system

Abstract

We provide the system. [Solution] Means of collecting data on the living environment, A means of analyzing collected data using a generative model to predict user demand, A means for generating a shared resource utilization plan based on predicted demand, Based on the above usage plan, means of notifying users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern living environment, it may be economically burdensome for individual residents to separately own and manage high-quality goods and services alone, and the effective utilization of resources may not be sufficient. Therefore, it has become a major issue to effectively utilize the overall resources while maintaining the quality of life of the residents.

Means for Solving the Problems

[0005] This invention provides a system that utilizes data collected within a residential environment and uses a generative model to predict the needs of each resident. This system enables efficient and economical operation by creating a shared resource utilization plan based on the predicted demand and notifying residents. Furthermore, the system includes a function that allows users to make reservations themselves, and improves the accuracy and convenience of the system by incorporating feedback after use into future demand forecasts.

[0006] "Residential environment" refers to the interior and surrounding environment of a dwelling where residents live, and includes facilities and conditions.

[0007] "Data" refers to the collection of information gathered, including purchase history, travel patterns, and records of services used.

[0008] "Generative models" refer to artificial intelligence techniques used to analyze collected data and predict future demand.

[0009] "Predicting demand" refers to the act of estimating how much of a service or product will be needed at a particular time.

[0010] "Shared resources" refer to goods or services that can be used jointly by multiple residents.

[0011] A "utilization plan" refers to a plan that proposes the optimal use of shared resources based on predicted demand.

[0012] "Notification" refers to a means of informing users of information, and includes formats such as push notifications and email.

[0013] "Reservation" refers to the procedure or function of securing shared resources in advance.

[0014] "Feedback" refers to the opinions and evaluations that users provide after using a service, and is used to predict future usage and improve the service.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. <T [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [[ID=H0000086]] [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] 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.

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

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

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

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

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that utilizes data within a residential environment to efficiently manage shared resources. This system performs a series of actions including data collection, analysis, planning, notification, reservation, and feedback by residents. A specific embodiment of this system is described below.

[0037] Data collection

[0038] The server collects data related to the residents' lives, including purchase history, vehicle usage, and frequency of delivery service use. This data is obtained through sensors installed in the residential environment and APIs of relevant online platforms.

[0039] Data Analysis

[0040] The collected data is preprocessed by the server and input into a generative model. This model uses machine learning to predict, for example, the demand for shared resources on specific days of the week or at specific times of day.

[0041] Creating a Usage Plan

[0042] Based on the analysis results, the server automatically generates a plan that allows each resident to use shared resources most efficiently. This promotes usage that avoids peak hours and maximizes resource utilization.

[0043] Notifications and reservations

[0044] The generated usage plan is notified to each resident via their device. Based on the notification, users can easily make reservations according to their preferences. This process is provided through a user-friendly interface using an application.

[0045] Feedback Collection

[0046] After use, the terminal requests feedback from the resident. This feedback is sent to the server and used to improve the accuracy of future demand forecasts and plan generation.

[0047] Specific example

[0048] For example, if multiple residents need a car on a weekday, the server predicts peak demand based on past data. This allows it to create an optimal car-sharing schedule and propose it to each resident. Users can then make reservations based on the suggestions, ensuring smooth travel. After use, user feedback is collected and used to improve future schedules.

[0049] Thus, embodiments of the present invention enable the effective use of data on the living environment and the efficient use of shared resources among residents.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The server collects data on residents' behavior in real time through sensors and APIs installed in the living environment. This data includes, for example, records of vehicle entry and exit in parking lots and usage status of smart home appliances.

[0053] Step 2:

[0054] The server preprocesses the collected data and converts it into a format suitable for input to generative models. Preprocessing includes noise filtering and missing value imputation. This improves data quality and formats it for analysis.

[0055] Step 3:

[0056] The server inputs pre-processed data into a generative model to perform demand forecasting. This model learns from past patterns and has the ability to predict when demand for a particular service will be high, depending on the time of day or date.

[0057] Step 4:

[0058] The server automatically generates a resource utilization plan based on the prediction results. Specifically, it creates a schedule that considers resource allocation to avoid peak demand times and the optimal order of use.

[0059] Step 5:

[0060] The device notifies residents of the generated usage plan and displays the plan's contents. Notifications are sent via push notifications and email, and users can view the details on their device.

[0061] Step 6:

[0062] Users select their desired options from the notified usage plan and reserve resources through the terminal interface. Reservations can be completed with simple operations, taking user convenience into consideration.

[0063] Step 7:

[0064] The terminal collects feedback from users after they have used shared resources. This feedback reflects user opinions on the ease of use of the resources and the suitability of the plan, and will be used to improve the system in the future.

[0065] Step 8:

[0066] The server analyzes the collected feedback and incorporates it into demand forecasting models and usage plan generation to improve the system's accuracy. This enables more accurate forecasts and higher quality services in subsequent processing.

[0067] (Example 1)

[0068] 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."

[0069] In residential environments, efficient management and utilization of shared resources require accurately forecasting residents' needs and developing appropriate usage plans based on those forecasts. Furthermore, a system that allows users to intuitively make reservations, along with a mechanism to improve forecast accuracy through post-use feedback, is necessary. Conventional systems lack sufficient integration of these processes, resulting in inadequate resource management.

[0070] 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.

[0071] In this invention, the server includes means for collecting information about the living environment, means for preprocessing the collected information and analyzing it using a generative model to predict demand, and means for automatically generating a shared resource utilization plan based on the predicted demand. This makes it possible to accurately predict the demand of residents and create an efficient resource utilization plan.

[0072] "Residential environment" refers to the various conditions and circumstances related to daily life that occur within and around individual residences.

[0073] "Information" refers to a collection of numerical and string data related to residents' lives and behaviors, including data and records acquired through sensors and APIs.

[0074] "Means of collection" refers to systems for collecting information through devices installed in the living environment or via networks.

[0075] "Preprocessing" refers to a series of processes that remove noise and missing values ​​from raw data and convert it into a format that can be used for analysis.

[0076] A "generative model" refers to a computational process that uses machine learning algorithms to predict demand from collected information.

[0077] "Analysis" refers to the activity of interpreting collected information using statistical and machine learning methods to extract useful insights.

[0078] "Means of predicting demand" refers to functions that use generative models to predict the behavior and demands of residents under specific conditions.

[0079] "Shared resources" refer to facilities and services that residents can use together, such as vehicles and common facilities.

[0080] "Means for automatically generating usage plans" refers to a system that independently creates an appropriate resource utilization schedule based on predicted demand.

[0081] "Notification" refers to the act of sending the generated usage plan as information to the resident's device to inform them.

[0082] "Feedback" refers to the process of collecting user experiences and opinions from residents after they have used a resource.

[0083] This system enables the effective collection and analysis of information in the living environment and the optimal management of shared resources. A specific embodiment is shown below.

[0084] The server first collects information about daily life from sensors installed in the living environment and APIs accessed via the internet. This information includes data on electricity consumption, vehicle usage, and delivery request history. A Python library is used as the software for efficiently extracting the data.

[0085] The collected data is preprocessed on the server. This includes normalizing the data using the Pandas library and filtering outliers. This preprocessed information is then input into a generative AI model using machine learning algorithms. The generative AI model analyzes residents' behavior patterns and performs demand forecasting.

[0086] Based on demand forecasts, the server automatically generates a shared resource usage plan. Because this plan is tailored to each resident, efficient resource utilization is possible. This usage plan is notified to residents via their devices. The devices utilize a smartphone app to inform users of their schedules and enable intuitive reservations.

[0087] After use, users provide feedback via their device. This feedback is sent to the server and used to improve the accuracy of the entire system by informing future demand forecasts and usage plans.

[0088] As a concrete example, if multiple residents want to use a vehicle on a weekday, the server could predict demand based on past data and suggest the optimal usage time for each user. Users could then easily make a reservation based on this and provide feedback after use. Through this process, efficient management of shared resources in the residential environment can be achieved.

[0089] An example of a prompt to the generating AI model is, "Predict the usage preferences of residents on a specific day next week and suggest the optimal usage schedule." This prompt allows the model to predict specific actions and provide information useful for planning.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The server collects information from sensors installed in the residential environment and from APIs accessed via the internet. This input includes electricity usage data from smart meters and purchase history from online platforms. The server receives this information and stores it in a database. Specifically, the server automatically sends API requests at a specified time during the night to retrieve the latest data.

[0093] Step 2:

[0094] The server preprocesses the collected data. Unfiltered raw data is provided as input. The server cleanses the data using the Python Pandas library to remove noise and outliers. The normalized data is prepared as output and input into the generative AI model. In operation, the server applies an outlier detection algorithm to each dataset to ensure data integrity.

[0095] Step 3:

[0096] The server inputs pre-processed data into a generating AI model to perform demand forecasting. The input is a cleansed dataset, and the output is the forecast result. The model uses machine learning algorithms to calculate the demand for shared resources on specified days and times. Specifically, the server runs the forecasting model weekly to analyze demand trends.

[0097] Step 4:

[0098] The server automatically generates usage plans using prediction results. The input is the prediction result, and the output is a detailed usage schedule. The server constructs a plan that avoids peak demand and enables efficient resource allocation. In operation, the server optimizes each resident's schedule using an algorithm to achieve maximum efficiency.

[0099] Step 5:

[0100] The terminal notifies residents of the usage plan generated by the server. The input is the usage plan data, and the output is notification information for the user. The terminal sends push notifications to residents via a smartphone app. Specifically, the terminal provides an interface that prompts the notified user to take action.

[0101] Step 6:

[0102] Users make reservations based on the information they receive in the notification. The input is the usage plan provided in the notification, and the output is the reservation confirmation information. Users select the time and resources provided on the app on their device to complete the reservation. Specifically, users use the app's calendar function to quickly make the best reservation to fit their schedule.

[0103] Step 7:

[0104] The device collects feedback from users after use. The input is the user's experience and evaluation, and the output is feedback data. The device displays an evaluation window within the app, soliciting user feedback. Specifically, a pop-up appears immediately after use, creating an environment where users can quickly provide evaluations.

[0105] Step 8:

[0106] The server analyzes the collected feedback and uses it to generate future demand forecasts and plans. The input is feedback data, and the output is an improved forecasting model. The server scrutinizes the feedback and uses it to adjust the parameters of the generated AI model. Specifically, the server repeatedly trains the model based on new feedback data to improve forecasting accuracy.

[0107] (Application Example 1)

[0108] 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."

[0109] In modern residential and industrial environments, maximizing the efficiency of shared resources and manufacturing equipment is essential. However, accurately forecasting individual demand and developing appropriate utilization plans is challenging, leading to inefficient resource use and reduced productivity due to unnecessary maintenance. Furthermore, a lack of proper notification and feedback processing to users and equipment hinders improvements in the accuracy of future utilization planning.

[0110] 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.

[0111] In this invention, the server includes means for collecting data within residential and industrial environments, means for analyzing the collected data using a machine learning generative model to predict demand, and means for generating a utilization plan for shared resources and manufacturing equipment based on the predicted demand. This enables the efficient utilization of shared resources and manufacturing equipment and the implementation of an appropriate maintenance plan.

[0112] "Residential and industrial environments" refer to the collection of physical structures and surrounding facilities where people live and work, and are places where data for improving quality of life and productivity is collected.

[0113] "Means of data collection" refers to the process of acquiring information about the behavior of residents and workers, usage patterns, environmental conditions, etc., using sensors and online platforms.

[0114] A "machine learning generative model" is a system that combines algorithms used to analyze collected data and predict future demand and usage patterns.

[0115] "Methods for forecasting demand" refer to methods that use historical data and machine learning results to predict future resource usage and needs.

[0116] "Means for generating utilization plans for shared resources and manufacturing equipment" refers to a process that automatically creates optimal resource utilization and equipment operating schedules in response to predicted demand.

[0117] "Appropriate notifications and feedback" refer to means of information exchange that provide necessary information to users and work equipment based on the generated usage plans and results, and to extract areas for improvement necessary for creating the next plan.

[0118] The system implementing this invention is intended to achieve efficient resource management in residential and industrial environments. Servers, terminals, and users are the main components.

[0119] The server first collects data on resident and worker behavior, usage patterns, and environmental conditions through various sensors and online platforms. This data is preprocessed on the server and input into a machine learning platform (e.g., TENSORFLOW® or PyTorch) using Python. A machine learning generative model analyzes this data to predict future demand and usage patterns. Based on the results, an optimal utilization plan for shared resources and manufacturing equipment is automatically created.

[0120] The terminal's role is to notify each user and work device of the usage plan generated by the server. This notification allows individual users and devices to use resources and perform maintenance according to the schedule. Furthermore, feedback is collected after use and sent to the server, which is then reflected in the generation of the next plan.

[0121] As a concrete example, consider the use of tools on a factory production line. Based on past data, the server predicts the frequency of tool use and plans the necessary maintenance schedule. This plan is then communicated to workers via terminals. In this way, efficient factory operations are supported.

[0122] An example of a prompt to input into the generating AI model is, "Based on the frequency and conditions of use of this tool, please suggest the optimal maintenance schedule for the next week." Using this example, a concrete plan will be generated, making it easier to implement.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server collects user behavior data and environmental data through sensors installed in residential and industrial environments, as well as through APIs on online platforms. Input data includes records of usage frequency and conditions, which are then aggregated on the server.

