system

Abstract

We provide the system. [Solution] A terminal equipped with sensors to detect user movements, A server that analyzes detected motion data and generates a response corresponding to the motion, A feedback mechanism for communicating the generated response to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a congested environment or a situation where voice cannot be used, there is a problem that it is difficult for a user to communicate smoothly with an electronic device or an artificial intelligence agent. Even in such a situation, there is a need for a means that allows the user to intuitively operate and communicate without stress.

Means for Solving the Problems

[0005] This invention provides a terminal equipped with a sensor that detects the user's head movements, and transmits the detected movement data to a server. The server analyzes the received data and generates a response corresponding to the movement. The generated response is provided to the user through a feedback mechanism. This makes it possible to accurately convey the user's intentions and obtain an appropriate response even in environments where voice is unavailable.

[0006] A "sensor" is a device used to detect a user's movements or state, and in this invention in particular, it measures changes in head movements in real time.

[0007] A "terminal" is an electronic device that a user directly operates or carries, and that has the function of detecting its operation and processing or transmitting its data.

[0008] "Motion data" refers to data that quantifies or structures information about the user's body movements, particularly head movements, and is the data that is subject to analysis.

[0009] A "server" is a computing device that connects to terminals via a network, analyzes received data, and generates appropriate responses.

[0010] "Analysis means" refers to software or hardware functions used to process received data and understand its content, playing a role in interpreting operational data and determining a response.

[0011] A "feedback mechanism" is a mechanism or method for transmitting a response generated by a server to a user, presenting information to the user in the form of sound, vibration, or visual.

[0012] A "communication device" is a module that has the function of sending and receiving data between a terminal and a server, and transmits information via a network such as the internet. [Brief explanation of the drawing]

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

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

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

[0016] In the following embodiments, a 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, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, 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 provides a system that detects the user's head movements and communicates with an AI agent based on these movements. The processing of the program of this system will be described in detail below.

[0035] Gesture detection

[0036] The device uses sensors built into the earphones to monitor the user's head movements in real time. These sensors measure acceleration and angular velocity, acquiring the movement as digital data. The device records each of the user's actions—shaking their head, nodding, or tilting their head—as separate data points.

[0037] Specific example: When a user nods their head in response to "yes," the device recognizes this movement as a "positive response."

[0038] Data analysis and transmission

[0039] The recognized motion data is pre-processed on the terminal to remove noise and identify the type of motion. This information is then sent to the server via the internet. The terminal is designed to packetize the data, ensuring low latency and stable communication.

[0040] Specific example: If the device detects a "head bobbing" motion, that motion data is sent to the server as a "positive response".

[0041] Response generation and feedback

[0042] The server analyzes the received operational data to determine an appropriate response. This analysis includes an algorithm that checks the type and content of the data and determines the user's intent. After the server generates a response, that information is fed back to the terminal.

[0043] Specific example: The server confirms that it is a "positive response" and generates the instruction "Approved. Please select the next step," which is then sent to the terminal.

[0044] Notification to the user

[0045] The device receives a response from the server and provides feedback to the user through voice or vibration. This allows the user to confirm the appropriate action and proceed to the next step.

[0046] Specific example: The device provides the user with a voice feedback message saying, "Please select the next step."

[0047] In this way, one embodiment of the invention makes it possible for users to interact naturally with an AI agent even in situations where voice is unavailable.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The device uses sensors built into the earphones to detect the user's head movements in real time. When the user nods, shakes their head from side to side, or tilts their head, the sensors measure the acceleration and angular velocity and capture it as digital data.

[0051] Step 2:

[0052] The device preprocesses the acquired motion data. Preprocessing includes denoising the data obtained from the sensors and identifying the type of motion. This classifies whether the motion is positive, negative, or selective.

[0053] Step 3:

[0054] The terminal packets pre-processed operational data and sends it to the server via the internet. This communication incorporates error correction technology to ensure data reliability.

[0055] Step 4:

[0056] The server analyzes the received behavioral data. Using analysis tools, it interprets the user's intent represented by the data and generates an appropriate response corresponding to the action. The server refers to a predefined gesture mapping database to link actions and responses.

[0057] Step 5:

[0058] The server sends the generated response to the terminal. The response data consists of instructions for proceeding to the next step and is presented in a user-friendly format.

[0059] Step 6:

[0060] The device notifies the user of the response data received from the server. This notification is provided via earphones as audio or vibration feedback. This allows the user to confirm the response and proceed to the next action.

[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] Many modern interfaces are designed with voice input in mind, making it difficult to meet the needs of diverse users or in environments where voice input is unavailable. Therefore, there is a need to develop interfaces that enable natural communication without relying on voice.

[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 analyzing pre-processed behavioral data, means for transmitting the generated response, and means for classifying the behavior into multiple types. This enables users to intuitively communicate with the AI ​​even in situations where voice is unavailable.

[0066] A "sensor" is a device used to detect the user's head movements in real time.

[0067] "Pre-processing" refers to the process of performing initial processing, such as noise reduction, on detected head movements.

[0068] An "information processing device" is a device that receives pre-processed data and performs analysis and response generation.

[0069] A "notification means" is a means of communicating the generated response to the user.

[0070] A "classification means" is an algorithm or device for classifying a user's head movements into several types and selecting an appropriate response.

[0071] "Communication means" refers to technology or devices for transmitting detected head movement data to an information processing device via the Internet.

[0072] The system in this invention detects the user's head movements and generates a response corresponding to those movements, thereby achieving natural communication. The following describes embodiments for carrying out this invention.

[0073] The user wears a device equipped with sensors. This device has a built-in accelerometer and gyroscope, which allows for real-time detection of the user's movements such as head movements, nodding, and tilting. The terminal improves the accuracy of the digital data obtained from these sensors by first performing preprocessing, such as noise reduction. The preprocessed data is then classified by type and transmitted to a server via the internet.

[0074] The server uses a generative AI model to analyze the received head movement data. This analysis employs sophisticated algorithms to determine what the movements mean. For example, if it recognizes an "affirmative response," the server generates an appropriate response such as "Approved. Please select the next step." These responses are sent back to the terminal via the network and provided to the user through voice or vibration feedback. Even without voice input, the user can intuitively guide to the next action.

[0075] As a concrete example, even if a user has difficulty responding verbally during a meeting, they can indicate "yes" by nodding their head. This action is immediately recognized as an "affirmative response," and further instructions are generated. An example of a prompt might be, "Please describe the method for generating the AI ​​agent's response based on the user's actions." In this way, non-verbal and natural communication becomes possible.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device uses sensors to detect the user's head movements, thereby acquiring motion data. Specifically, an accelerometer and gyroscope work together to capture motion patterns such as head shakes and nods. The input is real-time motion, and the output is raw data in digital format.

[0079] Step 2:

[0080] The terminal performs preprocessing on the acquired raw data. This processing includes noise reduction and data normalization. For example, it removes minute vibrations from the sensor and environmental noise. The input is raw data from the sensor, and the output is clean, denoised data.

[0081] Step 3:

[0082] The device analyzes pre-processed data and identifies the type of action. It uses an algorithm to classify the data into categories such as "positive response" and "negative response." For example, nodding is identified as "positive." The input is clean data, and the output is classified action data.

[0083] Step 4:

[0084] The terminal packets the classified behavioral data and sends it to the server. A stable communication protocol is used to ensure that the necessary data is transmitted reliably. The input is the classified behavioral data, and the output is the data packets sent to the server.

[0085] Step 5:

[0086] The server analyzes the received data packets and generates an appropriate response using a generative AI model. The response algorithm is executed based on the content of the data. For example, for data indicating an "affirmative response," it generates the instruction "approved." The input is the action data packet, and the output is the generated response.

[0087] Step 6:

[0088] The server sends the generated response to the terminal. This communication requires immediacy, and a high-speed network is used. The input is the generated response, and the output is the data sent to the terminal.

[0089] Step 7:

[0090] The terminal transmits the response received from the server to the user. Feedback using voice and vibration allows the user to recognize the next action. For example, a voice device might play a message such as "Please select the next step." The input is the response data from the server, and the output is the feedback to the user.

[0091] (Application Example 1)

[0092] 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."

[0093] In recent years, there has been a growing need for more natural and contactless ways to control devices within the home, beyond just voice and hand gestures. However, traditional control methods have made it difficult for users to comfortably operate devices in many situations. In particular, there is a need for more intuitive control of home devices when hands are occupied or when voice control is not possible in a quiet environment.

[0094] 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.

[0095] In this invention, the server includes a measuring device means for detecting the user's physical movements, an information processing device means for analyzing the detected movement information and generating a response corresponding to the movement, a return information means for communicating the generated response to the user, an analysis means for classifying multiple movements and determining a response according to each classification, and a control means for controlling devices in the home based on the user's movement input. This enables the user to operate devices in the home with natural movements without using voice.

[0096] A "user" is an individual or entity that uses a system to input actions and receives responses or control based on those actions.

[0097] "Physical movement" refers to physical actions that a user intentionally performs, such as neck movements or changes in posture.

[0098] A "measuring device" is a sensor device that collects the user's physical movements as digital data in real time.

[0099] "Motion information" refers to digital data of the user's physical movements detected by a measuring device.

[0100] An "information processing device" is a computer or server that analyzes operational information and generates an appropriate response.

[0101] A "feedback information means" is a method or device for notifying the user of the generated response, and provides feedback through sound or light.

[0102] An "analysis method" is an algorithm or process that classifies operational information into multiple types and selects an appropriate response according to each type.

[0103] "Control means" refers to mechanisms and protocols for operating devices within the home based on user input.

[0104] "Home devices" refers to electrical appliances and services used in typical households, including air conditioners, lighting, and smart home appliances.

[0105] "Natural movements" are simple physical actions that users can perform on a daily basis without the need for special training or additional means.

[0106] This invention is a system that detects a user's natural movements within the home and controls home devices based on those movements. The system consists of a measuring device, an information processing device, and a return information means.

[0107] The measuring device uses sensors embedded in earphones or other devices to detect the user's body movements in real time. The sensors measure acceleration and angular velocity, and store the acquired motion information as digital data. This includes body movements such as the user shaking their head, nodding, or tilting their head.

[0108] The information processing device analyzes the motion information transmitted from the measuring device. The motion information is preprocessed through a noise-canceling filter and then classified into several types by the analysis means. The information processing device generates an appropriate response based on the type of motion and transmits that information back to the information means.

[0109] The feedback mechanism is responsible for notifying the user of the generated response. This involves conveying information to the user through audio or optical feedback.

[0110] For example, a user can perform a simple physical movement, and digital devices in the home will respond instantly, such as turning off the air conditioner or adjusting the TV volume. This system allows users to intuitively operate devices without using their hands.

[0111] Based on an example of a prompt message, the system will perform an action such as, "Detect that the user nods and turn on the room lights."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The device detects the user's physical movements using measuring equipment. The input is the user's physical movements, which are acquired as digital data by the sensor. This data consists of numerical data related to acceleration and angular velocity.

[0115] Step 2:

[0116] The device preprocesses the acquired motion data using a noise-canceling filter. The input is unprocessed motion data, and unnecessary noise is removed from the raw data obtained from the sensor to output clean motion data.

[0117] Step 3:

[0118] The terminal analyzes pre-processed motion data and classifies it into multiple motion types using an analysis tool. The input is pre-processed motion data, the algorithm recognizes the motion pattern, and outputs the result as a motion type.

[0119] Step 4:

[0120] The server analyzes the action type transmitted from the terminal, and the information processing device generates an appropriate response based on this. The input is the action type, and the optimal response content is generated using a generative AI model and output in prompt format.

[0121] Step 5:

[0122] The server sends the generated response to the terminal, which then uses a return information mechanism to notify the user. The input is the response data from the server, and the output is communicated to the user as audio or optical feedback.

