system

Abstract

We provide the system. [Solution] A means for collecting signal data obtained from a wireless communication network, An analysis means for processing the aforementioned signal data to estimate the movement of surrounding objects and people, A means for detecting an event and generating a notification based on the aforementioned analysis results, Means for transmitting the aforementioned notification to the user device, When an anomaly is detected, a means with an emergency contact function, A means of providing an information board that allows users to visualize their activity patterns, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional physical sensor technology, it is necessary to install many sensors to detect the movement of objects and people in the environment, resulting in high costs. In addition, it is easily affected by the installation location and external environment, and there may be concerns about privacy. Furthermore, these sensors often require direct line of sight or contact, and there are problems in detection in environments with darkness or obstacles.

Means for Solving the Problems

[0005] This invention provides a system for non-contact detection of the movement of objects and people in an environment using signal data received from a wireless network. The system includes means for collecting signal data, means for analyzing changes in the signal using a machine learning algorithm, means for detecting events and generating notifications based on the analysis results, and means for transmitting notifications to a user device. This configuration enables high-precision motion detection at low cost, protects privacy, and allows for motion detection in darkness and through obstructions.

[0006] A "wireless network" is a network that uses radio waves to communicate data, and is a general term for technologies that enable information to be sent and received between multiple devices without the need for cable connections.

[0007] "Signal data" refers to information that describes the physical characteristics of data transmitted and received over a wireless network, such as signal strength, phase, and delay.

[0008] A "machine learning algorithm" refers to a computational method that allows computers to learn patterns and relationships from empirical data and make predictions and judgments.

[0009] "Analysis" is the process of identifying specific patterns or characteristics based on collected data and deriving meaningful information from them.

[0010] An "event" refers to a specific action or state change detected within a system, and is distinguished by whether or not it matches pre-defined conditions.

[0011] "Notifications" refer to messages or alerts that provide information to the user based on detected events.

[0012] A "user device" is an electronic device owned by an individual and used for receiving and displaying information, and generally includes smartphones, tablets, and computers. [Brief explanation of the drawing]

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

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

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

[0016] 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.

[0017] 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.

[0018] 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.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system that uses signals from a wireless network to non-contactually detect the movement of objects and people in an environment. The system includes a server and user devices, and performs signal data collection, analysis, event detection, and notification transmission.

[0035] System Embodiment

[0036] Data collection of signals

[0037] The server obtains signal data from the WiFi router that communicates with the terminal via the wireless network. This data includes signal strength, phase changes, and delay information.

[0038] Analysis of signal data

[0039] The server uses machine learning algorithms to analyze changes in signal data. This analysis identifies the movement of objects and people within the environment. For example, it can capture changes in reflections as a person moves through a room and identify their movement.

[0040] Event detection and notification

[0041] The server identifies specific patterns based on the analysis results and detects abnormal movements or changes in circumstances as events. When an event is detected, the server sends a notification to the user device. This notification includes the type of event (e.g., intrusion detection, fall of an elderly person, etc.), the location where it occurred, and the estimated severity.

[0042] Notifications and actions for users

[0043] Users receive notifications through a dedicated application. Within the application, users can view detailed information and take appropriate action as needed. For example, they can review surveillance camera footage for home security or contact care services.

[0044] Specific example

[0045] For example, if this system is implemented in a household where an elderly person lives alone, the server will continuously monitor the WiFi signal in the living space and, upon detecting a pattern that differs from normal movement (e.g., a sudden fall), will immediately send an alert to the user's device. The user (family member or caregiver) who receives the notification can check the details through the app and, if necessary, quickly rush to the scene. In this way, the present invention makes it possible to provide a highly accurate and low-cost motion detection system using a wireless network.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects signal data from WiFi routers within the wireless network. This includes signal strength, channel status information (CSI), and signal phase change data. This information is continuously monitored in real time.

[0049] Step 2:

[0050] The server preprocesses the collected signal data. This involves noise reduction and data normalization, preparing the data for analysis. Once the data is ready, it is sent to the next analysis stage.

[0051] Step 3:

[0052] The server inputs pre-processed data into a machine learning algorithm. This algorithm utilizes a pre-trained model to identify movement within the environment from changes in signals. The model analyzes new data based on known patterns and extracts movement characteristics.

[0053] Step 4:

[0054] The server detects specific events based on the results of algorithmic analysis. For example, if a change exceeding a certain level is detected, it determines whether it indicates the movement of a person or an object. Based on this result, the importance of the detected event is evaluated.

[0055] Step 5:

[0056] The server generates notifications based on detected events. These notifications include the event type, location, timestamp, and severity level, allowing users to quickly understand the situation.

[0057] Step 6:

[0058] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other interface installed by the user.

[0059] Step 7:

[0060] Users check notifications received on their devices and decide on appropriate actions as needed. For example, in the case of intrusion detection, they might check their home security system, or in the case of an elderly monitoring system, they might contact care services.

[0061] (Example 1)

[0062] 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."

[0063] In modern safety technology, accurately and non-contactually detecting movements and states within a space and rapidly transmitting that information remains a challenging task. In particular, areas such as personal safety and home security demand immediacy and accuracy, but conventional technologies fail to adequately meet these requirements. Therefore, there is a need for technologies that achieve highly accurate motion detection and rapid information transmission.

[0064] 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.

[0065] In this invention, the server includes means for collecting signal information from a wireless communication network via an information terminal, analysis means for analyzing the signal information to estimate the movement of objects or individuals in space, and means for detecting events and generating information based on the analysis results. This makes it possible to detect movements and states in space with high accuracy without contact and to quickly transmit necessary information to the user.

[0066] An "information terminal" is a general term for a device that collects signal information and processes data via a wireless communication network.

[0067] A "wireless communication network" refers to a network that uses radio waves to send and receive information, and is a means of communication that does not require a wired connection.

[0068] "Signal information" refers to characteristic data of radio waves acquired via a wireless communication network, including signal strength, phase change, and delay information.

[0069] "Analysis means" refers to techniques or algorithms for processing collected signal information and estimating the movement of objects or individuals in space.

[0070] "Objects or individuals" refer to items or people that are the target of detection within a space, and are the subjects of analysis necessary to estimate their actions and states.

[0071] "Event detection" refers to the process of recognizing specific actions or changes in circumstances based on analyzed signal information.

[0072] "Information creation" refers to the act of generating useful data and notifications for users based on detected events.

[0073] "User" refers to an individual or organization that receives the information or services provided by this invention.

[0074] This invention is a system for non-contact detection of the movement of objects or individuals in space using signal information from a wireless communication network. The system includes an information terminal (e.g., a smartphone or dedicated device) and a server. The information terminal collects signal information from the wireless communication network and transmits it to the server. The server analyzes the signal information using a machine learning algorithm to detect changes in the environment. The signal information includes signal strength, phase change, and delay information.

[0075] Software such as Python or TENSORFLOW® is used for the analysis process. The server analyzes the temporal changes in signals to identify specific behavioral patterns (e.g., people walking or furniture moving). If the analysis detects abnormal behavior or situations (e.g., unauthorized entry, potential falls), event information is generated.

[0076] This generated information, along with its importance and location, is sent to the information terminal. The user receives the notification through an application and checks the situation. This application allows users to view detailed information and take additional actions (e.g., check surveillance cameras or make an emergency call).

[0077] As a concrete example, this system could be implemented in a home where elderly people live. The server continuously monitors the Wi-Fi signal in the living space, and if it detects a pattern that deviates from normal movement (e.g., a sudden fall), it immediately sends an alert to the user's device. The user (e.g., family member or caregiver) can check the situation through the application and take action to provide assistance if necessary.

[0078] An example of a prompt for a generative AI model is: "Describe a system that uses wireless network signals to detect the movement of people in an environment. As a specific scenario, give an example of its use in a home where elderly people live, and describe in detail the notification process in that case." This prompt instructs the AI ​​to provide more detailed information about how the system works and its applications.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects signal information from the wireless communication network via an information terminal. The input to this step is radio wave information emitted from the wireless communication network, and the output is a dataset of signal information. Specifically, the server periodically acquires signal strength, phase, and delay information and stores it in a database.

[0082] Step 2:

[0083] The server analyzes the collected signal information using machine learning algorithms. The input to this step is a dataset of collected signal information, and the output is the analysis results showing the behavioral patterns in space. Specifically, the server uses Python or TensorFlow to analyze signal intensity fluctuations and phase changes to identify specific patterns and anomalies.

[0084] Step 3:

[0085] The server detects specific events based on the analysis results. The input for this step is the analyzed behavioral pattern, and the output is the detected event information. Specifically, the server compares it to a pre-configured threshold, logs any anomalies as events, and sets a flag.

[0086] Step 4:

[0087] The server generates information based on detected event information and sends a notification to the user's information terminal. The input for this step is the detected event information, and the output is the notification message to the user. Specifically, the server generates the content of the notification message and sends it to the user's terminal via push notification.

[0088] Step 5:

[0089] The user receives notifications through an application on their device and takes the necessary actions. The input for this step is the notification message sent from the server, and the output is the user's action. Specifically, the user opens the app to check the notification content and, depending on the situation, can check the surveillance camera footage or take emergency action.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] To ensure elderly people live safely at home, there is a need for a system that can detect falls and abnormal behavior in real time and respond quickly. Furthermore, a challenge is to provide an efficient monitoring system that allows family members and caregivers to remotely understand the elderly person's activity patterns and respond immediately in emergencies.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for collecting signal data acquired from a wireless communication network, analysis means for processing the signal data to estimate the movement of surrounding objects and people, and means for detecting events and generating notifications based on the analysis results. This makes it possible to quickly detect abnormalities in elderly people and send necessary notifications.

[0095] A "wireless communication network" is a communication system that uses wireless technology to send and receive data.

[0096] "Signal data" refers to a collection of information acquired through wireless communication, and by analyzing its changes, it is possible to estimate the behavior within the environment.

[0097] "Analysis means" refers to the function of a system that includes algorithms and programs for processing signal data to estimate the movement of surrounding objects and people.

[0098] An "event" refers to a specific phenomenon or occurrence detected based on the actions or changes of the surrounding environment.

[0099] A "notification" is information generated based on detected events, and it is a message intended to convey that information to the user.

[0100] A "user device" is an electronic device used to receive and display notifications, such as a smartphone.

[0101] "Activity patterns" refer to the tendencies in behaviors and actions that a particular individual exhibits in their daily life.

[0102] An "information board" is an interface or dashboard used to visually display data and allow users to understand the situation.

[0103] This system collects and analyzes signal data from wireless communication networks to detect abnormal behavior in elderly individuals. The server collects signal data obtained through a typical Wi-Fi router and analyzes the data using a machine learning algorithm. This machine learning algorithm is implemented using TensorFlow and models signal changes to accurately predict behavior. Based on the analysis results, if an abnormal event occurs, the server uses Firebase to send a real-time notification to the user's device.

[0104] A device, such as a smartphone, displays received notifications to the user and provides an information board to understand the activity patterns of elderly individuals. This information board visualizes past data, making it easy to check behavioral trends in daily life. It also includes an emergency contact function to enable a quick response in emergencies.

[0105] As a concrete example, if an unusual walking pattern is detected when an elderly person goes to the toilet at night, an alert is immediately sent to the family, who can then check the situation through the app. This process provides effective guidance for optimizing the system's operation by inputting pre-prepared prompt messages into a generating AI model. The prompt message used is: "Design an AI model that analyzes the daily movement patterns of elderly people and detects abnormal movements. Refer to the example that utilizes WiFi signals as an anomaly detection system." This system operates by combining a WiFi router and a smartphone as hardware, and TensorFlow and Firebase as software.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server collects signal data from the WiFi router in real time. It takes WiFi signal strength, phase changes, and delay information as input and stores this data for analysis.