[0126] Step 2:

[0127] The server preprocesses the collected data and filters out redundant information. This process also includes data imputation and anomaly detection. The preprocessed data is then input into a machine learning model to forecast future demand. The output is a forecast showing demand patterns for specific days and times.

[0128] Step 3:

[0129] The server generates an optimal utilization plan for shared resources and manufacturing equipment based on demand forecasts. Specifically, it determines the timing of use and maintenance schedules for each resource. This plan is designed to maximize utilization efficiency and eliminate waste.

[0130] Step 4:

[0131] The server sends the generated usage plan to the terminal, notifying the user and the work equipment. This notification includes a specific usage schedule and maintenance information, allowing the user to adjust their actions accordingly.

[0132] Step 5:

[0133] The device collects user feedback after use. This feedback includes actual usage details and satisfaction with the plan, and is sent to the server.

[0134] Step 6:

[0135] The server uses feedback data to improve the accuracy of future demand forecasts and usage plans. This feedback process facilitates model improvement and plan optimization. The output leads to more refined future plans and an improved user experience.

[0136] 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.

[0137] This invention provides a system that incorporates an emotion engine to recognize users' emotions, with the aim of effectively utilizing shared resources within a residential environment. This system comprehensively analyzes residents' behavioral data and emotional states, creates and notifies them of an optimal utilization plan, thereby enabling a more fulfilling living experience. Specific embodiments are described below.

[0138] Data collection

[0139] The server collects various types of data from the living environment. This data includes physical behavioral data collected via sensors and emotional data collected by an emotion engine that recognizes the user's emotions. For example, it can use cameras and voice input devices to analyze the user's facial expressions and voice tone, and estimate their emotional state in real time.

[0140] Data Analysis

[0141] The collected data is preprocessed by the server and fed into a generative model. The generative model analyzes users' past behavioral patterns and emotional data to predict future demand. This analysis utilizes machine learning algorithms, taking into account what resources are needed when users are in specific emotional states.

[0142] Creating a Usage Plan

[0143] The server generates a usage plan based on demand forecasts and taking into account the user's emotional state. The plan includes resource allocation corresponding to emotional changes predicted by the emotion engine, such as recommending the use of relaxation facilities during stressful periods.

[0144] Notifications and reservations

[0145] The optimized usage plan is notified to the device, and the user can review the details. At this time, the notification is presented in a format tailored to the user's emotional state, allowing the user to intuitively accept the content. For example, if the emotional engine detects a high level of stress, it can simply present relaxation suggestions on the device.

[0146] Feedback Collection

[0147] After use, the terminal collects user feedback. This feedback reflects the user's experience and emotional state, and the server uses this to forecast future demand and generate usage plans, thereby improving the system.

[0148] Specific example

[0149] When a resident feels tired after work on a weekday, the server recognizes this state based on data from the emotion engine. It then generates a usage plan to reserve a relaxation area and notifies the user via a soothing voice tone. The user can then review the suggested plan on their device and smoothly enjoy their relaxation time. In this way, combining the emotion engine makes it possible to provide a more personalized service.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The server utilizes sensors installed in the living environment and input devices from an emotion engine to collect behavioral and emotional data of residents in real time. For example, it monitors facial expressions through cameras and analyzes voice tone through voice analysis to obtain the user's current emotional state.

[0153] Step 2:

[0154] The server preprocesses the collected behavioral and sentiment data, removing noise and outliers to prepare it for generative models. This includes organizing the data chronologically and imputing missing values ​​as needed.

[0155] Step 3:

[0156] The server uses pre-processed data and leverages generative models to predict future user needs and emotional states. The generative models extract patterns from historical data and calculate what services residents will need and when.

[0157] Step 4:

[0158] The server automatically generates a shared resource utilization plan based on predicted demand and emotional state. This plan includes service options that take the user's emotions into account; for example, if high stress levels are predicted, it might suggest the use of relaxation facilities.

[0159] Step 5:

[0160] The device notifies the user of the generated usage plan and displays the content in a format appropriate to their emotional state. This notification is delivered through friendly voice guidance and an intuitive interface, making it easy for the user to accept and use.

[0161] Step 6:

[0162] Users can review the usage plan provided through their device and easily reserve their desired resources. Reservations are offered with an emotionally resonant approach, contributing to an improved user experience.

[0163] Step 7:

[0164] After use, the device collects feedback from the user and records information about their experience and emotional state. This feedback is sent to the server as important data.

[0165] Step 8:

[0166] The server analyzes feedback data and incorporates it into future demand forecasts and usage plans, continuously improving the system's accuracy. This improvement process enables the system to continue providing users with more optimized services.

[0167] (Example 2)

[0168] 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".

[0169] In modern living environments, there is a need to understand residents' emotional states and behavioral patterns in real time and to effectively allocate the most suitable resources accordingly. However, conventional systems have difficulty responding flexibly to such individual emotional states, resulting in decreased resident satisfaction. This invention aims to solve these problems and provide residents with a more personalized living experience.

[0170] 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.

[0171] In this invention, the server includes means for collecting operational information of the living environment, means for preprocessing the collected operational information and analyzing it using a generating AI model, and means for predicting demand based on the user's emotional state and past behavioral patterns. This makes it possible to generate an optimal utilization plan for shared resources and notify the user according to their status.

[0172] "Action information" refers to data related to the user's behavior and emotions, collected using sensors and devices within the living environment.

[0173] "Preprocessing" refers to the process of removing noise from collected motion information and preparing it for analysis.

[0174] A "generative AI model" refers to a machine learning technique used to predict future demand by analyzing users' behavioral patterns and emotional states from past data.

[0175] "Emotional state" refers to the user's current psychological and emotional state, and is generally analyzed from facial expressions, tone of voice, and actions.

[0176] "Demand forecasting" refers to the process of planning based on analyzed data, anticipating the resources and services that users will need in the future.

[0177] "Shared resources" refer to services and facilities that can be used jointly by users within a residential environment.

[0178] A "utilization plan" refers to a schedule of resource and service usage optimized for the user, created based on demand forecasts.

[0179] "Notification" refers to the act of communicating information to inform users about the generated usage plan.

[0180] "Feedback" refers to information provided to report users' experiences and feelings after using a product or service, and is used to improve future usage plans.

[0181] This invention is a system for effectively utilizing shared resources within a residential environment, generating demand forecasts and usage plans based on the user's emotional state. This system is primarily implemented via a server and terminals.

[0182] The server collects motion information from motion sensors, cameras, and voice input devices within the living environment. This allows for the acquisition of detailed data about the user's behavior patterns and emotional states. The collected motion information is preprocessed in a database, undergoing noise reduction and data normalization.

[0183] Next, the pre-processed data is analyzed by a generative AI model. This model has the ability to predict what resources will be needed in the future based on the user's past behavioral data and emotional state. In particular, the AI ​​model captures fluctuations in demand corresponding to specific emotional states and proposes an appropriate resource utilization plan.

[0184] The generated usage plan is customized based on the user's current emotional state and notified to the device. The device presents the information in a way that the user can intuitively understand, either visually or audibly. For example, if high stress levels are detected, a plan recommending the use of relaxation facilities will be presented.

[0185] After the usage plan is executed, users provide feedback via their terminals. This feedback is used to improve future demand forecasts and usage plans, and the server collects it and uses it as foundational data to enhance system efficiency.

[0186] As a concrete example, when a user feels fatigued after work, the server recognizes a high-stress state from the tone of their voice via a voice input device. Based on this information, it inputs prompt messages such as "Relaxation programs available immediately upon returning home" into an AI model, and suggests booking a relaxation area on the terminal. In this way, the user can easily accept a personalized plan and enjoy a comfortable living experience.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The server collects motion information from sensors and devices within the living environment. This motion information includes user facial expression data from cameras, motion data from motion sensors, and voice tone data from voice input devices. The input is raw data from sensors and devices, which is used as the basis for subsequent analysis.

[0190] Step 2:

[0191] The server preprocesses the collected behavioral data. Specifically, it removes noise and imputes missing values. The input is raw data, and the output is a clean and consistent dataset. This processing prepares the data to be suitable for generative AI models.

[0192] Step 3:

[0193] The server feeds pre-processed data into a generating AI model for analysis. This analysis uses past behavioral patterns and emotional states to predict future emotional states and demands. The input is pre-processed data, and the output is future demands and recommended shared resources. Specifically, it predicts what resources should be used and when, depending on the emotional state.

[0194] Step 4:

[0195] The server creates a usage plan based on the generated demand forecast. This plan includes the optimal allocation of resources according to the user's emotional state. The input is the analysis result, and the output is a customized usage plan. For example, relaxation facilities might be recommended for users experiencing high stress levels.

[0196] Step 5:

[0197] The server notifies the terminal of the generated usage plan. The terminal presents the plan to the user in an intuitive way using a visual interface and voice guidance. The input is the usage plan, and the output is the provision of information to the user. This allows the user to easily review the proposed plan.

[0198] Step 6:

[0199] Users utilize resources according to their usage plan and then provide feedback through their terminal. Input is information about the user's experience and feelings, and output is feedback data. This feedback is sent to the server and used to help generate future demand forecasts and plans.

[0200] (Application Example 2)

[0201] 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".

[0202] In modern living environments, the means of providing personalized services tailored to an individual's emotional state are limited. In particular, resource and service utilization plans in residential environments are often based solely on simple availability without considering the user's emotional state, resulting in insufficient user satisfaction. It is necessary to address these challenges and realize the provision of more individualized services.

[0203] 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.

[0204] In this invention, the server includes means for collecting data on the living environment, means for analyzing the collected data using a generative model and predicting user demand, means for generating a plan for the use of shared resources and services based on the predicted demand and the user's emotional state, means for providing notifications in a format appropriate to the emotional state based on the plan, and means for detecting the user's emotional state and creating a service provision plan including personalized meal suggestions. This makes it possible to provide personalized resources and services according to the user's emotional state.

[0205] "Means of collecting data on the living environment" refers to systems that use sensors and devices installed within a residence to acquire information about physical behavior and emotional states.

[0206] "A means of analyzing collected data using generative models to predict user demand" refers to a process that utilizes machine learning algorithms with acquired data to analyze users' past behavioral patterns and emotions in order to predict future needs.

[0207] "Means for generating a plan for the use of shared resources and services based on predicted demand and the emotional state of users" refers to an algorithm that creates optimal resource allocation and service proposals based on emotion recognition and demand forecasting.

[0208] "Means of providing notifications in a format appropriate to the user's emotional state, based on the above usage plan" refers to a function that customizes the content and format of notifications according to the user's current emotional state, conveying information in a way that is intuitively understandable.

[0209] "A means of detecting the emotional state of users and creating a service provision plan that includes individualized meal suggestions" refers to a system that recognizes emotions in real time from facial expressions, voice, etc., and suggests meals and services that are appropriate to that state.

[0210] To realize this invention, a server, a terminal, and a user interface device are required. The system is designed to support the optimal use of resources and services in the living environment based on the user's emotional state.

[0211] The server collects diverse data, including the user's emotional state, from sensors and devices installed within the residence (e.g., cameras and microphones). This data is processed through image processing libraries (OpenCV) and speech analysis libraries to estimate emotions by analyzing facial expressions and voice tone in real time. Next, it analyzes past data using machine learning libraries (TensorFlow or PyTorch) to predict user requests. Based on the predictive information generated by this series of data processing steps, the server creates a plan for the use of shared resources and services.

[0212] The terminal notifies the user of the usage plan generated by the server in a format that is appropriate to their emotional state. The notifications are designed to be intuitively understandable to the user; for example, when relaxation is needed, suggestions are made in a soft tone.

[0213] Users can easily reserve and confirm the use of suggested services and resources through their devices. Furthermore, feedback is collected after use, and the server uses this information to inform future planning.

[0214] For example, when a user returns home exhausted after a long day at work, the server recognizes this emotional state. It then suggests nutritious meals and relaxing environments through the user's device. The user can review these suggestions on the device and, if necessary, order delivery with a single click.

[0215] An example of a prompt message for a generative AI model is: "The user's current emotional state is fatigue. Please suggest the most suitable meal for this state."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The server collects the user's facial expressions and voice tone in real time through cameras and microphones installed in the living environment. Receiving this data as input, it performs facial expression analysis using image processing with the OpenCV library and tone analysis using a voice analysis library. This results in an output that estimates the user's emotional state.

[0219] Step 2:

[0220] The server takes estimated sentiment data and the user's past behavior history as input and analyzes the data using a machine learning library (TensorFlow or PyTorch) to predict the user's future demand. This provides predictive information necessary for creating future usage plans as output.

[0221] Step 3:

[0222] The server generates a usage plan using predicted demand information and sentiment states. The generated usage plan includes necessary resource allocations and service delivery plans. This plan is output to prepare data for the next step.

[0223] Step 4:

[0224] The device presents the usage plan received from the server to the user in a format optimized for them. For notifications, it considers the user's current emotional state and communicates information visually or audibly using voice assistants and screen displays. The user acknowledges this as input and selects an action as needed.

[0225] Step 5:

[0226] Users utilize the services and resources suggested through their devices. Reservations can be easily made through operations on the device. The user's choices are collected as data that is fed back into future predictions.

