses this information to generate future suggestions.

[0712] "Feedback" refers to the information a user provides when approving, rejecting, or modifying a proposal.

[0713] This invention relates to an information processing system that manages schedules and tasks based on the user's emotional state. This system is realized by processing data collected from the user's terminal using an emotion engine and a generative AI model on a server.

[0714] First, the device uses hardware such as sensors, microphones, and cameras to collect user behavior data, audio information, and video information. This allows data related to the user's emotional state to be obtained in a natural way. For example, voice tone when using a voice assistant and facial expression monitoring via camera are performed. This data is collected with the user's permission.

[0715] Next, the device sends the collected data to the server using a secure communication protocol. The server stores the received data in a database and prepares it for analysis. This analysis uses an emotion engine running in a programming environment such as Python, and the data is quantified or categorized. For example, voice emotion analysis using TensorFlow or facial recognition technology using PyTorch may be utilized.

[0716] Furthermore, the server uses a generative AI model to generate suggestions that respond to the user's emotions. An example of a prompt used in this process is, "If the user is tired, what relaxation methods should be suggested?" The system uses such prompts to create specific action suggestions.

[0717] Users can receive suggestions via their devices and approve or modify them. This feedback is then used again on the server for future suggestions, improving the system's adaptability.

[0718] This system can improve the user experience by providing flexible schedule management that takes user emotions into account.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The device acquires user behavior data, voice information, and camera footage. It uses built-in sensors, microphones, and cameras to respond to user requests and perform periodic scans. Specifically, it recognizes speech, converts it to text, analyzes voice tone, and reads user facial expressions from camera footage to generate emotion data. The input for this step is user actions and environmental information, while the output is emotion data.

[0722] Step 2:

[0723] The device sends the acquired emotional data to the server using a secure communication protocol. This step protects privacy by encrypting the data before transmission. The input is the emotional data generated in the previous step, and the output is data securely stored on the server.

[0724] Step 3:

[0725] The server stores the received emotion data in a database and begins analysis by the emotion engine. The emotion engine generates and quantifies emotion labels from voice tone and facial expressions, for example, using a machine learning library. The input in this step is the emotion data sent from the terminal, and the output is the interpreted emotion labels and numerical data.

[0726] Step 4:

[0727] The server generates suggestions using a generative AI model based on data analyzed by the emotion engine. Specifically, it inputs a prompt message to the generative AI model asking, "What suggestions would be appropriate when this user is feeling stressed?" and obtains individually tailored suggestions. The input is the analyzed emotion data, and the output is the generated suggestions.

[0728] Step 5:

[0729] The server sends the generated proposal to the terminal and notifies the user. The user can review the notified proposal and approve or modify it as needed. The input is the proposal generated in step 4, and the output is the user's feedback.

[0730] Step 6:

[0731] The server collects user feedback and incorporates it into the generation of future suggestions. The feedback is analyzed to improve the quality of the suggestions and stored in a database. The input is user feedback, and the output is adaptive data used for future suggestions.

[0732] (Application Example 2)

[0733] 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".

[0734] Conventional information processing systems face the challenge of making suggestions that take into account the user's emotional state when managing their schedule and tasks. If a user is experiencing stress or discomfort, suggestions that ignore these feelings are not only unhelpful but may even worsen their experience. Therefore, there is a need to improve user satisfaction and provide more personalized suggestions.

[0735] 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.

[0736] In this invention, the server includes means for collecting information from the user's device through multiple information sources, means for analyzing the collected information and determining the user's schedule and task priorities, and means for recognizing the user's emotions and adjusting the suggested content based on those emotions. This makes it possible to provide appropriate suggestions according to the user's emotional state and improve the user experience.

[0737] An "information processing device" is a device that analyzes information collected from a user's device and generates appropriate suggestions.

[0738] "User's devices" refers to terminals and devices that users use on a daily basis, and these are the targets of information collection.