[0109] Step 2:

[0110] The server analyzes the collected signal data using a machine learning algorithm based on TensorFlow. Using the signal data collected in the previous step as input, it models the changes in the data and estimates the behavioral patterns within the environment. This process yields the estimated behavioral results as output.

[0111] Step 3:

[0112] The server determines whether an anomaly is detected based on the analyzed operating patterns. In this step, the estimated results are used as input to detect events. The detected anomalies are output, providing the type and severity of the anomaly, which serves as the basic data for generating notifications.

[0113] Step 4:

[0114] When the server detects an anomaly, it uses Firebase to send a notification to the user's device. The server uses the details of the detected anomaly as input to generate a real-time alert on the user's device. The notification message is then sent to the user as output.

[0115] Step 5:

[0116] The terminal receives notifications from the server and displays them to the user. It uses notification data sent from the server as input and visually represents it on the screen. This step provides output that allows the user to check the situation and take appropriate action.

[0117] Step 6:

[0118] Users can view the daily activity patterns of elderly individuals through an information board on their terminal. Past data history obtained from the terminal is used as input, and a detailed activity dashboard is displayed as output, visualizing the trends.

[0119] Step 7:

[0120] The server sets prompt statements to optimize the generated AI model throughout the operation of the entire system. These prompt statements are used as input to obtain feedback that enhances the system's effectiveness. The results are then reflected in the next analysis and output.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is a system that uses wireless network signals to detect the movement of objects and people in the environment, while simultaneously recognizing the user's emotions by combining them with an emotion engine. This configuration makes it possible to more accurately understand the user's situation and provide feedback tailored to their individual needs.

[0123] System Embodiment

[0124] Data collection of signals

[0125] The server collects signal data in real time from WiFi routers via the wireless network. It also acquires data from other sensor devices as needed to support more detailed environmental analysis.

[0126] Data analysis and motion detection

[0127] The server uses machine learning algorithms to analyze collected signal data and identify movement within the environment. This includes human movement and specific motion patterns, and the server estimates what each of these might mean.

[0128] Emotion recognition by an emotion engine

[0129] The emotion engine embedded in the server analyzes the user's emotional state based on signal data and other sensor data. This engine comprehensively evaluates the user's facial expressions, voice, behavioral patterns, and other factors to identify their current emotion.

[0130] Generating notifications and feedback

[0131] Based on the analysis results, the server generates notifications that correspond to detected events and the user's emotional state. These notifications are sent to the user's device and are tailored to the user's situation. For example, if the server suspects the user is stressed, a notification encouraging calmness will be generated.

[0132] User notifications and adaptive responses

[0133] Users receive notifications on their devices and check their status through the content of those notifications. This system learns from user feedback, enabling more accurate emotion recognition and adaptive responses.

[0134] Specific example

[0135] As an example, consider the use of this system in a home security and care environment. The server detects changes in the movement patterns of people within the living space and, using an emotion engine, senses when a resident is in an unstable emotional state. In this case, it generates a notification and sends it to the family's device, providing real-time information about the situation. This allows the family to take appropriate countermeasures based on the situation.

[0136] This invention combines wireless networks and emotion recognition technology to realize a system with a wider range of applications, enabling the provision of useful information in various usage scenarios.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server collects signal data from WiFi routers and other sensor devices via a wireless network. This includes signal strength and channel status information (CSI), which are then integrated and processed with data from other sensors.

[0140] Step 2:

[0141] The server preprocesses the collected signal data. This preprocessing includes denoising and normalizing the data. This prepares the data for subsequent analysis.

[0142] Step 3:

[0143] The server uses pre-processed data to run machine learning algorithms and analyze the movement of objects and people in the environment. This analysis allows for the detection of specific movement patterns and abnormal movements.

[0144] Step 4:

[0145] The server uses an emotion engine to estimate the user's emotional state. In addition to signal data, it also analyzes video and audio data if available, performing facial recognition and voice analysis.

[0146] Step 5:

[0147] The server evaluates the importance of an event based on the analyzed movement and the user's emotional state, and generates an appropriate notification. The notification includes the type of event, its location, the user's emotional state, and the recommended response.

[0148] Step 6:

[0149] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other appropriate interface.

[0150] Step 7:

[0151] The user checks the notifications received on their device. Based on the content of the notifications, they take appropriate action according to their emotional state and surrounding circumstances. For example, if stress is detected, suggestions will be made to encourage actions to relax.

[0152] (Example 2)

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

[0154] In modern society, understanding the movement of objects and people in the environment in real time, and recognizing the emotional state of individual users, is a crucial challenge in order to ensure the health and safety of individuals. However, conventional technologies have struggled to accurately analyze movement and emotions and provide appropriate feedback.

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

[0156] In this invention, the server includes means for receiving wireless data and acquiring information, analysis means for processing the information and estimating the behavior of objects in space, and means for detecting events and generating instructions based on the analysis results. This makes it possible to understand the movements of objects and people in the environment and to provide appropriate feedback according to the user's emotional state.

[0157] "Wireless data" refers to a collection of information transmitted and received via wireless communication within a given space, including signal strength and device connection status.

[0158] "Means for acquiring information" refers to a device or system that has the function of receiving data via wireless communication and extracting necessary information.

[0159] "Analysis means" refers to the technologies and algorithms used to process acquired information and analyze the behavior of objects and people in a space.

[0160] "Means of detecting events" refers to the process of identifying specific events or situations from analyzed data and prompting the next steps based on that identification.

[0161] "Means for generating instructions" refers to functions that create relevant notifications and feedback based on detected events and the user's emotional state.

[0162] "User emotional state" refers to the user's current psychological or emotional state, estimated from their facial expressions, voice, behavioral patterns, etc.

[0163] "Feedback" refers to information and suggestions provided to users based on analysis results, and is tailored to the user's psychological or behavioral needs.

[0164] This invention is a system that combines wireless data and emotion analysis technology to detect movement within the environment and the user's emotional state with high accuracy, and to provide appropriate feedback based on that.

[0165] The server collects information via hardware designed to receive wireless data from Wi-Fi routers and other wireless communication devices. This collected information is processed by analysis software incorporating machine learning algorithms. Specifically, deep learning models and statistical methods are used to analyze the behavior of objects and people in the environment and identify their movements and patterns.

[0166] The server is equipped with an emotion engine that comprehensively evaluates the user's facial expressions, voice, and behavioral patterns. This allows the engine to recognize the user's current emotional state and generate instructions and notifications based on that state. For example, if the server detects a user's stress level, it may generate feedback suggesting relaxation methods. These notifications and feedback are sent to the user's device, allowing the user to adjust their behavior accordingly.

[0167] One specific example of its use is in home security and care environments. The server can detect changes in patterns or emotional instability among people in the living space and send notifications to family members' devices. This provides real-time information about the situation and helps families take appropriate action.

[0168] Possible prompts for the generating AI model include, "Analyze movement and emotional data in the current living environment and create suggestions for stress reduction." This prompt allows the AI ​​model to generate basic data for providing appropriate feedback based on the user's emotional state. In this way, the present invention aims to effectively utilize wireless networks and emotion recognition technology to provide information that meets the user's needs.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The server receives signal data from wireless communication devices. Inputs include wireless signals from WiFi routers and other sensors. Data processing involves logging signal strength and connection status from each device and monitoring their changes in real time. The output is signal strength data.

[0172] Step 2:

[0173] The server analyzes signal data using machine learning algorithms. The input is the wireless signal data collected in step 1. As part of the data calculation, a deep learning model is used for pattern recognition to estimate the movement of objects and people in the environment. The output is the analysis results regarding the identified movements and their patterns.

[0174] Step 3:

[0175] The server uses an emotion engine to estimate the user's emotional state. Inputs include the analysis results from step 2, and additional sensor data from the camera and microphone. Data processing involves applying facial expression and voice analysis algorithms to evaluate behavioral patterns. The output provides information about the user's emotional state.

[0176] Step 4:

[0177] The server generates appropriate notifications based on the analysis results obtained. The input is the analysis results of movement and emotion. As data generation, it creates customized notifications based on template messages, tailored to the user's situation. The output is a notification that includes specific feedback and suggestions.

[0178] Step 5:

[0179] The server sends the generated notification to the user's device. The input is the notification generated in step 4. Specifically, the message is delivered using a push notification service. The output is the notification displayed on the user's device.

[0180] Step 6:

[0181] The user receives a notification on their device and checks its contents. The input is the content of the notification received on the device. Specifically, the user opens the notification center on their smartphone, checks the provided feedback, and modifies their actions as needed. The output is the adaptive action the user takes based on the notification.

[0182] (Application Example 2)

[0183] 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".

[0184] In modern society, monitoring and ensuring the safety of elderly people who require care is a crucial issue. However, conventional surveillance cameras and sensors are insufficient for accurately capturing their movements or interpreting their emotional states, which can prevent prompt and appropriate responses when abnormalities occur. Therefore, there is a need to balance the safety of the elderly with reducing the burden on caregivers.

[0185] 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.

[0186] In this invention, the server includes means for collecting signal information acquired from a wireless communication network; analysis means for processing the signal information to estimate the actions of objects and individuals in the environment; means for detecting events based on the analysis results, recognizing the user's emotional state using an emotion engine, and generating notifications; and means for transmitting the notifications to a user device and providing adaptive feedback. This makes it possible to accurately grasp the actions and emotional states of elderly people in real time and prompt caregivers to respond quickly.

[0187] A "wireless communication network" is a network system used to send and receive data and signals wirelessly.

[0188] "Signal information" refers to data acquired through wireless communication networks, including information such as location and movement.

[0189] "Analysis means" refers to a device or method for processing collected signal information and estimating the actions of objects or individuals within the environment.

[0190] An "emotion engine" is software or a system that analyzes a user's emotional state based on signal information and other sensing data.

[0191] A "user device" is a terminal that a user carries or installs for use, and is a device that receives and displays notifications.

[0192] Adaptive feedback is a response that suggests the most appropriate information or instructions for the user based on their situation and emotions at that particular time.

[0193] To implement this invention, a system for acquiring signal information via a wireless communication network is first required. The server collects this signal information and uses analysis means to estimate the actions of objects and individuals in the environment. This analysis utilizes machine learning algorithms that model changes in signal information. Next, the server uses an emotion engine to analyze the user's emotional state from the signal information. This emotion engine is used to evaluate the correlation between action data and emotional data.

[0194] Based on the analysis results, the server generates notifications corresponding to the detected events and emotional states and sends them to the user's device. The user's device receives and displays these notifications, providing the user with situational adaptive feedback. This feedback allows the user to confirm their own actions and emotional states and adjust their behavior as needed.

[0195] To implement this system, software written in programming languages ​​such as Python will be used, along with user devices such as smartphones and tablets. Machine learning algorithms such as TensorFlow and PyTorch will be selected. For example, if an elderly person is about to fall at home and anxiety is indicated along with the movement, the system will send a notification to the caregiver's device stating "Movement: risk of fall, Emotion: anxiety."

[0196] An example of a specific prompt: "An elderly person is showing signs of being at risk of falling at home and is expressing anxiety. What can be done?"

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server acquires signal information in real time via a wireless communication network. The input is raw data from a WiFi router, and the output is a time-series of signal data. This data is used in subsequent analysis to estimate the actions of subjects or individuals.

[0200] Step 2:

[0201] The server processes the acquired signal information using analysis tools. Specifically, it uses a machine learning model to analyze signal changes and performs data processing to estimate the operation. The input is the signal data acquired in step 1, and the output is the estimated operation information.

[0202] Step 3:

[0203] The server evaluates the user's emotional state based on behavioral information estimated using an emotion engine. This evaluation applies an algorithm based on the user's behavioral patterns. The input is the behavioral information from step 2, and the output is the user's emotional state.