[0227] Step 6:

[0228] The server collects feedback from users via terminals after each service or resource is used. This feedback is then used as data for future demand forecasting and usage planning. This allows the system to be continuously improved and personalized service delivery to be enhanced.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention is a system that utilizes data within a residential environment to efficiently manage shared resources. This system performs a series of actions including data collection, analysis, planning, notification, reservation, and feedback by residents. A specific embodiment of this system is described below.

[0246] Data collection

[0247] The server collects data related to the residents' lives, including purchase history, vehicle usage, and frequency of delivery service use. This data is obtained through sensors installed in the residential environment and APIs on relevant online platforms.

[0248] Data Analysis

[0249] The collected data is preprocessed by the server and input into a generative model. This model uses machine learning to predict, for example, the demand for shared resources on specific days of the week or at specific times of day.

[0250] Creating a Usage Plan

[0251] Based on the analysis results, the server automatically generates a plan that allows each resident to use shared resources most efficiently. This promotes usage that avoids peak hours and maximizes resource utilization.

[0252] Notifications and reservations

[0253] The generated usage plan is notified to each resident via their device. Based on the notification, users can easily make reservations according to their preferences. This process is provided through a user-friendly interface using an application.

[0254] Feedback Collection

[0255] After use, the terminal requests feedback from the resident. This feedback is sent to the server and used to improve the accuracy of future demand forecasts and plan generation.

[0256] Specific example

[0257] For example, if multiple residents need a car on a weekday, the server predicts peak demand based on past data. This allows it to create an optimal car-sharing schedule and propose it to each resident. Users can then make reservations based on the suggestions, ensuring smooth travel. After use, user feedback is collected and used to improve future schedules.

[0258] Thus, embodiments of the present invention enable the effective use of data on the living environment and the efficient use of shared resources among residents.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The server collects data on residents' behavior in real time through sensors and APIs installed in the living environment. This data includes, for example, records of vehicle entry and exit in parking lots and usage status of smart home appliances.

[0262] Step 2:

[0263] The server preprocesses the collected data and converts it into a format suitable for input to generative models. Preprocessing includes noise filtering and missing value imputation. This improves data quality and formats it for analysis.

[0264] Step 3:

[0265] The server inputs pre-processed data into a generative model to perform demand forecasting. This model learns from past patterns and has the ability to predict when demand for a particular service will be high, depending on the time of day or date.

[0266] Step 4:

[0267] The server automatically generates a resource utilization plan based on the prediction results. Specifically, it creates a schedule that considers resource allocation to avoid peak demand times and the optimal order of use.

[0268] Step 5:

[0269] The device notifies residents of the generated usage plan and displays the plan's contents. Notifications are sent via push notifications and email, and users can view the details on their device.

[0270] Step 6:

[0271] Users select their desired options from the notified usage plan and reserve resources through the terminal interface. Reservations can be completed with simple operations, taking user convenience into consideration.

[0272] Step 7:

[0273] The terminal collects feedback from users after they have used shared resources. This feedback reflects user opinions on the ease of use of the resources and the suitability of the plan, and will be used to improve the system in the future.

[0274] Step 8:

[0275] The server analyzes the collected feedback and incorporates it into demand forecasting models and usage plan generation to improve the system's accuracy. This enables more accurate forecasts and higher quality services in subsequent processing.

[0276] (Example 1)

[0277] 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."

[0278] In a living environment, in order to efficiently manage and utilize shared resources, it is necessary to accurately predict the needs of residents and appropriately formulate utilization plans based on them. In addition, a system that allows users to intuitively make reservations and a mechanism that utilizes feedback after use to improve prediction accuracy are also required. In conventional systems, these processes are not sufficiently integrated, and optimal resource management has not been achieved.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes means for collecting information on the living environment, means for preprocessing the collected information and analyzing it by a generation model to predict demand, and means for automatically generating a utilization plan for shared resources based on the predicted demand. As a result, it becomes possible to accurately predict the needs of residents and formulate an efficient resource utilization plan.

[0281] The "living environment" refers to various conditions and situations related to life that occur inside and around individual dwellings.

[0282] The "information" refers to a collection of numerical values and character strings related to the life and behavior of residents, including data and records obtained through sensors and APIs.

[0283] The "means for collecting" refers to a mechanism for integrating information via devices installed in the living environment or networks.

[0284] The "preprocessing" refers to a series of processes for removing noise and missing values contained in raw data and converting it into a form that can be used for analysis.

[0285] The "generation model" refers to a calculation process for predicting demand from information collected using a machine learning algorithm.

[0286] "Analysis" refers to the activity of interpreting the collected information using statistical and machine learning methods and extracting useful insights.

[0287] "Means for predicting demand" refers to the function for predicting the behavior and requirements of residents under specific conditions using a generative model.

[0288] "Shared resources" refer to facilities and services that residents can use jointly, such as vehicles and shared facilities.

[0289] "Means for automatically generating usage plans" refers to the mechanism by which the system independently creates an appropriate schedule for resource utilization based on the predicted demand.

[0290] "Notification" refers to the act of transmitting the generated usage plan as information to the residents' terminals to inform them.

[0291] "Feedback" refers to the process of collecting the usage experiences and opinions obtained from residents after the use of resources.

[0292] This system enables the effective collection, analysis, and optimal management of information in the living environment. The following shows its specific embodiments.

[0293] The server first collects information related to daily life from sensors installed in the living environment and APIs through the Internet. This information includes data on power consumption, vehicle usage status, delivery receipt history, etc. As software for information collection, Python libraries are used to efficiently absorb data.

[0294] The collected data is preprocessed on the server. This includes the process of normalizing the data and filtering out outliers using the Pandas library. This preprocessed information is input into a generative AI model using machine learning algorithms. The generative AI model analyzes the behavior patterns of residents and conducts demand prediction.

[0295] Based on demand forecasts, the server automatically generates a shared resource usage plan. Because this plan is tailored to each resident, efficient resource utilization is possible. This usage plan is notified to residents via their devices. The devices utilize a smartphone app to inform users of their schedules and enable intuitive reservations.

[0296] After use, users provide feedback via their device. This feedback is sent to the server and used to improve the accuracy of the entire system by informing future demand forecasts and usage plans.

[0297] As a concrete example, if multiple residents want to use a vehicle on a weekday, the server could predict demand based on past data and suggest the optimal usage time for each user. Users could then easily make a reservation based on this and provide feedback after use. Through this process, efficient management of shared resources in the residential environment can be achieved.

[0298] An example of a prompt to the generating AI model is, "Predict the usage preferences of residents on a specific day next week and suggest the optimal usage schedule." This prompt allows the model to predict specific actions and provide information useful for planning.

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] The server collects information from sensors installed in the residential environment and from APIs accessed via the internet. This input includes electricity usage data from smart meters and purchase history from online platforms. The server receives this information and stores it in a database. Specifically, the server automatically sends API requests at a specified time during the night to retrieve the latest data.

[0302] Step 2:

[0303] The server preprocesses the collected data. As input, raw, unfiltered data is provided. The server uses the Pandas library in Python to perform data cleansing and remove noise and outliers. The normalized data is prepared as output and input into the generative AI model. As an operation, the server applies an outlier detection algorithm to each dataset to ensure data integrity.

[0304] Step 3:

[0305] The server inputs the preprocessed data into the generative AI model to perform demand forecasting. The input is the cleansed dataset, and the output is the prediction result. The model uses a machine learning algorithm to calculate the demand for shared resources at a given day of the week and time period. Specifically, the server runs the prediction model weekly to analyze demand trends.

[0306] Step 4:

[0307] The server automatically generates a usage plan using the prediction result. The input is the prediction result, and a detailed usage schedule is generated as output. The server constructs a plan that avoids peak demand and enables efficient resource allocation. As an operation, the server optimizes the schedule of each resident by an algorithm to achieve maximum efficiency.

[0308] Step 5:

[0309] The terminal notifies the resident of the usage plan generated by the server. The input is the data of the usage plan, and the output is the notification information for the user. The terminal sends a push notification to the resident via a smartphone app. In a specific operation, the terminal provides an interface that prompts the user who received the notification to take an action.

[0310] Step 6:

[0311] Users make reservations based on the information they receive in the notification. The input is the usage plan provided in the notification, and the output is the reservation confirmation information. Users select the time and resources provided on the app on their device to complete the reservation. Specifically, users use the app's calendar function to quickly make the best reservation to fit their schedule.

[0312] Step 7:

[0313] The device collects feedback from users after use. The input is the user's experience and evaluation, and the output is feedback data. The device displays an evaluation window within the app, soliciting user feedback. Specifically, a pop-up appears immediately after use, creating an environment where users can quickly provide evaluations.

[0314] Step 8:

[0315] The server analyzes the collected feedback and uses it to generate future demand forecasts and plans. The input is feedback data, and the output is an improved forecasting model. The server scrutinizes the feedback and uses it to adjust the parameters of the generated AI model. Specifically, the server repeatedly trains the model based on new feedback data to improve forecasting accuracy.

[0316] (Application Example 1)

[0317] 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."

[0318] In modern residential and industrial environments, maximizing the efficiency of shared resources and manufacturing equipment is essential. However, accurately forecasting individual demand and developing appropriate utilization plans is challenging, leading to inefficient resource use and reduced productivity due to unnecessary maintenance. Furthermore, a lack of proper notification and feedback processing to users and equipment hinders improvements in the accuracy of future utilization planning.

[0319] 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.

[0320] In this invention, the server includes means for collecting data within residential and industrial environments, means for analyzing the collected data using a machine learning generative model to predict demand, and means for generating a utilization plan for shared resources and manufacturing equipment based on the predicted demand. This enables the efficient utilization of shared resources and manufacturing equipment and the implementation of an appropriate maintenance plan.

[0321] "Residential and industrial environments" refer to the collection of physical structures and surrounding facilities where people live and work, and are places where data for improving quality of life and productivity is collected.

[0322] "Means of data collection" refers to the process of acquiring information about the behavior of residents and workers, usage patterns, environmental conditions, etc., using sensors and online platforms.

[0323] A "machine learning generative model" is a system that combines algorithms used to analyze collected data and predict future demand and usage patterns.

[0324] "Methods for forecasting demand" refer to methods that use historical data and machine learning results to predict future resource usage and needs.

[0325] "Means for generating utilization plans for shared resources and manufacturing equipment" refers to a process that automatically creates optimal resource utilization and equipment operating schedules in response to predicted demand.

[0326] "Appropriate notifications and feedback" refer to means of information exchange that provide necessary information to users and work equipment based on the generated usage plans and results, and to extract areas for improvement necessary for creating the next plan.

[0327] The system implementing this invention is intended to achieve efficient resource management in residential and industrial environments. Servers, terminals, and users are the main components.

[0328] The server first collects data on resident and worker behavior, usage patterns, and environmental conditions through various sensors and online platforms. This data is preprocessed on the server and input into a machine learning platform (e.g., TensorFlow or PyTorch) using Python. A machine learning generative model analyzes this data to predict future demand and usage patterns. Based on the results, an optimal utilization plan for shared resources and manufacturing equipment is automatically created.

[0329] The terminal's role is to notify each user and work device of the usage plan generated by the server. This notification allows individual users and devices to use resources and perform maintenance according to the schedule. Furthermore, feedback is collected after use and sent to the server, which is then reflected in the generation of the next plan.

[0330] As a concrete example, consider the use of tools on a factory production line. Based on past data, the server predicts the frequency of tool use and plans the necessary maintenance schedule. This plan is then communicated to workers via terminals. In this way, efficient factory operations are supported.

[0331] An example of a prompt to input into the generating AI model is, "Based on the frequency and conditions of use of this tool, please suggest the optimal maintenance schedule for the next week." Using this example, a concrete plan will be generated, making it easier to implement.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The server collects user behavior data and environmental data through sensors installed in residential and industrial environments, as well as through APIs on online platforms. Input data includes records of usage frequency and conditions, which are then aggregated on the server.

[0335] Step 2:

[0336] The server preprocesses the collected data and filters out redundant information. This process also includes data imputation and anomaly detection. The preprocessed data is then input into a machine learning model to forecast future demand. The output is a forecast showing demand patterns for specific days and times.

[0337] Step 3:

[0338] The server generates an optimal utilization plan for shared resources and manufacturing equipment based on demand forecasts. Specifically, it determines the timing of use and maintenance schedules for each resource. This plan is designed to maximize utilization efficiency and eliminate waste.

[0339] Step 4:

[0340] The server sends the generated usage plan to the terminal, notifying the user and the work equipment. This notification includes a specific usage schedule and maintenance information, allowing the user to adjust their actions accordingly.

[0341] Step 5:

[0342] The device collects user feedback after use. This feedback includes actual usage details and satisfaction with the plan, and is sent to the server.

[0343] Step 6:

[0344] The server uses feedback data to improve the accuracy of future demand forecasts and usage plans. This feedback process facilitates model improvement and plan optimization. The output leads to more refined future plans and an improved user experience.

[0345] 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.

[0346] This invention provides a system that incorporates an emotion engine to recognize users' emotions, with the aim of effectively utilizing shared resources within a residential environment. This system comprehensively analyzes residents' behavioral data and emotional states, creates and notifies them of an optimal utilization plan, thereby enabling a more fulfilling living experience. Specific embodiments are described below.