[0123] Step 6:

[0124] Based on user feedback, the system controls devices within the home. This means the input is user feedback, and the output is the result of specific device operations. For example, a nod of the head might turn on a light.

[0125] 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.

[0126] This invention provides a system that detects the user's head movements, combines them with an emotion engine to understand the user's intentions, and communicates with an AI agent. The processing of this system's program is described in detail below.

[0127] Gesture and emotion detection

[0128] The device continuously monitors the user's head movements through sensors built into the earphones. It also features an emotion engine that detects the user's emotional state based on their facial expressions and voice patterns. This allows the device to understand not only their actions but also their emotional state simultaneously.

[0129] Specific example: If a user shakes their head and says "no" with a stern expression, the emotion engine will recognize this as a "negative and unpleasant" emotion.

[0130] Data analysis and transmission

[0131] The terminal preprocesses the acquired motion and emotion data, removing noise and then identifying the type of motion and emotion category. This information is packetized and sent to the server via the internet. The terminal ensures reliable and efficient communication with minimal errors.

[0132] Specific example: If the emotion engine detects a "negative action" accompanied by "negative and unpleasant" emotions, that data is sent to the server.

[0133] Response generation and feedback

[0134] The server analyzes the received behavioral and emotional data and determines the optimal response. Using a dedicated algorithm, the server undergoes a process to deeply understand the user's intentions and emotions. The generated response is sent to the terminal and provided as feedback to the user.

[0135] Specific example: When the server receives a "no" response accompanied by "negative and unpleasant" feelings, it generates feedback saying, "Understood, I'm sorry if I offended you."

[0136] Adaptive notifications to users

[0137] The device receives feedback from the server and notifies the user in a way that is appropriate to their emotions. The feedback is delivered via voice or vibration, taking into account the user's current emotions.

[0138] Specific example: A voice message plays from the device saying, "We apologize, we will improve so that this does not cause any offense."

[0139] This configuration allows users to engage in richer interactions with AI agents, including not only normal motion recognition but also emotional engagement. This enables more intuitive and emotionally resonant communication.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The device detects the user's head movements using sensors built into the earphones. Simultaneously, an emotion engine collects the user's facial expressions and voice characteristics and evaluates their emotions in real time. If the user nods in agreement, that data is recorded digitally on the device.

[0143] Step 2:

[0144] The terminal then enters the preprocessing stage for the acquired behavioral and emotional data. Here, noise is removed, and the type of behavior (positive, negative, choice) and emotion (joy, sadness, surprise, etc.) are identified. This data is then compiled into packets for analysis.

[0145] Step 3:

[0146] The terminal sends pre-processed data to the server. The system is designed to maintain data integrity and ensure effective delivery to the server during communication over the internet.

[0147] Step 4:

[0148] The server analyzes the received behavioral and emotional data. Here, the server applies multiple algorithms to determine the appropriate response based on the user's intentions and emotions. For example, if a "positive behavior" matches the emotion of "joy," it generates positive feedback.

[0149] Step 5:

[0150] The server sends the generated response to the terminal. The response includes special instructions and information tailored to the user's situation.

[0151] Step 6:

[0152] The device receives responses from the server and provides users with emotionally appropriate notifications. In particular, it uses voice output and vibration feedback to convey information in a way that resonates with the user's emotions. If the user shows a positive response to the system, the device will play a voice message saying, "I gladly accept this choice."

[0153] (Example 2)

[0154] 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".

[0155] Accurately understanding a user's intentions, including not only their actions but also their emotional state, and providing them with appropriate and emotionally sensitive responses has been difficult with conventional technologies. There is a need for systems that enable more intuitive and natural communication in such user interactions.

[0156] 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.

[0157] In this invention, the server includes means for utilizing a terminal equipped with sensors for detecting the user's actions and emotional state, computation means for analyzing the detected action data and emotional data and generating responses corresponding to the actions and emotions, and feedback means for appropriately communicating the generated responses to the user and notifying them in a manner that takes the user's emotions into consideration. This enables a deeper understanding of the user's intentions and communication that is more empathetic to their emotions.

[0158] A "user" is a person who uses the system and whose actions and emotions are detected.

[0159] "Motion" refers to physical movements expressed by the user moving their head or body, which are detected by sensors.

[0160] "Emotional state" refers to the internal psychological state expressed through the user's facial expressions and vocal patterns.

[0161] A "sensor" is a device installed in a terminal to detect the user's actions and emotional state.

[0162] A "terminal" is an electronic device equipped with sensors to detect the user's actions and emotions.

[0163] "Data" refers to information about detected user behavior and emotions, which is used for analysis and response generation.

[0164] "Computation means" refers to the computational techniques and methods used by the server to analyze the data detected and generate a corresponding response.

[0165] A "feedback mechanism" is a method of communicating the generated response to the user and notifying them in a way that is adapted to the user's current situation and emotions.

[0166] "Communication means" refers to the technologies and protocols used to transmit detected data to a server via the internet.

[0167] This invention provides a system that detects user actions and emotions and generates responses corresponding to those emotions.

[0168] Hardware configuration

[0169] The device is equipped with sensors to detect the user's head movements. These sensors, based on sensor technology, are capable of detecting head movements and angles. The device also includes an emotion engine to recognize emotional states, analyzing emotions through facial expressions and voice patterns.

[0170] Software Configuration

[0171] The server receives behavioral and emotional data transmitted from the terminal. The received data is processed by an internal generative AI model and data analysis software to generate a response adapted to the user's behavior and emotions. This response is then sent back to the terminal for prompt feedback to the user.

[0172] For example, if a user shakes their head and makes an unpleasant expression, the device detects a "negative action" and an "unpleasant emotion." Based on this, the server generates a response such as, "I'm sorry if I made you uncomfortable."

[0173] Example of a prompt

[0174] Examples of prompt messages include the following:

[0175] "The user is shaking their head and has a grim expression. What kind of feedback should we provide to this user?"

[0176] In this way, the system can better understand the user's state and interact with the user in a way that is sensitive to their emotions. This technology enables more natural and intuitive communication.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The device continuously monitors the user's head movements using sensors built into the earphones. The sensors acquire user movement and angle information as input data. Based on this data, the amount and speed of head movement are calculated, and the type of movement (e.g., nodding, side-to-side shaking) is identified. For example, if the user shakes their head from side to side, this movement is categorized as a "negative movement."

[0180] Step 2:

[0181] The device analyzes facial expressions and voice patterns to detect the user's emotional state using an emotion engine. Input data includes the user's facial features and voice waveforms. This data is analyzed by the emotion engine, which identifies emotional categories such as smiles, surprise, and anger. For example, if the user has a stern expression, it is tagged as an "unpleasant emotion."

[0182] Step 3:

[0183] The device aggregates the acquired behavioral and emotional data and applies a noise reduction filter to form a clean dataset. The input consists of raw behavioral and emotional data. The filtering process removes noise, resulting in output organized into behavioral and emotional categories. Specifically, data packets containing "negative behavior" and "unpleasant emotions" are generated.

[0184] Step 4:

[0185] The terminal packets clean data and sends it to the server over the internet using a reliable communication protocol. The input data consists of packetized behavioral and emotional information. Communication error correction is performed to ensure that the data reaches the server safely. Specifically, when a data packet is sent to the server and an error is detected, a retransmission process is performed.

[0186] Step 5:

[0187] The server analyzes the received data. The input data includes behavioral and emotional data packets from the terminal. A generative AI model uses this to delve deeper into the user's intent and calculate the optimal response. For example, a response such as "I'm sorry if I offended you" is generated.

[0188] Step 6:

[0189] The server sends the generated response to the terminal, and the terminal relays the response to the user. The input data is the generated response message. The terminal appropriately provides feedback to the user through voice or vibration. Specifically, the terminal plays a voice message saying, "I'm sorry."

[0190] (Application Example 2)

[0191] 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".

[0192] Current user interfaces lack the ability to comprehensively understand user behavior and emotions and provide appropriate real-time responses. Therefore, there is a need for systems that are more empathetic to user emotions and enable more intuitive and human-like interactions.

[0193] 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.

[0194] In this invention, the server includes an information processing device equipped with sensors for detecting the user's actions and emotional state; an information processing device that analyzes the detected action data and emotional data and generates a response corresponding to the user's intentions; and an information processing device that communicates the generated response to the user and notifies them in a manner adapted to their emotions. This makes it possible to provide appropriate responses in real time based on the user's actions and emotions.

[0195] A "user" is a person who performs operations or gives instructions via information processing equipment or information and communication networks.

[0196] "Motion data" refers to information about the user's body movements detected by sensors.

[0197] "Emotional data" refers to information about a user's emotional state that is inferred from their facial expressions, voice, and actions.

[0198] A "sensor" is a device or instrument used to detect a user's actions or emotions.

[0199] An "information processing device" is a computing device that analyzes input data from a user and generates an appropriate response.

[0200] An "information and communication network" is a means of communication for transmitting data over a network.

[0201] "Response" refers to the output information from an information processing device based on detected and analyzed motion data and emotion data.

[0202] A "notification" is information provided to a user through visual or auditory means.

[0203] This invention relates to a system that detects user actions and emotions and generates appropriate responses based on them. The system includes the following components:

[0204] First, the user utilizes a device equipped with sensors. This device incorporates various sensors to detect head movements and facial expressions. Specifically, sensors such as accelerometers and cameras acquire data on the user's movements and facial expressions. This data forms a crucial foundation for monitoring the user's movements and emotional state in real time.

[0205] The motion and emotion data acquired by the sensors is analyzed by an information processing device. This device runs data analysis algorithms using Python and artificial intelligence models for emotion estimation using PyTorch. As a result of the analysis, the user's actions and emotions are clearly identified, and the information necessary for response is extracted.

[0206] The server generates a response tailored to the user based on the analyzed data. This response uses natural language processing technology to ensure it reflects the user's emotions and intentions. For example, if the user looks troubled, the server might generate a suggestion such as, "Is there anything I can help you with?"

[0207] The generated response is communicated to the end user via an information processing device. The notification is provided visually or audibly, utilizing a speech synthesizer or display. A stable communication method is used for transmission, minimizing response delays.

[0208] As a concrete example, a scenario could be envisioned where a home robot reads the user's anxiety from their face and movements and suggests, "Shall I play some relaxing music?" An example of a prompt to input into the generative AI model would be, "The user is shaking their head from side to side and showing a confused expression. Please generate appropriate feedback."

[0209] This configuration makes it possible to achieve intuitive and human-like interactions based on the user's actions and emotions.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The device detects the user's movements and facial expressions in real time. It receives head movement data and facial expression data acquired by sensors as input, and outputs them as accelerometer data and image data. This allows for accurate recording of the user's physical movements and facial expressions.

[0213] Step 2:

[0214] The terminal preprocesses the acquired motion data and facial expression data. The inputs are the accelerometer data and image data obtained in step 1. By removing noise and formatting the data, the output is processed data in a state where the user's movements can be classified. Here, for example, smoothing and noise filtering are performed.

[0215] Step 3:

[0216] The device analyzes pre-processed data and uses an emotion engine to estimate the user's emotional state. It takes processed motion data and facial expression data as input and generates emotion categories (e.g., "joy," "anxiety," "anger") as output. This allows for an understanding of the user's inner state.

[0217] Step 4:

[0218] The terminal packets all the analysis data and sends it to the server via the internet. The input consists of emotion categories and behavioral data, which are output as data packets. This ensures reliable and efficient data transfer.

[0219] Step 5:

[0220] The server analyzes the received data packets and generates a response based on the user's actions and emotions. The input is the data packets sent in step 4. By using a generative AI model, the output is a natural language response that is optimal for the user. Specifically, example prompt sentences are analyzed and appropriate feedback is determined.

