system

Abstract

We provide the system. [Solution] A means of estimating a person's emotional state by analyzing audio data acquired by a collection device, A means of recognizing facial expressions by analyzing image data acquired by a camera device, A means for generating a notification based on the analyzed emotional state and facial expressions of a person and sending it to an external device, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention relates to a system for remotely monitoring the elderly in an aging society, and particularly aims to provide a technology capable of grasping the emotional state of the elderly leading their daily lives and reducing loneliness and ensuring safety. This problem is caused by the current situation that when the elderly live alone, there is a lack of means for their family members to recognize their emotional state in real time.

Means for Solving the Problems

[0006] A "data acquisition device" is a device that includes hardware or software for acquiring data such as audio and images.

[0007] "Voice data" refers to information about a person's voice acquired through a voice collection device such as a microphone.

[0008] "Analysis" is the process of checking and verifying the information contained in data and converting it into useful information such as a person's emotional state.

[0009] "Emotional state" refers to a person's psychological and emotional state, including joy, sadness, anger, and so on.

[0010] A "camera device" refers to an optical instrument used to collect image data, and is primarily used to generate video and still images.

[0011] "Image data" refers to visual information acquired through a camera device, and includes both still images and videos.

[0012] "Facial expression recognition" is a technology that analyzes image data, identifies the features of a person's face, and determines their facial expression.

[0013] A "notification" is information or a message generated by a system that informs the user about a specific event or state.

[0014] An "external device" refers to a device, such as a computer or smartphone, that is located outside the system and receives or displays information.

Brief Description of the Drawings

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

Modes for Carrying Out the Invention

[0016] Next, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] This invention consists of a system comprising multiple devices, which in particular uses voice data and image data to identify the emotional state of a user (an elderly person) and, if necessary, sends notifications to an external device. This system mainly consists of the following elements.

[0037] First, the device is installed in the user's environment and collects audio and image data during daily life. The device has a built-in microphone and camera, which acquire audio and visual information, respectively. The collected data is temporarily stored as digital data within the device.

[0038] The terminal then transmits this digital data to the server at fixed intervals or in real time. The server converts the received audio data into text using a speech recognition module and simultaneously runs an emotion analysis algorithm to estimate the user's emotional state. In parallel, it uses face detection and facial expression analysis algorithms on the image data to identify the user's facial expressions.

[0039] The server categorizes and records the user's emotional state based on the analysis results. In particular, if the server determines that the user is experiencing a specific emotional state (e.g., stress or loneliness), it generates a notification based on that information. This notification is sent to an external device (for example, a smartphone owned by a family member of the user), allowing the recipient to respond quickly.

[0040] To give a specific example, if the device detects sadness in the user's voice and the server analyzes it and determines that the user is experiencing "sadness," the server immediately notifies the user's family. Upon receiving the notification, the family can then take immediate follow-up action, which is expected to alleviate the user's feelings of loneliness.

[0041] In this way, the system of the present invention remotely monitors the emotional state of the user and transmits necessary information to an external party, thereby providing a new monitoring solution for an aging society.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects ambient sound via a microphone. The microphone converts the user's voice into digital audio data in real time and stores it. It also uses a camera to capture the user's face and continuously capture image data. This data is stored in temporary storage.

[0045] Step 2:

[0046] The device transfers collected audio and image data to the server at regular intervals. The audio data is encrypted and securely transmitted along with the image data via a wireless or wired network connection.

[0047] Step 3:

[0048] The server passes the received audio data through a speech recognition module and converts it into text. Based on this text data, an emotion analysis algorithm evaluates the user's emotional state. Emotional components such as joy, anger, and sadness are extracted.

[0049] Step 4:

[0050] Simultaneously, the server performs face detection and expression analysis on the image data. It extracts facial feature data and identifies what kind of expression the user is making. Results such as smiling, surprised, or expressionless are output.

[0051] Step 5:

[0052] The server integrates the results of voice and facial expression analysis to estimate and classify the user's current emotional state. This includes evaluating whether the change is significant compared to past data.

[0053] Step 6:

[0054] If the server determines that a user's emotional state exceeds a pre-set threshold, it generates a notification based on this and sends it to an external device. This external device may include a smartphone belonging to a family member of the user.

[0055] Step 7:

[0056] The user (family member) who receives the notification can take the necessary action based on the alert. For example, they might contact the parent directly or engage in further communication.

[0057] In this way, the system works in conjunction with each step, enabling it to understand the emotional state of users in remote locations and provide support accordingly.

[0058] (Example 1)

[0059] 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."

[0060] In an aging society, there is a need to detect negative emotional states such as loneliness and stress in the elderly early on and to promptly inform their families and caregivers. However, conventional technologies have made it difficult to monitor emotional states in real time and efficiently notify accurate information. Therefore, an efficient method is needed to better ensure the safety and security of the elderly.

[0061] 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.

[0062] In this invention, the server includes means for interpreting acoustic information acquired by an audio input device to estimate a person's emotional state, means for interpreting visual information acquired by an image acquisition device to recognize facial expressions, means for generating a notification based on the interpreted emotional state and facial expressions of the person and transmitting it to an external device, and means for providing information so that an immediate response can be taken when the notification detects a negative emotional state. This makes it possible to monitor the emotional state of elderly people in real time and to quickly notify family members or caregivers as needed.

[0063] A "sound input device" is a device used to acquire the user's acoustic information, and typically includes a microphone.

[0064] A "video acquisition device" is a device used to acquire a user's visual information, and typically includes a camera.

[0065] "Acoustic information" refers to digital sound data acquired by sound input devices, and primarily includes human speech and ambient sounds.

[0066] "Visual information" refers to digital data of images acquired by image acquisition devices, and primarily focuses on human faces, expressions, and body movements.

[0067] "Emotional state" refers to the type and intensity of a person's emotions, estimated through the analysis of acoustic and visual information.

[0068] A "notification" is a message generated based on an interpreted emotional state, intended to convey information to the relevant parties.

[0069] "External devices" refer to devices used to receive notifications, and typically include communication devices such as smartphones and tablets.

[0070] A "negative emotional state" refers to a state of exhibiting undesirable emotions such as stress or loneliness.