[0739] "Information source" refers to the source of various forms of information collected by the user's device.

[0740] "Information" refers to data about users, which is the subject of collection and analysis.

[0741] "Analysis" is a method of processing collected information to understand the user's state and behavior.

[0742] "Schedule" refers to actions or events that the user plans to perform.

[0743] "Task prioritization" refers to the order in which tasks within a schedule are organized based on their importance and urgency.

[0744] "Proposed content" refers to recommendations and action plans that should be presented to users.

[0745] "Emotional recognition" is the process of identifying an emotional state from a user's facial expressions and voice.

[0746] "Adjusting the proposal" means optimizing the proposal based on the emotions that have been perceived.

[0747] "User experience" refers to the overall feeling and impression gained when using a system.

[0748] The system for carrying out this invention includes an advanced information processing device for providing personalized suggestions that take into account the user's emotions. The server receives facial expression data and voice data collected from the user's terminal via camera and microphone, and securely stores this data. In particular, information is transmitted and received using security protocols to ensure data protection.

[0749] The server uses machine learning algorithms and emotion recognition engines to analyze the received data. For example, it uses OpenCV and Dlib to analyze facial features from camera footage, and Google Cloud Speech-to-Text to analyze emotions from audio data, quantifying or categorizing them. Based on the analysis results, the server generates suggestions appropriate to the user's emotional state.

[0750] Furthermore, the generated suggestions are used with a generation AI model (e.g., a GPT-based model) to create detailed responses tailored to the user's individual circumstances. In operating this AI model, pre-collected and analyzed data is provided as prompts to improve the accuracy of the responses.

[0751] As a concrete example, when a user returns home from an outing such as shopping, the robot might suggest, "You look tired. I'll play some relaxing music. Is there a genre you'd like to listen to?" Furthermore, examples of prompts provided to the AI ​​model include sentences like, "The user seems stressed; I will continue to offer suggestions to help them relax."

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The device uses a camera and microphone to collect the user's facial expressions and voice data. The input is real-time video and audio, and the output is this raw data. This data is temporarily stored locally for subsequent processing.

[0755] Step 2:

[0756] The terminal transmits the collected raw data to the server using a secure communication protocol. The input is raw data, and the output is encrypted data packets. This communication uses the latest security technologies to prevent data eavesdropping and tampering.

[0757] Step 3:

[0758] The server activates an emotion recognition engine to analyze the received data. Specifically, it uses OpenCV and Dlib to analyze facial features from video data and Google Cloud Speech-to-Text to analyze speech tone from audio data. The input is the transmitted video and audio data, and the output is a numerical or categorized emotion index.

[0759] Step 4:

[0760] The server generates suggestions based on sentiment data. It applies a generative AI model (GPT series) and provides prompts to generate responses. The input consists of sentiment indicators and the user's past behavioral patterns, while the output is a customized suggestion message. In this operation, the AI ​​model constructs appropriate responses related to the emotional state.

[0761] Step 5:

[0762] The server sends the generated suggestions to the terminal. The terminal notifies the user of the suggestions via voice or display. The input is the suggestion message from the server, and the output is a notification message to the user. In this step, the terminal provides direct feedback to the user using its speaker or display.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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."

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] An information processing device includes means for collecting data from a user's terminal through multiple data sources,

[0787] A means for analyzing the aforementioned data and determining the priority of the user's schedule and tasks,

[0788] A means of generating multiple proposals based on the analysis results and notifying the user,

[0789] Means for enabling the user to approve or modify the aforementioned proposal,

[0790] Based on the user's approval, means for updating the schedule and coordinating information between linked applications,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the information processing device has means for communicating using a security protocol.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the information processing device has means for analyzing the user's behavior patterns using a machine learning algorithm and reflecting this in the next suggestion.