[0204] Step 4:

[0205] The server generates notifications based on emotional state and behavioral information. Specifically, it formats the content, including the type of event detected and the emotional state, into a notification. The input is the emotional state and behavioral information from step 3, and the output is the generated notification message.

[0206] Step 5:

[0207] The user device displays the notification message received from the server. The input received by the terminal is the notification message generated in step 4, and the output is displayed as adaptive feedback to the user.

[0208] Step 6:

[0209] The user understands the current situation through notification messages displayed on the device and takes action as needed. Specific actions include adjusting behavior based on the notification content. The input is the notification message from the device, and the output is the user's adjusted actions.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system that uses signals from a wireless network to non-contactually detect the movement of objects and people in an environment. This system includes a server and user devices, and performs signal data collection, analysis, event detection, and notification transmission.

[0227] System Embodiment

[0228] Data collection of signals

[0229] The server obtains signal data from the WiFi router that communicates with the terminal via the wireless network. This data includes signal strength, phase changes, and delay information.

[0230] Analysis of signal data

[0231] The server uses machine learning algorithms to analyze changes in signal data. This analysis identifies the movement of objects and people within the environment. For example, it can capture changes in reflections as a person moves through a room and identify their movement.

[0232] Event detection and notification

[0233] The server identifies specific patterns based on the analysis results and detects abnormal movements or changes in circumstances as events. When an event is detected, the server sends a notification to the user device. This notification includes the type of event (e.g., intrusion detection, fall of an elderly person, etc.), the location where it occurred, and the estimated severity.

[0234] Notifications and actions for users

[0235] Users receive notifications through a dedicated application. Within the application, users can view detailed information and take appropriate action as needed. For example, they can review surveillance camera footage for home security or contact care services.

[0236] Specific example

[0237] For example, if this system is implemented in a household where an elderly person lives alone, the server will continuously monitor the WiFi signal in the living space and, upon detecting a pattern that differs from normal movement (e.g., a sudden fall), will immediately send an alert to the user's device. The user (family member or caregiver) who receives the notification can check the details through the app and, if necessary, quickly rush to the scene. In this way, the present invention makes it possible to provide a highly accurate and low-cost motion detection system using a wireless network.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The server collects signal data from WiFi routers within the wireless network. This includes signal strength, channel status information (CSI), and signal phase change data. This information is continuously monitored in real time.

[0241] Step 2:

[0242] The server preprocesses the collected signal data. This involves noise reduction and data normalization, preparing the data for analysis. Once the data is ready, it is sent to the next analysis stage.

[0243] Step 3:

[0244] The server inputs pre-processed data into a machine learning algorithm. This algorithm utilizes a pre-trained model to identify movement within the environment from changes in signals. The model analyzes new data based on known patterns and extracts movement characteristics.

[0245] Step 4:

[0246] The server detects specific events based on the results of algorithmic analysis. For example, if a change exceeding a certain level is detected, it determines whether it indicates the movement of a person or an object. Based on this result, the importance of the detected event is evaluated.

[0247] Step 5:

[0248] The server generates notifications based on detected events. These notifications include the event type, location, timestamp, and severity level, allowing users to quickly understand the situation.

[0249] Step 6:

[0250] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other interface installed by the user.

[0251] Step 7:

[0252] Users check notifications received on their devices and decide on appropriate actions as needed. For example, in the case of intrusion detection, they might check their home security system, or in the case of an elderly monitoring system, they might contact care services.

[0253] (Example 1)

[0254] 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."

[0255] In modern safety technology, accurately and non-contactually detecting movements and states within a space and rapidly transmitting that information remains a challenging task. In particular, areas such as personal safety and home security demand immediacy and accuracy, but conventional technologies fail to adequately meet these requirements. Therefore, there is a need for technologies that achieve highly accurate motion detection and rapid information transmission.

[0256] 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.

[0257] In this invention, the server includes means for collecting signal information from a wireless communication network via an information terminal, analysis means for analyzing the signal information to estimate the movement of objects or individuals in space, and means for detecting events and generating information based on the analysis results. This makes it possible to detect movements and states in space with high accuracy without contact and to quickly transmit necessary information to the user.

[0258] An "information terminal" is a general term for a device that collects signal information and processes data via a wireless communication network.

[0259] A "wireless communication network" refers to a network that uses radio waves to send and receive information, and is a means of communication that does not require a wired connection.

[0260] "Signal information" refers to characteristic data of radio waves acquired via a wireless communication network, including signal strength, phase change, and delay information.

[0261] "Analysis means" refers to techniques or algorithms for processing collected signal information and estimating the movement of objects or individuals in space.

[0262] "Objects or individuals" refer to items or people that are the target of detection within a space, and are the subjects of analysis necessary to estimate their actions and states.

[0263] "Event detection" refers to the process of recognizing specific actions or changes in circumstances based on analyzed signal information.

[0264] "Information creation" refers to the act of generating useful data and notifications for users based on detected events.

[0265] "User" refers to an individual or organization that receives the information or services provided by this invention.

[0266] This invention is a system for non-contact detection of the movement of objects or individuals in space using signal information from a wireless communication network. The system includes an information terminal (e.g., a smartphone or dedicated device) and a server. The information terminal collects signal information from the wireless communication network and transmits it to the server. The server analyzes the signal information using a machine learning algorithm to detect changes in the environment. The signal information includes signal strength, phase change, and delay information.

[0267] Software such as Python or TensorFlow is used for the analysis process. The server analyzes the temporal changes in signals and identifies specific behavioral patterns (e.g., people walking or furniture moving). If the analysis detects abnormal behavior or situations (e.g., unauthorized entry, potential falls), event information is generated.

[0268] This generated information, along with its importance and location, is sent to the information terminal. The user receives the notification through an application and checks the situation. This application allows users to view detailed information and take additional actions (e.g., check surveillance cameras or make an emergency call).

[0269] As a concrete example, this system could be implemented in a home where elderly people live. The server continuously monitors the Wi-Fi signal in the living space, and if it detects a pattern that deviates from normal movement (e.g., a sudden fall), it immediately sends an alert to the user's device. The user (e.g., family member or caregiver) can check the situation through the application and take action to provide assistance if necessary.

[0270] An example of a prompt for a generative AI model is: "Describe a system that uses wireless network signals to detect the movement of people in an environment. As a specific scenario, give an example of its use in a home where elderly people live, and describe in detail the notification process in that case." This prompt instructs the AI ​​to provide more detailed information about how the system works and its applications.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The server collects signal information from the wireless communication network via an information terminal. The input to this step is radio wave information emitted from the wireless communication network, and the output is a dataset of signal information. Specifically, the server periodically acquires signal strength, phase, and delay information and stores it in a database.

[0274] Step 2:

[0275] The server analyzes the collected signal information using machine learning algorithms. The input to this step is a dataset of collected signal information, and the output is the analysis results showing the behavioral patterns in space. Specifically, the server uses Python or TensorFlow to analyze signal intensity fluctuations and phase changes to identify specific patterns and anomalies.

[0276] Step 3:

[0277] The server detects specific events based on the analysis results. The input for this step is the analyzed behavioral pattern, and the output is the detected event information. Specifically, the server compares it to a pre-configured threshold, logs any anomalies as events, and flags them.

[0278] Step 4:

[0279] The server generates information based on detected event information and sends a notification to the user's information terminal. The input for this step is the detected event information, and the output is the notification message to the user. Specifically, the server generates the content of the notification message and sends it to the user's terminal via push notification.

[0280] Step 5:

[0281] The user receives notifications through an application on their device and takes the necessary actions. The input for this step is the notification message sent from the server, and the output is the user's action. Specifically, the user opens the app to check the notification content and, depending on the situation, can check the surveillance camera footage or take emergency action.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] When an elderly person lives safely at home, there is a need for means to detect falls and abnormal behaviors in real time and respond promptly. In addition, it is an issue to provide an efficient monitoring system for family members and caregivers to grasp the activity patterns of the elderly remotely and respond immediately in case of an emergency.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting signal data acquired from a wireless communication network, analysis means for processing the signal data to estimate the movement of surrounding objects and people, and means for detecting an event based on the analysis result and generating a notification. Thereby, it becomes possible to quickly detect abnormalities of the elderly and send necessary notifications.

[0287] The "wireless communication network" is a communication system that transmits and receives data using wireless technology.

[0288] The "signal data" is a set of information acquired through wireless communication, and the operation in the environment is estimated by analyzing its changes.

[0289] The "analysis means" is a function of a system including algorithms and programs for processing signal data to estimate the movement of surrounding objects and people.

[0290] The "event" refers to a specific phenomenon or occurrence detected based on the surrounding operations and changes.

[0291] The "notification" is information generated based on the detected event and is a message for transmitting it to the user.

[0292] A "user device" is an electronic device used to receive and display notifications, such as a smartphone.

[0293] "Activity patterns" refer to the tendencies in behaviors and actions that a particular individual exhibits in their daily life.

[0294] An "information board" is an interface or dashboard used to visually display data and allow users to understand the situation.

[0295] This system collects and analyzes signal data from wireless communication networks to detect abnormal behavior in elderly individuals. The server collects signal data obtained through a typical Wi-Fi router and analyzes the data using a machine learning algorithm. This machine learning algorithm is implemented using TensorFlow and models signal changes to accurately predict behavior. Based on the analysis results, if an abnormal event occurs, the server uses Firebase to send a real-time notification to the user's device.

[0296] A device, such as a smartphone, displays received notifications to the user and provides an information board to understand the activity patterns of elderly individuals. This information board visualizes past data, making it easy to check behavioral trends in daily life. It also includes an emergency contact function to enable a quick response in emergencies.

[0297] As a concrete example, if an unusual walking pattern is detected when an elderly person goes to the toilet at night, an alert is immediately sent to the family, who can then check the situation through the app. This process provides effective guidance for optimizing the system's operation by inputting pre-prepared prompt messages into a generating AI model. The prompt message used is: "Design an AI model that analyzes the daily movement patterns of elderly people and detects abnormal movements. Refer to the example that utilizes WiFi signals as an anomaly detection system." This system operates by combining a WiFi router and a smartphone as hardware, and TensorFlow and Firebase as software.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects signal data from the WiFi router in real time. It takes WiFi signal strength, phase changes, and delay information as input and stores this data for analysis.

[0301] Step 2:

[0302] The server analyzes the collected signal data using a machine learning algorithm based on TensorFlow. Using the signal data collected in the previous step as input, it models the changes in the data and estimates the behavioral patterns within the environment. This process yields the estimated behavioral results as output.

[0303] Step 3:

[0304] The server determines whether an anomaly is detected based on the analyzed operating patterns. In this step, the estimated results are used as input to detect the event. The detected anomalies are output, providing the type and severity of the anomaly, which serves as the basic data for generating notifications.

[0305] Step 4:

[0306] When the server detects an abnormality, it uses Firebase to send a notification to the user terminal. Using the details of the event, which is the result of the abnormality detection, as input, it generates an alert in real time on the user device. As output, a notification message is sent to the user.

[0307] Step 5:

[0308] The terminal receives the notification from the server and displays it to the user. Using the notification data sent from the server as input, it visually represents it on the screen. In this step, information for the user to check the situation and respond as needed is provided as output.

[0309] Step 6:

[0310] The user checks the activity patterns of the daily life of the elderly through the information board on the terminal. Using the past data history obtained from the terminal as input, a detailed activity dashboard is shown as output to visualize the trend.

[0311] Step 7:

[0312] The server sets a prompt sentence for optimizing the generated AI model through the operation of the entire system. Using this prompt sentence as input, it obtains feedback for enhancing the system effect. And the result is output by being reflected in the next analysis.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0314] This invention is a system that uses wireless network signals to detect the movement of objects and people in the environment, while simultaneously recognizing the user's emotions by combining them with an emotion engine. This configuration makes it possible to more accurately understand the user's situation and provide feedback tailored to their individual needs.