[0071] This invention provides a system that effectively monitors the emotional state of elderly individuals and promptly notifies relevant parties as needed. This system primarily consists of terminal devices, a server device, and external equipment.

[0072] The terminal is installed in the user's living environment and includes an audio input device and a video acquisition device. The audio input device is a microphone that collects the user's speech and background sounds. The video acquisition device is a camera that captures the user's face and facial expressions. These devices acquire the user's acoustic and visual information as digital data in their daily life.

[0073] The acquired acoustic and visual information is encrypted and periodically sent to the server. The server uses speech recognition software (e.g., a speech recognition API) to convert the acoustic information into text data. During this process, an emotion analysis algorithm is applied to estimate the user's emotional state. The server also uses libraries such as OpenCV and Dlib for image processing to perform face detection and facial expression analysis, identifying the user's facial expressions from the visual information.

[0074] The server classifies the user's emotional state based on the analysis results. If a negative emotional state is detected, the server immediately generates a notification and sends it to an external device (e.g., a family member's smartphone). The notification includes information about the user's state and recommended actions.

[0075] As a concrete example, if a user expresses feelings of loneliness in their daily life, the acoustic information is collected by the device and, based on emotion analysis, is determined to be "loneliness" by the server. Based on this information, the server sends a notification to the user's family to encourage support. This enables a quick and appropriate response.

[0076] An example of a prompt message might be, "Please describe the specific implementation method of a system that monitors the emotional state of elderly people using audio and image data."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The device acquires acoustic and visual information from the user's daily life using an audio input device and a video acquisition device. Inputs include the user's speech and facial images, and outputs consist of digital audio and image data. Specifically, it records the user's voice with a microphone and captures images of the user's facial expressions and surroundings with a camera.

[0080] Step 2:

[0081] The device encrypts the collected acoustic and visual information. The input consists of the digital acoustic and image data acquired in step 1. The output is encrypted data. Specifically, it performs operations to protect the data using an encryption algorithm.

[0082] Step 3:

[0083] The terminal sends encrypted acoustic and visual information to the server. The input is the data encrypted in step 2, and the output is the data received on the server. Specifically, the operation involves transferring data using a secure communication protocol.

[0084] Step 4:

[0085] The server converts received acoustic information into text data using speech recognition software. The input is encrypted acoustic data sent from the terminal, and the output is text data. This process involves calling a speech recognition API to perform data analysis.

[0086] Step 5:

[0087] The server applies a sentiment analysis algorithm to estimate the user's emotional state based on the converted character data. The input is the text data obtained in step 4, and the output is information indicating the user's emotional state. Specifically, the server performs text analysis using a sentiment analysis library.

[0088] Step 6:

[0089] The server performs face detection and facial expression analysis using the transmitted image data. The input is encrypted image data sent from the terminal, and the output is information about the user's facial expressions. Specific operations include image processing using OpenCV and Dlib.

[0090] Step 7:

[0091] The server integrates emotional state and facial expression information to classify the user's emotional state. The input is the data obtained in steps 5 and 6, and the output is the emotional state category. Specifically, it aggregates this information and processes it to classify emotions according to the judgment criteria.

[0092] Step 8:

[0093] If a user is classified as being in a negative emotional state, the server generates a notification and sends it to an external device. The input is the emotional state information obtained in step 7, and the output is the notification and its transmission. Specifically, the system creates the message content and sends the notification to family members' smartphones or other devices via the network.

[0094] (Application Example 1)

[0095] 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."

[0096] In an aging society, there is a need to appropriately monitor and promptly support elderly people who are experiencing loneliness and stress. However, conventional technologies are not sufficient to identify emotional states in real time or to quickly notify family members in remote locations. Solving this problem and creating an environment where elderly people can live with peace of mind is essential.

[0097] 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.

[0098] In this invention, the server includes means for analyzing voice signals to estimate emotional states, means for analyzing facial expressions, and means for generating warnings and transmitting them to a communication device. This makes it possible to identify the emotional state of elderly people in real time and quickly inform family members or caregivers who are in remote locations.

[0099] A "collection device" is equipment installed to acquire audio and image signals, and includes microphones and cameras.

[0100] "Analysis" is the process of analyzing signals acquired by a data collection device and extracting detailed information about the target.

[0101] "Emotional state" refers to an individual's mental state and includes emotions such as joy, sadness, and stress.

[0102] "Facial expression" refers to the emotions and reactions that appear on a person's face, and includes things like smiles and frowns.

[0103] A "warning" is a notification message generated based on analyzed emotional states and facial expressions, and is sent to family members or caregivers as needed.

[0104] "Communication device" refers to a device used to transmit generated warnings to a remote location, and includes smartphones and communication networks via the internet.

[0105] An "application program" is software installed on smartphones or other devices to perform specific functions.

[0106] "Recommended actions" refer to specific actions or procedures suggested to users who have received a warning, with the aim of improving the situation.

[0107] The system for implementing this invention provides a solution for identifying the emotional state of elderly individuals and notifying their families and caregivers. The system mainly consists of the following elements:

[0108] Hardware configuration

[0109] 1. Data Collection Device: A terminal equipped with a microphone and camera will be used to acquire audio and image signals. This terminal will be installed in the living space of the elderly person to collect information about their daily life.

[0110] 2. Communication device: Used to send alerts to external devices such as smartphones and tablets. Data is transmitted via Wi-Fi or mobile networks.

[0111] Software Configuration

[0112] 1. Speech Analysis Module: This module utilizes the Google® Cloud Speech-to-Text API to analyze speech signals, convert them to text, and estimate emotional states. The analyzed data is then subjected to sentiment analysis using the Hugging Face converter library.

[0113] 2. Image Analysis Module: Using the open-source libraries OpenCV and Dlib, this module analyzes facial expressions within image signals. This allows for the identification of the user's facial expressions and emotions.

[0114] 3. Notification Generation Module: Based on analyzed emotional states and facial expressions, it sends notifications to external devices using Firebase Cloud Messaging. These notifications also include recommendations for user actions.