[0221] Step 6:

[0222] The server sends the generated response to the terminal. The generated response is the input, and the data packet sent back to the terminal is the output. This completes the communication with the user.

[0223] Step 7:

[0224] The terminal notifies the user of the received response. It receives response data from the server as input and provides it to the user as output via voice or display. Specifically, a speech synthesizer is activated, and the response is displayed as a voice message or as text on the display.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention provides a system that detects the user's head movements and communicates with an AI agent based on these movements. The processing of the program of this system will be described in detail below.

[0242] Gesture detection

[0243] The device uses sensors built into the earphones to monitor the user's head movements in real time. These sensors measure acceleration and angular velocity, acquiring the movement as digital data. The device records each of the user's actions—shaking their head, nodding, or tilting their head—as separate data points.

[0244] Specific example: When a user nods their head in response to "yes," the device recognizes this movement as a "positive response."

[0245] Data analysis and transmission

[0246] The recognized motion data is pre-processed on the terminal to remove noise and identify the type of motion. This information is then sent to the server via the internet. The terminal is designed to packetize the data, ensuring low latency and stable communication.

[0247] Specific example: If the device detects a "head bobbing" motion, that motion data is sent to the server as a "positive response".

[0248] Response generation and feedback

[0249] The server analyzes the received operational data to determine an appropriate response. This analysis includes an algorithm that checks the type and content of the data and determines the user's intent. After the server generates a response, that information is fed back to the terminal.

[0250] Specific example: The server confirms that it is a "positive response" and generates the instruction "Approved. Please select the next step," which is then sent to the terminal.

[0251] Notification to the user

[0252] The device receives a response from the server and provides feedback to the user through voice or vibration. This allows the user to confirm the appropriate action and proceed to the next step.

[0253] Specific example: The device provides the user with a voice feedback message saying, "Please select the next step."

[0254] In this way, one embodiment of the invention makes it possible for users to interact naturally with an AI agent even in situations where voice is unavailable.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The device uses sensors built into the earphones to detect the user's head movements in real time. When the user nods, shakes their head from side to side, or tilts their head, the sensors measure the acceleration and angular velocity and capture it as digital data.

[0258] Step 2:

[0259] The device preprocesses the acquired motion data. Preprocessing includes denoising the data obtained from the sensors and identifying the type of motion. This classifies whether the motion is positive, negative, or selective.

[0260] Step 3:

[0261] The terminal packets pre-processed operational data and sends it to the server via the internet. This communication incorporates error correction technology to ensure data reliability.

[0262] Step 4:

[0263] The server analyzes the received behavioral data. Using analysis tools, it interprets the user's intent represented by the data and generates an appropriate response corresponding to the action. The server refers to a predefined gesture mapping database to link actions and responses.

[0264] Step 5:

[0265] The server sends the generated response to the terminal. The response data consists of instructions for proceeding to the next step and is presented in a user-friendly format.

[0266] Step 6:

[0267] The device notifies the user of the response data received from the server. This notification is provided via earphones as audio or vibration feedback. This allows the user to confirm the response and proceed to the next action.

[0268] (Example 1)

[0269] 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."

[0270] Many modern interfaces are designed with voice input in mind, making it difficult to meet the needs of diverse users or in environments where voice input is unavailable. Therefore, there is a need to develop interfaces that enable natural communication without relying on voice.

[0271] 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.

[0272] In this invention, the server includes means for analyzing pre-processed behavioral data, means for transmitting the generated response, and means for classifying the behavior into multiple types. This enables users to intuitively communicate with the AI ​​even in situations where voice is unavailable.

[0273] A "sensor" is a device used to detect the user's head movements in real time.

[0274] "Pre-processing" refers to the process of performing initial processing, such as noise reduction, on detected head movements.

[0275] An "information processing device" is a device that receives pre-processed data and performs analysis and response generation.

[0276] A "notification means" is a means of communicating the generated response to the user.

[0277] A "classification means" is an algorithm or device for classifying a user's head movements into several types and selecting an appropriate response.

[0278] "Communication means" refers to technology or devices for transmitting detected head movement data to an information processing device via the Internet.

[0279] The system in this invention detects the user's head movements and generates a response corresponding to those movements, thereby achieving natural communication. The following describes embodiments for carrying out this invention.

[0280] The user wears a device equipped with sensors. This device has a built-in accelerometer and gyroscope, which allows for real-time detection of the user's movements such as head movements, nodding, and tilting. The terminal improves the accuracy of the digital data obtained from these sensors by first performing preprocessing, such as noise reduction. The preprocessed data is then classified by type and transmitted to a server via the internet.

[0281] The server uses a generative AI model to analyze the received head movement data. This analysis employs sophisticated algorithms to determine what the movements mean. For example, if it recognizes an "affirmative response," the server generates an appropriate response such as "Approved. Please select the next step." These responses are sent back to the terminal via the network and provided to the user through voice or vibration feedback. Even without voice input, the user can intuitively guide to the next action.

[0282] As a specific example, even when it is difficult for the user to respond verbally during a meeting, the user can indicate "yes" by nodding vertically. This action is immediately recognized as an "affirmative response", and further instructions are generated. As an example of a prompt sentence, something like "Please describe a method for generating the response of an AI agent based on the user's actions." can be considered. In this way, non-verbal and natural communication becomes possible.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The terminal uses sensors to detect the user's head movements. Thereby, motion data is acquired. As specific movements, the accelerometer and gyroscope work in conjunction to capture motion patterns such as the user's head nods and shakes. The input is real-time motion, and the output is raw data in digital format.

[0286] Step 2:

[0287] The terminal performs preprocessing on the acquired raw data. This processing includes noise removal and data normalization. For example, minute vibrations from the sensor and environmental noise are removed. The input is raw data from the sensor, and the output is clean data with noise removed.

[0288] Step 3:

[0289] The terminal analyzes the preprocessed data to identify the type of movement. An algorithm is used to classify the data into "affirmative response", "negative response", etc. As a specific example, when the head is nodded vertically, it is identified as "affirmative". The input is clean data, and the output is classified motion data.

[0290] Step 4:

[0291] The terminal packets the classified behavioral data and sends it to the server. A stable communication protocol is used to ensure that the necessary data is transmitted reliably. The input is the classified behavioral data, and the output is the data packets sent to the server.

[0292] Step 5:

[0293] The server analyzes the received data packets and generates an appropriate response using a generative AI model. The response algorithm is executed based on the content of the data. For example, for data indicating an "affirmative response," it generates the instruction "approved." The input is the action data packet, and the output is the generated response.

[0294] Step 6:

[0295] The server sends the generated response to the terminal. This communication requires immediacy, and a high-speed network is used. The input is the generated response, and the output is the data sent to the terminal.

[0296] Step 7:

[0297] The terminal transmits the response received from the server to the user. Feedback using voice and vibration allows the user to recognize the next action. For example, a voice device might play a message such as "Please select the next step." The input is the response data from the server, and the output is the feedback to the user.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] In recent years, there has been an increasing need to perform operations within the home not only by voice or manual operation, but also in a more natural and contactless manner. However, with conventional operating means, it has been difficult for users to comfortably operate devices in many situations. In particular, when the hands are occupied or voice operation is not possible in a quiet environment, there is a demand for more intuitive control of home devices.

[0301] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0302] In this invention, the server includes a measuring device means for detecting a user's body movement, an information processing device means for analyzing the detected movement information and generating a response corresponding to the movement, a feedback information means for transmitting the generated response to the user, an analysis means for classifying a plurality of movements and determining a response corresponding to each classification, and a control means for controlling devices within the home based on the user's movement input. Thereby, the user can operate home devices with natural movements without using voice.

[0303] A "user" is an individual or entity that inputs an action using the system and receives a response or control based thereon. [[ID=X]]

[0304] [[ID=X]] A "body movement" is a physical behavior such as a movement of the neck or a change in posture that a user intentionally performs.

[0305] A "measuring device" is a sensor device that collects a user's body movement as digital data in real time.

[0306] "Movement information" is digital data of a user's body movement detected by a measuring device.

[0307] An "information processing device" is a computer or server for analyzing movement information and generating an appropriate response.

[0308] Note: There seem to be some consecutive tags without text between them in the original. I've translated the text as accurately as possible while maintaining the tags and line breaks. If there are any specific requirements regarding those empty tags, please let me know.A "feedback information means" is a method or device for notifying the user of the generated response, and provides feedback through sound or light.

[0309] An "analysis method" is an algorithm or process that classifies operational information into multiple types and selects an appropriate response according to each type.

[0310] "Control means" refers to mechanisms and protocols for operating devices within the home based on user input.

[0311] "Home devices" refers to electrical appliances and services used in typical households, including air conditioners, lighting, and smart home appliances.

[0312] "Natural movements" are simple physical actions that users can perform on a daily basis without the need for special training or additional means.

[0313] This invention is a system that detects a user's natural movements within the home and controls home devices based on those movements. The system consists of a measuring device, an information processing device, and a return information means.

[0314] The measuring device uses sensors embedded in earphones or other devices to detect the user's body movements in real time. The sensors measure acceleration and angular velocity, and store the acquired motion information as digital data. This includes body movements such as the user shaking their head, nodding, or tilting their head.

[0315] The information processing device analyzes the motion information transmitted from the measuring device. The motion information is preprocessed through a noise-canceling filter and then classified into several types by the analysis means. The information processing device generates an appropriate response based on the type of motion and transmits that information back to the information means.

[0316] The feedback mechanism is responsible for notifying the user of the generated response. This involves conveying information to the user through audio or optical feedback.

[0317] For example, a user can perform a simple physical movement, and digital devices in the home will respond instantly, such as turning off the air conditioner or adjusting the TV volume. This system allows users to intuitively operate devices without using their hands.

[0318] Based on an example of a prompt message, the system will perform an action such as, "Detect that the user nods and turn on the room lights."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The device detects the user's physical movements using measuring equipment. The input is the user's physical movements, which are acquired as digital data by the sensor. This data consists of numerical data related to acceleration and angular velocity.

[0322] Step 2:

[0323] The device preprocesses the acquired motion data using a noise-canceling filter. The input is unprocessed motion data, and unnecessary noise is removed from the raw data obtained from the sensor to output clean motion data.

[0324] Step 3:

[0325] The terminal analyzes pre-processed motion data and classifies it into multiple motion types using an analysis tool. The input is pre-processed motion data, the algorithm recognizes the motion pattern, and outputs the result as a motion type.

[0326] Step 4:

[0327] The server analyzes the action type transmitted from the terminal, and the information processing device generates an appropriate response based on this. The input is the action type, and the optimal response content is generated using a generative AI model and output in prompt format.

[0328] Step 5:

[0329] The server sends the generated response to the terminal, which then uses a return information mechanism to notify the user. The input is the response data from the server, and the output is communicated to the user as audio or optical feedback.

[0330] Step 6:

[0331] Based on user feedback, the system controls devices within the home. This means the input is user feedback, and the output is the result of specific device operations. For example, a nod of the head might turn on a light.

[0332] 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.

[0333] This invention provides a system that detects the user's head movements, combines them with an emotion engine to understand the user's intentions, and communicates with an AI agent. The processing of this system's program is described in detail below.

[0334] Gesture and emotion detection

[0335] The device continuously monitors the user's head movements through sensors built into the earphones. It also features an emotion engine that detects the user's emotional state based on their facial expressions and voice patterns. This allows the device to understand not only their actions but also their emotional state simultaneously.

[0336] Specific example: If a user shakes their head and says "no" with a stern expression, the emotion engine will recognize this as a "negative and unpleasant" emotion.

[0337] Data analysis and transmission

[0338] The terminal preprocesses the acquired motion and emotion data, removing noise and then identifying the type of motion and emotion category. This information is packetized and sent to the server via the internet. The terminal ensures reliable and efficient communication with minimal errors.