[0315] System Embodiment

[0316] Data collection of signals

[0317] The server collects signal data in real time from WiFi routers via the wireless network. It also acquires data from other sensor devices as needed to support more detailed environmental analysis.

[0318] Data analysis and motion detection

[0319] The server uses machine learning algorithms to analyze collected signal data and identify movement within the environment. This includes human movement and specific motion patterns, and the server estimates what each of these might mean.

[0320] Emotion recognition by an emotion engine

[0321] The emotion engine embedded in the server analyzes the user's emotional state based on signal data and other sensor data. This engine comprehensively evaluates the user's facial expressions, voice, behavioral patterns, and other factors to identify their current emotion.

[0322] Generating notifications and feedback

[0323] Based on the analysis results, the server generates notifications that correspond to detected events and the user's emotional state. These notifications are sent to the user's device and are tailored to the user's situation. For example, if the server suspects the user is stressed, a notification encouraging calmness will be generated.

[0324] User notifications and adaptive responses

[0325] Users receive notifications on their devices and check their status through the content of those notifications. This system learns from user feedback, enabling more accurate emotion recognition and adaptive responses.

[0326] Specific example

[0327] As an example, consider the use of this system in a home security and care environment. The server detects changes in the movement patterns of people within the living space and, using an emotion engine, senses when a resident is in an unstable emotional state. In this case, it generates a notification and sends it to the family's device, providing real-time information about the situation. This allows the family to take appropriate countermeasures based on the situation.

[0328] This invention combines wireless networks and emotion recognition technology to realize a system with a wider range of applications, enabling the provision of useful information in various usage scenarios.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server collects signal data from WiFi routers and other sensor devices via a wireless network. This includes signal strength and channel status information (CSI), which are then integrated and processed with data from other sensors.

[0332] Step 2:

[0333] The server preprocesses the collected signal data. This preprocessing includes denoising and normalizing the data. This prepares the data for subsequent analysis.

[0334] Step 3:

[0335] The server uses pre-processed data to run machine learning algorithms and analyze the movement of objects and people in the environment. This analysis allows for the detection of specific movement patterns and abnormal movements.

[0336] Step 4:

[0337] The server uses an emotion engine to estimate the user's emotional state. In addition to signal data, it also analyzes video and audio data if available, performing facial recognition and voice analysis.

[0338] Step 5:

[0339] The server evaluates the importance of an event based on the analyzed movement and the user's emotional state, and generates an appropriate notification. The notification includes the type of event, its location, the user's emotional state, and the recommended response.

[0340] Step 6:

[0341] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other appropriate interface.

[0342] Step 7:

[0343] The user checks the notifications received on their device. Based on the content of the notifications, they take appropriate action according to their emotional state and surrounding circumstances. For example, if stress is detected, suggestions will be made to encourage actions to relax.

[0344] (Example 2)

[0345] 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".

[0346] In modern society, understanding the movement of objects and people in the environment in real time, and recognizing the emotional state of individual users, is a crucial challenge in order to ensure the health and safety of individuals. However, conventional technologies have struggled to accurately analyze movement and emotions and provide appropriate feedback.

[0347] 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.

[0348] In this invention, the server includes means for receiving wireless data and acquiring information, analysis means for processing the information and estimating the behavior of objects in space, and means for detecting events and generating instructions based on the analysis results. This makes it possible to understand the movements of objects and people in the environment and to provide appropriate feedback according to the user's emotional state.

[0349] "Wireless data" refers to a collection of information transmitted and received via wireless communication within a given space, including signal strength and device connection status.

[0350] "Means for acquiring information" refers to a device or system that has the function of receiving data via wireless communication and extracting necessary information.

[0351] "Analysis means" refers to the technologies and algorithms used to process acquired information and analyze the behavior of objects and people in a space.

[0352] "Means of detecting events" refers to the process of identifying specific events or situations from analyzed data and prompting the next steps based on that identification.

[0353] "Means for generating instructions" refers to functions that create relevant notifications and feedback based on detected events and the user's emotional state.

[0354] "User emotional state" refers to the user's current psychological or emotional state, estimated from their facial expressions, voice, behavioral patterns, etc.

[0355] "Feedback" refers to information and suggestions provided to users based on analysis results, and is tailored to the user's psychological or behavioral needs.

[0356] This invention is a system that combines wireless data and emotion analysis technology to detect movement within the environment and the user's emotional state with high accuracy, and to provide appropriate feedback based on that.

[0357] The server collects information via hardware designed to receive wireless data from Wi-Fi routers and other wireless communication devices. This collected information is processed by analysis software incorporating machine learning algorithms. Specifically, deep learning models and statistical methods are used to analyze the behavior of objects and people in the environment and identify their movements and patterns.

[0358] The server is equipped with an emotion engine that comprehensively evaluates the user's facial expressions, voice, and behavioral patterns. This allows the engine to recognize the user's current emotional state and generate instructions and notifications based on that state. For example, if the server detects a user's stress level, it may generate feedback suggesting relaxation methods. These notifications and feedback are sent to the user's device, allowing the user to adjust their behavior accordingly.

[0359] One specific example of its use is in home security and care environments. The server can detect changes in patterns or emotional instability among people in the living space and send notifications to family members' devices. This provides real-time information about the situation and helps families take appropriate action.

[0360] Possible prompts for the generating AI model include, "Analyze movement and emotional data in the current living environment and create suggestions for stress reduction." This prompt allows the AI ​​model to generate basic data for providing appropriate feedback based on the user's emotional state. In this way, the present invention aims to effectively utilize wireless networks and emotion recognition technology to provide information that meets the user's needs.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The server receives signal data from wireless communication devices. Inputs include wireless signals from WiFi routers and other sensors. Data processing involves logging signal strength and connection status from each device and monitoring their changes in real time. The output is signal strength data.

[0364] Step 2:

[0365] The server analyzes signal data using machine learning algorithms. The input is the wireless signal data collected in step 1. As part of the data calculation, a deep learning model is used for pattern recognition to estimate the movement of objects and people in the environment. The output is the analysis results regarding the identified movements and their patterns.

[0366] Step 3:

[0367] The server uses an emotion engine to estimate the user's emotional state. Inputs include the analysis results from step 2, and additional sensor data from the camera and microphone. Data processing involves applying facial expression and voice analysis algorithms to evaluate behavioral patterns. The output provides information about the user's emotional state.

[0368] Step 4:

[0369] The server generates appropriate notifications based on the analysis results obtained. The input is the analysis results of movement and emotion. As data generation, it creates customized notifications based on template messages, tailored to the user's situation. The output is a notification that includes specific feedback and suggestions.

[0370] Step 5:

[0371] The server sends the generated notification to the user's device. The input is the notification generated in step 4. Specifically, the message is delivered using a push notification service. The output is the notification displayed on the user's device.

[0372] Step 6:

[0373] The user receives a notification on their device and checks its contents. The input is the content of the notification received on the device. Specifically, the user opens the notification center on their smartphone, checks the provided feedback, and modifies their actions as needed. The output is the adaptive action the user takes based on the notification.

[0374] (Application Example 2)

[0375] 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."

[0376] In modern society, monitoring and ensuring the safety of elderly people who require care is a crucial issue. However, conventional surveillance cameras and sensors are insufficient for accurately capturing their movements or interpreting their emotional states, which can prevent prompt and appropriate responses when abnormalities occur. Therefore, there is a need to balance the safety of the elderly with reducing the burden on caregivers.

[0377] 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.

[0378] In this invention, the server includes means for collecting signal information acquired from a wireless communication network; analysis means for processing the signal information to estimate the actions of objects and individuals in the environment; means for detecting events based on the analysis results, recognizing the user's emotional state using an emotion engine, and generating notifications; and means for transmitting the notifications to a user device and providing adaptive feedback. This makes it possible to accurately grasp the actions and emotional states of elderly people in real time and prompt caregivers to respond quickly.

[0379] A "wireless communication network" is a network system used to send and receive data and signals wirelessly.

[0380] "Signal information" refers to data acquired through wireless communication networks, including information such as location and movement.

[0381] "Analysis means" refers to a device or method for processing collected signal information and estimating the actions of objects or individuals within the environment.

[0382] An "emotion engine" is software or a system that analyzes a user's emotional state based on signal information and other sensing data.

[0383] A "user device" is a terminal that a user carries or installs for use, and is a device that receives and displays notifications.

[0384] Adaptive feedback is a response that suggests the most appropriate information or instructions for the user based on their situation and emotions at that particular time.

[0385] To implement this invention, a system for acquiring signal information via a wireless communication network is first required. The server collects this signal information and uses analysis means to estimate the actions of objects and individuals in the environment. This analysis utilizes machine learning algorithms that model changes in signal information. Next, the server uses an emotion engine to analyze the user's emotional state from the signal information. This emotion engine is used to evaluate the correlation between action data and emotional data.

[0386] Based on the analysis results, the server generates notifications corresponding to the detected events and emotional states and sends them to the user's device. The user's device receives and displays these notifications, providing the user with situational adaptive feedback. This feedback allows the user to confirm their own actions and emotional states and adjust their behavior as needed.

[0387] To implement this system, software written in programming languages ​​such as Python will be used, along with user devices such as smartphones and tablets. Machine learning algorithms such as TensorFlow and PyTorch will be selected. For example, if an elderly person is about to fall at home and anxiety is indicated along with the movement, the system will send a notification to the caregiver's device stating "Movement: risk of fall, Emotion: anxiety."

[0388] An example of a specific prompt: "An elderly person is showing signs of being at risk of falling at home and is expressing anxiety. What can be done?"

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The server acquires signal information in real time via a wireless communication network. The input is raw data from a WiFi router, and the output is a time-series of signal data. This data is used in subsequent analysis to estimate the actions of subjects or individuals.

[0392] Step 2:

[0393] The server processes the acquired signal information using analysis tools. Specifically, it uses a machine learning model to analyze signal changes and performs data processing to estimate the operation. The input is the signal data acquired in step 1, and the output is the estimated operation information.

[0394] Step 3:

[0395] The server evaluates the user's emotional state based on behavioral information estimated using an emotion engine. This evaluation applies an algorithm based on the user's behavioral patterns. The input is the behavioral information from step 2, and the output is the user's emotional state.

[0396] Step 4:

[0397] The server generates notifications based on emotional state and behavioral information. Specifically, it formats the content, including the type of event detected and the emotional state, into a notification. The input is the emotional state and behavioral information from step 3, and the output is the generated notification message.

[0398] Step 5:

[0399] The user device displays the notification message received from the server. The input received by the terminal is the notification message generated in step 4, and the output is displayed as adaptive feedback to the user.

[0400] Step 6:

[0401] The user understands the current situation through notification messages displayed on the device and takes action as needed. Specific actions include adjusting behavior based on the notification content. The input is the notification message from the device, and the output is the user's adjusted actions.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention is a system that uses signals from a wireless network to non-contactually detect the movement of objects and people in an environment. This system includes a server and user devices, and performs signal data collection, analysis, event detection, and notification transmission.

[0419] System Embodiment

[0420] Data collection of signals

[0421] The server obtains signal data from the WiFi router that communicates with the terminal via the wireless network. This data includes signal strength, phase changes, and delay information.

[0422] Analysis of signal data

[0423] The server uses machine learning algorithms to analyze changes in signal data. This analysis identifies the movement of objects and people within the environment. For example, it can capture changes in reflections as a person moves through a room and identify their movement.

[0424] Event detection and notification

[0425] The server identifies specific patterns based on the analysis results and detects abnormal movements or changes in circumstances as events. When an event is detected, the server sends a notification to the user device. This notification includes the type of event (e.g., intrusion detection, fall of an elderly person, etc.), the location where it occurred, and the estimated severity.

[0426] Notifications and actions for users

[0427] Users receive notifications through a dedicated application. Within the application, users can view detailed information and take appropriate action as needed. For example, they can review surveillance camera footage for home security or contact care services.