[0347] Data collection

[0348] The server collects various types of data from the living environment. This data includes physical behavioral data collected via sensors and emotional data collected by an emotion engine that recognizes the user's emotions. For example, it can use cameras and voice input devices to analyze the user's facial expressions and voice tone, and estimate their emotional state in real time.

[0349] Data Analysis

[0350] The collected data is preprocessed by the server and fed into a generative model. The generative model analyzes users' past behavioral patterns and emotional data to predict future demand. This analysis utilizes machine learning algorithms, taking into account what resources are needed when users are in specific emotional states.

[0351] Creating a Usage Plan

[0352] The server generates a usage plan based on demand forecasts and taking into account the user's emotional state. The plan includes resource allocation corresponding to emotional changes predicted by the emotion engine, such as recommending the use of relaxation facilities during stressful periods.

[0353] Notifications and reservations

[0354] The optimized usage plan is notified to the device, and the user can review the details. At this time, the notification is presented in a format tailored to the user's emotional state, allowing the user to intuitively accept the content. For example, if the emotional engine detects a high level of stress, it can simply present relaxation suggestions on the device.

[0355] Feedback Collection

[0356] After use, the terminal collects user feedback. This feedback reflects the user's experience and emotional state, and the server uses this to forecast future demand and generate usage plans, thereby improving the system.

[0357] Specific example

[0358] When a resident feels tired after work on a weekday, the server recognizes this state based on data from the emotion engine. It then generates a usage plan to reserve a relaxation area and notifies the user via a soothing voice tone. The user can then review the suggested plan on their device and smoothly enjoy their relaxation time. In this way, combining the emotion engine makes it possible to provide a more personalized service.

[0359] The following describes the processing flow.

[0360] Step 1:

[0361] The server utilizes sensors installed in the living environment and input devices from an emotion engine to collect behavioral and emotional data of residents in real time. For example, it monitors facial expressions through cameras and analyzes voice tone through voice analysis to obtain the user's current emotional state.

[0362] Step 2:

[0363] The server preprocesses the collected behavioral and sentiment data, removing noise and outliers to prepare it for generative models. This includes organizing the data chronologically and imputing missing values ​​as needed.

[0364] Step 3:

[0365] The server uses pre-processed data and leverages generative models to predict future user needs and emotional states. The generative models extract patterns from historical data and calculate what services residents will need and when.

[0366] Step 4:

[0367] The server automatically generates a shared resource utilization plan based on predicted demand and emotional state. This plan includes service options that take the user's emotions into account; for example, if high stress levels are predicted, it might suggest the use of relaxation facilities.

[0368] Step 5:

[0369] The device notifies the user of the generated usage plan and displays the content in a format appropriate to their emotional state. This notification is delivered through friendly voice guidance and an intuitive interface, making it easy for the user to accept and use.

[0370] Step 6:

[0371] Users can review the usage plan provided through their device and easily reserve their desired resources. Reservations are offered with an emotionally resonant approach, contributing to an improved user experience.

[0372] Step 7:

[0373] After use, the device collects feedback from the user and records information about their experience and emotional state. This feedback is sent to the server as important data.

[0374] Step 8:

[0375] The server analyzes feedback data and incorporates it into future demand forecasts and usage plans, continuously improving the system's accuracy. This improvement process enables the system to continue providing users with more optimized services.

[0376] (Example 2)

[0377] 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".

[0378] In modern living environments, there is a need to understand residents' emotional states and behavioral patterns in real time and to effectively allocate the most suitable resources accordingly. However, conventional systems have difficulty responding flexibly to such individual emotional states, resulting in decreased resident satisfaction. This invention aims to solve these problems and provide residents with a more personalized living experience.

[0379] 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.

[0380] In this invention, the server includes means for collecting operational information of the living environment, means for preprocessing the collected operational information and analyzing it using a generating AI model, and means for predicting demand based on the user's emotional state and past behavioral patterns. This makes it possible to generate an optimal utilization plan for shared resources and notify the user according to their status.

[0381] "Action information" refers to data related to the user's behavior and emotions, collected using sensors and devices within the living environment.

[0382] "Preprocessing" refers to the process of removing noise from collected motion information and preparing it for analysis.

[0383] A "generative AI model" refers to a machine learning technique used to predict future demand by analyzing users' behavioral patterns and emotional states from past data.

[0384] "Emotional state" refers to the user's current psychological and emotional state, and is generally analyzed from facial expressions, tone of voice, and actions.

[0385] "Demand forecasting" refers to the process of planning based on analyzed data, anticipating the resources and services that users will need in the future.

[0386] "Shared resources" refer to services and facilities that can be used jointly by users within a residential environment.

[0387] A "utilization plan" refers to a schedule of resource and service usage optimized for the user, created based on demand forecasts.

[0388] "Notification" refers to the act of communicating information to inform users about the generated usage plan.

[0389] "Feedback" refers to information provided to report users' experiences and feelings after using a product or service, and is used to improve future usage plans.

[0390] This invention is a system for effectively utilizing shared resources within a residential environment, generating demand forecasts and usage plans based on the user's emotional state. This system is primarily implemented via a server and terminals.

[0391] The server collects motion information from motion sensors, cameras, and voice input devices within the living environment. This allows for the acquisition of detailed data about the user's behavior patterns and emotional states. The collected motion information is preprocessed in a database, undergoing noise reduction and data normalization.

[0392] Next, the pre-processed data is analyzed by a generative AI model. This model has the ability to predict what resources will be needed in the future based on the user's past behavioral data and emotional state. In particular, the AI ​​model captures fluctuations in demand corresponding to specific emotional states and proposes an appropriate resource utilization plan.

[0393] The generated usage plan is customized based on the user's current emotional state and notified to the device. The device presents the information in a way that the user can intuitively understand, either visually or audibly. For example, if high stress levels are detected, a plan recommending the use of relaxation facilities will be presented.

[0394] After the usage plan is executed, users provide feedback via their terminals. This feedback is used to improve future demand forecasts and usage plans, and the server collects it and uses it as foundational data to enhance system efficiency.

[0395] As a concrete example, when a user feels fatigued after work, the server recognizes a high-stress state from the tone of their voice via a voice input device. Based on this information, it inputs prompt messages such as "Relaxation programs available immediately upon returning home" into an AI model, and suggests booking a relaxation area on the terminal. In this way, the user can easily accept a personalized plan and enjoy a comfortable living experience.

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] Step 1:

[0398] The server collects motion information from sensors and devices within the living environment. This motion information includes user facial expression data from cameras, motion data from motion sensors, and voice tone data from voice input devices. The input is raw data from sensors and devices, which is used as the basis for subsequent analysis.

[0399] Step 2:

[0400] The server preprocesses the collected behavioral data. Specifically, it removes noise and imputes missing values. The input is raw data, and the output is a clean and consistent dataset. This processing prepares the data to be suitable for generative AI models.

[0401] Step 3:

[0402] The server feeds pre-processed data into a generating AI model for analysis. This analysis uses past behavioral patterns and emotional states to predict future emotional states and demands. The input is pre-processed data, and the output is future demands and recommended shared resources. Specifically, it predicts what resources should be used and when, depending on the emotional state.

[0403] Step 4:

[0404] The server creates a usage plan based on the generated demand forecast. This plan includes the optimal allocation of resources according to the user's emotional state. The input is the analysis result, and the output is a customized usage plan. For example, relaxation facilities might be recommended for users experiencing high stress levels.

[0405] Step 5:

[0406] The server notifies the terminal of the generated usage plan. The terminal presents the plan to the user in an intuitive way using a visual interface and voice guidance. The input is the usage plan, and the output is the provision of information to the user. This allows the user to easily review the proposed plan.

[0407] Step 6:

[0408] Users utilize resources according to their usage plan and then provide feedback through their terminal. Input is information about the user's experience and feelings, and output is feedback data. This feedback is sent to the server and used to help generate future demand forecasts and plans.

[0409] (Application Example 2)

[0410] 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."

[0411] In modern living environments, the means of providing personalized services tailored to an individual's emotional state are limited. In particular, resource and service utilization plans in residential environments are often based solely on simple availability without considering the user's emotional state, resulting in insufficient user satisfaction. It is necessary to address these challenges and realize the provision of more individualized services.

[0412] 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.

[0413] In this invention, the server includes means for collecting data on the living environment, means for analyzing the collected data using a generative model and predicting user demand, means for generating a plan for the use of shared resources and services based on the predicted demand and the user's emotional state, means for providing notifications in a format appropriate to the emotional state based on the plan, and means for detecting the user's emotional state and creating a service provision plan including personalized meal suggestions. This makes it possible to provide personalized resources and services according to the user's emotional state.

[0414] "Means of collecting data on the living environment" refers to systems that use sensors and devices installed within a residence to acquire information about physical behavior and emotional states.

[0415] "A means of analyzing collected data using generative models to predict user demand" refers to a process that utilizes machine learning algorithms with acquired data to analyze users' past behavioral patterns and emotions in order to predict future needs.

[0416] "Means for generating a plan for the use of shared resources and services based on predicted demand and the emotional state of users" refers to an algorithm that creates optimal resource allocation and service proposals based on emotion recognition and demand forecasting.

[0417] "Means of providing notifications in a format appropriate to the user's emotional state, based on the above usage plan" refers to a function that customizes the content and format of notifications according to the user's current emotional state, conveying information in a way that is intuitively understandable.

[0418] "A means of detecting the emotional state of users and creating a service provision plan that includes individualized meal suggestions" refers to a system that recognizes emotions in real time from facial expressions, voice, etc., and suggests meals and services that are appropriate to that state.

[0419] To realize this invention, a server, a terminal, and a user interface device are required. The system is designed to support the optimal use of resources and services in the living environment based on the user's emotional state.

[0420] The server collects diverse data, including the user's emotional state, from sensors and devices installed within the residence (e.g., cameras and microphones). This data is processed through image processing libraries (OpenCV) and speech analysis libraries to estimate emotions by analyzing facial expressions and voice tone in real time. Next, it analyzes past data using machine learning libraries (TensorFlow or PyTorch) to predict user requests. Based on the predictive information generated by this series of data processing steps, the server creates a plan for the use of shared resources and services.

[0421] The terminal notifies the user of the usage plan generated by the server in a format that is appropriate to their emotional state. The notifications are designed to be intuitively understandable to the user; for example, when relaxation is needed, suggestions are made in a soft tone.

[0422] Users can easily reserve and confirm the use of suggested services and resources through their devices. Furthermore, feedback is collected after use, and the server uses this information to inform future planning.

[0423] For example, when a user returns home exhausted after a long day at work, the server recognizes this emotional state. It then suggests nutritious meals and relaxing environments through the user's device. The user can review these suggestions on the device and, if necessary, order delivery with a single click.

[0424] An example of a prompt message for a generative AI model is: "The user's current emotional state is fatigue. Please suggest the most suitable meal for this state."

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

[0426] Step 1:

[0427] The server collects the user's facial expressions and voice tone in real time through cameras and microphones installed in the living environment. Receiving this data as input, it performs facial expression analysis using image processing with the OpenCV library and tone analysis using a voice analysis library. This results in an output that estimates the user's emotional state.

[0428] Step 2:

[0429] The server takes estimated sentiment data and the user's past behavior history as input and analyzes the data using a machine learning library (TensorFlow or PyTorch) to predict the user's future demand. This provides predictive information necessary for creating future usage plans as output.

[0430] Step 3:

[0431] The server generates a usage plan using predicted demand information and sentiment states. The generated usage plan includes necessary resource allocations and service delivery plans. This plan is output to prepare data for the next step.

[0432] Step 4:

[0433] The device presents the usage plan received from the server to the user in a format optimized for them. For notifications, it considers the user's current emotional state and communicates information visually or audibly using voice assistants and screen displays. The user acknowledges this as input and selects an action as needed.

[0434] Step 5:

[0435] Users utilize the services and resources suggested through their devices. Reservations can be easily made through operations on the device. The user's choices are collected as data that is fed back into future predictions.

[0436] Step 6:

[0437] The server collects feedback from users via terminals after each service or resource is used. This feedback is then used as data for future demand forecasting and usage planning. This allows the system to be continuously improved and personalized service delivery to be enhanced.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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".

[0454] This invention is a system that utilizes data within a residential environment to efficiently manage shared resources. This system performs a series of actions including data collection, analysis, planning, notification, reservation, and feedback by residents. A specific embodiment of this system is described below.

[0455] Data collection

[0456] The server collects data related to the residents' lives, including purchase history, vehicle usage, and frequency of delivery service use. This data is obtained through sensors installed in the residential environment and APIs on relevant online platforms.

[0457] Data Analysis

[0458] The collected data is preprocessed by the server and input into a generative model. This model uses machine learning to predict, for example, the demand for shared resources on specific days of the week or at specific times of day.

[0459] Creating a Usage Plan

[0460] Based on the analysis results, the server automatically generates a plan that allows each resident to use shared resources most efficiently. This promotes usage that avoids peak hours and maximizes resource utilization.

[0461] Notifications and reservations

[0462] The generated usage plan is notified to each resident via their device. Based on the notification, users can easily make reservations according to their preferences. This process is provided through a user-friendly interface using an application.