[0339] Specific example: If the emotion engine detects a "negative action" accompanied by "negative and unpleasant" emotions, that data is sent to the server.

[0340] Response generation and feedback

[0341] The server analyzes the received behavioral and emotional data and determines the optimal response. Using a dedicated algorithm, the server undergoes a process to deeply understand the user's intentions and emotions. The generated response is sent to the terminal and provided as feedback to the user.

[0342] Specific example: When the server receives a "no" response accompanied by "negative and unpleasant" feelings, it generates feedback saying, "Understood, I'm sorry if I offended you."

[0343] Adaptive notifications to users

[0344] The device receives feedback from the server and notifies the user in a way that is appropriate to their emotions. The feedback is delivered via voice or vibration, taking into account the user's current emotions.

[0345] Specific example: A voice message plays from the device saying, "We apologize, we will improve so that this does not cause any offense."

[0346] This configuration allows users to engage in richer interactions with AI agents, including not only normal motion recognition but also emotional engagement. This enables more intuitive and emotionally resonant communication.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] The device detects the user's head movements using sensors built into the earphones. Simultaneously, an emotion engine collects the user's facial expressions and voice characteristics and evaluates their emotions in real time. If the user nods in agreement, that data is recorded digitally on the device.

[0350] Step 2:

[0351] The terminal then enters the preprocessing stage for the acquired behavioral and emotional data. Here, noise is removed, and the type of behavior (positive, negative, choice) and emotion (joy, sadness, surprise, etc.) are identified. This data is then compiled into packets for analysis.

[0352] Step 3:

[0353] The terminal sends pre-processed data to the server. The system is designed to maintain data integrity and ensure effective delivery to the server during communication over the internet.

[0354] Step 4:

[0355] The server analyzes the received behavioral and emotional data. Here, the server applies multiple algorithms to determine the appropriate response based on the user's intentions and emotions. For example, if a "positive behavior" matches the emotion of "joy," it generates positive feedback.

[0356] Step 5:

[0357] The server sends the generated response to the terminal. The response includes special instructions and information tailored to the user's situation.

[0358] Step 6:

[0359] The device receives responses from the server and provides users with emotionally appropriate notifications. In particular, it uses voice output and vibration feedback to convey information in a way that resonates with the user's emotions. If the user shows a positive response to the system, the device will play a voice message saying, "I gladly accept this choice."

[0360] (Example 2)

[0361] 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".

[0362] Accurately understanding a user's intentions, including not only their actions but also their emotional state, and providing them with appropriate and emotionally sensitive responses has been difficult with conventional technologies. There is a need for systems that enable more intuitive and natural communication in such user interactions.

[0363] 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.

[0364] In this invention, the server includes means for utilizing a terminal equipped with sensors for detecting the user's actions and emotional state, computation means for analyzing the detected action data and emotional data and generating responses corresponding to the actions and emotions, and feedback means for appropriately communicating the generated responses to the user and notifying them in a manner that takes the user's emotions into consideration. This enables a deeper understanding of the user's intentions and communication that is more empathetic to their emotions.

[0365] A "user" is a person who uses the system and whose actions and emotions are detected.

[0366] "Motion" refers to physical movements expressed by the user moving their head or body, which are detected by sensors.

[0367] "Emotional state" refers to the internal psychological state expressed through the user's facial expressions and vocal patterns.

[0368] A "sensor" is a device installed in a terminal to detect the user's actions and emotional state.

[0369] A "terminal" is an electronic device equipped with sensors to detect the user's actions and emotions.

[0370] "Data" refers to information about detected user behavior and emotions, which is used for analysis and response generation.

[0371] "Computation means" refers to the computational techniques and methods used by the server to analyze the data detected and generate a corresponding response.

[0372] A "feedback mechanism" is a method of communicating the generated response to the user and notifying them in a way that is adapted to the user's current situation and emotions.

[0373] "Communication means" refers to the technologies and protocols used to transmit detected data to a server via the internet.

[0374] This invention provides a system that detects user actions and emotions and generates responses corresponding to those emotions.

[0375] Hardware configuration

[0376] The device is equipped with sensors to detect the user's head movements. These sensors, based on sensor technology, are capable of detecting head movements and angles. The device also includes an emotion engine to recognize emotional states, analyzing emotions through facial expressions and voice patterns.

[0377] Software Configuration

[0378] The server receives behavioral and emotional data transmitted from the terminal. The received data is processed by an internal generative AI model and data analysis software to generate a response adapted to the user's behavior and emotions. This response is then sent back to the terminal for prompt feedback to the user.

[0379] For example, if a user shakes their head and makes an unpleasant expression, the device detects a "negative action" and an "unpleasant emotion." Based on this, the server generates a response such as, "I'm sorry if I made you uncomfortable."

[0380] Example of a prompt

[0381] Examples of prompt messages include the following:

[0382] "The user is shaking their head and has a grim expression. What kind of feedback should we provide to this user?"

[0383] In this way, the system can better understand the user's state and interact with the user in a way that is sensitive to their emotions. This technology enables more natural and intuitive communication.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] The device continuously monitors the user's head movements using sensors built into the earphones. The sensors acquire user movement and angle information as input data. Based on this data, the amount and speed of head movement are calculated, and the type of movement (e.g., nodding, side-to-side shaking) is identified. For example, if the user shakes their head from side to side, this movement is categorized as a "negative movement."

[0387] Step 2:

[0388] The device analyzes facial expressions and voice patterns to detect the user's emotional state using an emotion engine. Input data includes the user's facial features and voice waveforms. This data is analyzed by the emotion engine, which identifies emotional categories such as smiles, surprise, and anger. For example, if the user has a stern expression, it is tagged as an "unpleasant emotion."

[0389] Step 3:

[0390] The device aggregates the acquired behavioral and emotional data and applies a noise reduction filter to form a clean dataset. The input consists of raw behavioral and emotional data. The filtering process removes noise, resulting in output organized into behavioral and emotional categories. Specifically, data packets containing "negative behavior" and "unpleasant emotions" are generated.

[0391] Step 4:

[0392] The terminal packets clean data and sends it to the server over the internet using a reliable communication protocol. The input data consists of packetized behavioral and emotional information. Communication error correction is performed to ensure that the data reaches the server safely. Specifically, when a data packet is sent to the server and an error is detected, a retransmission process is performed.

[0393] Step 5:

[0394] The server analyzes the received data. The input data includes behavioral and emotional data packets from the terminal. A generative AI model uses this to delve deeper into the user's intent and calculate the optimal response. For example, a response such as "I'm sorry if I offended you" is generated.

[0395] Step 6:

[0396] The server sends the generated response to the terminal, and the terminal relays the response to the user. The input data is the generated response message. The terminal appropriately provides feedback to the user through voice or vibration. Specifically, the terminal plays a voice message saying, "I'm sorry."

[0397] (Application Example 2)

[0398] 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."

[0399] Current user interfaces lack the ability to comprehensively understand user behavior and emotions and provide appropriate real-time responses. Therefore, there is a need for systems that are more empathetic to user emotions and enable more intuitive and human-like interactions.

[0400] 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.

[0401] In this invention, the server includes an information processing device equipped with sensors for detecting the user's actions and emotional state; an information processing device that analyzes the detected action data and emotional data and generates a response corresponding to the user's intentions; and an information processing device that communicates the generated response to the user and notifies them in a manner adapted to their emotions. This makes it possible to provide appropriate responses in real time based on the user's actions and emotions.

[0402] A "user" is a person who performs operations or gives instructions via information processing equipment or information and communication networks.

[0403] "Motion data" refers to information about the user's body movements detected by sensors.

[0404] "Emotional data" refers to information about a user's emotional state that is inferred from their facial expressions, voice, and actions.

[0405] A "sensor" is a device or instrument used to detect a user's actions or emotions.

[0406] An "information processing device" is a computing device that analyzes input data from a user and generates an appropriate response.

[0407] An "information and communication network" is a means of communication for transmitting data over a network.

[0408] "Response" refers to the output information from an information processing device based on detected and analyzed motion data and emotion data.

[0409] A "notification" is information provided to a user through visual or auditory means.

[0410] This invention relates to a system that detects user actions and emotions and generates appropriate responses based on them. The system includes the following components:

[0411] First, the user utilizes a device equipped with sensors. This device incorporates various sensors to detect head movements and facial expressions. Specifically, sensors such as accelerometers and cameras acquire data on the user's movements and facial expressions. This data forms a crucial foundation for monitoring the user's movements and emotional state in real time.

[0412] The motion and emotion data acquired by the sensors is analyzed by an information processing device. This device runs data analysis algorithms using Python and artificial intelligence models for emotion estimation using PyTorch. As a result of the analysis, the user's actions and emotions are clearly identified, and the information necessary for response is extracted.

[0413] The server generates a response tailored to the user based on the analyzed data. This response uses natural language processing technology to ensure it reflects the user's emotions and intentions. For example, if the user looks troubled, the server might generate a suggestion such as, "Is there anything I can help you with?"

[0414] The generated response is communicated to the end user via an information processing device. The notification is provided visually or audibly, utilizing a speech synthesizer or display. A stable communication method is used for transmission, minimizing response delays.

[0415] As a concrete example, a scenario could be envisioned where a home robot reads the user's anxiety from their face and movements and suggests, "Shall I play some relaxing music?" An example of a prompt to input into the generative AI model would be, "The user is shaking their head from side to side and showing a confused expression. Please generate appropriate feedback."

[0416] This configuration makes it possible to achieve intuitive and human-like interactions based on the user's actions and emotions.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The device detects the user's movements and facial expressions in real time. It receives head movement data and facial expression data acquired by sensors as input, and outputs them as accelerometer data and image data. This allows for accurate recording of the user's physical movements and facial expressions.

[0420] Step 2:

[0421] The terminal preprocesses the acquired motion data and facial expression data. The inputs are the accelerometer data and image data obtained in step 1. By removing noise and formatting the data, the output is processed data in a state where the user's movements can be classified. Here, for example, smoothing and noise filtering are performed.

[0422] Step 3:

[0423] The device analyzes pre-processed data and uses an emotion engine to estimate the user's emotional state. It takes processed motion data and facial expression data as input and generates emotion categories (e.g., "joy," "anxiety," "anger") as output. This allows for an understanding of the user's inner state.

[0424] Step 4:

[0425] The terminal packets all the analysis data and sends it to the server via the internet. The input consists of emotion categories and behavioral data, which are output as data packets. This ensures reliable and efficient data transfer.

[0426] Step 5:

[0427] The server analyzes the received data packets and generates a response based on the user's actions and emotions. The input is the data packets sent in step 4. By using a generative AI model, the output is a natural language response that is optimal for the user. Specifically, example prompt sentences are analyzed and appropriate feedback is determined.

[0428] Step 6:

[0429] The server sends the generated response to the terminal. The generated response is the input, and the data packet sent back to the terminal is the output. This completes the communication with the user.

[0430] Step 7:

[0431] The terminal notifies the user of the received response. It receives response data from the server as input and provides it to the user as output via voice or display. Specifically, a speech synthesizer is activated, and the response is displayed as a voice message or as text on the display.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] This invention provides a system that detects the user's head movements and communicates with an AI agent based on these movements. The processing of the program of this system will be described in detail below.

[0449] Gesture detection

[0450] The device uses sensors built into the earphones to monitor the user's head movements in real time. These sensors measure acceleration and angular velocity, acquiring the movement as digital data. The device records each of the user's actions—shaking their head, nodding, or tilting their head—as separate data points.

[0451] Specific example: When a user nods their head in response to "yes," the device recognizes this movement as a "positive response."