[0428] Specific example

[0429] For example, if this system is implemented in a household where an elderly person lives alone, the server will continuously monitor the WiFi signal in the living space and, upon detecting a pattern that differs from normal movement (e.g., a sudden fall), will immediately send an alert to the user's device. The user (family member or caregiver) who receives the notification can check the details through the app and, if necessary, quickly rush to the scene. In this way, the present invention makes it possible to provide a highly accurate and low-cost motion detection system using a wireless network.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The server collects signal data from WiFi routers within the wireless network. This includes signal strength, channel status information (CSI), and signal phase change data. This information is continuously monitored in real time.

[0433] Step 2:

[0434] The server preprocesses the collected signal data. This involves noise reduction and data normalization, preparing the data for analysis. Once the data is ready, it is sent to the next analysis stage.

[0435] Step 3:

[0436] The server inputs pre-processed data into a machine learning algorithm. This algorithm utilizes a pre-trained model to identify movement within the environment from changes in signals. The model analyzes new data based on known patterns and extracts movement characteristics.

[0437] Step 4:

[0438] The server detects specific events based on the results of algorithmic analysis. For example, if a change exceeding a certain level is detected, it determines whether it indicates the movement of a person or an object. Based on this result, the importance of the detected event is evaluated.

[0439] Step 5:

[0440] The server generates notifications based on detected events. These notifications include the event type, location, timestamp, and severity level, allowing users to quickly understand the situation.

[0441] Step 6:

[0442] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other interface installed by the user.

[0443] Step 7:

[0444] Users check notifications received on their devices and decide on appropriate actions as needed. For example, in the case of intrusion detection, they might check their home security system, or in the case of an elderly monitoring system, they might contact care services.

[0445] (Example 1)

[0446] 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."

[0447] In modern safety technology, accurately and non-contactually detecting movements and states within a space and rapidly transmitting that information remains a challenging task. In particular, areas such as personal safety and home security demand immediacy and accuracy, but conventional technologies fail to adequately meet these requirements. Therefore, there is a need for technologies that achieve highly accurate motion detection and rapid information transmission.

[0448] 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.

[0449] In this invention, the server includes means for collecting signal information from a wireless communication network via an information terminal, analysis means for analyzing the signal information to estimate the movement of objects or individuals in space, and means for detecting events and generating information based on the analysis results. This makes it possible to detect movements and states in space with high accuracy without contact and to quickly transmit necessary information to the user.

[0450] An "information terminal" is a general term for a device that collects signal information and processes data via a wireless communication network.

[0451] A "wireless communication network" refers to a network that uses radio waves to send and receive information, and is a means of communication that does not require a wired connection.

[0452] "Signal information" refers to characteristic data of radio waves acquired via a wireless communication network, including signal strength, phase change, and delay information.

[0453] "Analysis means" refers to techniques or algorithms for processing collected signal information and estimating the movement of objects or individuals in space.

[0454] "Objects or individuals" refer to items or people that are the target of detection within a space, and are the subjects of analysis necessary to estimate their actions and states.

[0455] "Event detection" refers to the process of recognizing specific actions or changes in circumstances based on analyzed signal information.

[0456] "Information creation" refers to the act of generating useful data and notifications for users based on detected events.

[0457] "User" refers to an individual or organization that receives the information or services provided by this invention.

[0458] This invention is a system for non-contact detection of the movement of objects or individuals in space using signal information from a wireless communication network. The system includes an information terminal (e.g., a smartphone or dedicated device) and a server. The information terminal collects signal information from the wireless communication network and transmits it to the server. The server analyzes the signal information using a machine learning algorithm to detect changes in the environment. The signal information includes signal strength, phase change, and delay information.

[0459] Software such as Python or TensorFlow is used for the analysis process. The server analyzes the temporal changes in signals and identifies specific behavioral patterns (e.g., people walking or furniture moving). If the analysis detects abnormal behavior or situations (e.g., unauthorized entry, potential falls), event information is generated.

[0460] This generated information, along with its importance and location, is sent to the information terminal. The user receives the notification through an application and checks the situation. This application allows users to view detailed information and take additional actions (e.g., check surveillance cameras or make an emergency call).

[0461] As a concrete example, this system could be implemented in a home where elderly people live. The server continuously monitors the Wi-Fi signal in the living space, and if it detects a pattern that deviates from normal movement (e.g., a sudden fall), it immediately sends an alert to the user's device. The user (e.g., family member or caregiver) can check the situation through the application and take action to provide assistance if necessary.

[0462] An example of a prompt for a generative AI model is: "Describe a system that uses wireless network signals to detect the movement of people in an environment. As a specific scenario, give an example of its use in a home where elderly people live, and describe in detail the notification process in that case." This prompt instructs the AI ​​to provide more detailed information about how the system works and its applications.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The server collects signal information from the wireless communication network via an information terminal. The input to this step is radio wave information emitted from the wireless communication network, and the output is a dataset of signal information. Specifically, the server periodically acquires signal strength, phase, and delay information and stores it in a database.

[0466] Step 2:

[0467] The server analyzes the collected signal information using machine learning algorithms. The input to this step is a dataset of collected signal information, and the output is the analysis results showing the behavioral patterns in space. Specifically, the server uses Python or TensorFlow to analyze signal intensity fluctuations and phase changes to identify specific patterns and anomalies.

[0468] Step 3:

[0469] The server detects specific events based on the analysis results. The input for this step is the analyzed behavioral pattern, and the output is the detected event information. Specifically, the server compares it to a pre-configured threshold, logs any anomalies as events, and flags them.

[0470] Step 4:

[0471] The server generates information based on detected event information and sends a notification to the user's information terminal. The input for this step is the detected event information, and the output is the notification message to the user. Specifically, the server generates the content of the notification message and sends it to the user's terminal via push notification.

[0472] Step 5:

[0473] The user receives notifications through an application on their device and takes the necessary actions. The input for this step is the notification message sent from the server, and the output is the user's action. Specifically, the user opens the app to check the notification content and, depending on the situation, can check the surveillance camera footage or take emergency action.

[0474] (Application Example 1)

[0475] 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."

[0476] To ensure elderly people live safely at home, there is a need for a system that can detect falls and abnormal behavior in real time and respond quickly. Furthermore, a challenge is to provide an efficient monitoring system that allows family members and caregivers to remotely understand the elderly person's activity patterns and respond immediately in emergencies.

[0477] 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.

[0478] In this invention, the server includes means for collecting signal data acquired from a wireless communication network, analysis means for processing the signal data to estimate the movement of surrounding objects and people, and means for detecting events and generating notifications based on the analysis results. This makes it possible to quickly detect abnormalities in elderly people and send necessary notifications.

[0479] A "wireless communication network" is a communication system that uses wireless technology to send and receive data.

[0480] "Signal data" refers to a collection of information acquired through wireless communication, and by analyzing its changes, it is possible to estimate the behavior within the environment.

[0481] "Analysis means" refers to the function of a system that includes algorithms and programs for processing signal data to estimate the movement of surrounding objects and people.

[0482] An "event" refers to a specific phenomenon or occurrence detected based on the actions or changes of the surrounding environment.

[0483] A "notification" is information generated based on detected events, and it is a message intended to convey that information to the user.

[0484] A "user device" is an electronic device used to receive and display notifications, such as a smartphone.

[0485] "Activity patterns" refer to the tendencies in behaviors and actions that a particular individual exhibits in their daily life.

[0486] An "information board" is an interface or dashboard used to visually display data and allow users to understand the situation.

[0487] This system collects and analyzes signal data from wireless communication networks to detect abnormal behavior in elderly individuals. The server collects signal data obtained through a typical Wi-Fi router and analyzes the data using a machine learning algorithm. This machine learning algorithm is implemented using TensorFlow and models signal changes to accurately predict behavior. Based on the analysis results, if an abnormal event occurs, the server uses Firebase to send a real-time notification to the user's device.

[0488] A device, such as a smartphone, displays received notifications to the user and provides an information board to understand the activity patterns of elderly individuals. This information board visualizes past data, making it easy to check behavioral trends in daily life. It also includes an emergency contact function to enable a quick response in emergencies.

[0489] As a concrete example, if an unusual walking pattern is detected when an elderly person goes to the toilet at night, an alert is immediately sent to the family, who can then check the situation through the app. This process provides effective guidance for optimizing the system's operation by inputting pre-prepared prompt messages into a generating AI model. The prompt message used is: "Design an AI model that analyzes the daily movement patterns of elderly people and detects abnormal movements. Refer to the example that utilizes WiFi signals as an anomaly detection system." This system operates by combining a WiFi router and a smartphone as hardware, and TensorFlow and Firebase as software.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The server collects signal data from the WiFi router in real time. It takes WiFi signal strength, phase changes, and delay information as input and stores this data for analysis.

[0493] Step 2:

[0494] The server analyzes the collected signal data using a machine learning algorithm based on TensorFlow. Using the signal data collected in the previous step as input, it models the changes in the data and estimates the behavioral patterns within the environment. This process yields the estimated behavioral results as output.

[0495] Step 3:

[0496] The server determines whether an anomaly is detected based on the analyzed operating patterns. In this step, the estimated results are used as input to detect the event. The detected anomalies are output, providing the type and severity of the anomaly, which serves as the basic data for generating notifications.

[0497] Step 4:

[0498] When the server detects an anomaly, it uses Firebase to send a notification to the user's device. The server uses the details of the detected anomaly as input to generate a real-time alert on the user's device. The notification message is then sent to the user as output.

[0499] Step 5:

[0500] The terminal receives notifications from the server and displays them to the user. It uses notification data sent from the server as input and visually represents it on the screen. This step provides output that allows the user to check the situation and take appropriate action.

[0501] Step 6:

[0502] Users can view the daily activity patterns of elderly individuals through an information board on their terminal. Past data history obtained from the terminal is used as input, and a detailed activity dashboard is displayed as output, visualizing the trends.

[0503] Step 7:

[0504] The server sets prompt statements to optimize the generated AI model throughout the operation of the entire system. These prompt statements are used as input to obtain feedback that enhances the system's effectiveness. The results are then reflected in the next analysis and output.

[0505] 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.

[0506] This invention is a system that uses wireless network signals to detect the movement of objects and people in the environment, while simultaneously recognizing the user's emotions by combining them with an emotion engine. This configuration makes it possible to more accurately understand the user's situation and provide feedback tailored to their individual needs.

[0507] System Embodiment

[0508] Data collection of signals

[0509] The server collects signal data in real time from WiFi routers via the wireless network. It also acquires data from other sensor devices as needed to support more detailed environmental analysis.

[0510] Data analysis and motion detection

[0511] The server uses machine learning algorithms to analyze collected signal data and identify movement within the environment. This includes human movement and specific motion patterns, and the server estimates what each of these might mean.

[0512] Emotion recognition by an emotion engine

[0513] The emotion engine embedded in the server analyzes the user's emotional state based on signal data and other sensor data. This engine comprehensively evaluates the user's facial expressions, voice, behavioral patterns, and other factors to identify their current emotion.

[0514] Generating notifications and feedback

[0515] Based on the analysis results, the server generates notifications that correspond to detected events and the user's emotional state. These notifications are sent to the user's device and are tailored to the user's situation. For example, if the server suspects the user is stressed, a notification encouraging calmness will be generated.

[0516] User notifications and adaptive responses

[0517] Users receive notifications on their devices and check their status through the content of those notifications. This system learns from user feedback, enabling more accurate emotion recognition and adaptive responses.

[0518] Specific example

[0519] As an example, consider the use of this system in a home security and care environment. The server detects changes in the movement patterns of people within the living space and, using an emotion engine, senses when a resident is in an unstable emotional state. In this case, it generates a notification and sends it to the family's device, providing real-time information about the situation. This allows the family to take appropriate countermeasures based on the situation.