[0463] Feedback Collection

[0464] After use, the terminal requests feedback from the resident. This feedback is sent to the server and used to improve the accuracy of future demand forecasts and plan generation.

[0465] Specific example

[0466] For example, if multiple residents need a car on a weekday, the server predicts peak demand based on past data. This allows it to create an optimal car-sharing schedule and propose it to each resident. Users can then make reservations based on the suggestions, ensuring smooth travel. After use, user feedback is collected and used to improve future schedules.

[0467] Thus, embodiments of the present invention enable the effective use of data on the living environment and the efficient use of shared resources among residents.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The server collects data on residents' behavior in real time through sensors and APIs installed in the living environment. This data includes, for example, records of vehicle entry and exit in parking lots and usage status of smart home appliances.

[0471] Step 2:

[0472] The server preprocesses the collected data and converts it into a format suitable for input to generative models. Preprocessing includes noise filtering and missing value imputation. This improves data quality and formats it for analysis.

[0473] Step 3:

[0474] The server inputs pre-processed data into a generative model to perform demand forecasting. This model learns from past patterns and has the ability to predict when demand for a particular service will be high, depending on the time of day or date.

[0475] Step 4:

[0476] The server automatically generates a resource utilization plan based on the prediction results. Specifically, it creates a schedule that considers resource allocation to avoid peak demand times and the optimal order of use.

[0477] Step 5:

[0478] The device notifies residents of the generated usage plan and displays the plan's contents. Notifications are sent via push notifications and email, and users can view the details on their device.

[0479] Step 6:

[0480] Users select their desired options from the notified usage plan and reserve resources through the terminal interface. Reservations can be completed with simple operations, taking user convenience into consideration.

[0481] Step 7:

[0482] The terminal collects feedback from users after they have used shared resources. This feedback reflects user opinions on the ease of use of the resources and the suitability of the plan, and will be used to improve the system in the future.

[0483] Step 8:

[0484] The server analyzes the collected feedback and incorporates it into demand forecasting models and usage plan generation to improve the system's accuracy. This enables more accurate forecasts and higher quality services in subsequent processing.

[0485] (Example 1)

[0486] 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."

[0487] In residential environments, efficient management and utilization of shared resources require accurately forecasting residents' needs and developing appropriate usage plans based on those forecasts. Furthermore, a system that allows users to intuitively make reservations, along with a mechanism to improve forecast accuracy through post-use feedback, is necessary. Conventional systems lack sufficient integration of these processes, resulting in inadequate resource management.

[0488] 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.

[0489] In this invention, the server includes means for collecting information about the living environment, means for preprocessing the collected information and analyzing it using a generative model to predict demand, and means for automatically generating a shared resource utilization plan based on the predicted demand. This makes it possible to accurately predict the demand of residents and create an efficient resource utilization plan.

[0490] "Residential environment" refers to the various conditions and circumstances related to daily life that occur within and around individual residences.

[0491] "Information" refers to a collection of numerical and string data related to residents' lives and behaviors, including data and records acquired through sensors and APIs.

[0492] "Means of collection" refers to systems for collecting information through devices installed in the living environment or via networks.

[0493] "Preprocessing" refers to a series of processes that remove noise and missing values ​​from raw data and convert it into a format that can be used for analysis.

[0494] A "generative model" refers to a computational process that uses machine learning algorithms to predict demand from collected information.

[0495] "Analysis" refers to the activity of interpreting collected information using statistical and machine learning methods to extract useful insights.

[0496] "Means of predicting demand" refers to functions that use generative models to predict the behavior and demands of residents under specific conditions.

[0497] "Shared resources" refer to facilities and services that residents can use together, such as vehicles and common facilities.

[0498] "Means for automatically generating utilization plans" refers to a system that independently creates an appropriate resource utilization schedule based on predicted demand.

[0499] "Notification" refers to the act of sending the generated usage plan as information to the resident's device to inform them.

[0500] "Feedback" refers to the process of collecting user experiences and opinions from residents after they have used a resource.

[0501] This system enables the effective collection and analysis of information in the living environment and the optimal management of shared resources. A specific embodiment is shown below.

[0502] The server first collects information about daily life from sensors installed in the living environment and APIs accessed via the internet. This information includes data on electricity consumption, vehicle usage, and delivery request history. A Python library is used as the software for efficiently extracting the data.

[0503] The collected data is preprocessed on the server. This includes normalizing the data using the Pandas library and filtering outliers. This preprocessed information is then input into a generative AI model using machine learning algorithms. The generative AI model analyzes residents' behavior patterns and performs demand forecasting.

[0504] Based on demand forecasts, the server automatically generates a shared resource usage plan. Because this plan is tailored to each resident, efficient resource utilization is possible. This usage plan is notified to residents via their devices. The devices utilize a smartphone app to inform users of their schedules and enable intuitive reservations.

[0505] After use, users provide feedback via their device. This feedback is sent to the server and used to improve the accuracy of the entire system by informing future demand forecasts and usage plans.

[0506] As a concrete example, if multiple residents want to use a vehicle on a weekday, the server could predict demand based on past data and suggest the optimal usage time for each user. Users could then easily make a reservation based on this and provide feedback after use. Through this process, efficient management of shared resources in the residential environment can be achieved.

[0507] An example of a prompt to a generating AI model is, "Predict the usage preferences of residents on a specific day next week and suggest the optimal usage schedule." This prompt allows the model to predict specific actions and provide information useful for planning.

[0508] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0509] Step 1:

[0510] The server collects information from sensors installed in the residential environment and from APIs accessed via the internet. This input includes electricity usage data from smart meters and purchase history from online platforms. The server receives this information and stores it in a database. Specifically, the server automatically sends API requests at a specified time during the night to retrieve the latest data.

[0511] Step 2:

[0512] The server preprocesses the collected data. Unfiltered raw data is provided as input. The server cleanses the data using the Python Pandas library to remove noise and outliers. The normalized data is prepared as output and input into the generative AI model. In operation, the server applies an outlier detection algorithm to each dataset to ensure data integrity.

[0513] Step 3:

[0514] The server inputs pre-processed data into a generating AI model to perform demand forecasting. The input is a cleansed dataset, and the output is the forecast result. The model uses machine learning algorithms to calculate the demand for shared resources on specified days and times. Specifically, the server runs the forecasting model weekly to analyze demand trends.

[0515] Step 4:

[0516] The server automatically generates usage plans using prediction results. The input is the prediction result, and the output is a detailed usage schedule. The server constructs a plan that avoids peak demand and enables efficient resource allocation. In operation, the server optimizes each resident's schedule using an algorithm to achieve maximum efficiency.

[0517] Step 5:

[0518] The terminal notifies residents of the usage plan generated by the server. The input is the usage plan data, and the output is notification information for the user. The terminal sends push notifications to residents via a smartphone app. Specifically, the terminal provides an interface that prompts the notified user to take action.

[0519] Step 6:

[0520] Users make reservations based on the information they receive in the notification. The input is the usage plan provided in the notification, and the output is the reservation confirmation information. Users select the time and resources provided on the app on their device to complete the reservation. Specifically, users use the app's calendar function to quickly make the best reservation to fit their schedule.

[0521] Step 7:

[0522] The device collects feedback from users after use. The input is the user's experience and evaluation, and the output is feedback data. The device displays an evaluation window within the app, soliciting user feedback. Specifically, a pop-up appears immediately after use, creating an environment where users can quickly provide evaluations.

[0523] Step 8:

[0524] The server analyzes the collected feedback and uses it to generate future demand forecasts and plans. The input is feedback data, and the output is an improved forecasting model. The server scrutinizes the feedback and uses it to adjust the parameters of the generated AI model. Specifically, the server repeatedly trains the model based on new feedback data to improve forecasting accuracy.

[0525] (Application Example 1)

[0526] 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."

[0527] In modern residential and industrial environments, maximizing the efficiency of shared resources and manufacturing equipment is essential. However, accurately forecasting individual demand and developing appropriate utilization plans is challenging, leading to inefficient resource use and reduced productivity due to unnecessary maintenance. Furthermore, a lack of proper notification and feedback processing to users and equipment hinders improvements in the accuracy of future utilization planning.

[0528] 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.

[0529] In this invention, the server includes means for collecting data within residential and industrial environments, means for analyzing the collected data using a machine learning generative model to predict demand, and means for generating a utilization plan for shared resources and manufacturing equipment based on the predicted demand. This enables the efficient utilization of shared resources and manufacturing equipment and the implementation of an appropriate maintenance plan.

[0530] "Residential and industrial environments" refer to the collection of physical structures and surrounding facilities where people live and work, and are places where data for improving quality of life and productivity is collected.

[0531] "Means of data collection" refers to the process of acquiring information about the behavior of residents and workers, usage patterns, environmental conditions, etc., using sensors and online platforms.

[0532] A "machine learning generative model" is a system that combines algorithms used to analyze collected data and predict future demand and usage patterns.

[0533] "Methods for predicting demand" refer to methods that estimate future resource usage and needs based on historical data and machine learning results.

[0534] "Means for generating utilization plans for shared resources and manufacturing equipment" refers to a process that automatically creates optimal resource utilization and equipment operating schedules in response to predicted demand.

[0535] "Appropriate notifications and feedback" refer to means of information exchange that provide necessary information to users and work equipment based on the generated usage plans and results, and to extract areas for improvement necessary for creating the next plan.

[0536] The system implementing this invention is intended to achieve efficient resource management in residential and industrial environments. Servers, terminals, and users are the main components.

[0537] The server first collects data on resident and worker behavior, usage patterns, and environmental conditions through various sensors and online platforms. This data is preprocessed on the server and input into a machine learning platform (e.g., TensorFlow or PyTorch) using Python. A machine learning generative model analyzes this data to predict future demand and usage patterns. Based on the results, an optimal utilization plan for shared resources and manufacturing equipment is automatically created.

[0538] The terminal's role is to notify each user and work device of the usage plan generated by the server. This notification allows individual users and devices to use resources and perform maintenance according to the schedule. Furthermore, feedback is collected after use and sent to the server, which is then reflected in the generation of the next plan.

[0539] As a concrete example, consider the use of tools on a factory production line. Based on past data, the server predicts the frequency of tool use and plans the necessary maintenance schedule. This plan is then communicated to workers via terminals. In this way, efficient factory operations are supported.

[0540] An example of a prompt to input into the generating AI model is, "Based on the frequency and conditions of use of this tool, please suggest the optimal maintenance schedule for the next week." Using this example, a concrete plan will be generated, making it easier to implement.

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

[0542] Step 1:

[0543] The server collects user behavior data and environmental data through sensors installed in residential and industrial environments, as well as through APIs on online platforms. Input data includes records of usage frequency and conditions, which are then aggregated on the server.

[0544] Step 2:

[0545] The server preprocesses the collected data and filters out redundant information. This process also includes data imputation and anomaly detection. The preprocessed data is then input into a machine learning model to forecast future demand. The output is a forecast showing demand patterns for specific days and times.

[0546] Step 3:

[0547] The server generates an optimal utilization plan for shared resources and manufacturing equipment based on demand forecasts. Specifically, it determines the timing of use and maintenance schedules for each resource. This plan is designed to maximize utilization efficiency and eliminate waste.

[0548] Step 4:

[0549] The server sends the generated usage plan to the terminal, notifying the user and the work equipment. This notification includes a specific usage schedule and maintenance information, allowing the user to adjust their actions accordingly.

[0550] Step 5:

[0551] The device collects user feedback after use. This feedback includes actual usage details and satisfaction with the plan, and is sent to the server.

[0552] Step 6:

[0553] The server uses feedback data to improve the accuracy of future demand forecasts and usage plans. This feedback process facilitates model improvement and plan optimization. The output leads to more refined future plans and an improved user experience.

[0554] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0555] This invention provides a system that incorporates an emotion engine to recognize users' emotions, with the aim of effectively utilizing shared resources within a residential environment. This system comprehensively analyzes residents' behavioral data and emotional states, creates and notifies them of an optimal utilization plan, thereby enabling a more fulfilling living experience. Specific embodiments are described below.

[0556] Data collection

[0557] The server collects various types of data from the living environment. This data includes physical behavioral data collected via sensors and emotional data collected by an emotion engine that recognizes the user's emotions. For example, it can use cameras and voice input devices to analyze the user's facial expressions and voice tone, and estimate their emotional state in real time.

[0558] Data Analysis

[0559] The collected data is preprocessed by the server and fed into a generative model. The generative model analyzes users' past behavioral patterns and emotional data to predict future demand. This analysis utilizes machine learning algorithms, taking into account what resources are needed when users are in specific emotional states.

[0560] Creating a Usage Plan

[0561] The server generates a usage plan based on demand forecasts and taking into account the user's emotional state. The plan includes resource allocation corresponding to emotional changes predicted by the emotion engine, such as recommending the use of relaxation facilities during stressful periods.

[0562] Notifications and reservations

[0563] The optimized usage plan is notified to the device, and the user can review the details. At this time, the notification is presented in a format tailored to the user's emotional state, allowing the user to intuitively accept the content. For example, if the emotional engine detects a high level of stress, it can simply present relaxation suggestions on the device.