[0115] Examples

[0116] For example, suppose an elderly person frequently mutters "I'm lonely." The device collects this audio, and if the server identifies "loneliness," a notification is immediately sent to the family's smartphone. Upon receiving this warning, the family can communicate with the elderly person via video call, such as Skype.

[0117] Examples of prompt statements

[0118] It analyzes the user's emotional state and generates prompts that detect whether their statements indicate loneliness or stress.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The terminal acquires the voice signals of elderly individuals through a collection device. The acquired voice data is temporarily stored as digital data on the spot. This data serves as input and is used for subsequent processing.

[0122] Step 2:

[0123] The terminal acquires image signals of elderly people's faces through a collection device. The acquired image data is also temporarily stored as digital data. This data serves as input and is used for face recognition and sentiment analysis.

[0124] Step 3:

[0125] The device sends audio data to the server at regular intervals. The server uses the Google Cloud Speech-to-Text API to convert this audio data into text. The input is audio data, and the output is the converted text. This text becomes the input for the generating AI model.

[0126] Step 4:

[0127] The server uses a generative AI model to analyze the emotional state from the converted text. The input is text data, and the output is the identified emotional state. Prompt sentences are also used to improve the accuracy of the emotion estimation.

[0128] Step 5:

[0129] The server uses OpenCV and Dlib to analyze facial expressions from transmitted image data. The input is image data, and the output is the identified facial expression. This allows for a more detailed understanding of the user's emotional state.

[0130] Step 6:

[0131] The server integrates the results of voice and image analysis to derive an overall emotional state. This analysis result forms the basis for subsequent notification generation.

[0132] Step 7:

[0133] The server uses Firebase Cloud Messaging to generate a notification message based on the analyzed sentiment state and send it to the communication device. The input is the analysis result, and the output is the sent notification message. This notification also includes a specified recommended action.

[0134] Step 8:

[0135] Users (such as family members or caregivers) receive the notifications sent and take follow-up actions as needed. Through this process, it becomes possible to provide an environment where elderly people can live with peace of mind.

[0136] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0137] This invention is an emotion recognition system that combines an emotion engine to collect user voice and image data and analyze that data to accurately understand the user's emotional state. This system mainly consists of a data collection device, a server, an emotion engine, and external devices.

[0138] First, the device functions as a data collection device, acquiring everyday voice and image data from the user. The device incorporates a high-performance microphone and camera, collecting voice data in real time and capturing the user's face. The data is then securely transmitted to a server at regular intervals. Encryption technology is used to prevent information leakage during data transmission.

[0139] The server analyzes the received audio and image data. The audio data is converted to text using speech recognition technology, and the emotional state of the user is analyzed from the tone and rhythm of their voice via an emotion engine. For the image data, face detection and facial expression recognition technology are used to identify specific facial expressions of the user.

[0140] The emotion engine is the central element of this system. This engine learns from emotion data collected and analyzed in the past and can predict changes and patterns in the user's emotional state by comparing it with current data. Furthermore, if a certain emotional state persists for a long period of time, it generates an alert and notifies the user and their family in an appropriate manner.

[0141] To give a specific example, if the emotion engine detects signs of stress at a very high frequency in the user's voice, the server will use the emotion engine's historical data analysis to determine whether the stress exceeds the normal range. When this information is notified to the user's family, they can take specific actions to alleviate the user's stress.

[0142] This invention enables highly accurate emotional monitoring of users, thereby providing support to help users live safely without feeling lonely.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The device collects audio and images in the user's living space. Specifically, a microphone built into the device collects the user's voice as digital data, and simultaneously, a camera captures the user's face as a still image or video. This data is temporarily stored in the device's storage.

[0146] Step 2:

[0147] The device sends the collected audio and image data to the server. The transmitted data is encrypted to ensure security. The data is sent in batches at predetermined intervals or streamed in real time.

[0148] Step 3:

[0149] The server analyzes the received data. For audio data, it is first converted to text via a speech recognition module. Then, an emotion engine analyzes the tone, pitch, and tempo to estimate the user's emotional state.

[0150] Step 4:

[0151] The server simultaneously analyzes the image data using facial recognition and expression analysis algorithms. It extracts facial features and obtains information to classify them into expression categories such as smiling, surprise, anger, and sadness.

[0152] Step 5:

[0153] The emotion engine integrates analysis results obtained from voice and facial expressions to determine the user's overall emotional state. It compares this to past datasets to evaluate whether the current emotional state is abnormal. It also tracks changes over time.

[0154] Step 6:

[0155] The server generates a notification when a user's emotional state exceeds a pre-set threshold or when an anomaly is detected. Based on the alert information from the emotion engine, the notification may include recommended actions.

[0156] Step 7:

[0157] A notification is sent to an external device and displayed to the user's family or related parties. Family members (notification recipients) can then take necessary actions based on the notification. Specific examples include calling or messaging the user, or adjusting visit plans.

[0158] This entire process allows for real-time and continuous monitoring of the user's emotional state, enabling the provision of support as needed.

[0159] (Example 2)

[0160] 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 will be referred to as the "terminal."

[0161] In modern society, continuously observing people's emotional states in their daily lives and providing timely feedback is extremely beneficial. However, current technology makes it difficult to efficiently detect and provide feedback on emotional changes and continuous emotional states. Furthermore, there is a lack of solutions to ensure the security of collected data while making effective recommendations to users.

[0162] 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.

[0163] In this invention, the server includes means for analyzing sound data collected by a voice acquisition device and estimating the user's emotional state, means for analyzing video data collected by a video acquisition device and recognizing expressions, and means for generating warnings based on the analyzed emotional state and expressions and transmitting them to an information output device. This makes it possible to observe the user's emotions with high accuracy while ensuring the security of the collected sound and video data, and to provide notifications and recommended actions as needed.

[0164] A "sound acquisition device" is a device for collecting sound data, and includes high-precision microphones and other such equipment.

[0165] An "image acquisition device" is a device for collecting video data, and includes high-precision cameras and other similar devices.

[0166] "Sound data" refers to digital signals that include the tone and pitch of a person's voice, and is used to analyze emotional states.

[0167] "Video data" refers to video recordings that include human facial expressions and movements, and is used for expression recognition through video analysis.