[0520] This invention combines wireless networks and emotion recognition technology to realize a system with a wider range of applications, enabling the provision of useful information in various usage scenarios.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The server collects signal data from WiFi routers and other sensor devices via a wireless network. This includes signal strength and channel status information (CSI), which are then integrated and processed with data from other sensors.

[0524] Step 2:

[0525] The server preprocesses the collected signal data. This preprocessing includes denoising and normalizing the data. This prepares the data for subsequent analysis.

[0526] Step 3:

[0527] The server uses pre-processed data to run machine learning algorithms and analyze the movement of objects and people in the environment. This analysis allows for the detection of specific movement patterns and abnormal movements.

[0528] Step 4:

[0529] The server uses an emotion engine to estimate the user's emotional state. In addition to signal data, it also analyzes video and audio data if available, performing facial recognition and voice analysis.

[0530] Step 5:

[0531] The server evaluates the importance of an event based on the analyzed movement and the user's emotional state, and generates an appropriate notification. The notification includes the type of event, its location, the user's emotional state, and the recommended response.

[0532] Step 6:

[0533] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other appropriate interface.

[0534] Step 7:

[0535] The user checks the notifications received on their device. Based on the content of the notifications, they take appropriate action according to their emotional state and surrounding circumstances. For example, if stress is detected, suggestions will be made to encourage actions to relax.

[0536] (Example 2)

[0537] 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."

[0538] In modern society, understanding the movement of objects and people in the environment in real time, and recognizing the emotional state of individual users, is a crucial challenge in order to ensure the health and safety of individuals. However, conventional technologies have struggled to accurately analyze movement and emotions and provide appropriate feedback.

[0539] 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.

[0540] In this invention, the server includes means for receiving wireless data and acquiring information, analysis means for processing the information and estimating the behavior of objects in space, and means for detecting events and generating instructions based on the analysis results. This makes it possible to understand the movements of objects and people in the environment and to provide appropriate feedback according to the user's emotional state.

[0541] "Wireless data" refers to a collection of information transmitted and received via wireless communication within a given space, including signal strength and device connection status.

[0542] "Means for acquiring information" refers to a device or system that has the function of receiving data via wireless communication and extracting necessary information.

[0543] "Analysis means" refers to the technologies and algorithms used to process acquired information and analyze the behavior of objects and people in a space.

[0544] "Means of detecting events" refers to the process of identifying specific events or situations from analyzed data and prompting the next steps based on that identification.

[0545] "Means for generating instructions" refers to functions that create relevant notifications and feedback based on detected events and the user's emotional state.

[0546] "User emotional state" refers to the user's current psychological or emotional state, estimated from their facial expressions, voice, behavioral patterns, etc.

[0547] "Feedback" refers to information and suggestions provided to users based on analysis results, and is tailored to the user's psychological or behavioral needs.

[0548] This invention is a system that combines wireless data and emotion analysis technology to detect movement within the environment and the user's emotional state with high accuracy, and to provide appropriate feedback based on that.

[0549] The server collects information via hardware designed to receive wireless data from Wi-Fi routers and other wireless communication devices. This collected information is processed by analysis software incorporating machine learning algorithms. Specifically, deep learning models and statistical methods are used to analyze the behavior of objects and people in the environment and identify their movements and patterns.

[0550] The server is equipped with an emotion engine that comprehensively evaluates the user's facial expressions, voice, and behavioral patterns. This allows the engine to recognize the user's current emotional state and generate instructions and notifications based on that state. For example, if the server detects a user's stress level, it may generate feedback suggesting relaxation methods. These notifications and feedback are sent to the user's device, allowing the user to adjust their behavior accordingly.

[0551] One specific example of its use is in home security and care environments. The server can detect changes in patterns or emotional instability among people in the living space and send notifications to family members' devices. This provides real-time information about the situation and helps families take appropriate action.

[0552] Possible prompts for the generating AI model include, "Analyze movement and emotional data in the current living environment and create suggestions for stress reduction." This prompt allows the AI ​​model to generate basic data for providing appropriate feedback based on the user's emotional state. In this way, the present invention aims to effectively utilize wireless networks and emotion recognition technology to provide information that meets the user's needs.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The server receives signal data from wireless communication devices. Inputs include wireless signals from WiFi routers and other sensors. Data processing involves logging signal strength and connection status from each device and monitoring their changes in real time. The output is signal strength data.

[0556] Step 2:

[0557] The server analyzes signal data using machine learning algorithms. The input is the wireless signal data collected in step 1. As part of the data calculation, a deep learning model is used for pattern recognition to estimate the movement of objects and people in the environment. The output is the analysis results regarding the identified movements and their patterns.

[0558] Step 3:

[0559] The server uses an emotion engine to estimate the user's emotional state. Inputs include the analysis results from step 2, and additional sensor data from the camera and microphone. Data processing involves applying facial expression and voice analysis algorithms to evaluate behavioral patterns. The output provides information about the user's emotional state.

[0560] Step 4:

[0561] The server generates appropriate notifications based on the analysis results obtained. The input is the analysis results of movement and emotion. As data generation, it creates customized notifications based on template messages, tailored to the user's situation. The output is a notification that includes specific feedback and suggestions.

[0562] Step 5:

[0563] The server sends the generated notification to the user's device. The input is the notification generated in step 4. Specifically, the message is delivered using a push notification service. The output is the notification displayed on the user's device.

[0564] Step 6:

[0565] The user receives a notification on their device and checks its contents. The input is the content of the notification received on the device. Specifically, the user opens the notification center on their smartphone, checks the provided feedback, and modifies their actions as needed. The output is the adaptive action the user takes based on the notification.

[0566] (Application Example 2)

[0567] 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."

[0568] In modern society, monitoring and ensuring the safety of elderly people who require care is a crucial issue. However, conventional surveillance cameras and sensors are insufficient for accurately capturing their movements or interpreting their emotional states, which can prevent prompt and appropriate responses when abnormalities occur. Therefore, there is a need to balance the safety of the elderly with reducing the burden on caregivers.

[0569] 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.

[0570] In this invention, the server includes means for collecting signal information acquired from a wireless communication network; analysis means for processing the signal information to estimate the actions of objects and individuals in the environment; means for detecting events based on the analysis results, recognizing the user's emotional state using an emotion engine, and generating notifications; and means for transmitting the notifications to a user device and providing adaptive feedback. This makes it possible to accurately grasp the actions and emotional states of elderly people in real time and prompt caregivers to respond quickly.

[0571] A "wireless communication network" is a network system used to send and receive data and signals wirelessly.

[0572] "Signal information" refers to data acquired through wireless communication networks, including information such as location and movement.

[0573] "Analysis means" refers to a device or method for processing collected signal information and estimating the actions of objects or individuals within the environment.

[0574] An "emotion engine" is software or a system that analyzes a user's emotional state based on signal information and other sensing data.

[0575] A "user device" is a terminal that a user carries or installs for use, and is a device that receives and displays notifications.

[0576] Adaptive feedback is a response that suggests the most appropriate information or instructions for the user based on their situation and emotions at that particular time.

[0577] To implement this invention, a system for acquiring signal information via a wireless communication network is first required. The server collects this signal information and uses analysis means to estimate the actions of objects and individuals in the environment. This analysis utilizes machine learning algorithms that model changes in signal information. Next, the server uses an emotion engine to analyze the user's emotional state from the signal information. This emotion engine is used to evaluate the correlation between action data and emotional data.

[0578] Based on the analysis results, the server generates notifications corresponding to the detected events and emotional states and sends them to the user's device. The user's device receives and displays these notifications, providing the user with situational adaptive feedback. This feedback allows the user to confirm their own actions and emotional states and adjust their behavior as needed.

[0579] To implement this system, software written in programming languages ​​such as Python will be used, along with user devices such as smartphones and tablets. Machine learning algorithms such as TensorFlow and PyTorch will be selected. For example, if an elderly person is about to fall at home and anxiety is indicated along with the movement, the system will send a notification to the caregiver's device stating "Movement: risk of fall, Emotion: anxiety."

[0580] An example of a specific prompt: "An elderly person is showing signs of being at risk of falling at home and is expressing anxiety. What can be done?"

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The server acquires signal information in real time via a wireless communication network. The input is raw data from a WiFi router, and the output is a time-series of signal data. This data is used in subsequent analysis to estimate the actions of subjects or individuals.

[0584] Step 2:

[0585] The server processes the acquired signal information using analysis tools. Specifically, it uses a machine learning model to analyze signal changes and performs data processing to estimate the operation. The input is the signal data acquired in step 1, and the output is the estimated operation information.

[0586] Step 3:

[0587] The server evaluates the user's emotional state based on behavioral information estimated using an emotion engine. This evaluation applies an algorithm based on the user's behavioral patterns. The input is the behavioral information from step 2, and the output is the user's emotional state.

[0588] Step 4:

[0589] The server generates notifications based on emotional state and behavioral information. Specifically, it formats the content, including the type of event detected and the emotional state, into a notification. The input is the emotional state and behavioral information from step 3, and the output is the generated notification message.

[0590] Step 5:

[0591] The user device displays the notification message received from the server. The input received by the terminal is the notification message generated in step 4, and the output is displayed as adaptive feedback to the user.

[0592] Step 6:

[0593] The user understands the current situation through notification messages displayed on the device and takes action as needed. Specific actions include adjusting behavior based on the notification content. The input is the notification message from the device, and the output is the user's adjusted actions.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] This invention is a system that uses signals from a wireless network to non-contactually detect the movement of objects and people in an environment. This system includes a server and user devices, and performs signal data collection, analysis, event detection, and notification transmission.

[0612] System Embodiment

[0613] Data collection of signals

[0614] The server obtains signal data from the WiFi router that communicates with the terminal via the wireless network. This data includes signal strength, phase changes, and delay information.

[0615] Analysis of signal data

[0616] The server uses machine learning algorithms to analyze changes in signal data. This analysis identifies the movement of objects and people within the environment. For example, it can capture changes in reflections as a person moves through a room and identify their movement.

[0617] Event detection and notification

[0618] The server identifies specific patterns based on the analysis results and detects abnormal movements or changes in circumstances as events. When an event is detected, the server sends a notification to the user device. This notification includes the type of event (e.g., intrusion detection, fall of an elderly person, etc.), the location where it occurred, and the estimated severity.

[0619] Notifications and actions for users

[0620] Users receive notifications through a dedicated application. Within the application, users can view detailed information and take appropriate action as needed. For example, they can review surveillance camera footage for home security or contact care services.

[0621] Specific example

[0622] For example, if this system is implemented in a household where an elderly person lives alone, the server will continuously monitor the WiFi signal in the living space and, upon detecting a pattern that differs from normal movement (e.g., a sudden fall), will immediately send an alert to the user's device. The user (family member or caregiver) who receives the notification can check the details through the app and, if necessary, quickly rush to the scene. In this way, the present invention makes it possible to provide a highly accurate and low-cost motion detection system using a wireless network.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The server collects signal data from WiFi routers within the wireless network. This includes signal strength, channel status information (CSI), and signal phase change data. This information is continuously monitored in real time.

[0626] Step 2:

[0627] The server preprocesses the collected signal data. This involves noise reduction and data normalization, preparing the data for analysis. Once the data is ready, it is sent to the next analysis stage.

[0628] Step 3:

[0629] The server inputs pre-processed data into a machine learning algorithm. This algorithm utilizes a pre-trained model to identify movement within the environment from changes in signals. The model analyzes new data based on known patterns and extracts movement characteristics.

[0630] Step 4:

[0631] The server detects specific events based on the results of algorithmic analysis. For example, if a change exceeding a certain level is detected, it determines whether it indicates the movement of a person or an object. Based on this result, the importance of the detected event is evaluated.