[0564] Feedback Collection

[0565] After use, the terminal collects user feedback. This feedback reflects the user's experience and emotional state, and the server uses this to forecast future demand and generate usage plans, thereby improving the system.

[0566] Specific example

[0567] When a resident feels tired after work on a weekday, the server recognizes this state based on data from the emotion engine. It then generates a usage plan to reserve a relaxation area and notifies the user via a soothing voice tone. The user can then review the suggested plan on their device and smoothly enjoy their relaxation time. In this way, combining the emotion engine makes it possible to provide a more personalized service.

[0568] The following describes the processing flow.

[0569] Step 1:

[0570] The server utilizes sensors installed in the living environment and input devices from an emotion engine to collect behavioral and emotional data of residents in real time. For example, it monitors facial expressions through cameras and analyzes voice tone through voice analysis to obtain the user's current emotional state.

[0571] Step 2:

[0572] The server preprocesses the collected behavioral and sentiment data, removing noise and outliers to prepare it for generative models. This includes organizing the data chronologically and imputing missing values ​​as needed.

[0573] Step 3:

[0574] The server uses pre-processed data and leverages generative models to predict future user needs and emotional states. The generative models extract patterns from historical data and calculate what services residents will need and when.

[0575] Step 4:

[0576] The server automatically generates a shared resource utilization plan based on predicted demand and emotional state. This plan includes service options that take the user's emotions into account; for example, if high stress levels are predicted, it might suggest the use of relaxation facilities.

[0577] Step 5:

[0578] The device notifies the user of the generated usage plan and displays the content in a format appropriate to their emotional state. This notification is delivered through friendly voice guidance and an intuitive interface, making it easy for the user to accept and use.

[0579] Step 6:

[0580] Users can review the usage plan provided through their device and easily reserve their desired resources. Reservations are offered with an emotionally resonant approach, contributing to an improved user experience.

[0581] Step 7:

[0582] After use, the device collects feedback from the user and records information about their experience and emotional state. This feedback is sent to the server as important data.

[0583] Step 8:

[0584] The server continuously improves the system's accuracy by analyzing feedback data and incorporating it into future demand forecasts and usage plans. This improvement process enables the system to continue providing users with more optimized services.

[0585] (Example 2)

[0586] 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."

[0587] In modern living environments, there is a need to understand residents' emotional states and behavioral patterns in real time and to effectively allocate the most suitable resources accordingly. However, conventional systems have difficulty responding flexibly to such individual emotional states, resulting in decreased resident satisfaction. This invention aims to solve these problems and provide residents with a more personalized living experience.

[0588] 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.

[0589] In this invention, the server includes means for collecting operational information of the living environment, means for preprocessing the collected operational information and analyzing it using a generating AI model, and means for predicting demand based on the user's emotional state and past behavioral patterns. This makes it possible to generate an optimal utilization plan for shared resources and notify the user according to their status.

[0590] "Action information" refers to data related to the user's behavior and emotions, collected using sensors and devices within the living environment.

[0591] "Preprocessing" refers to the process of removing noise from collected motion information and preparing it for analysis.

[0592] A "generative AI model" refers to a machine learning technique used to predict future demand by analyzing users' behavioral patterns and emotional states from past data.

[0593] "Emotional state" refers to the user's current psychological and emotional state, and is generally analyzed from facial expressions, tone of voice, and actions.

[0594] "Demand forecasting" refers to the process of planning based on analyzed data, anticipating the resources and services that users will need in the future.

[0595] "Shared resources" refer to services and facilities that can be used jointly by users within a residential environment.

[0596] A "utilization plan" refers to a schedule of resource and service usage optimized for the user, created based on demand forecasts.

[0597] "Notification" refers to the act of communicating information to inform users about the generated usage plan.

[0598] "Feedback" refers to information provided to report users' experiences and feelings after using a product or service, and is used to improve future usage plans.

[0599] This invention is a system for effectively utilizing shared resources within a residential environment, generating demand forecasts and usage plans based on the user's emotional state. This system is primarily implemented via a server and terminals.

[0600] The server collects motion information from motion sensors, cameras, and voice input devices within the living environment. This allows for the acquisition of detailed data about the user's behavior patterns and emotional states. The collected motion information is preprocessed in a database, undergoing noise reduction and data normalization.

[0601] Next, the pre-processed data is analyzed by a generative AI model. This model has the ability to predict what resources will be needed in the future based on the user's past behavioral data and emotional state. In particular, the AI ​​model captures fluctuations in demand corresponding to specific emotional states and proposes an appropriate resource utilization plan.

[0602] The generated usage plan is customized based on the user's current emotional state and notified to the device. The device presents the information in a way that the user can intuitively understand, either visually or audibly. For example, if high stress levels are detected, a plan recommending the use of relaxation facilities will be presented.

[0603] After the usage plan is executed, users provide feedback via their terminals. This feedback is used to improve future demand forecasts and usage plans, and the server collects it and uses it as foundational data to enhance system efficiency.

[0604] As a concrete example, when a user feels fatigued after work, the server recognizes a high-stress state from the tone of their voice via a voice input device. Based on this information, it inputs prompt messages such as "Relaxation programs available immediately upon returning home" into an AI model, and suggests booking a relaxation area on the terminal. In this way, the user can easily accept a personalized plan and enjoy a comfortable living experience.

[0605] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0606] Step 1:

[0607] The server collects motion information from sensors and devices within the living environment. This motion information includes user facial expression data from cameras, motion data from motion sensors, and voice tone data from voice input devices. The input is raw data from sensors and devices, which is used as the basis for subsequent analysis.

[0608] Step 2:

[0609] The server preprocesses the collected behavioral data. Specifically, it removes noise and imputes missing values. The input is raw data, and the output is a clean and consistent dataset. This processing prepares the data to be suitable for generative AI models.

[0610] Step 3:

[0611] The server feeds pre-processed data into a generating AI model for analysis. This analysis uses past behavioral patterns and emotional states to predict future emotional states and demands. The input is pre-processed data, and the output is future demands and recommended shared resources. Specifically, it predicts what resources should be used and when, depending on the emotional state.

[0612] Step 4:

[0613] The server creates a usage plan based on the generated demand forecast. This plan includes the optimal allocation of resources according to the user's emotional state. The input is the analysis result, and the output is a customized usage plan. For example, relaxation facilities might be recommended for users experiencing high stress levels.

[0614] Step 5:

[0615] The server notifies the terminal of the generated usage plan. The terminal presents the plan to the user in an intuitive way using a visual interface and voice guidance. The input is the usage plan, and the output is the provision of information to the user. This allows the user to easily review the proposed plan.

[0616] Step 6:

[0617] Users utilize resources according to their usage plan and then provide feedback through their device. Input is information about the user's experience and feelings, and output is feedback data. This feedback is sent to the server and used to improve future demand forecasting and planning.

[0618] (Application Example 2)

[0619] 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."

[0620] In modern living environments, the means of providing personalized services tailored to an individual's emotional state are limited. In particular, resource and service utilization plans in residential environments are often based solely on simple availability without considering the user's emotional state, resulting in insufficient user satisfaction. It is necessary to address these challenges and realize the provision of more individualized services.

[0621] 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.

[0622] In this invention, the server includes means for collecting data on the living environment, means for analyzing the collected data using a generative model and predicting user demand, means for generating a plan for the use of shared resources and services based on the predicted demand and the user's emotional state, means for providing notifications in a format appropriate to the emotional state based on the plan, and means for detecting the user's emotional state and creating a service provision plan including personalized meal suggestions. This makes it possible to provide personalized resources and services according to the user's emotional state.

[0623] "Means of collecting data on the living environment" refers to systems that use sensors and devices installed within a residence to acquire information about physical behavior and emotional states.

[0624] "A means of analyzing collected data using generative models to predict user demand" refers to a process that utilizes machine learning algorithms with acquired data to analyze users' past behavioral patterns and emotions in order to predict future needs.

[0625] "Means for generating a plan for the use of shared resources and services based on predicted demand and the emotional state of users" refers to an algorithm that creates optimal resource allocation and service proposals based on emotion recognition and demand forecasting.

[0626] "Means of providing notifications in a format appropriate to the user's emotional state, based on the above usage plan" refers to a function that customizes the content and format of notifications according to the user's current emotional state, conveying information in a way that is intuitively understandable.

[0627] "A means of detecting the emotional state of users and creating a service provision plan that includes individualized meal suggestions" refers to a system that recognizes emotions in real time from facial expressions, voice, etc., and suggests meals and services that are appropriate to that state.

[0628] To realize this invention, a server, a terminal, and a user interface device are required. The system is designed to support the optimal use of resources and services in the living environment based on the user's emotional state.

[0629] The server collects diverse data, including the user's emotional state, from sensors and devices installed within the residence (e.g., cameras and microphones). This data is processed through image processing libraries (OpenCV) and speech analysis libraries to estimate emotions by analyzing facial expressions and voice tone in real time. Next, it analyzes historical data using machine learning libraries (TensorFlow or PyTorch) to predict user requests. Based on the predictive information generated by this series of data processing steps, the server creates a plan for the use of shared resources and services.

[0630] The terminal notifies the user of the usage plan generated by the server in a format that is appropriate to their emotional state. The notifications are designed to be intuitively understandable to the user; for example, when relaxation is needed, suggestions are made in a soft tone.

[0631] Users can easily reserve and confirm the use of suggested services and resources through their devices. Furthermore, feedback is collected after use, and the server uses this information to inform future planning.

[0632] For example, when a user returns home exhausted after a long day at work, the server recognizes this emotional state. It then suggests nutritious meals and relaxing environments through the user's device. The user can review these suggestions on the device and, if necessary, order delivery with a single click.

[0633] An example of a prompt message for a generative AI model is: "The user's current emotional state is fatigue. Please suggest a meal that is best suited to this state."

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The server collects the user's facial expressions and voice tone in real time through cameras and microphones installed in the living environment. Receiving this data as input, it performs facial expression analysis using image processing with the OpenCV library and tone analysis using a voice analysis library. This results in an output that estimates the user's emotional state.

[0637] Step 2:

[0638] The server takes estimated sentiment data and the user's past behavior history as input and analyzes the data using a machine learning library (TensorFlow or PyTorch) to predict the user's future demand. This provides predictive information necessary for creating future usage plans as output.

[0639] Step 3:

[0640] The server generates a usage plan using predicted demand information and sentiment states. The generated usage plan includes necessary resource allocations and service delivery plans. This plan is output to prepare data for the next step.

[0641] Step 4:

[0642] The device presents the usage plan received from the server to the user in a format optimized for them. For notifications, it considers the user's current emotional state and communicates information visually or audibly using voice assistants and screen displays. The user acknowledges this as input and selects an action as needed.

[0643] Step 5:

[0644] Users utilize the services and resources suggested through their devices. Reservations can be easily made through operations on the device. The user's choices are collected as data that is fed back into future predictions.

[0645] Step 6:

[0646] The server collects feedback from users via terminals after each service or resource is used. This feedback is then used as data for future demand forecasting and usage planning. This allows the system to be continuously improved and personalized service delivery to be enhanced.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] [Fourth Embodiment]

[0651] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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".

[0664] This invention is a system that utilizes data within a residential environment to efficiently manage shared resources. This system performs a series of actions including data collection, analysis, planning, notification, reservation, and feedback by residents. A specific embodiment of this system is described below.

[0665] Data collection

[0666] The server collects data related to the residents' lives, including purchase history, vehicle usage, and frequency of delivery service use. This data is obtained through sensors installed in the residential environment and APIs on relevant online platforms.

[0667] Data Analysis

[0668] The collected data is preprocessed by the server and input into a generative model. This model uses machine learning to predict, for example, the demand for shared resources on specific days of the week or at specific times of day.

[0669] Creating a Usage Plan

[0670] Based on the analysis results, the server automatically generates a plan that allows each resident to use shared resources most efficiently. This promotes usage that avoids peak hours and maximizes resource utilization.

[0671] Notifications and reservations

[0672] The generated usage plan is notified to each resident via their device. Based on the notification, users can easily make reservations according to their preferences. This process is provided through a user-friendly interface using an application.

[0673] Feedback Collection

[0674] After use, the terminal requests feedback from the resident. This feedback is sent to the server and used to improve the accuracy of future demand forecasts and plan generation.

[0675] Specific example

[0676] For example, if multiple residents need a car on a weekday, the server predicts peak demand based on past data. This allows it to create an optimal car-sharing schedule and propose it to each resident. Users can then make reservations based on the suggestions, ensuring smooth travel. After use, user feedback is collected and used to improve future schedules.

[0677] Thus, embodiments of the present invention enable the effective use of data on the living environment and the efficient use of shared resources among residents.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The server collects data on residents' behavior in real time through sensors and APIs installed in the living environment. This data includes, for example, records of vehicle entry and exit in parking lots and usage status of smart home appliances.

[0681] Step 2:

[0682] The server preprocesses the collected data and converts it into a format suitable for input to generative models. Preprocessing includes noise filtering and missing value imputation. This improves data quality and formats it for analysis.

[0683] Step 3:

[0684] The server inputs pre-processed data into a generative model to perform demand forecasting. This model learns from past patterns and has the ability to predict when demand for a particular service will be high, depending on the time of day or date.