[0168] "Emotional state" refers to an individual's psychological or emotional state, estimated through the analysis of audio and video data.

[0169] "Expression" refers to emotional expressions through an individual's facial and body movements, as recognized through video analysis.

[0170] A "warning" is a notification or alert generated based on analysis results, which indicates an abnormal emotional state or long-term emotional change.

[0171] An "information output device" is a device used to transmit generated warnings and notifications, and includes, for example, smartphones and personal computers.

[0172] This emotion recognition system consists of a terminal, a server, and external devices. The specific hardware includes an audio acquisition device with a high-precision microphone and a video acquisition device with a high-resolution camera. The software utilizes an audio recognition engine, a facial expression recognition algorithm, and an emotion engine.

[0173] The device is responsible for continuously collecting the user's everyday audio and video data. This allows for real-time acquisition of information indicating the user's emotional state. Audio data collected by the audio acquisition device is converted into text by a speech recognition engine, and then the tone and rhythm of the voice are analyzed. Video data is recognized by an expression recognition algorithm, and the user's facial expressions are recognized from this data to estimate their emotional state.

[0174] The server receives analytical data transmitted from the terminal and uses an emotion engine to compare it with past data. This allows for highly accurate prediction and analysis of changes and patterns in the user's emotional state. Furthermore, if a particular emotional state persists, the server generates a warning based on that information and notifies the information output device. Encryption technology plays a crucial role in maintaining data security throughout this process.

[0175] For example, if the emotion engine detects that a user's stress level is high, the server analyzes that information and, if necessary, sends a notification to the user's family. This allows the user's family to take appropriate action and reduce the user's mental burden.

[0176] Example prompt: "Detect signs of stress from the user's voice data and analyze whether the condition exceeds the normal range."

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

[0178] Step 1:

[0179] The device collects audio and video data from the user. When the user expresses emotions verbally, the audio acquisition device records the sound in real time, and simultaneously, the video acquisition device records the user's facial expressions. The inputs in this process are the user's raw audio and video data. The outputs are the collected, unprocessed audio and video data.

[0180] Step 2:

[0181] The device sends the collected audio data to a speech recognition engine, which converts it into text data. In this process, the speech recognition engine analyzes sound waves, converts them into strings, and extracts the tone and rhythm of the user's voice. The input is audio data, and the output is the converted text data and emotional characteristics data.

[0182] Step 3:

[0183] The server receives text data and sentiment characteristic data sent from the terminal. The server feeds this data into the sentiment engine and estimates the user's emotional state by comparing it with past database data. The data input consists of text and sentiment characteristics, and the output is the result of the emotional state analysis.

[0184] Step 4:

[0185] The device processes the collected video data using an expression recognition algorithm to analyze the user's facial expressions. It extracts facial features from the video data and links them to emotion recognition. The input is video data, and the output is recognized expression data.

[0186] Step 5:

[0187] The server sends facial expression data to the emotion engine and integrates it with the analysis results of the sound data. In this step, the server analyzes the user's overall emotional state in detail and ensures data consistency. The input is the analysis results of the facial expression data and sound data, and the output is the overall emotion analysis result.

[0188] Step 6:

[0189] The server generates a warning based on the analysis results and sends it to the information output device. For example, if the results indicate prolonged stress, it will send a notification suggesting appropriate countermeasures to the user or their family. The input is the analysis results, and the output is a notification containing the generated warning and recommended actions.

[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 the "server," and the smart device 14 will be referred to as the "terminal."

[0192] Currently, many brick-and-mortar stores require human staff to provide appropriate service based on customers' facial expressions and tone of voice in order to improve the quality of customer service. However, accurately understanding a customer's emotional state and providing appropriate service in real time is difficult. Furthermore, because it relies on human judgment, the quality of service can be inconsistent depending on the skills and experience of individual staff members. As a result, maintaining a consistent level of customer service across the entire store is a challenge.

[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 means for analyzing voice data to estimate a person's emotional state, means for analyzing image data to recognize facial expressions, means for generating a notification based on the analysis results and transmitting it to an external device, and means for prompting nearby people who receive the notification to respond based on the emotional information. This makes it possible to accurately grasp the emotional state of customers in real time and automatically notify store staff of the emotional information, thereby improving the quality of customer service.

[0195] A "collection device" is a device consisting of hardware and software for acquiring audio and image data.

[0196] "Audio data" refers to acoustic information, such as speech, collected from users.

[0197] "A person's emotional state" refers to their psychological state as inferred from things like their voice and facial expressions.

[0198] A "camera device" is a device intended for capturing still images or videos.

[0199] "Image data" is a collection of visual information acquired by a camera device.

[0200] "Facial expression" refers to the expression and changes in the face, and reflects a part of a person's emotions.

[0201] A "notification" is information generated based on analysis results and sent to the intended recipient.

[0202] An "external device" is a device that is connected outside the system and capable of receiving information.

[0203] "Emotional information" refers to detailed data about each person's emotional state obtained through analysis.

[0204] A "means to prompt action" refers to a mechanism that instructs and supports recipients to take appropriate action based on notifications from the system.

[0205] This invention is implemented in the form of a customer service support system for physical stores. This system collects, analyzes, and notifies voice and image data to grasp the customer's emotional state in real time and use this information to improve customer service.

[0206] The terminal functions as a data collection device for acquiring audio and image data. Specifically, it uses a camera and microphone connected to the terminal to collect customer facial expressions and voice. The collected data is transmitted to the server in real time. Encryption technology is used during this transmission process to ensure the security of the data.

[0207] The server analyzes the received audio data using audio analysis software (e.g., AudioAnalyzer) to estimate the customer's emotional state. It also uses image analysis software (e.g., OpenCV) to recognize facial expressions from image data. These analysis results are further evaluated by a built-in emotion engine to gain a detailed understanding of the customer's current emotional state.

[0208] Based on the analyzed emotional information, the server generates a notification and presents the appropriate action to take to the store staff. This notification is sent in real time to devices held by staff in the physical store, such as tablets and smartphones. This allows store staff to instantly understand the customer's emotional state and respond flexibly and accurately.