[0796] "Example 1"

[0797] (Claim 1)

[0798] An information processing system provides means for collecting information from a user's operating device via multiple information sources,

[0799] A means for processing the aforementioned information and determining the user's schedule and work priorities,

[0800] A means for generating multiple suggestions based on the processing results and notifying the user,

[0801] Means to enable users to approve or modify the aforementioned proposal,

[0802] Based on the user's approval, means for updating the plan and coordinating information among relevant application software,

[0803] A means for a device to automatically detect and monitor the user's electronic communications and activity history in the background,

[0804] A means by which the server uses a generated AI model to predict user behavior and reflect that in the suggestions,

[0805] Means for maintaining harmony with other application software and information infrastructure,

[0806] A means by which a device sends suggestions to a user using push notifications,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, wherein the information processing system has means for communication using a security protocol.

[0810] (Claim 3)

[0811] The system according to claim 1, wherein the information processing system has means for processing the user's behavior patterns using machine learning technology and reflecting this in the generation of the next suggestion.

[0812] "Application Example 1"

[0813] (Claim 1)

[0814] The information processing device includes means for collecting information from the user's communication device through multiple information sources,

[0815] A means for analyzing the aforementioned information and determining the user's schedule and work priorities,

[0816] A means of generating multiple recommendations based on the analysis results and notifying the user,

[0817] Means to enable users to approve or modify the aforementioned recommendations,

[0818] Based on the user's approval, a means for updating the schedule and coordinating information between linked application programs,

[0819] Through the citizen life assistant application program, a means to analyze the surrounding situation and propose the optimal course of action,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, wherein the information processing device has means for communicating using a security protocol.

[0823] (Claim 3)

[0824] The system according to claim 1, wherein the information processing device has means for analyzing the user's behavior patterns using a machine learning algorithm and reflecting this in the next recommendation.

[0825] "Example 2 of combining an emotion engine"

[0826] (Claim 1)

[0827] An information processing device collects data from a user's terminal through multiple data sources and has means for identifying an emotional state.

[0828] A means for adjusting the priority of a user's schedule and tasks using an emotion engine that analyzes the aforementioned data,

[0829] Based on the analysis results, a means of generating and notifying the user of suggestions that correspond to their emotional state using a generative AI model,

[0830] Means for allowing users to approve or modify the aforementioned proposal and for collecting feedback,

[0831] Means for updating the schedule and adjusting information based on the user's approval and feedback,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the information processing device has means for securely managing emotional data by communicating using a security protocol.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the information processing device has means for analyzing the user's behavior and emotional patterns using a machine learning algorithm and reflecting this in the next suggestion.

[0837] "Application example 2 when combining with an emotional engine"

[0838] (Claim 1)

[0839] An information processing device is a means of collecting information from a user's device through multiple information sources,

[0840] A means for analyzing the aforementioned information and determining the user's schedule and work priorities,

[0841] A means for generating multiple proposals based on the aforementioned analysis results and notifying the user,

[0842] Means to enable users to approve or modify the aforementioned proposal,

[0843] Based on the user's approval, means for updating the plan and coordinating information between linked application programs,

[0844] A means for recognizing the user's emotions and adjusting the proposed content based on those emotions,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the information processing device has means for performing communication using a protection protocol.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the information processing device has means for analyzing the user's behavior patterns using machine learning techniques and reflecting this in future suggestions. [Explanation of symbols]

[0850] 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. An information processing device includes means for collecting data from a user's terminal through multiple data sources, A means for analyzing the aforementioned data and determining the priority of the user's schedule and tasks, A means of generating multiple proposals based on the analysis results and notifying the user, Means for enabling the user to approve or modify the aforementioned proposal, Based on the user's approval, means for updating the schedule and coordinating information between linked applications, A system that includes this.

2. The system according to claim 1, wherein the information processing device has means for communicating using a security protocol.

3. The system according to claim 1, wherein the information processing device has means for analyzing the user's behavior patterns using a machine learning algorithm and reflecting this in the next suggestion.

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