[0685] Step 4:

[0686] The server automatically generates a resource utilization plan based on the prediction results. Specifically, it creates a schedule that considers resource allocation to avoid peak demand times and the optimal order of use.

[0687] Step 5:

[0688] The device notifies residents of the generated usage plan and displays the plan's contents. Notifications are sent via push notifications and email, and users can view the details on their device.

[0689] Step 6:

[0690] Users select their desired options from the notified usage plan and reserve resources through the terminal interface. Reservations can be completed with simple operations, taking user convenience into consideration.

[0691] Step 7:

[0692] The terminal collects feedback from users after they have used shared resources. This feedback reflects user opinions on the ease of use of the resources and the suitability of the plan, and will be used to improve the system in the future.

[0693] Step 8:

[0694] The server analyzes the collected feedback and incorporates it into demand forecasting models and usage plan generation to improve the system's accuracy. This enables more accurate forecasts and higher quality services in subsequent processing.

[0695] (Example 1)

[0696] 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".

[0697] In residential environments, efficient management and utilization of shared resources require accurately forecasting residents' needs and developing appropriate usage plans based on those forecasts. Furthermore, a system that allows users to intuitively make reservations, along with a mechanism to improve forecast accuracy through post-use feedback, is necessary. Conventional systems lack sufficient integration of these processes, resulting in inadequate resource management.

[0698] 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.

[0699] In this invention, the server includes means for collecting information about the living environment, means for preprocessing the collected information and analyzing it using a generative model to predict demand, and means for automatically generating a shared resource utilization plan based on the predicted demand. This makes it possible to accurately predict the demand of residents and create an efficient resource utilization plan.

[0700] "Residential environment" refers to the various conditions and circumstances related to daily life that occur within and around individual residences.

[0701] "Information" refers to a collection of numerical and string data related to residents' lives and behaviors, including data and records acquired through sensors and APIs.

[0702] "Means of collection" refers to systems for collecting information through devices installed in the living environment or via networks.

[0703] "Preprocessing" refers to a series of processes that remove noise and missing values ​​from raw data and convert it into a format that can be used for analysis.

[0704] A "generative model" refers to a computational process that uses machine learning algorithms to predict demand from collected information.

[0705] "Analysis" refers to the activity of interpreting collected information using statistical and machine learning methods to extract useful insights.

[0706] "Means of predicting demand" refers to functions that use generative models to predict the behavior and demands of residents under specific conditions.

[0707] "Shared resources" refer to facilities and services that residents can use together, such as vehicles and common facilities.

[0708] "Means for automatically generating utilization plans" refers to a system that independently creates an appropriate resource utilization schedule based on predicted demand.

[0709] "Notification" refers to the act of sending the generated usage plan as information to the resident's device to inform them.

[0710] "Feedback" refers to the process of collecting user experiences and opinions from residents after they have used a resource.

[0711] This system enables the effective collection and analysis of information in the living environment and the optimal management of shared resources. A specific embodiment is shown below.

[0712] The server first collects information about daily life from sensors installed in the living environment and APIs accessed via the internet. This information includes data on electricity consumption, vehicle usage, and delivery request history. A Python library is used as the software for efficiently extracting the data.

[0713] The collected data is preprocessed on the server. This includes normalizing the data using the Pandas library and filtering outliers. This preprocessed information is then input into a generative AI model using machine learning algorithms. The generative AI model analyzes residents' behavior patterns and performs demand forecasting.

[0714] Based on demand forecasts, the server automatically generates a shared resource usage plan. Because this plan is tailored to each resident, efficient resource utilization is possible. This usage plan is notified to residents via their devices. The devices utilize a smartphone app to inform users of their schedules and enable intuitive reservations.

[0715] After use, users provide feedback via their device. This feedback is sent to the server and used to improve the accuracy of the entire system by informing future demand forecasts and usage plans.

[0716] As a concrete example, if multiple residents want to use a vehicle on a weekday, the server could predict demand based on past data and suggest the optimal usage time for each user. Users could then easily make a reservation based on this and provide feedback after use. Through this process, efficient management of shared resources in the residential environment can be achieved.

[0717] An example of a prompt to a generating AI model is, "Predict the usage preferences of residents on a specific day next week and suggest the optimal usage schedule." This prompt allows the model to predict specific actions and provide information useful for planning.

[0718] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0719] Step 1:

[0720] The server collects information from sensors installed in the residential environment and from APIs accessed via the internet. This input includes electricity usage data from smart meters and purchase history from online platforms. The server receives this information and stores it in a database. Specifically, the server automatically sends API requests at a specified time during the night to retrieve the latest data.

[0721] Step 2:

[0722] The server preprocesses the collected data. Unfiltered raw data is provided as input. The server cleanses the data using the Python Pandas library to remove noise and outliers. The normalized data is prepared as output and input into the generative AI model. In operation, the server applies an outlier detection algorithm to each dataset to ensure data integrity.

[0723] Step 3:

[0724] The server inputs pre-processed data into a generating AI model to perform demand forecasting. The input is a cleansed dataset, and the output is the forecast result. The model uses machine learning algorithms to calculate the demand for shared resources on specified days and times. Specifically, the server runs the forecasting model weekly to analyze demand trends.

[0725] Step 4:

[0726] The server automatically generates usage plans using prediction results. The input is the prediction result, and the output is a detailed usage schedule. The server constructs a plan that avoids peak demand and enables efficient resource allocation. In operation, the server optimizes each resident's schedule using an algorithm to achieve maximum efficiency.

[0727] Step 5:

[0728] The terminal notifies residents of the usage plan generated by the server. The input is the usage plan data, and the output is notification information for the user. The terminal sends push notifications to residents via a smartphone app. Specifically, the terminal provides an interface that prompts the notified user to take action.

[0729] Step 6:

[0730] Users make reservations based on the information they receive in the notification. The input is the usage plan provided in the notification, and the output is the reservation confirmation information. Users select the time and resources provided on the app on their device to complete the reservation. Specifically, users use the app's calendar function to quickly make the best reservation to fit their schedule.

[0731] Step 7:

[0732] The device collects feedback from users after use. The input is the user's experience and evaluation, and the output is feedback data. The device displays an evaluation window within the app, soliciting user feedback. Specifically, a pop-up appears immediately after use, creating an environment where users can quickly provide evaluations.

[0733] Step 8:

[0734] The server analyzes the collected feedback and uses it to generate future demand forecasts and plans. The input is feedback data, and the output is an improved forecasting model. The server scrutinizes the feedback and uses it to adjust the parameters of the generated AI model. Specifically, the server repeatedly trains the model based on new feedback data to improve forecasting accuracy.

[0735] (Application Example 1)

[0736] 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".

[0737] In modern residential and industrial environments, maximizing the efficiency of shared resources and manufacturing equipment is essential. However, accurately forecasting individual demand and developing appropriate utilization plans is challenging, leading to inefficient resource use and reduced productivity due to unnecessary maintenance. Furthermore, a lack of proper notification and feedback processing to users and equipment hinders improvements in the accuracy of future utilization planning.

[0738] 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.

[0739] In this invention, the server includes means for collecting data within residential and industrial environments, means for analyzing the collected data using a machine learning generative model to predict demand, and means for generating a utilization plan for shared resources and manufacturing equipment based on the predicted demand. This enables the efficient utilization of shared resources and manufacturing equipment and the implementation of an appropriate maintenance plan.

[0740] "Residential and industrial environments" refer to the collection of physical structures and surrounding facilities where people live and work, and are places where data for improving quality of life and productivity is collected.

[0741] "Means of data collection" refers to the process of acquiring information about the behavior of residents and workers, usage patterns, environmental conditions, etc., using sensors and online platforms.

[0742] A "machine learning generative model" is a system that combines algorithms used to analyze collected data and predict future demand and usage patterns.

[0743] "Methods for predicting demand" refer to methods that estimate future resource usage and needs based on historical data and machine learning results.

[0744] "Means for generating utilization plans for shared resources and manufacturing equipment" refers to a process that automatically creates optimal resource utilization and equipment operating schedules in response to predicted demand.

[0745] "Appropriate notifications and feedback" refer to means of information exchange that provide necessary information to users and work equipment based on the generated usage plans and results, and to extract areas for improvement necessary for creating the next plan.

[0746] The system implementing this invention is intended to achieve efficient resource management in residential and industrial environments. Servers, terminals, and users are the main components.

[0747] The server first collects data on resident and worker behavior, usage patterns, and environmental conditions through various sensors and online platforms. This data is preprocessed on the server and input into a machine learning platform (e.g., TensorFlow or PyTorch) using Python. A machine learning generative model analyzes this data to predict future demand and usage patterns. Based on the results, an optimal utilization plan for shared resources and manufacturing equipment is automatically created.

[0748] The terminal's role is to notify each user and work device of the usage plan generated by the server. This notification allows individual users and devices to use resources and perform maintenance according to the schedule. Furthermore, feedback is collected after use and sent to the server, which is then reflected in the generation of the next plan.

[0749] As a concrete example, consider the use of tools on a factory production line. Based on past data, the server predicts the frequency of tool use and plans the necessary maintenance schedule. This plan is then communicated to workers via terminals. In this way, efficient factory operations are supported.

[0750] An example of a prompt to input into the generating AI model is, "Based on the frequency and conditions of use of this tool, please suggest the optimal maintenance schedule for the next week." Using this example, a concrete plan will be generated, making it easier to implement.

[0751] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0752] Step 1:

[0753] The server collects user behavior data and environmental data through sensors installed in residential and industrial environments, as well as through APIs on online platforms. Input data includes records of usage frequency and conditions, which are then aggregated on the server.

[0754] Step 2:

[0755] The server preprocesses the collected data and filters out redundant information. This process also includes data imputation and anomaly detection. The preprocessed data is then input into a machine learning model to forecast future demand. The output is a forecast showing demand patterns for specific days and times.

[0756] Step 3:

[0757] The server generates an optimal utilization plan for shared resources and manufacturing equipment based on demand forecasts. Specifically, it determines the timing of use and maintenance schedules for each resource. This plan is designed to maximize utilization efficiency and eliminate waste.

[0758] Step 4:

[0759] The server sends the generated usage plan to the terminal, notifying the user and the work equipment. This notification includes a specific usage schedule and maintenance information, allowing the user to adjust their actions accordingly.

[0760] Step 5:

[0761] The device collects user feedback after use. This feedback includes actual usage details and satisfaction with the plan, and is sent to the server.

[0762] Step 6:

[0763] The server uses feedback data to improve the accuracy of future demand forecasts and usage plans. This feedback process facilitates model improvement and plan optimization. The output leads to more refined future plans and an improved user experience.

[0764] 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.

[0765] This invention provides a system that incorporates an emotion engine to recognize users' emotions, with the aim of effectively utilizing shared resources within a residential environment. This system comprehensively analyzes residents' behavioral data and emotional states, creates and notifies them of an optimal utilization plan, thereby enabling a more fulfilling living experience. Specific embodiments are described below.

[0766] Data collection

[0767] The server collects various types of data from the living environment. This data includes physical behavioral data collected via sensors and emotional data collected by an emotion engine that recognizes the user's emotions. For example, it can use cameras and voice input devices to analyze the user's facial expressions and voice tone, and estimate their emotional state in real time.

[0768] Data Analysis

[0769] The collected data is preprocessed by the server and fed into a generative model. The generative model analyzes users' past behavioral patterns and emotional data to predict future demand. This analysis utilizes machine learning algorithms, taking into account what resources are needed when users are in specific emotional states.

[0770] Creating a Usage Plan

[0771] The server generates a usage plan based on demand forecasts and taking into account the user's emotional state. The plan includes resource allocation corresponding to emotional changes predicted by the emotion engine, such as recommending the use of relaxation facilities during stressful periods.

[0772] Notifications and reservations

[0773] The optimized usage plan is notified to the device, and the user can review the details. At this time, the notification is presented in a format tailored to the user's emotional state, allowing the user to intuitively accept the content. For example, if the emotional engine detects a high level of stress, it can simply present relaxation suggestions on the device.

[0774] Feedback Collection

[0775] After use, the terminal collects user feedback. This feedback reflects the user's experience and emotional state, and the server uses this to forecast future demand and generate usage plans, thereby improving the system.

[0776] Specific example

[0777] When a resident feels tired after work on a weekday, the server recognizes this state based on data from the emotion engine. It then generates a usage plan to reserve a relaxation area and notifies the user via a soothing voice tone. The user can then review the suggested plan on their device and smoothly enjoy their relaxation time. In this way, combining the emotion engine makes it possible to provide a more personalized service.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The server utilizes sensors installed in the living environment and input devices from an emotion engine to collect behavioral and emotional data of residents in real time. For example, it monitors facial expressions through cameras and analyzes voice tone through voice analysis to obtain the user's current emotional state.

[0781] Step 2:

[0782] The server preprocesses the collected behavioral and sentiment data, removing noise and outliers to prepare it for generative models. This includes organizing the data chronologically and imputing missing values ​​as needed.

[0783] Step 3:

[0784] The server uses pre-processed data and leverages generative models to predict future user needs and emotional states. The generative models extract patterns from historical data and calculate what services residents will need and when.