[0209] For example, if a customer shows signs of negative emotions (such as dissatisfaction or stress), the server immediately analyzes this and sends an alert to the employee's device. Based on this information, the employee can follow up by offering polite assistance to the customer or suggesting ways to improve service. In this way, the present invention contributes to improving the quality of customer service and maximizing customer satisfaction.

[0210] An example of a prompt for a generative AI model is, "Think about designing real-time feedback necessary to analyze a customer's emotional state in real time and improve the quality of service." This prompt assigns the AI ​​model the task of designing emotion recognition and the corresponding feedback.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The terminal uses camera equipment and microphones placed inside the store to collect image and audio data of customers. The input is ambient sound and video, and the output is the acquired raw audio and image files. In this step, the terminal captures the data in real time and records the collected data in a buffer.

[0214] Step 2:

[0215] The terminal encrypts the collected audio and image data and sends it to the server. The input is the raw data obtained in step 1, and the output is encrypted data packets. In this step, the terminal uses an existing encryption library to protect data privacy and ensure the security of the transmitted data.

[0216] Step 3:

[0217] The server receives encrypted audio data, decrypts it, and analyzes it. The input is encrypted data, and the output is an estimated result of the emotional state derived from the audio. In this step, the server uses AudioAnalyzer to extract audio features and a generative AI model to estimate the emotional state.

[0218] Step 4:

[0219] The server receives encrypted image data, decrypts it, and analyzes it. The input is encrypted data, and the output is the result of facial recognition obtained from the image. In this step, the server uses OpenCV to perform face detection and extract facial features, which are then analyzed by the emotion engine.

[0220] Step 5:

[0221] The server integrates customer emotional state information based on the analysis results and generates a notification if action is required. The input is the emotional recognition data obtained in steps 3 and 4, and the output is an alert message for the store staff. In this step, the server uses a generated AI model to detect patterns of emotional state changes and incorporates appropriate information into the notification.

[0222] Step 6:

[0223] The server sends the generated notification to a terminal within the store (for example, a smartphone or tablet carried by an employee). The input is the notification message generated in step 5, and the output is the alert message received by the employee. In this step, the server sends the notification in real time using a communication protocol.

[0224] Step 7:

[0225] The user (store clerk) checks the received notification and takes action according to the customer's emotional state. The input is the received notification message, and the output is the user's action and its consequences. In this step, the store clerk adjusts their customer service and takes specific actions to improve the quality of service.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention consists of a system comprising multiple devices, which in particular uses voice data and image data to identify the emotional state of a user (an elderly person) and, if necessary, sends notifications to an external device. This system mainly consists of the following elements.

[0243] First, the device is installed in the user's environment and collects audio and image data during daily life. The device has a built-in microphone and camera, which acquire audio and visual information, respectively. The collected data is temporarily stored as digital data within the device.

[0244] The terminal then transmits this digital data to the server at fixed intervals or in real time. The server converts the received audio data into text using a speech recognition module and simultaneously runs an emotion analysis algorithm to estimate the user's emotional state. In parallel, it uses face detection and facial expression analysis algorithms on the image data to identify the user's facial expressions.

[0245] The server categorizes and records the user's emotional state based on the analysis results. In particular, if the server determines that the user is experiencing a specific emotional state (e.g., stress or loneliness), it generates a notification based on that information. This notification is sent to an external device (for example, a smartphone owned by a family member of the user), allowing the recipient to respond quickly.

[0246] To give a specific example, if the device detects sadness in the user's voice and the server analyzes it and determines that the user is experiencing "sadness," the server immediately notifies the user's family. Upon receiving the notification, the family can then take immediate follow-up action, which is expected to alleviate the user's feelings of loneliness.

[0247] In this way, the system of the present invention provides a new monitoring solution for an aging society by remotely monitoring the emotional state of the user and transmitting necessary information to an external party.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The device collects ambient sound via a microphone. The microphone converts the user's voice into digital audio data in real time and stores it. It also uses a camera to capture the user's face and continuously capture image data. This data is stored in temporary storage.

[0251] Step 2:

[0252] The device transfers collected audio and image data to the server at regular intervals. The audio data is encrypted and securely transmitted along with the image data via a wireless or wired network connection.

[0253] Step 3:

[0254] The server passes the received audio data through a speech recognition module and converts it into text. Based on this text data, an emotion analysis algorithm evaluates the user's emotional state. Emotional components such as joy, anger, and sadness are extracted.

[0255] Step 4:

[0256] Simultaneously, the server performs face detection and expression analysis on the image data. It extracts facial feature data and identifies what kind of expression the user is making. Results such as smiling, surprised, or expressionless are output.

[0257] Step 5:

[0258] The server integrates the results of voice and facial expression analysis to estimate and classify the user's current emotional state. This includes evaluating whether the change is significant compared to past data.

[0259] Step 6:

[0260] If the server determines that a user's emotional state exceeds a pre-set threshold, it generates a notification based on this and sends it to an external device. This external device may include a smartphone belonging to a family member of the user.

[0261] Step 7:

[0262] The user (family member) who receives the notification can take the necessary action based on the alert. For example, they might contact their parent directly or engage in further communication.

[0263] In this way, the system works in conjunction with each step, enabling it to understand the emotional state of users in remote locations and provide support accordingly.

[0264] (Example 1)

[0265] 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."

[0266] In an aging society, there is a need to detect negative emotional states such as loneliness and stress in the elderly early on and to promptly inform their families and caregivers. However, conventional technologies have made it difficult to monitor emotional states in real time and efficiently notify accurate information. Therefore, an efficient method is needed to better ensure the safety and security of the elderly.

[0267] 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.

[0268] In this invention, the server includes means for interpreting acoustic information acquired by an audio input device to estimate a person's emotional state, means for interpreting visual information acquired by an image acquisition device to recognize facial expressions, means for generating a notification based on the interpreted emotional state and facial expressions of the person and transmitting it to an external device, and means for providing information so that an immediate response can be taken when the notification detects a negative emotional state. This makes it possible to monitor the emotional state of elderly people in real time and to quickly notify family members or caregivers as needed.

[0269] A "sound input device" is a device used to acquire the user's acoustic information, and typically includes a microphone.