[0452] Data analysis and transmission

[0453] The recognized motion data is pre-processed on the terminal to remove noise and identify the type of motion. This information is then sent to the server via the internet. The terminal is designed to packetize the data, ensuring low latency and stable communication.

[0454] Specific example: If the device detects a "head bobbing" motion, that motion data is sent to the server as a "positive response".

[0455] Response generation and feedback

[0456] The server analyzes the received operational data to determine an appropriate response. This analysis includes an algorithm that checks the type and content of the data and determines the user's intent. After the server generates a response, that information is fed back to the terminal.

[0457] Specific example: The server confirms that it is a "positive response" and generates the instruction "Approved. Please select the next step," which is then sent to the terminal.

[0458] Notification to the user

[0459] The device receives a response from the server and provides feedback to the user through voice or vibration. This allows the user to confirm the appropriate action and proceed to the next step.

[0460] Specific example: The device provides the user with a voice feedback message saying, "Please select the next step."

[0461] In this way, one embodiment of the invention makes it possible for users to interact naturally with an AI agent even in situations where voice is unavailable.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The device uses sensors built into the earphones to detect the user's head movements in real time. When the user nods, shakes their head from side to side, or tilts their head, the sensors measure the acceleration and angular velocity and capture it as digital data.

[0465] Step 2:

[0466] The device preprocesses the acquired motion data. Preprocessing includes denoising the data obtained from the sensors and identifying the type of motion. This classifies whether the motion is positive, negative, or selective.

[0467] Step 3:

[0468] The terminal packets pre-processed operational data and sends it to the server via the internet. This communication incorporates error correction technology to ensure data reliability.

[0469] Step 4:

[0470] The server analyzes the received behavioral data. Using analysis tools, it interprets the user's intent represented by the data and generates an appropriate response corresponding to the action. The server refers to a predefined gesture mapping database to link actions and responses.

[0471] Step 5:

[0472] The server sends the generated response to the terminal. The response data consists of instructions for proceeding to the next step and is presented in a user-friendly format.

[0473] Step 6:

[0474] The device notifies the user of the response data received from the server. This notification is provided via earphones as audio or vibration feedback. This allows the user to confirm the response and proceed to the next action.

[0475] (Example 1)

[0476] 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."

[0477] Many modern interfaces are designed with voice input in mind, making it difficult to meet the needs of diverse users or in environments where voice input is unavailable. Therefore, there is a need to develop interfaces that enable natural communication without relying on voice.

[0478] 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.

[0479] In this invention, the server includes means for analyzing pre-processed behavioral data, means for transmitting the generated response, and means for classifying the behavior into multiple types. This enables users to intuitively communicate with the AI ​​even in situations where voice is unavailable.

[0480] A "sensor" is a device used to detect the user's head movements in real time.

[0481] "Pre-processing" refers to the process of performing initial processing, such as noise reduction, on detected head movements.

[0482] An "information processing device" is a device that receives pre-processed data and performs analysis and response generation.

[0483] A "notification means" is a means of communicating the generated response to the user.

[0484] A "classification means" is an algorithm or device for classifying a user's head movements into several types and selecting an appropriate response.

[0485] "Communication means" refers to technology or devices for transmitting detected head movement data to an information processing device via the Internet.

[0486] The system in this invention detects the user's head movements and generates a response corresponding to those movements, thereby achieving natural communication. The following describes embodiments for carrying out this invention.

[0487] The user wears a device equipped with sensors. This device has a built-in accelerometer and gyroscope, which allows for real-time detection of the user's movements such as head movements, nodding, and tilting. The terminal improves the accuracy of the digital data obtained from these sensors by first performing preprocessing, such as noise reduction. The preprocessed data is then classified by type and transmitted to a server via the internet.

[0488] The server uses a generative AI model to analyze the received head movement data. This analysis employs sophisticated algorithms to determine what the movements mean. For example, if it recognizes an "affirmative response," the server generates an appropriate response such as "Approved. Please select the next step." These responses are sent back to the terminal via the network and provided to the user through voice or vibration feedback. Even without voice input, the user can intuitively guide to the next action.

[0489] As a concrete example, even if a user has difficulty responding verbally during a meeting, they can indicate "yes" by nodding their head. This action is immediately recognized as an "affirmative response," and further instructions are generated. An example of a prompt might be, "Please describe the method for generating the AI ​​agent's response based on the user's actions." In this way, non-verbal and natural communication becomes possible.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The device uses sensors to detect the user's head movements, thereby acquiring motion data. Specifically, an accelerometer and gyroscope work together to capture motion patterns such as head shakes and nods. The input is real-time motion, and the output is raw data in digital format.

[0493] Step 2:

[0494] The terminal performs preprocessing on the acquired raw data. This processing includes noise reduction and data normalization. For example, it removes minute vibrations from the sensor and environmental noise. The input is raw data from the sensor, and the output is clean, denoised data.

[0495] Step 3:

[0496] The device analyzes pre-processed data and identifies the type of action. It uses an algorithm to classify the data into categories such as "positive response" and "negative response." For example, nodding is identified as "positive." The input is clean data, and the output is classified action data.

[0497] Step 4:

[0498] The terminal packets the classified behavioral data and sends it to the server. A stable communication protocol is used to ensure that the necessary data is transmitted reliably. The input is the classified behavioral data, and the output is the data packets sent to the server.

[0499] Step 5:

[0500] The server analyzes the received data packets and generates an appropriate response using a generative AI model. The response algorithm is executed based on the content of the data. For example, for data indicating an "affirmative response," it generates the instruction "approved." The input is the action data packet, and the output is the generated response.

[0501] Step 6:

[0502] The server sends the generated response to the terminal. This communication requires immediacy, and a high-speed network is used. The input is the generated response, and the output is the data sent to the terminal.

[0503] Step 7:

[0504] The terminal transmits the response received from the server to the user. Feedback using voice and vibration allows the user to recognize the next action. For example, a voice device might play a message such as "Please select the next step." The input is the response data from the server, and the output is the feedback to the user.

[0505] (Application Example 1)

[0506] 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."

[0507] In recent years, there has been a growing need for more natural and contactless ways to control devices within the home, beyond just voice and hand gestures. However, traditional control methods have made it difficult for users to comfortably operate devices in many situations. In particular, there is a need for more intuitive control of home devices when hands are occupied or when voice control is not possible in a quiet environment.

[0508] 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.

[0509] In this invention, the server includes a measuring device means for detecting the user's physical movements, an information processing device means for analyzing the detected movement information and generating a response corresponding to the movement, a return information means for communicating the generated response to the user, an analysis means for classifying multiple movements and determining a response according to each classification, and a control means for controlling devices in the home based on the user's movement input. This enables the user to operate devices in the home with natural movements without using voice.

[0510] A "user" is an individual or entity that uses a system to input actions and receives responses or control based on those actions.

[0511] "Physical movement" refers to physical actions that a user intentionally performs, such as neck movements or changes in posture.

[0512] A "measuring device" is a sensor device that collects the user's physical movements as digital data in real time.

[0513] "Motion information" refers to digital data of the user's physical movements detected by a measuring device.

[0514] An "information processing device" is a computer or server that analyzes operational information and generates an appropriate response.

[0515] A "feedback information means" is a method or device for notifying the user of the generated response, and provides feedback through sound or light.

[0516] An "analysis method" is an algorithm or process that classifies operational information into multiple types and selects an appropriate response according to each type.

[0517] "Control means" refers to mechanisms and protocols for operating devices within the home based on user input.

[0518] "Home devices" refers to electrical appliances and services used in typical households, including air conditioners, lighting, and smart home appliances.

[0519] "Natural movements" are simple physical actions that users can perform on a daily basis without the need for special training or additional means.

[0520] This invention is a system that detects a user's natural movements within the home and controls home devices based on those movements. The system consists of a measuring device, an information processing device, and a return information means.

[0521] The measuring device uses sensors embedded in earphones or other devices to detect the user's body movements in real time. The sensors measure acceleration and angular velocity, and store the acquired motion information as digital data. This includes body movements such as the user shaking their head, nodding, or tilting their head.

[0522] The information processing device analyzes the motion information transmitted from the measuring device. The motion information is preprocessed through a noise-canceling filter and then classified into several types by the analysis means. The information processing device generates an appropriate response based on the type of motion and transmits that information back to the information means.

[0523] The feedback mechanism is responsible for notifying the user of the generated response. This involves conveying information to the user through audio or optical feedback.

[0524] For example, a user can perform a simple physical movement, and digital devices in the home will respond instantly, such as turning off the air conditioner or adjusting the TV volume. This system allows users to intuitively operate devices without using their hands.

[0525] Based on an example of a prompt message, the system will perform an action such as, "Detect that the user nods and turn on the room lights."

[0526] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0527] Step 1:

[0528] The device detects the user's physical movements using measuring equipment. The input is the user's physical movements, which are acquired as digital data by the sensor. This data consists of numerical data related to acceleration and angular velocity.

[0529] Step 2:

[0530] The device preprocesses the acquired motion data using a noise-canceling filter. The input is unprocessed motion data, and unnecessary noise is removed from the raw data obtained from the sensor to output clean motion data.

[0531] Step 3:

[0532] The terminal analyzes pre-processed motion data and classifies it into multiple motion types using an analysis tool. The input is pre-processed motion data, the algorithm recognizes the motion pattern, and outputs the result as a motion type.

[0533] Step 4:

[0534] The server analyzes the action type transmitted from the terminal, and the information processing device generates an appropriate response based on this. The input is the action type, and the optimal response content is generated using a generative AI model and output in prompt format.

[0535] Step 5:

[0536] The server sends the generated response to the terminal, which then uses a return information mechanism to notify the user. The input is the response data from the server, and the output is communicated to the user as audio or optical feedback.

[0537] Step 6:

[0538] Based on user feedback, the system controls devices within the home. This means the input is user feedback, and the output is the result of specific device operations. For example, a nod of the head might turn on a light.

[0539] 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.

[0540] This invention provides a system that detects the user's head movements, combines them with an emotion engine to understand the user's intentions, and communicates with an AI agent. The processing of this system's program is described in detail below.

[0541] Gesture and emotion detection

[0542] The device continuously monitors the user's head movements through sensors built into the earphones. It also features an emotion engine that detects the user's emotional state based on their facial expressions and voice patterns. This allows the device to understand not only their actions but also their emotional state simultaneously.

[0543] Specific example: If a user shakes their head and says "no" with a stern expression, the emotion engine will recognize this as a "negative and unpleasant" emotion.

[0544] Data analysis and transmission

[0545] The terminal preprocesses the acquired motion and emotion data, removing noise and then identifying the type of motion and emotion category. This information is packetized and sent to the server via the internet. The terminal ensures reliable and efficient communication with minimal errors.

[0546] Specific example: If the emotion engine detects a "negative action" accompanied by "negative and unpleasant" emotions, that data is sent to the server.

[0547] Response generation and feedback

[0548] The server analyzes the received behavioral and emotional data and determines the optimal response. Using a dedicated algorithm, the server undergoes a process to deeply understand the user's intentions and emotions. The generated response is sent to the terminal and provided as feedback to the user.

[0549] Specific example: When the server receives a "no" response accompanied by "negative and unpleasant" feelings, it generates feedback saying, "Understood, I'm sorry if I offended you."

[0550] Adaptive notifications to users

[0551] The device receives feedback from the server and notifies the user in a way that is appropriate to their emotions. The feedback is delivered via voice or vibration, taking into account the user's current emotions.

[0552] Specific example: A voice message plays from the device saying, "We apologize, we will improve so that this does not cause any offense."

[0553] This configuration allows users to engage in richer interactions with AI agents, including not only normal motion recognition but also emotional engagement. This enables more intuitive and emotionally resonant communication.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The device detects the user's head movements using sensors built into the earphones. Simultaneously, an emotion engine collects the user's facial expressions and voice characteristics and evaluates their emotions in real time. If the user nods in agreement, that data is recorded digitally on the device.