[0632] Step 5:

[0633] The server generates notifications based on detected events. These notifications include the event type, location, timestamp, and severity level, allowing users to quickly understand the situation.

[0634] Step 6:

[0635] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other interface installed by the user.

[0636] Step 7:

[0637] Users check notifications received on their devices and decide on appropriate actions as needed. For example, in the case of intrusion detection, they might check their home security system, or in the case of an elderly monitoring system, they might contact care services.

[0638] (Example 1)

[0639] 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".

[0640] In modern safety technology, accurately and non-contactually detecting movements and states within a space and rapidly transmitting that information remains a challenging task. In particular, areas such as personal safety and home security demand immediacy and accuracy, but conventional technologies fail to adequately meet these requirements. Therefore, there is a need for technologies that achieve highly accurate motion detection and rapid information transmission.

[0641] 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.

[0642] In this invention, the server includes means for collecting signal information from a wireless communication network via an information terminal, analysis means for analyzing the signal information to estimate the movement of objects or individuals in space, and means for detecting events and generating information based on the analysis results. This makes it possible to detect movements and states in space with high accuracy without contact and to quickly transmit necessary information to the user.

[0643] An "information terminal" is a general term for a device that collects signal information and processes data via a wireless communication network.

[0644] A "wireless communication network" refers to a network that uses radio waves to send and receive information, and is a means of communication that does not require a wired connection.

[0645] "Signal information" refers to characteristic data of radio waves acquired via a wireless communication network, including signal strength, phase change, and delay information.

[0646] "Analysis means" refers to techniques or algorithms for processing collected signal information and estimating the movement of objects or individuals in space.

[0647] "Objects or individuals" refer to items or people that are the target of detection within a space, and are the subjects of analysis necessary to estimate their actions and states.

[0648] "Event detection" refers to the process of recognizing specific actions or changes in circumstances based on analyzed signal information.

[0649] "Information creation" refers to the act of generating useful data and notifications for users based on detected events.

[0650] "User" refers to an individual or organization that receives the information or services provided by this invention.

[0651] This invention is a system for non-contact detection of the movement of objects or individuals in space using signal information from a wireless communication network. The system includes an information terminal (e.g., a smartphone or dedicated device) and a server. The information terminal collects signal information from the wireless communication network and transmits it to the server. The server analyzes the signal information using a machine learning algorithm to detect changes in the environment. The signal information includes signal strength, phase change, and delay information.

[0652] Software such as Python or TensorFlow is used for the analysis process. The server analyzes the temporal changes in signals and identifies specific behavioral patterns (e.g., people walking or furniture moving). If the analysis detects abnormal behavior or situations (e.g., unauthorized entry, potential falls), event information is generated.

[0653] This generated information, along with its importance and location, is sent to the information terminal. The user receives the notification through an application and checks the situation. This application allows users to view detailed information and take additional actions (e.g., check surveillance cameras or make an emergency call).

[0654] As a concrete example, this system could be implemented in a home where elderly people live. The server continuously monitors the Wi-Fi signal in the living space, and if it detects a pattern that deviates from normal movement (e.g., a sudden fall), it immediately sends an alert to the user's device. The user (e.g., family member or caregiver) can check the situation through the application and take action to provide assistance if necessary.

[0655] An example of a prompt for a generative AI model is: "Describe a system that uses wireless network signals to detect the movement of people in an environment. As a specific scenario, give an example of its use in a home where elderly people live, and describe in detail the notification process in that case." This prompt instructs the AI ​​to provide more detailed information about how the system works and its applications.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The server collects signal information from the wireless communication network via an information terminal. The input to this step is radio wave information emitted from the wireless communication network, and the output is a dataset of signal information. Specifically, the server periodically acquires signal strength, phase, and delay information and stores it in a database.

[0659] Step 2:

[0660] The server analyzes the collected signal information using machine learning algorithms. The input to this step is a dataset of collected signal information, and the output is the analysis results showing the behavioral patterns in space. Specifically, the server uses Python or TensorFlow to analyze signal intensity fluctuations and phase changes to identify specific patterns and anomalies.

[0661] Step 3:

[0662] The server detects specific events based on the analysis results. The input for this step is the analyzed behavioral pattern, and the output is the detected event information. Specifically, the server compares it to a pre-configured threshold, logs any anomalies as events, and flags them.

[0663] Step 4:

[0664] The server generates information based on detected event information and sends a notification to the user's information terminal. The input for this step is the detected event information, and the output is the notification message to the user. Specifically, the server generates the content of the notification message and sends it to the user's terminal via push notification.

[0665] Step 5:

[0666] The user receives notifications through an application on their device and takes the necessary actions. The input for this step is the notification message sent from the server, and the output is the user's action. Specifically, the user opens the app to check the notification content and, depending on the situation, can check the surveillance camera footage or take emergency action.

[0667] (Application Example 1)

[0668] 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".

[0669] To ensure elderly people live safely at home, there is a need for a system that can detect falls and abnormal behavior in real time and respond quickly. Furthermore, a challenge is to provide an efficient monitoring system that allows family members and caregivers to remotely understand the elderly person's activity patterns and respond immediately in emergencies.

[0670] 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.

[0671] In this invention, the server includes means for collecting signal data acquired from a wireless communication network, analysis means for processing the signal data to estimate the movement of surrounding objects and people, and means for detecting events and generating notifications based on the analysis results. This makes it possible to quickly detect abnormalities in elderly people and send necessary notifications.

[0672] A "wireless communication network" is a communication system that uses wireless technology to send and receive data.

[0673] "Signal data" refers to a collection of information acquired through wireless communication, and by analyzing its changes, it is possible to estimate the behavior within the environment.

[0674] "Analysis means" refers to the function of a system that includes algorithms and programs for processing signal data to estimate the movement of surrounding objects and people.

[0675] An "event" refers to a specific phenomenon or occurrence detected based on the actions or changes of the surrounding environment.

[0676] A "notification" is information generated based on detected events, and it is a message intended to convey that information to the user.

[0677] A "user device" is an electronic device used to receive and display notifications, such as a smartphone.

[0678] "Activity patterns" refer to the tendencies in behaviors and actions that a particular individual exhibits in their daily life.

[0679] An "information board" is an interface or dashboard used to visually display data and allow users to understand the situation.

[0680] This system collects and analyzes signal data from wireless communication networks to detect abnormal behavior in elderly individuals. The server collects signal data obtained through a typical Wi-Fi router and analyzes the data using a machine learning algorithm. This machine learning algorithm is implemented using TensorFlow and models signal changes to accurately predict behavior. Based on the analysis results, if an abnormal event occurs, the server uses Firebase to send a real-time notification to the user's device.

[0681] A device, such as a smartphone, displays received notifications to the user and provides an information board to understand the activity patterns of elderly individuals. This information board visualizes past data, making it easy to check behavioral trends in daily life. It also includes an emergency contact function to enable a quick response in emergencies.

[0682] As a concrete example, if an unusual walking pattern is detected when an elderly person goes to the toilet at night, an alert is immediately sent to the family, who can then check the situation through the app. This process provides effective guidance for optimizing the system's operation by inputting pre-prepared prompt messages into a generating AI model. The prompt message used is: "Design an AI model that analyzes the daily movement patterns of elderly people and detects abnormal movements. Refer to the example that utilizes WiFi signals as an anomaly detection system." This system operates by combining a WiFi router and a smartphone as hardware, and TensorFlow and Firebase as software.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The server collects signal data from the WiFi router in real time. It takes WiFi signal strength, phase changes, and delay information as input and stores this data for analysis.

[0686] Step 2:

[0687] The server analyzes the collected signal data using a machine learning algorithm based on TensorFlow. Using the signal data collected in the previous step as input, it models the changes in the data and estimates the behavioral patterns within the environment. This process yields the estimated behavioral results as output.

[0688] Step 3:

[0689] The server determines whether an anomaly is detected based on the analyzed operating patterns. In this step, the estimated results are used as input to detect the event. The detected anomalies are output, providing the type and severity of the anomaly, which serves as the basic data for generating notifications.

[0690] Step 4:

[0691] When the server detects an anomaly, it uses Firebase to send a notification to the user's device. The server uses the details of the detected anomaly as input to generate a real-time alert on the user's device. The notification message is then sent to the user as output.

[0692] Step 5:

[0693] The terminal receives notifications from the server and displays them to the user. It uses notification data sent from the server as input and visually represents it on the screen. This step provides output that allows the user to check the situation and take appropriate action.

[0694] Step 6:

[0695] Users can view the daily activity patterns of elderly individuals through an information board on their terminal. Past data history obtained from the terminal is used as input, and a detailed activity dashboard is displayed as output, visualizing the trends.

[0696] Step 7:

[0697] The server sets prompt statements to optimize the generated AI model throughout the operation of the entire system. These prompt statements are used as input to obtain feedback that enhances the system's effectiveness. The results are then reflected in the next analysis and output.

[0698] 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.

[0699] This invention is a system that uses wireless network signals to detect the movement of objects and people in the environment, while simultaneously recognizing the user's emotions by combining them with an emotion engine. This configuration makes it possible to more accurately understand the user's situation and provide feedback tailored to their individual needs.

[0700] System Embodiment

[0701] Data collection of signals

[0702] The server collects signal data in real time from WiFi routers via the wireless network. It also acquires data from other sensor devices as needed to support more detailed environmental analysis.

[0703] Data analysis and motion detection

[0704] The server uses machine learning algorithms to analyze collected signal data and identify movement within the environment. This includes human movement and specific motion patterns, and the server estimates what each of these might mean.

[0705] Emotion recognition by an emotion engine

[0706] The emotion engine embedded in the server analyzes the user's emotional state based on signal data and other sensor data. This engine comprehensively evaluates the user's facial expressions, voice, behavioral patterns, and other factors to identify their current emotion.

[0707] Generating notifications and feedback

[0708] Based on the analysis results, the server generates notifications that correspond to detected events and the user's emotional state. These notifications are sent to the user's device and are tailored to the user's situation. For example, if the server suspects the user is stressed, a notification encouraging calmness will be generated.

[0709] User notifications and adaptive responses

[0710] Users receive notifications on their devices and check their status through the content of those notifications. This system learns from user feedback, enabling more accurate emotion recognition and adaptive responses.

[0711] Specific example

[0712] As an example, consider the use of this system in a home security and care environment. The server detects changes in the movement patterns of people within the living space and, using an emotion engine, senses when a resident is in an unstable emotional state. In this case, it generates a notification and sends it to the family's device, providing real-time information about the situation. This allows the family to take appropriate countermeasures based on the situation.

[0713] This invention combines wireless networks and emotion recognition technology to realize a system with a wider range of applications, enabling the provision of useful information in various usage scenarios.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The server collects signal data from WiFi routers and other sensor devices via a wireless network. This includes signal strength and channel status information (CSI), which are then integrated and processed with data from other sensors.

[0717] Step 2:

[0718] The server preprocesses the collected signal data. This preprocessing includes denoising and normalizing the data. This prepares the data for subsequent analysis.

[0719] Step 3:

[0720] The server uses pre-processed data to run machine learning algorithms and analyze the movement of objects and people in the environment. This analysis allows for the detection of specific movement patterns and abnormal movements.

[0721] Step 4:

[0722] The server uses an emotion engine to estimate the user's emotional state. In addition to signal data, it also analyzes video and audio data if available, performing facial recognition and voice analysis.

[0723] Step 5:

[0724] The server evaluates the importance of an event based on the analyzed movement and the user's emotional state, and generates an appropriate notification. The notification includes the type of event, its location, the user's emotional state, and the recommended response.

[0725] Step 6:

[0726] The server sends the generated notification to the user's device. The notification is displayed through a dedicated application or other appropriate interface.

[0727] Step 7:

[0728] The user checks the notifications received on their device. Based on the content of the notifications, they take appropriate action according to their emotional state and surrounding circumstances. For example, if stress is detected, suggestions will be made to encourage actions to relax.