[0270] A "video acquisition device" is a device used to acquire a user's visual information, and typically includes a camera.

[0271] "Acoustic information" refers to digital sound data acquired by sound input devices, and primarily includes human speech and ambient sounds.

[0272] "Visual information" refers to digital data of images acquired by image acquisition devices, and primarily focuses on human faces, expressions, and body movements.

[0273] "Emotional state" refers to the type and intensity of a person's emotions, estimated through the analysis of acoustic and visual information.

[0274] A "notification" is a message generated based on an interpreted emotional state, intended to convey information to the relevant parties.

[0275] "External devices" refer to devices used to receive notifications, and typically include communication devices such as smartphones and tablets.

[0276] A "negative emotional state" refers to a state of exhibiting undesirable emotions such as stress or loneliness.

[0277] This invention provides a system that effectively monitors the emotional state of elderly individuals and promptly notifies relevant parties as needed. This system primarily consists of terminal devices, a server device, and external equipment.

[0278] The terminal is installed in the user's living environment and includes an audio input device and a video acquisition device. The audio input device is a microphone that collects the user's speech and background sounds. The video acquisition device is a camera that captures the user's face and facial expressions. These devices acquire the user's acoustic and visual information as digital data in their daily life.

[0279] The acquired acoustic information and visual information are encrypted and periodically transmitted to the server. The server uses speech recognition software (e.g., speech recognition API) to convert the acoustic information into character data. At this time, an emotion analysis algorithm is applied to estimate the user's emotional state. Also, the server uses libraries such as OpenCV and Dlib for image processing to perform face detection and expression analysis, and identify the user's expression from the visual information.

[0280] The server classifies the user's emotional state based on the analysis results. At this time, if a negative emotional state is detected, the server immediately generates a notification and transmits it to an external device (e.g., the family member's smartphone). The notification includes information about the user's state and recommended actions.

[0281] As a specific example, when the user utters a voice expressing loneliness in daily life, the acoustic information is collected by the terminal and determined as "loneliness" by the server based on emotion analysis. Based on this information, the server sends a notification to the user's family to prompt support. This enables a prompt and appropriate response.

[0282] As an example of the prompt sentence, something like "Please explain the specific implementation method of a system for monitoring the emotional state of the elderly using voice and image data" can be considered.

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

[0284] Step 1:

[0285] The terminal uses a sound input device and a video acquisition device to acquire acoustic information and visual information from the user's daily life. The input is the user's speech and face video, and digital acoustic data and image data are generated as output. Specifically, the operation of recording the user's voice with a microphone and shooting the user's expression and surrounding video with a camera is performed.

[0286] Step 2:

[0287] The terminal encrypts the collected acoustic information and visual information. As input, the digital acoustic data and image data obtained in step 1 are used. The output is the encrypted data. As a specific operation, an operation of protecting the data using an encryption algorithm is performed.

[0288] Step 3:

[0289] The terminal sends the encrypted acoustic information and visual information to the server. The input is the data encrypted in step 2, and the output is the data reception on the server. As a specific operation, an operation of transferring the data using a secure communication protocol is performed.

[0290] Step 4:

[0291] The server converts the received acoustic information into character data using speech recognition software. The input is the encrypted acoustic data sent from the terminal, and the output is the data in text format. At this time, a specific operation of calling the speech recognition API for data analysis is involved.

[0292] Step 5:

[0293] The server applies a sentiment analysis algorithm based on the converted character data to estimate the user's sentiment state. The input is the text data obtained in step 4, and the output is the information indicating the user's sentiment state. As a specific operation, a process of performing text analysis using a sentiment analysis library is implemented.

[0294] Step 6:

[0295] The server performs face detection and expression analysis using the transmitted image data. The input is the encrypted image data sent from the terminal, and the output is the information regarding the user's expression. As a specific operation, it includes operations of performing image processing using OpenCV or Dlib.

[0296] Step 7:

[0297] The server integrates emotional state and facial expression information to classify the user's emotional state. The input is the data obtained in steps 5 and 6, and the output is the emotional state category. Specifically, it aggregates this information and processes it to classify emotions according to the judgment criteria.

[0298] Step 8:

[0299] If a user is classified as being in a negative emotional state, the server generates a notification and sends it to an external device. The input is the emotional state information obtained in step 7, and the output is the notification and its transmission. Specifically, the system creates the message content and sends the notification to family members' smartphones or other devices via the network.

[0300] (Application Example 1)

[0301] 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."

[0302] In an aging society, there is a need to appropriately monitor and promptly support elderly people who are experiencing loneliness and stress. However, conventional technologies are not sufficient to identify emotional states in real time or to quickly notify family members in remote locations. Solving this problem and creating an environment where elderly people can live with peace of mind is essential.

[0303] 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.

[0304] In this invention, the server includes means for analyzing voice signals to estimate emotional states, means for analyzing facial expressions, and means for generating warnings and transmitting them to a communication device. This makes it possible to identify the emotional state of elderly people in real time and quickly inform family members or caregivers who are in remote locations.

[0305] The "collection device" is a device installed to acquire audio signals and image signals, including microphones, cameras, etc.

[0306] "Analysis" is a process of analyzing the signals acquired by the collection device and extracting the detailed information of interest.

[0307] "Emotional state" refers to the state of an individual's mind, including emotions such as joy, sadness, stress, etc.

[0308] "Facial expression" refers to the emotions and reactions shown on a person's face, including smiling faces, frowning faces, etc.

[0309] "Warning" is a notification message generated based on the analyzed emotional state and facial expression, and is sent to family members or caregivers as needed.

[0310] "Communication device" is a device used to transmit the generated warning to a remote location, including smartphones and communication networks via the Internet.

[0311] "Application program" is software installed on a smartphone or other device to execute specific functions.

[0312] "Recommended operation" is a specific action or procedure proposed to the user who received the warning, aiming to improve the situation.

[0313] The system for implementing this invention provides a solution for identifying the emotional state of the elderly and notifying family members and caregivers. The system mainly consists of the following elements.