[0785] Step 4:

[0786] The server automatically generates a shared resource utilization plan based on predicted demand and emotional state. This plan includes service options that take the user's emotions into account; for example, if high stress levels are predicted, it might suggest the use of relaxation facilities.

[0787] Step 5:

[0788] The device notifies the user of the generated usage plan and displays the content in a format appropriate to their emotional state. This notification is delivered through friendly voice guidance and an intuitive interface, making it easy for the user to accept and use.

[0789] Step 6:

[0790] Users can review the usage plan provided through their device and easily reserve their desired resources. Reservations are offered with an emotionally resonant approach, contributing to an improved user experience.

[0791] Step 7:

[0792] After use, the device collects feedback from the user and records information about their experience and emotional state. This feedback is sent to the server as important data.

[0793] Step 8:

[0794] The server continuously improves the system's accuracy by analyzing feedback data and incorporating it into future demand forecasts and usage plans. This improvement process enables the system to continue providing users with more optimized services.

[0795] (Example 2)

[0796] 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".

[0797] In modern living environments, there is a need to understand residents' emotional states and behavioral patterns in real time and to effectively allocate the most suitable resources accordingly. However, conventional systems have difficulty responding flexibly to such individual emotional states, resulting in decreased resident satisfaction. This invention aims to solve these problems and provide residents with a more personalized living experience.

[0798] 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.

[0799] In this invention, the server includes means for collecting operational information of the living environment, means for preprocessing the collected operational information and analyzing it using a generating AI model, and means for predicting demand based on the user's emotional state and past behavioral patterns. This makes it possible to generate an optimal utilization plan for shared resources and notify the user according to their status.

[0800] "Action information" refers to data related to the user's behavior and emotions, collected using sensors and devices within the living environment.

[0801] "Preprocessing" refers to the process of removing noise from collected motion information and preparing it for analysis.

[0802] A "generative AI model" refers to a machine learning technique used to predict future demand by analyzing users' behavioral patterns and emotional states from past data.

[0803] "Emotional state" refers to the user's current psychological and emotional state, and is generally analyzed from facial expressions, tone of voice, and actions.

[0804] "Demand forecasting" refers to the process of planning based on analyzed data, anticipating the resources and services that users will need in the future.

[0805] "Shared resources" refer to services and facilities that can be used jointly by users within a residential environment.

[0806] A "utilization plan" refers to a schedule of resource and service usage optimized for the user, created based on demand forecasts.

[0807] "Notification" refers to the act of communicating information to inform users about the generated usage plan.

[0808] "Feedback" refers to information provided to report users' experiences and feelings after using a product or service, and is used to improve future usage plans.

[0809] This invention is a system for effectively utilizing shared resources within a residential environment, generating demand forecasts and usage plans based on the user's emotional state. This system is primarily implemented via a server and terminals.

[0810] The server collects motion information from motion sensors, cameras, and voice input devices within the living environment. This allows for the acquisition of detailed data about the user's behavior patterns and emotional states. The collected motion information is preprocessed in a database, undergoing noise reduction and data normalization.

[0811] Next, the pre-processed data is analyzed by a generative AI model. This model has the ability to predict what resources will be needed in the future based on the user's past behavioral data and emotional state. In particular, the AI ​​model captures fluctuations in demand corresponding to specific emotional states and proposes an appropriate resource utilization plan.

[0812] The generated usage plan is customized based on the user's current emotional state and notified to the device. The device presents the information in a way that the user can intuitively understand, either visually or audibly. For example, if high stress levels are detected, a plan recommending the use of relaxation facilities will be presented.

[0813] After the usage plan is executed, users provide feedback via their terminals. This feedback is used to improve future demand forecasts and usage plans, and the server collects it and uses it as foundational data to enhance system efficiency.

[0814] As a concrete example, when a user feels fatigued after work, the server recognizes a high-stress state from the tone of their voice via a voice input device. Based on this information, it inputs prompt messages such as "Relaxation programs available immediately upon returning home" into an AI model, and suggests booking a relaxation area on the terminal. In this way, the user can easily accept a personalized plan and enjoy a comfortable living experience.

[0815] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0816] Step 1:

[0817] The server collects motion information from sensors and devices within the living environment. This motion information includes user facial expression data from cameras, motion data from motion sensors, and voice tone data from voice input devices. The input is raw data from sensors and devices, which is used as the basis for subsequent analysis.

[0818] Step 2:

[0819] The server preprocesses the collected behavioral data. Specifically, it removes noise and imputes missing values. The input is raw data, and the output is a clean and consistent dataset. This processing prepares the data to be suitable for generative AI models.

[0820] Step 3:

[0821] The server feeds pre-processed data into a generating AI model for analysis. This analysis uses past behavioral patterns and emotional states to predict future emotional states and demands. The input is pre-processed data, and the output is future demands and recommended shared resources. Specifically, it predicts what resources should be used and when, depending on the emotional state.

[0822] Step 4:

[0823] The server creates a usage plan based on the generated demand forecast. This plan includes the optimal allocation of resources according to the user's emotional state. The input is the analysis result, and the output is a customized usage plan. For example, relaxation facilities might be recommended for users experiencing high stress levels.

[0824] Step 5:

[0825] The server notifies the terminal of the generated usage plan. The terminal presents the plan to the user in an intuitive way using a visual interface and voice guidance. The input is the usage plan, and the output is the provision of information to the user. This allows the user to easily review the proposed plan.

[0826] Step 6:

[0827] Users utilize resources according to their usage plan and then provide feedback through their device. Input is information about the user's experience and feelings, and output is feedback data. This feedback is sent to the server and used to improve future demand forecasting and planning.

[0828] (Application Example 2)

[0829] 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".

[0830] In modern living environments, the means of providing personalized services tailored to an individual's emotional state are limited. In particular, resource and service utilization plans in residential environments are often based solely on simple availability without considering the user's emotional state, resulting in insufficient user satisfaction. It is necessary to address these challenges and realize the provision of more individualized services.

[0831] 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.

[0832] In this invention, the server includes means for collecting data on the living environment, means for analyzing the collected data using a generative model and predicting user demand, means for generating a plan for the use of shared resources and services based on the predicted demand and the user's emotional state, means for providing notifications in a format appropriate to the emotional state based on the plan, and means for detecting the user's emotional state and creating a service provision plan including personalized meal suggestions. This makes it possible to provide personalized resources and services according to the user's emotional state.

[0833] "Means of collecting data on the living environment" refers to systems that use sensors and devices installed within a residence to acquire information about physical behavior and emotional states.

[0834] "A means of analyzing collected data using generative models to predict user demand" refers to a process that utilizes machine learning algorithms with acquired data to analyze users' past behavioral patterns and emotions in order to predict future needs.

[0835] "Means for generating a plan for the use of shared resources and services based on predicted demand and the emotional state of users" refers to an algorithm that creates optimal resource allocation and service proposals based on emotion recognition and demand forecasting.

[0836] "Means of providing notifications in a format appropriate to the user's emotional state, based on the above usage plan" refers to a function that customizes the content and format of notifications according to the user's current emotional state, conveying information in a way that is intuitively understandable.

[0837] "A means of detecting the emotional state of users and creating a service provision plan that includes individualized meal suggestions" refers to a system that recognizes emotions in real time from facial expressions, voice, etc., and suggests meals and services that are appropriate to that state.

[0838] To realize this invention, a server, a terminal, and a user interface device are required. The system is designed to support the optimal use of resources and services in the living environment based on the user's emotional state.

[0839] The server collects diverse data, including the user's emotional state, from sensors and devices installed within the residence (e.g., cameras and microphones). This data is processed through image processing libraries (OpenCV) and speech analysis libraries to estimate emotions by analyzing facial expressions and voice tone in real time. Next, it analyzes historical data using machine learning libraries (TensorFlow or PyTorch) to predict user requests. Based on the predictive information generated by this series of data processing steps, the server creates a plan for the use of shared resources and services.

[0840] The terminal notifies the user of the usage plan generated by the server in a format that is appropriate to their emotional state. The notifications are designed to be intuitively understandable to the user; for example, when relaxation is needed, suggestions are made in a soft tone.

[0841] Users can easily reserve and confirm the use of suggested services and resources through their devices. Furthermore, feedback is collected after use, and the server uses this information to inform future planning.

[0842] For example, when a user returns home exhausted after a long day at work, the server recognizes this emotional state. It then suggests nutritious meals and relaxing environments through the user's device. The user can review these suggestions on the device and, if necessary, order delivery with a single click.

[0843] An example of a prompt message for a generative AI model is: "The user's current emotional state is fatigue. Please suggest a meal that is best suited to this state."

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The server collects the user's facial expressions and voice tone in real time through cameras and microphones installed in the living environment. Receiving this data as input, it performs facial expression analysis using image processing with the OpenCV library and tone analysis using a voice analysis library. This results in an output that estimates the user's emotional state.

[0847] Step 2:

[0848] The server takes estimated sentiment data and the user's past behavior history as input and analyzes the data using a machine learning library (TensorFlow or PyTorch) to predict the user's future demand. This provides predictive information necessary for creating future usage plans as output.

[0849] Step 3:

[0850] The server generates a usage plan using predicted demand information and sentiment states. The generated usage plan includes necessary resource allocations and service delivery plans. This plan is output to prepare data for the next step.

[0851] Step 4:

[0852] The device presents the usage plan received from the server to the user in a format optimized for them. For notifications, it considers the user's current emotional state and communicates information visually or audibly using voice assistants and screen displays. The user acknowledges this as input and selects an action as needed.

[0853] Step 5:

[0854] Users utilize the services and resources suggested through their devices. Reservations can be easily made through operations on the device. The user's choices are collected as data that is fed back into future predictions.

[0855] Step 6:

[0856] The server collects feedback from users via terminals after each service or resource is used. This feedback is then used as data for future demand forecasting and usage planning. This allows the system to be continuously improved and personalized service delivery to be enhanced.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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."

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] Means of collecting data on the living environment,

[0881] A means of analyzing collected data using a generative model to predict user demand,

[0882] A means for generating a shared resource utilization plan based on predicted demand,

[0883] Based on the above usage plan, means of notifying users,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, which allows users to reserve the use of shared resources.

[0887] (Claim 3)

[0888] The system according to claim 1, which collects feedback on usage plans and utilizes that feedback in generating future demand forecasts and usage plans.

[0889] "Example 1"

[0890] (Claim 1)

[0891] Means of collecting information on the living environment,

[0892] A means for preprocessing collected information and analyzing it using a generative model to predict demand,

[0893] A means for automatically generating a shared resource utilization plan based on predicted demand,

[0894] A means of notifying users of the generated usage plan and enabling users to make reservations as desired,

[0895] A means of collecting feedback after use and utilizing that feedback to improve the accuracy of demand forecasting and usage plan generation,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, which allows users to intuitively reserve the use of shared resources via a terminal.

[0899] (Claim 3)

[0900] The system according to claim 1, which performs demand forecasting using a generative model based on a machine learning algorithm.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] Means for collecting data within residential and industrial environments,

[0904] A means of predicting demand by analyzing collected data using machine learning generative models,

[0905] Means for generating a utilization plan for shared resources and manufacturing equipment based on predicted demand,

[0906] A means of notifying users and work equipment based on the above usage plan,

[0907] A means of collecting tool usage frequency and maintenance information and feeding it back into usage planning,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, wherein users can reserve the use of shared resources and manufacturing equipment.

[0911] (Claim 3)

[0912] The system according to claim 1, which collects feedback on usage plans and tool maintenance plans and utilizes that feedback to generate future demand forecasts and usage plans.

[0913] "Example 2 of combining an emotion engine"

[0914] (Claim 1)

[0915] Means for collecting operational information of the living environment,

[0916] A means for preprocessing collected motion information and analyzing it using a generated AI model,

[0917] A means of predicting demand based on the emotional state and past behavioral patterns of users,

[0918] A means for generating an optimal utilization plan for shared resources based on predicted demand and emotional states,

[0919] A means of notifying users of the generated usage plan according to their status,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, which allows users to reserve the use of shared resources based on usage notifications.

[0923] (Claim 3)

[0924] The system according to claim 1, which collects feedback on the user's experience and emotional state regarding the usage plan, and reflects that feedback in the generation of the next demand forecast and usage plan.

[0925] "Application example 2 when combining with an emotional engine"

[0926] (Claim 1)

[0927] Means of collecting data on the living environment,

[0928] A means of analyzing collected data using a generative model to predict user demand,

[0929] A means for generating a plan for the use of shared resources and services based on predicted demand and the emotional state of users,

[0930] Based on the above usage plan, means of providing notifications in a format appropriate to the emotional state,

[0931] A means for detecting the emotional state of users and creating a service delivery plan that includes individualized meal suggestions,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, which allows users to reserve the use of shared resources and services.

[0935] (Claim 3)

[0936] The system according to claim 1, which collects feedback on usage plans and utilizes that feedback in generating future demand forecasts and usage plans. [Explanation of symbols]

[0937] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting data on the living environment, A means of analyzing collected data using a generative model to predict user demand, A means for generating a shared resource utilization plan based on predicted demand, Based on the above usage plan, means of notifying users, A system that includes this.

2. The system according to claim 1, which allows users to reserve the use of shared resources.

3. The system according to claim 1, which collects feedback on usage plans and utilizes that feedback in generating future demand forecasts and usage plans.

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