[0729] (Example 2)

[0730] 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".

[0731] In modern society, understanding the movement of objects and people in the environment in real time, and recognizing the emotional state of individual users, is a crucial challenge in order to ensure the health and safety of individuals. However, conventional technologies have struggled to accurately analyze movement and emotions and provide appropriate feedback.

[0732] 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.

[0733] In this invention, the server includes means for receiving wireless data and acquiring information, analysis means for processing the information and estimating the behavior of objects in space, and means for detecting events and generating instructions based on the analysis results. This makes it possible to understand the movements of objects and people in the environment and to provide appropriate feedback according to the user's emotional state.

[0734] "Wireless data" refers to a collection of information transmitted and received via wireless communication within a given space, including signal strength and device connection status.

[0735] "Means for acquiring information" refers to a device or system that has the function of receiving data via wireless communication and extracting necessary information.

[0736] "Analysis means" refers to the technologies and algorithms used to process acquired information and analyze the behavior of objects and people in a space.

[0737] "Means of detecting events" refers to the process of identifying specific events or situations from analyzed data and prompting the next steps based on that identification.

[0738] "Means for generating instructions" refers to functions that create relevant notifications and feedback based on detected events and the user's emotional state.

[0739] "User emotional state" refers to the user's current psychological or emotional state, estimated from their facial expressions, voice, behavioral patterns, etc.

[0740] "Feedback" refers to information and suggestions provided to users based on analysis results, and is tailored to the user's psychological or behavioral needs.

[0741] This invention is a system that combines wireless data and emotion analysis technology to detect movement within the environment and the user's emotional state with high accuracy, and to provide appropriate feedback based on that.

[0742] The server collects information via hardware designed to receive wireless data from Wi-Fi routers and other wireless communication devices. This collected information is processed by analysis software incorporating machine learning algorithms. Specifically, deep learning models and statistical methods are used to analyze the behavior of objects and people in the environment and identify their movements and patterns.

[0743] The server is equipped with an emotion engine that comprehensively evaluates the user's facial expressions, voice, and behavioral patterns. This allows the engine to recognize the user's current emotional state and generate instructions and notifications based on that state. For example, if the server detects a user's stress level, it may generate feedback suggesting relaxation methods. These notifications and feedback are sent to the user's device, allowing the user to adjust their behavior accordingly.

[0744] One specific example of its use is in home security and care environments. The server can detect changes in patterns or emotional instability among people in the living space and send notifications to family members' devices. This provides real-time information about the situation and helps families take appropriate action.

[0745] Possible prompts for the generating AI model include, "Analyze movement and emotional data in the current living environment and create suggestions for stress reduction." This prompt allows the AI ​​model to generate basic data for providing appropriate feedback based on the user's emotional state. In this way, the present invention aims to effectively utilize wireless networks and emotion recognition technology to provide information that meets the user's needs.

[0746] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0747] Step 1:

[0748] The server receives signal data from wireless communication devices. Inputs include wireless signals from WiFi routers and other sensors. Data processing involves logging signal strength and connection status from each device and monitoring their changes in real time. The output is signal strength data.

[0749] Step 2:

[0750] The server analyzes signal data using machine learning algorithms. The input is the wireless signal data collected in step 1. As part of the data calculation, a deep learning model is used for pattern recognition to estimate the movement of objects and people in the environment. The output is the analysis results regarding the identified movements and their patterns.

[0751] Step 3:

[0752] The server uses an emotion engine to estimate the user's emotional state. Inputs include the analysis results from step 2, and additional sensor data from the camera and microphone. Data processing involves applying facial expression and voice analysis algorithms to evaluate behavioral patterns. The output provides information about the user's emotional state.

[0753] Step 4:

[0754] The server generates appropriate notifications based on the analysis results obtained. The input is the analysis results of movement and emotion. As data generation, it creates customized notifications based on template messages, tailored to the user's situation. The output is a notification that includes specific feedback and suggestions.

[0755] Step 5:

[0756] The server sends the generated notification to the user's device. The input is the notification generated in step 4. Specifically, the message is delivered using a push notification service. The output is the notification displayed on the user's device.

[0757] Step 6:

[0758] The user receives a notification on their device and checks its contents. The input is the content of the notification received on the device. Specifically, the user opens the notification center on their smartphone, checks the provided feedback, and modifies their actions as needed. The output is the adaptive action the user takes based on the notification.

[0759] (Application Example 2)

[0760] 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".

[0761] In modern society, monitoring and ensuring the safety of elderly people who require care is a crucial issue. However, conventional surveillance cameras and sensors are insufficient for accurately capturing their movements or interpreting their emotional states, which can prevent prompt and appropriate responses when abnormalities occur. Therefore, there is a need to balance the safety of the elderly with reducing the burden on caregivers.

[0762] 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.

[0763] In this invention, the server includes means for collecting signal information acquired from a wireless communication network; analysis means for processing the signal information to estimate the actions of objects and individuals in the environment; means for detecting events based on the analysis results, recognizing the user's emotional state using an emotion engine, and generating notifications; and means for transmitting the notifications to a user device and providing adaptive feedback. This makes it possible to accurately grasp the actions and emotional states of elderly people in real time and prompt caregivers to respond quickly.

[0764] A "wireless communication network" is a network system used to send and receive data and signals wirelessly.

[0765] "Signal information" refers to data acquired through wireless communication networks, including information such as location and movement.

[0766] "Analysis means" refers to a device or method for processing collected signal information and estimating the actions of objects or individuals within the environment.

[0767] An "emotion engine" is software or a system that analyzes a user's emotional state based on signal information and other sensing data.

[0768] A "user device" is a terminal that a user carries or installs for use, and is a device that receives and displays notifications.

[0769] Adaptive feedback is a response that suggests the most appropriate information or instructions for the user based on their situation and emotions at that particular time.

[0770] To implement this invention, a system for acquiring signal information via a wireless communication network is first required. The server collects this signal information and uses analysis means to estimate the actions of objects and individuals in the environment. This analysis utilizes machine learning algorithms that model changes in signal information. Next, the server uses an emotion engine to analyze the user's emotional state from the signal information. This emotion engine is used to evaluate the correlation between action data and emotional data.

[0771] Based on the analysis results, the server generates notifications corresponding to the detected events and emotional states and sends them to the user's device. The user's device receives and displays these notifications, providing the user with situational adaptive feedback. This feedback allows the user to confirm their own actions and emotional states and adjust their behavior as needed.

[0772] To implement this system, software written in programming languages ​​such as Python will be used, along with user devices such as smartphones and tablets. Machine learning algorithms such as TensorFlow and PyTorch will be selected. For example, if an elderly person is about to fall at home and anxiety is indicated along with the movement, the system will send a notification to the caregiver's device stating "Movement: risk of fall, Emotion: anxiety."

[0773] An example of a specific prompt: "An elderly person is showing signs of being at risk of falling at home and is expressing anxiety. What can be done?"

[0774] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0775] Step 1:

[0776] The server acquires signal information in real time via a wireless communication network. The input is raw data from a WiFi router, and the output is a time-series of signal data. This data is used in subsequent analysis to estimate the actions of subjects or individuals.

[0777] Step 2:

[0778] The server processes the acquired signal information using analysis tools. Specifically, it uses a machine learning model to analyze signal changes and performs data processing to estimate the operation. The input is the signal data acquired in step 1, and the output is the estimated operation information.

[0779] Step 3:

[0780] The server evaluates the user's emotional state based on behavioral information estimated using an emotion engine. This evaluation applies an algorithm based on the user's behavioral patterns. The input is the behavioral information from step 2, and the output is the user's emotional state.

[0781] Step 4:

[0782] The server generates notifications based on emotional state and behavioral information. Specifically, it formats the content, including the type of event detected and the emotional state, into a notification. The input is the emotional state and behavioral information from step 3, and the output is the generated notification message.

[0783] Step 5:

[0784] The user device displays the notification message received from the server. The input received by the terminal is the notification message generated in step 4, and the output is displayed as adaptive feedback to the user.

[0785] Step 6:

[0786] The user understands the current situation through notification messages displayed on the device and takes action as needed. Specific actions include adjusting behavior based on the notification content. The input is the notification message from the device, and the output is the user's adjusted actions.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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."

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means for collecting signal data received from a wireless network,

[0811] An analysis means for processing the aforementioned signal data to estimate the movement of objects and people in the environment,

[0812] A means for detecting an event and generating a notification based on the aforementioned analysis results,

[0813] Means for sending the aforementioned notification to the user device,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, characterized in that the analysis means models the change in the signal using a machine learning algorithm and grasps the operation with high accuracy.

[0817] (Claim 3)

[0818] The system according to claim 1, characterized in that it generates the notification as information including the type, location, and severity level of the detected event.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A means of collecting signal information from a wireless communication network via an information terminal,

[0822] An analysis means for analyzing the aforementioned signal information to estimate the movement of objects or individuals in space,

[0823] A means for detecting events and generating information based on the aforementioned analysis results,

[0824] Means for transmitting the aforementioned information to the user's device,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, characterized in that the analysis means models the fluctuations of the signal using information processing technology and grasps the operation with high accuracy.

[0828] (Claim 3)

[0829] The system according to claim 1, characterized in that it generates the aforementioned information as content including the type, location, and severity level of the detected event.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] A structure for collecting signal data acquired from a wireless communication network,

[0833] An analytical structure that processes the aforementioned signal data to estimate the movement of surrounding objects and people,

[0834] A structure that detects events and generates notifications based on the aforementioned analysis results,

[0835] A structure for transmitting the aforementioned notification to the user device,

[0836] A structure that has an emergency contact function when an abnormality is detected,

[0837] A structure that provides users with an information board that allows them to visualize their activity patterns,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, characterized in that the analysis structure uses machine learning techniques to model changes in signals and accurately understands their operation.

[0841] (Claim 3)

[0842] The system according to claim 1, characterized in that it generates the notification as information including the type, location, and severity level of the detected event.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] A means of receiving wireless data and acquiring information,

[0846] An analysis means for processing the aforementioned information and estimating the behavior of an object in space,

[0847] A means for detecting an event and generating instructions based on the aforementioned analysis results,

[0848] Means for transmitting the aforementioned instructions to the user terminal,

[0849] Means for generating the aforementioned instructions so that they include feedback corresponding to the user's emotional state,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, characterized in that the analysis means models the change in the signal using a learning algorithm and grasps the behavior with high accuracy.

[0853] (Claim 3)

[0854] The system according to claim 1, characterized in that it generates the aforementioned instructions as information including the type, location, and severity level of the detected event, and includes an adaptive response based on the user's emotions.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] A means for collecting signal information obtained from a wireless communication network,

[0858] An analysis means for processing the aforementioned signal information to estimate the actions of objects or individuals in the environment,

[0859] A means for detecting events based on the analysis results, recognizing the user's emotional state using an emotion engine, and generating notifications,

[0860] Means for transmitting the aforementioned notification to the user device and providing adaptive feedback,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that the analysis means uses a machine learning algorithm to model changes in signals, accurately grasp the behavior, and analyze the emotional state.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that it generates the notification as information including the type, location, importance level, and emotional state of the detected event, and prompts the caregiver to take appropriate action. [Explanation of symbols]

[0866] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting signal data obtained from a wireless communication network, An analysis means for processing the aforementioned signal data to estimate the movement of surrounding objects and people, A means for detecting an event and generating a notification based on the aforementioned analysis results, Means for transmitting the aforementioned notification to the user device, When an anomaly is detected, a means with an emergency contact function, A means of providing an information board that allows users to visualize their activity patterns, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means models the change in the signal using a machine learning method and accurately grasps the operation.

3. The system according to claim 1, characterized in that it generates the notification as information including the type, location, and severity level of the detected event.

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