[0314] Hardware configuration

[0315] 1. Data Collection Device: A terminal equipped with a microphone and camera will be used to acquire audio and image signals. This terminal will be installed in the living space of the elderly person to collect information about their daily life.

[0316] 2. Communication device: Used to send alerts to external devices such as smartphones and tablets. Data is transmitted via Wi-Fi or mobile networks.

[0317] Software Configuration

[0318] 1. Speech Analysis Module: This module utilizes the Google Cloud Speech-to-Text API to analyze speech signals, convert them to text, and estimate emotional states. The analyzed data is then subjected to sentiment analysis using the Hugging Face converter library.

[0319] 2. Image Analysis Module: Using the open-source libraries OpenCV and Dlib, this module analyzes facial expressions within image signals. This allows for the identification of the user's facial expressions and emotions.

[0320] 3. Notification Generation Module: Based on analyzed emotional states and facial expressions, it sends notifications to external devices using Firebase Cloud Messaging. These notifications also include recommendations for user actions.

[0321] Examples

[0322] For example, suppose an elderly person frequently mutters "I'm lonely." The device collects this audio, and if the server identifies "loneliness," a notification is immediately sent to the family's smartphone. Upon receiving this warning, the family can communicate with the elderly person via video call, such as Skype.

[0323] Examples of prompt statements

[0324] It analyzes the user's emotional state and generates prompts that detect whether their statements indicate loneliness or stress.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The terminal acquires the voice signals of elderly individuals through a collection device. The acquired voice data is temporarily stored as digital data on the spot. This data serves as input and is used for subsequent processing.

[0328] Step 2:

[0329] The terminal acquires image signals of elderly people's faces through a collection device. The acquired image data is also temporarily stored as digital data. This data serves as input and is used for face recognition and sentiment analysis.

[0330] Step 3:

[0331] The device sends audio data to the server at regular intervals. The server uses the Google Cloud Speech-to-Text API to convert this audio data into text. The input is audio data, and the output is the converted text. This text becomes the input for the generating AI model.

[0332] Step 4:

[0333] The server uses a generative AI model to analyze the emotional state from the converted text. The input is text data, and the output is the identified emotional state. Prompt sentences are also used to improve the accuracy of the emotion estimation.

[0334] Step 5:

[0335] The server uses OpenCV and Dlib to analyze facial expressions from transmitted image data. The input is image data, and the output is the identified facial expression. This allows for a more detailed understanding of the user's emotional state.

[0336] Step 6:

[0337] The server integrates the results of voice and image analysis to derive an overall emotional state. This analysis result forms the basis for subsequent notification generation.

[0338] Step 7:

[0339] The server uses Firebase Cloud Messaging to generate a notification message based on the analyzed sentiment state and send it to the communication device. The input is the analysis result, and the output is the sent notification message. This notification also includes a specified recommended action.

[0340] Step 8:

[0341] Users (such as family members or caregivers) receive the notifications sent and take follow-up actions as needed. Through this process, it becomes possible to provide an environment where elderly people can live with peace of mind.

[0342] 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.

[0343] This invention is an emotion recognition system that combines an emotion engine to collect user voice and image data and analyze that data to accurately understand the user's emotional state. This system mainly consists of a data collection device, a server, an emotion engine, and external devices.

[0344] First, the device functions as a data collection device, acquiring everyday voice and image data from the user. The device incorporates a high-performance microphone and camera, collecting voice data in real time and capturing the user's face. The data is then securely transmitted to a server at regular intervals. Encryption technology is used to prevent information leakage during data transmission.

[0345] The server analyzes the received audio and image data. The audio data is converted to text using speech recognition technology, and the emotional state of the user is analyzed from the tone and rhythm of their voice via an emotion engine. For the image data, face detection and facial expression recognition technology are used to identify specific facial expressions of the user.

[0346] The emotion engine is the central element of this system. This engine learns from emotion data collected and analyzed in the past and can predict changes and patterns in the user's emotional state by comparing it with current data. Furthermore, if a certain emotional state persists for a long period of time, it generates an alert and notifies the user and their family in an appropriate manner.

[0347] To give a specific example, if the emotion engine detects signs of stress at a very high frequency in the user's voice, the server will use the emotion engine's historical data analysis to determine whether the stress exceeds the normal range. When this information is notified to the user's family, they can take specific actions to alleviate the user's stress.

[0348] This invention enables highly accurate emotional monitoring of users, thereby providing support to help users live safely without feeling lonely.

[0349] The following describes the processing flow.

[0350] Step 1:

[0351] The device collects audio and images in the user's living space. Specifically, a microphone built into the device collects the user's voice as digital data, and simultaneously, a camera captures the user's face as a still image or video. This data is temporarily stored in the device's storage.

[0352] Step 2:

[0353] The device sends the collected audio and image data to the server. The transmitted data is encrypted to ensure security. The data is sent in batches at predetermined intervals or streamed in real time.

[0354] Step 3:

[0355] The server analyzes the received data. For audio data, it is first converted to text via a speech recognition module. Then, an emotion engine analyzes the tone, pitch, and tempo to estimate the user's emotional state.

[0356] Step 4:

[0357] The server simultaneously analyzes the image data using facial recognition and expression analysis algorithms. It extracts facial features and obtains information to classify them into expression categories such as smiling, surprise, anger, and sadness.

[0358] Step 5:

[0359] The emotion engine integrates analysis results obtained from voice and facial expressions to determine the user's overall emotional state. It compares this to past datasets to evaluate whether the current emotional state is abnormal. It also tracks changes over time.

[0360] Step 6:

[0361] The server generates a notification when a user's emotional state exceeds a pre-set threshold or when an anomaly is detected. Based on the alert information from the emotion engine, the notification may include recommended actions.

[0362] Step 7:

[0363] A notification is sent to an external device and displayed to the user's family or related parties. Family members (notification recipients) can then take necessary actions based on the notification. Specific examples include calling or messaging the user, or adjusting visit plans.

[0364] This entire process allows for real-time and continuous monitoring of the user's emotional state, enabling the provision of support as needed.

[0365] (Example 2)

[0366] 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".