[0557] Step 2:

[0558] The terminal then enters the preprocessing stage for the acquired behavioral and emotional data. Here, noise is removed, and the type of behavior (positive, negative, choice) and emotion (joy, sadness, surprise, etc.) are identified. This data is then compiled into packets for analysis.

[0559] Step 3:

[0560] The terminal sends pre-processed data to the server. The system is designed to maintain data integrity and ensure effective delivery to the server during communication over the internet.

[0561] Step 4:

[0562] The server analyzes the received behavioral and emotional data. Here, the server applies multiple algorithms to determine the appropriate response based on the user's intentions and emotions. For example, if a "positive behavior" matches the emotion of "joy," it generates positive feedback.

[0563] Step 5:

[0564] The server sends the generated response to the terminal. The response includes special instructions and information tailored to the user's situation.

[0565] Step 6:

[0566] The device receives responses from the server and provides users with emotionally appropriate notifications. In particular, it uses voice output and vibration feedback to convey information in a way that resonates with the user's emotions. If the user shows a positive response to the system, the device will play a voice message saying, "I gladly accept this choice."

[0567] (Example 2)

[0568] 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."

[0569] Accurately understanding a user's intentions, including not only their actions but also their emotional state, and providing them with appropriate and emotionally sensitive responses has been difficult with conventional technologies. There is a need for systems that enable more intuitive and natural communication in such user interactions.

[0570] 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.

[0571] In this invention, the server includes means for utilizing a terminal equipped with sensors for detecting the user's actions and emotional state, computation means for analyzing the detected action data and emotional data and generating responses corresponding to the actions and emotions, and feedback means for appropriately communicating the generated responses to the user and notifying them in a manner that takes the user's emotions into consideration. This enables a deeper understanding of the user's intentions and communication that is more empathetic to their emotions.

[0572] A "user" is a person who uses the system and whose actions and emotions are detected.

[0573] "Motion" refers to physical movements expressed by the user moving their head or body, which are detected by sensors.

[0574] "Emotional state" refers to the internal psychological state expressed through the user's facial expressions and vocal patterns.

[0575] A "sensor" is a device installed in a terminal to detect the user's actions and emotional state.

[0576] A "terminal" is an electronic device equipped with sensors to detect the user's actions and emotions.

[0577] "Data" refers to information about detected user behavior and emotions, which is used for analysis and response generation.

[0578] "Computation means" refers to the computational techniques and methods used by the server to analyze the data detected and generate a corresponding response.

[0579] A "feedback mechanism" is a method of communicating the generated response to the user and notifying them in a way that is adapted to the user's current situation and emotions.

[0580] "Communication means" refers to the technologies and protocols used to transmit detected data to a server via the internet.

[0581] This invention provides a system that detects user actions and emotions and generates responses corresponding to those emotions.

[0582] Hardware configuration

[0583] The device is equipped with sensors to detect the user's head movements. These sensors, based on sensor technology, are capable of detecting head movements and angles. The device also includes an emotion engine to recognize emotional states, analyzing emotions through facial expressions and voice patterns.

[0584] Software Configuration

[0585] The server receives behavioral and emotional data transmitted from the terminal. The received data is processed by an internal generative AI model and data analysis software to generate a response adapted to the user's behavior and emotions. This response is then sent back to the terminal for prompt feedback to the user.

[0586] For example, if a user shakes their head and makes an unpleasant expression, the device detects a "negative action" and an "unpleasant emotion." Based on this, the server generates a response such as, "I'm sorry if I made you uncomfortable."

[0587] Example of a prompt

[0588] Examples of prompt messages include the following:

[0589] "The user is shaking their head and has a grim expression. What kind of feedback should we provide to this user?"

[0590] In this way, the system can better understand the user's state and interact with the user in a way that is sensitive to their emotions. This technology enables more natural and intuitive communication.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] The device continuously monitors the user's head movements using sensors built into the earphones. The sensors acquire user movement and angle information as input data. Based on this data, the amount and speed of head movement are calculated, and the type of movement (e.g., nodding, side-to-side shaking) is identified. For example, if the user shakes their head from side to side, this movement is categorized as a "negative movement."

[0594] Step 2:

[0595] The device analyzes facial expressions and voice patterns to detect the user's emotional state using an emotion engine. Input data includes the user's facial features and voice waveforms. This data is analyzed by the emotion engine, which identifies emotional categories such as smiles, surprise, and anger. For example, if the user has a stern expression, it is tagged as an "unpleasant emotion."

[0596] Step 3:

[0597] The device aggregates the acquired behavioral and emotional data and applies a noise reduction filter to form a clean dataset. The input consists of raw behavioral and emotional data. The filtering process removes noise, resulting in output organized into behavioral and emotional categories. Specifically, data packets containing "negative behavior" and "unpleasant emotions" are generated.

[0598] Step 4:

[0599] The terminal packets clean data and sends it to the server over the internet using a reliable communication protocol. The input data consists of packetized behavioral and emotional information. Communication error correction is performed to ensure that the data reaches the server safely. Specifically, when a data packet is sent to the server and an error is detected, a retransmission process is performed.

[0600] Step 5:

[0601] The server analyzes the received data. The input data includes behavioral and emotional data packets from the terminal. A generative AI model uses this to delve deeper into the user's intent and calculate the optimal response. For example, a response such as "I'm sorry if I offended you" is generated.

[0602] Step 6:

[0603] The server sends the generated response to the terminal, and the terminal relays the response to the user. The input data is the generated response message. The terminal appropriately provides feedback to the user through voice or vibration. Specifically, the terminal plays a voice message saying, "I'm sorry."

[0604] (Application Example 2)

[0605] 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."

[0606] Current user interfaces lack the ability to comprehensively understand user behavior and emotions and provide appropriate real-time responses. Therefore, there is a need for systems that are more empathetic to user emotions and enable more intuitive and human-like interactions.

[0607] 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.

[0608] In this invention, the server includes an information processing device equipped with sensors for detecting the user's actions and emotional state; an information processing device that analyzes the detected action data and emotional data and generates a response corresponding to the user's intentions; and an information processing device that communicates the generated response to the user and notifies them in a manner adapted to their emotions. This makes it possible to provide appropriate responses in real time based on the user's actions and emotions.

[0609] A "user" is a person who performs operations or gives instructions via information processing equipment or information and communication networks.

[0610] "Motion data" refers to information about the user's body movements detected by sensors.

[0611] "Emotional data" refers to information about a user's emotional state that is inferred from their facial expressions, voice, and actions.

[0612] A "sensor" is a device or instrument used to detect a user's actions or emotions.

[0613] An "information processing device" is a computing device that analyzes input data from a user and generates an appropriate response.

[0614] An "information and communication network" is a means of communication for transmitting data over a network.

[0615] "Response" refers to the output information from an information processing device based on detected and analyzed motion data and emotion data.

[0616] A "notification" is information provided to a user through visual or auditory means.

[0617] This invention relates to a system that detects user actions and emotions and generates appropriate responses based on them. The system includes the following components:

[0618] First, the user utilizes a device equipped with sensors. This device incorporates various sensors to detect head movements and facial expressions. Specifically, sensors such as accelerometers and cameras acquire data on the user's movements and facial expressions. This data forms a crucial foundation for monitoring the user's movements and emotional state in real time.

[0619] The motion and emotion data acquired by the sensors is analyzed by an information processing device. This device runs data analysis algorithms using Python and artificial intelligence models for emotion estimation using PyTorch. As a result of the analysis, the user's actions and emotions are clearly identified, and the information necessary for response is extracted.

[0620] The server generates a response tailored to the user based on the analyzed data. This response uses natural language processing technology to ensure it reflects the user's emotions and intentions. For example, if the user looks troubled, the server might generate a suggestion such as, "Is there anything I can help you with?"

[0621] The generated response is communicated to the end user via an information processing device. The notification is provided visually or audibly, utilizing a speech synthesizer or display. A stable communication method is used for transmission, minimizing response delays.

[0622] As a concrete example, a scenario could be envisioned where a home robot reads the user's anxiety from their face and movements and suggests, "Shall I play some relaxing music?" An example of a prompt to input into the generative AI model would be, "The user is shaking their head from side to side and showing a confused expression. Please generate appropriate feedback."

[0623] This configuration makes it possible to achieve intuitive and human-like interactions based on the user's actions and emotions.

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The device detects the user's movements and facial expressions in real time. It receives head movement data and facial expression data acquired by sensors as input, and outputs them as accelerometer data and image data. This allows for accurate recording of the user's physical movements and facial expressions.

[0627] Step 2:

[0628] The terminal preprocesses the acquired motion data and facial expression data. The inputs are the accelerometer data and image data obtained in step 1. By removing noise and formatting the data, the output is processed data in a state where the user's movements can be classified. Here, for example, smoothing and noise filtering are performed.

[0629] Step 3:

[0630] The device analyzes pre-processed data and uses an emotion engine to estimate the user's emotional state. It takes processed motion data and facial expression data as input and generates emotion categories (e.g., "joy," "anxiety," "anger") as output. This allows for an understanding of the user's inner state.

[0631] Step 4:

[0632] The terminal packets all the analysis data and sends it to the server via the internet. The input consists of emotion categories and behavioral data, which are output as data packets. This ensures reliable and efficient data transfer.

[0633] Step 5:

[0634] The server analyzes the received data packets and generates a response based on the user's actions and emotions. The input is the data packets sent in step 4. By using a generative AI model, the output is a natural language response that is optimal for the user. Specifically, example prompt sentences are analyzed and appropriate feedback is determined.

[0635] Step 6:

[0636] The server sends the generated response to the terminal. The generated response is the input, and the data packet sent back to the terminal is the output. This completes the communication with the user.

[0637] Step 7:

[0638] The terminal notifies the user of the received response. It receives response data from the server as input and provides it to the user as output via voice or display. Specifically, a speech synthesizer is activated, and the response is displayed as a voice message or as text on the display.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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".

[0656] This invention provides a system that detects the user's head movements and communicates with an AI agent based on these movements. The processing of the program of this system will be described in detail below.

[0657] Gesture detection

[0658] The device uses sensors built into the earphones to monitor the user's head movements in real time. These sensors measure acceleration and angular velocity, acquiring the movement as digital data. The device records each of the user's actions—shaking their head, nodding, or tilting their head—as separate data points.

[0659] Specific example: When a user nods their head in response to "yes," the device recognizes this movement as a "positive response."

[0660] Data analysis and transmission

[0661] The recognized motion data is pre-processed on the terminal to remove noise and identify the type of motion. This information is then sent to the server via the internet. The terminal is designed to packetize the data, ensuring low latency and stable communication.

[0662] Specific example: If the device detects a "head bobbing" motion, that motion data is sent to the server as a "positive response".

[0663] Response generation and feedback

[0664] The server analyzes the received operational data to determine an appropriate response. This analysis includes an algorithm that checks the type and content of the data and determines the user's intent. After the server generates a response, that information is fed back to the terminal.

[0665] Specific example: The server confirms that it is a "positive response" and generates the instruction "Approved. Please select the next step," which is then sent to the terminal.

[0666] Notification to the user

[0667] The device receives a response from the server and provides feedback to the user through voice or vibration. This allows the user to confirm the appropriate action and proceed to the next step.

[0668] Specific example: The device provides the user with a voice feedback message saying, "Please select the next step."

[0669] In this way, one embodiment of the invention makes it possible for users to interact naturally with an AI agent even in situations where voice is unavailable.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The device uses sensors built into the earphones to detect the user's head movements in real time. When the user nods, shakes their head from side to side, or tilts their head, the sensors measure the acceleration and angular velocity and capture it as digital data.

[0673] Step 2:

[0674] The device preprocesses the acquired motion data. Preprocessing includes denoising the data obtained from the sensors and identifying the type of motion. This classifies whether the motion is positive, negative, or selective.

[0675] Step 3:

[0676] The terminal packets pre-processed operational data and sends it to the server via the internet. This communication incorporates error correction technology to ensure data reliability.

[0677] Step 4:

[0678] The server analyzes the received behavioral data. Using analysis tools, it interprets the user's intent represented by the data and generates an appropriate response corresponding to the action. The server refers to a predefined gesture mapping database to link actions and responses.

[0679] Step 5:

[0680] The server sends the generated response to the terminal. The response data consists of instructions for proceeding to the next step and is presented in a user-friendly format.

[0681] Step 6:

[0682] The device notifies the user of the response data received from the server. This notification is provided via earphones as audio or vibration feedback. This allows the user to confirm the response and proceed to the next action.

[0683] (Example 1)

[0684] 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".

[0685] Many modern interfaces are designed with voice input in mind, making it difficult to meet the needs of diverse users or in environments where voice input is unavailable. Therefore, there is a need to develop interfaces that enable natural communication without relying on voice.

[0686] 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.

[0687] In this invention, the server includes means for analyzing pre-processed behavioral data, means for transmitting the generated response, and means for classifying the behavior into multiple types. This enables users to intuitively communicate with the AI ​​even in situations where voice is unavailable.

[0688] A "sensor" is a device used to detect the user's head movements in real time.

[0689] "Pre-processing" refers to the process of performing initial processing, such as noise reduction, on detected head movements.

[0690] An "information processing device" is a device that receives pre-processed data and performs analysis and response generation.

[0691] A "notification means" is a means of communicating the generated response to the user.

[0692] A "classification means" is an algorithm or device for classifying a user's head movements into several types and selecting an appropriate response.

[0693] "Communication means" refers to technology or devices for transmitting detected head movement data to an information processing device via the Internet.

[0694] The system in this invention detects the user's head movements and generates a response corresponding to those movements, thereby achieving natural communication. The following describes embodiments for carrying out this invention.

[0695] The user wears a device equipped with sensors. This device has a built-in accelerometer and gyroscope, which allows for real-time detection of the user's movements such as head movements, nodding, and tilting. The terminal improves the accuracy of the digital data obtained from these sensors by first performing preprocessing, such as noise reduction. The preprocessed data is then classified by type and transmitted to a server via the internet.

[0696] The server uses a generative AI model to analyze the received head movement data. This analysis employs sophisticated algorithms to determine what the movements mean. For example, if it recognizes an "affirmative response," the server generates an appropriate response such as "Approved. Please select the next step." These responses are sent back to the terminal via the network and provided to the user through voice or vibration feedback. Even without voice input, the user can intuitively guide to the next action.

[0697] As a concrete example, even if a user has difficulty responding verbally during a meeting, they can indicate "yes" by nodding their head. This action is immediately recognized as an "affirmative response," and further instructions are generated. An example of a prompt might be, "Please describe the method for generating the AI ​​agent's response based on the user's actions." In this way, non-verbal and natural communication becomes possible.

[0698] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0699] Step 1:

[0700] The device uses sensors to detect the user's head movements, thereby acquiring motion data. Specifically, an accelerometer and gyroscope work together to capture motion patterns such as head shakes and nods. The input is real-time motion, and the output is raw data in digital format.

[0701] Step 2:

[0702] The terminal performs preprocessing on the acquired raw data. This processing includes noise reduction and data normalization. For example, it removes minute vibrations from the sensor and environmental noise. The input is raw data from the sensor, and the output is clean, denoised data.

[0703] Step 3:

[0704] The device analyzes pre-processed data and identifies the type of action. It uses an algorithm to classify the data into categories such as "positive response" and "negative response." For example, nodding is identified as "positive." The input is clean data, and the output is classified action data.

[0705] Step 4:

[0706] The terminal packets the classified behavioral data and sends it to the server. A stable communication protocol is used to ensure that the necessary data is transmitted reliably. The input is the classified behavioral data, and the output is the data packets sent to the server.

[0707] Step 5:

[0708] The server analyzes the received data packets and generates an appropriate response using a generative AI model. The response algorithm is executed based on the content of the data. For example, for data indicating an "affirmative response," it generates the instruction "approved." The input is the action data packet, and the output is the generated response.

[0709] Step 6:

[0710] The server sends the generated response to the terminal. This communication requires immediacy, and a high-speed network is used. The input is the generated response, and the output is the data sent to the terminal.

[0711] Step 7:

[0712] The terminal transmits the response received from the server to the user. Feedback using voice and vibration allows the user to recognize the next action. For example, a voice device might play a message such as "Please select the next step." The input is the response data from the server, and the output is the feedback to the user.

[0713] (Application Example 1)

[0714] 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".

[0715] In recent years, there has been a growing need for more natural and contactless ways to control devices within the home, beyond just voice and hand gestures. However, traditional control methods have made it difficult for users to comfortably operate devices in many situations. In particular, there is a need for more intuitive control of home devices when hands are occupied or when voice control is not possible in a quiet environment.

[0716] 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.

[0717] In this invention, the server includes a measuring device means for detecting the user's physical movements, an information processing device means for analyzing the detected movement information and generating a response corresponding to the movement, a return information means for communicating the generated response to the user, an analysis means for classifying multiple movements and determining a response according to each classification, and a control means for controlling devices in the home based on the user's movement input. This enables the user to operate devices in the home with natural movements without using voice.

[0718] A "user" is an individual or entity that uses a system to input actions and receives responses or control based on those actions.

[0719] "Physical movement" refers to physical actions that a user intentionally performs, such as neck movements or changes in posture.

[0720] A "measuring device" is a sensor device that collects the user's physical movements as digital data in real time.

[0721] "Motion information" refers to digital data of the user's physical movements detected by a measuring device.

[0722] An "information processing device" is a computer or server that analyzes operational information and generates an appropriate response.

[0723] A "feedback information means" is a method or device for notifying the user of the generated response, and provides feedback through sound or light.

[0724] An "analysis method" is an algorithm or process that classifies operational information into multiple types and selects an appropriate response according to each type.

[0725] "Control means" refers to mechanisms and protocols for operating devices within the home based on user input.

[0726] "Home devices" refers to electrical appliances and services used in typical households, including air conditioners, lighting, and smart home appliances.

[0727] "Natural movements" are simple physical actions that users can perform on a daily basis without the need for special training or additional means.

[0728] This invention is a system that detects a user's natural movements within the home and controls home devices based on those movements. The system consists of a measuring device, an information processing device, and a return information means.

[0729] The measuring device uses sensors embedded in earphones or other devices to detect the user's body movements in real time. The sensors measure acceleration and angular velocity, and store the acquired motion information as digital data. This includes body movements such as the user shaking their head, nodding, or tilting their head.

[0730] The information processing device analyzes the motion information transmitted from the measuring device. The motion information is preprocessed through a noise-canceling filter and then classified into several types by the analysis means. The information processing device generates an appropriate response based on the type of motion and transmits that information back to the information means.

[0731] The feedback mechanism is responsible for notifying the user of the generated response. This involves conveying information to the user through audio or optical feedback.

[0732] For example, a user can perform a simple physical movement, and digital devices in the home will respond instantly, such as turning off the air conditioner or adjusting the TV volume. This system allows users to intuitively operate devices without using their hands.

[0733] Based on an example of a prompt message, the system will perform an action such as, "Detect that the user nods and turn on the room lights."

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The device detects the user's physical movements using measuring equipment. The input is the user's physical movements, which are acquired as digital data by the sensor. This data consists of numerical data related to acceleration and angular velocity.

[0737] Step 2:

[0738] The device preprocesses the acquired motion data using a noise-canceling filter. The input is unprocessed motion data, and unnecessary noise is removed from the raw data obtained from the sensor to output clean motion data.

[0739] Step 3:

[0740] The terminal analyzes pre-processed motion data and classifies it into multiple motion types using an analysis tool. The input is pre-processed motion data, the algorithm recognizes the motion pattern, and outputs the result as a motion type.

[0741] Step 4:

[0742] The server analyzes the action type transmitted from the terminal, and the information processing device generates an appropriate response based on this. The input is the action type, and the optimal response content is generated using a generative AI model and output in prompt format.

[0743] Step 5:

[0744] The server sends the generated response to the terminal, which then uses a return information mechanism to notify the user. The input is the response data from the server, and the output is communicated to the user as audio or optical feedback.

[0745] Step 6:

[0746] Based on user feedback, the system controls devices within the home. This means the input is user feedback, and the output is the result of specific device operations. For example, a nod of the head might turn on a light.

[0747] 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.

[0748] This invention provides a system that detects the user's head movements, combines them with an emotion engine to understand the user's intentions, and communicates with an AI agent. The processing of this system's program is described in detail below.

[0749] Gesture and emotion detection

[0750] The device continuously monitors the user's head movements through sensors built into the earphones. It also features an emotion engine that detects the user's emotional state based on their facial expressions and voice patterns. This allows the device to understand not only their actions but also their emotional state simultaneously.

[0751] Specific example: If a user shakes their head and says "no" with a stern expression, the emotion engine will recognize this as a "negative and unpleasant" emotion.

[0752] Data analysis and transmission

[0753] The terminal preprocesses the acquired motion and emotion data, removing noise and then identifying the type of motion and emotion category. This information is packetized and sent to the server via the internet. The terminal ensures reliable and efficient communication with minimal errors.

[0754] Specific example: If the emotion engine detects a "negative action" accompanied by "negative and unpleasant" emotions, that data is sent to the server.

[0755] Response generation and feedback

[0756] The server analyzes the received behavioral and emotional data and determines the optimal response. Using a dedicated algorithm, the server undergoes a process to deeply understand the user's intentions and emotions. The generated response is sent to the terminal and provided as feedback to the user.

[0757] Specific example: When the server receives a "no" response accompanied by "negative and unpleasant" feelings, it generates feedback saying, "Understood, I'm sorry if I offended you."

[0758] Adaptive notifications to users

[0759] The device receives feedback from the server and notifies the user in a way that is appropriate to their emotions. The feedback is delivered via voice or vibration, taking into account the user's current emotions.

[0760] Specific example: A voice message plays from the device saying, "We apologize, we will improve so that this does not cause any offense."

[0761] This configuration allows users to engage in richer interactions with AI agents, including not only normal motion recognition but also emotional engagement. This enables more intuitive and emotionally resonant communication.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The device detects the user's head movements using sensors built into the earphones. Simultaneously, an emotion engine collects the user's facial expressions and voice characteristics and evaluates their emotions in real time. If the user nods in agreement, that data is recorded digitally on the device.

[0765] Step 2:

[0766] The terminal then enters the preprocessing stage for the acquired behavioral and emotional data. Here, noise is removed, and the type of behavior (positive, negative, choice) and emotion (joy, sadness, surprise, etc.) are identified. This data is then compiled into packets for analysis.

[0767] Step 3:

[0768] The terminal sends pre-processed data to the server. The system is designed to maintain data integrity and ensure effective delivery to the server during communication over the internet.

[0769] Step 4:

[0770] The server analyzes the received behavioral and emotional data. Here, the server applies multiple algorithms to determine the appropriate response based on the user's intentions and emotions. For example, if a "positive behavior" matches the emotion of "joy," it generates positive feedback.

[0771] Step 5:

[0772] The server sends the generated response to the terminal. The response includes special instructions and information tailored to the user's situation.

[0773] Step 6:

[0774] The device receives responses from the server and provides users with emotionally appropriate notifications. In particular, it uses voice output and vibration feedback to convey information in a way that resonates with the user's emotions. If the user shows a positive response to the system, the device will play a voice message saying, "I gladly accept this choice."

[0775] (Example 2)

[0776] 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".

[0777] Accurately understanding a user's intentions, including not only their actions but also their emotional state, and providing them with appropriate and emotionally sensitive responses has been difficult with conventional technologies. There is a need for systems that enable more intuitive and natural communication in such user interactions.

[0778] 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.

[0779] In this invention, the server includes means for utilizing a terminal equipped with sensors for detecting the user's actions and emotional state, computation means for analyzing the detected action data and emotional data and generating responses corresponding to the actions and emotions, and feedback means for appropriately communicating the generated responses to the user and notifying them in a manner that takes the user's emotions into consideration. This enables a deeper understanding of the user's intentions and communication that is more empathetic to their emotions.

[0780] A "user" is a person who uses the system and whose actions and emotions are detected.

[0781] "Motion" refers to physical movements expressed by the user moving their head or body, which are detected by sensors.

[0782] "Emotional state" refers to the internal psychological state expressed through the user's facial expressions and vocal patterns.

[0783] A "sensor" is a device installed in a terminal to detect the user's actions and emotional state.

[0784] A "terminal" is an electronic device equipped with sensors to detect the user's actions and emotions.

[0785] "Data" refers to information about detected user behavior and emotions, which is used for analysis and response generation.

[0786] "Computation means" refers to the computational techniques and methods used by the server to analyze the data detected and generate a corresponding response.

[0787] A "feedback mechanism" is a method of communicating the generated response to the user and notifying them in a way that is adapted to the user's current situation and emotions.

[0788] "Communication means" refers to the technologies and protocols used to transmit detected data to a server via the internet.

[0789] This invention provides a system that detects user actions and emotions and generates responses corresponding to those emotions.

[0790] Hardware configuration

[0791] The device is equipped with sensors to detect the user's head movements. These sensors, based on sensor technology, are capable of detecting head movements and angles. The device also includes an emotion engine to recognize emotional states, analyzing emotions through facial expressions and voice patterns.

[0792] Software Configuration

[0793] The server receives behavioral and emotional data transmitted from the terminal. The received data is processed by an internal generative AI model and data analysis software to generate a response adapted to the user's behavior and emotions. This response is then sent back to the terminal for prompt feedback to the user.

[0794] For example, if a user shakes their head and makes an unpleasant expression, the device detects a "negative action" and an "unpleasant emotion." Based on this, the server generates a response such as, "I'm sorry if I made you uncomfortable."

[0795] Example of a prompt

[0796] Examples of prompt messages include the following:

[0797] "The user is shaking their head and has a grim expression. What kind of feedback should we provide to this user?"

[0798] In this way, the system can better understand the user's state and interact with the user in a way that is sensitive to their emotions. This technology enables more natural and intuitive communication.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The device continuously monitors the user's head movements using sensors built into the earphones. The sensors acquire user movement and angle information as input data. Based on this data, the amount and speed of head movement are calculated, and the type of movement (e.g., nodding, side-to-side shaking) is identified. For example, if the user shakes their head from side to side, this movement is categorized as a "negative movement."

[0802] Step 2:

[0803] The device analyzes facial expressions and voice patterns to detect the user's emotional state using an emotion engine. Input data includes the user's facial features and voice waveforms. This data is analyzed by the emotion engine, which identifies emotional categories such as smiles, surprise, and anger. For example, if the user has a stern expression, it is tagged as an "unpleasant emotion."

[0804] Step 3:

[0805] The device aggregates the acquired behavioral and emotional data and applies a noise reduction filter to form a clean dataset. The input consists of raw behavioral and emotional data. The filtering process removes noise, resulting in output organized into behavioral and emotional categories. Specifically, data packets containing "negative behavior" and "unpleasant emotions" are generated.

[0806] Step 4:

[0807] The terminal packets clean data and sends it to the server over the internet using a reliable communication protocol. The input data consists of packetized behavioral and emotional information. Communication error correction is performed to ensure that the data reaches the server safely. Specifically, when a data packet is sent to the server and an error is detected, a retransmission process is performed.

[0808] Step 5:

[0809] The server analyzes the received data. The input data includes behavioral and emotional data packets from the terminal. A generative AI model uses this to delve deeper into the user's intent and calculate the optimal response. For example, a response such as "I'm sorry if I offended you" is generated.

[0810] Step 6:

[0811] The server sends the generated response to the terminal, and the terminal relays the response to the user. The input data is the generated response message. The terminal appropriately provides feedback to the user through voice or vibration. Specifically, the terminal plays a voice message saying, "I'm sorry."

[0812] (Application Example 2)

[0813] 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".

[0814] Current user interfaces lack the ability to comprehensively understand user behavior and emotions and provide appropriate real-time responses. Therefore, there is a need for systems that are more empathetic to user emotions and enable more intuitive and human-like interactions.

[0815] 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.

[0816] In this invention, the server includes an information processing device equipped with sensors for detecting the user's actions and emotional state; an information processing device that analyzes the detected action data and emotional data and generates a response corresponding to the user's intentions; and an information processing device that communicates the generated response to the user and notifies them in a manner adapted to their emotions. This makes it possible to provide appropriate responses in real time based on the user's actions and emotions.

[0817] A "user" is a person who performs operations or gives instructions via information processing equipment or information and communication networks.

[0818] "Motion data" refers to information about the user's body movements detected by sensors.

[0819] "Emotional data" refers to information about a user's emotional state that is inferred from their facial expressions, voice, and actions.

[0820] A "sensor" is a device or instrument used to detect a user's actions or emotions.

[0821] An "information processing device" is a computing device that analyzes input data from a user and generates an appropriate response.

[0822] An "information and communication network" is a means of communication for transmitting data over a network.

[0823] "Response" refers to the output information from an information processing device based on detected and analyzed motion data and emotion data.

[0824] A "notification" is information provided to a user through visual or auditory means.

[0825] This invention relates to a system that detects user actions and emotions and generates appropriate responses based on them. The system includes the following components:

[0826] First, the user utilizes a device equipped with sensors. This device incorporates various sensors to detect head movements and facial expressions. Specifically, sensors such as accelerometers and cameras acquire data on the user's movements and facial expressions. This data forms a crucial foundation for monitoring the user's movements and emotional state in real time.

[0827] The motion and emotion data acquired by the sensors is analyzed by an information processing device. This device runs data analysis algorithms using Python and artificial intelligence models for emotion estimation using PyTorch. As a result of the analysis, the user's actions and emotions are clearly identified, and the information necessary for response is extracted.

[0828] The server generates a response tailored to the user based on the analyzed data. This response uses natural language processing technology to ensure it reflects the user's emotions and intentions. For example, if the user looks troubled, the server might generate a suggestion such as, "Is there anything I can help you with?"

[0829] The generated response is communicated to the end user via an information processing device. The notification is provided visually or audibly, utilizing a speech synthesizer or display. A stable communication method is used for transmission, minimizing response delays.

[0830] As a concrete example, a scenario could be envisioned where a home robot reads the user's anxiety from their face and movements and suggests, "Shall I play some relaxing music?" An example of a prompt to input into the generative AI model would be, "The user is shaking their head from side to side and showing a confused expression. Please generate appropriate feedback."

[0831] This configuration makes it possible to achieve intuitive and human-like interactions based on the user's actions and emotions.

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] The device detects the user's movements and facial expressions in real time. It receives head movement data and facial expression data acquired by sensors as input, and outputs them as accelerometer data and image data. This allows for accurate recording of the user's physical movements and facial expressions.

[0835] Step 2:

[0836] The terminal preprocesses the acquired motion data and facial expression data. The inputs are the accelerometer data and image data obtained in step 1. By removing noise and formatting the data, the output is processed data in a state where the user's movements can be classified. Here, for example, smoothing and noise filtering are performed.

[0837] Step 3:

[0838] The device analyzes pre-processed data and uses an emotion engine to estimate the user's emotional state. It takes processed motion data and facial expression data as input and generates emotion categories (e.g., "joy," "anxiety," "anger") as output. This allows for an understanding of the user's inner state.

[0839] Step 4:

[0840] The terminal packets all the analysis data and sends it to the server via the internet. The input consists of emotion categories and behavioral data, which are output as data packets. This ensures reliable and efficient data transfer.

[0841] Step 5:

[0842] The server analyzes the received data packets and generates a response based on the user's actions and emotions. The input is the data packets sent in step 4. By using a generative AI model, the output is a natural language response that is optimal for the user. Specifically, example prompt sentences are analyzed and appropriate feedback is determined.

[0843] Step 6:

[0844] The server sends the generated response to the terminal. The generated response is the input, and the data packet sent back to the terminal is the output. This completes the communication with the user.

[0845] Step 7:

[0846] The terminal notifies the user of the received response. It receives response data from the server as input and provides it to the user as output via voice or display. Specifically, a speech synthesizer is activated, and the response is displayed as a voice message or as text on the display.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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."

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] The following is further disclosed regarding the embodiments described above.

[0869] (Claim 1)

[0870] A terminal equipped with sensors to detect user movements,

[0871] A server that analyzes detected motion data and generates a response corresponding to the motion,

[0872] A feedback mechanism for communicating the generated response to the user,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising analysis means for classifying user actions into multiple types and selecting a response corresponding to each type.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising communication means for transmitting detected motion data to a server via the Internet.

[0878] "Example 1"

[0879] (Claim 1)

[0880] A device equipped with sensors to detect the user's head movements in real time,

[0881] A means for preprocessing detected head movements and removing noise,

[0882] An information processing device including an algorithm for analyzing preprocessed data and generating a response,

[0883] A notification means for communicating the generated response to the user,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, comprising a classification means for classifying the user's head movements into multiple types and selecting a response according to each type.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising communication means for transmitting detected head movement data to an information processing device via the Internet.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] A measuring device for detecting the user's body movements,

[0892] An information processing device that analyzes detected motion information and generates a response corresponding to the motion,

[0893] A means for providing feedback information to the user to transmit the generated response,

[0894] An analysis means that classifies multiple actions and determines a response according to each classification,

[0895] A control means that controls a device in the home based on user input,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising communication means for transmitting operation information detected via a communication network to an information processing device.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein household devices are operated based on operational data from a measuring device.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A terminal equipped with sensors to detect the user's actions and emotional state,

[0904] A computation means for analyzing detected motion data and emotion data and generating responses corresponding to motion and emotion,

[0905] A feedback mechanism for appropriately communicating the generated response to the user and notifying them in a way that takes the user's feelings into consideration,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, comprising analytical means for classifying user behavior and emotions into multiple types and selecting a response corresponding to each type and emotion.

[0909] (Claim 3)

[0910] The system according to claim 1, comprising communication means for transmitting detected motion data and emotion data to a server via the Internet.

[0911] "Application example 2 when combining with an emotional engine"

[0912] (Claim 1)

[0913] An information processing device equipped with sensors for detecting the user's actions and emotional state,

[0914] An information processing device that analyzes detected motion data and emotion data and generates a response corresponding to the user's intent,

[0915] An information processing device that transmits the generated response to the user and notifies them in a way that is adapted to their emotions,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, comprising an analysis means for classifying the user's head movements and facial expressions into multiple emotion types and selecting the optimal response for each emotion type.

[0919] (Claim 3)

[0920] The system according to claim 1, further comprising communication means for transmitting detected motion data and emotion data to a remote information processing device via an information and communication network. [Explanation of Symbols]

[0921] 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 terminal equipped with sensors to detect user movements, A server that analyzes detected motion data and generates a response corresponding to the motion, A feedback mechanism for communicating the generated response to the user, A system that includes this.

2. The system according to claim 1, comprising analysis means for classifying user actions into multiple types and selecting a response corresponding to each type.

3. The system according to claim 1, further comprising communication means for transmitting detected motion data to a server via the Internet.

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