[0367] In modern society, continuously observing people's emotional states in their daily lives and providing timely feedback is extremely beneficial. However, current technology makes it difficult to efficiently detect and provide feedback on emotional changes and continuous emotional states. Furthermore, there is a lack of solutions to ensure the security of collected data while making effective recommendations to users.

[0368] 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.

[0369] In this invention, the server includes means for analyzing sound data collected by a voice acquisition device and estimating the user's emotional state, means for analyzing video data collected by a video acquisition device and recognizing expressions, and means for generating warnings based on the analyzed emotional state and expressions and transmitting them to an information output device. This makes it possible to observe the user's emotions with high accuracy while ensuring the security of the collected sound and video data, and to provide notifications and recommended actions as needed.

[0370] A "sound acquisition device" is a device for collecting sound data, and includes high-precision microphones and other such equipment.

[0371] An "image acquisition device" is a device for collecting video data, and includes high-precision cameras and other similar devices.

[0372] "Sound data" refers to digital signals that include the tone and pitch of a person's voice, and is used to analyze emotional states.

[0373] "Video data" refers to video recordings that include human facial expressions and movements, and is used for expression recognition through video analysis.

[0374] "Emotional state" refers to an individual's psychological or emotional state, estimated through the analysis of audio and video data.

[0375] "Expression" refers to emotional expressions through an individual's facial and body movements, as recognized through video analysis.

[0376] A "warning" is a notification or alert generated based on analysis results, which indicates an abnormal emotional state or long-term emotional change.

[0377] An "information output device" is a device used to transmit generated warnings and notifications, and includes, for example, smartphones and personal computers.

[0378] This emotion recognition system consists of a terminal, a server, and external devices. The specific hardware includes an audio acquisition device with a high-precision microphone and a video acquisition device with a high-resolution camera. The software utilizes an audio recognition engine, a facial expression recognition algorithm, and an emotion engine.

[0379] The device is responsible for continuously collecting the user's everyday audio and video data. This allows for real-time acquisition of information indicating the user's emotional state. Audio data collected by the audio acquisition device is converted into text by a speech recognition engine, and then the tone and rhythm of the voice are analyzed. Video data is recognized by an expression recognition algorithm, and the user's facial expressions are recognized from this data to estimate their emotional state.

[0380] The server receives analytical data transmitted from the terminal and uses an emotion engine to compare it with past data. This allows for highly accurate prediction and analysis of changes and patterns in the user's emotional state. Furthermore, if a particular emotional state persists, the server generates a warning based on that information and notifies the information output device. Encryption technology plays a crucial role in maintaining data security throughout this process.

[0381] For example, if the emotion engine detects that a user's stress level is high, the server analyzes that information and, if necessary, sends a notification to the user's family. This allows the user's family to take appropriate action and reduce the user's mental burden.

[0382] Example prompt: "Detect signs of stress from the user's voice data and analyze whether the condition exceeds the normal range."

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The device collects audio and video data from the user. When the user expresses emotions verbally, the audio acquisition device records the sound in real time, and simultaneously, the video acquisition device records the user's facial expressions. The inputs in this process are the user's raw audio and video data. The outputs are the collected, unprocessed audio and video data.

[0386] Step 2:

[0387] The device sends the collected audio data to a speech recognition engine, which converts it into text data. In this process, the speech recognition engine analyzes sound waves, converts them into strings, and extracts the tone and rhythm of the user's voice. The input is audio data, and the output is the converted text data and emotional characteristics data.

[0388] Step 3:

[0389] The server receives text data and sentiment characteristic data sent from the terminal. The server feeds this data into the sentiment engine and estimates the user's emotional state by comparing it with past database data. The data input consists of text and sentiment characteristics, and the output is the result of the emotional state analysis.

[0390] Step 4:

[0391] The device processes the collected video data using an expression recognition algorithm to analyze the user's facial expressions. It extracts facial features from the video data and links them to emotion recognition. The input is video data, and the output is recognized expression data.

[0392] Step 5:

[0393] The server sends facial expression data to the emotion engine and integrates it with the analysis results of the sound data. In this step, the server analyzes the user's overall emotional state in detail and ensures data consistency. The input is the analysis results of the facial expression data and sound data, and the output is the overall emotion analysis result.

[0394] Step 6:

[0395] The server generates a warning based on the analysis results and sends it to the information output device. For example, if the results indicate prolonged stress, it will send a notification suggesting appropriate countermeasures to the user or their family. The input is the analysis results, and the output is a notification containing the generated warning and recommended actions.

[0396] (Application Example 2)

[0397] 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."

[0398] Currently, many brick-and-mortar stores require human staff to provide appropriate service based on customers' facial expressions and tone of voice in order to improve the quality of customer service. However, accurately understanding a customer's emotional state and providing appropriate service in real time is difficult. Furthermore, because it relies on human judgment, the quality of service can be inconsistent depending on the skills and experience of individual staff members. As a result, maintaining a consistent level of customer service across the entire store is a challenge.

[0399] 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.

[0400] In this invention, the server includes means for analyzing voice data to estimate a person's emotional state, means for analyzing image data to recognize facial expressions, means for generating a notification based on the analysis results and transmitting it to an external device, and means for prompting nearby people who receive the notification to respond based on the emotional information. This makes it possible to accurately grasp the emotional state of customers in real time and automatically notify store staff of the emotional information, thereby improving the quality of customer service.

[0401] A "collection device" is a device consisting of hardware and software for acquiring audio and image data.

[0402] "Audio data" refers to acoustic information, such as speech, collected from users.

[0403] "A person's emotional state" refers to their psychological state as inferred from things like their voice and facial expressions.

[0404] A "camera device" is a device intended for capturing still images or videos.

[0405] "Image data" is a collection of visual information acquired by a camera device.

[0406] "Facial expression" refers to the expression and changes in the face, and reflects a part of a person's emotions.

[0407] A "notification" is information generated based on analysis results and sent to the intended recipient.

[0408] An "external device" is a device that is connected outside the system and capable of receiving information.

[0409] "Emotional information" refers to detailed data about each person's emotional state obtained through analysis.

[0410] A "means to prompt action" refers to a mechanism that instructs and supports recipients to take appropriate action based on notifications from the system.

[0411] This invention is implemented in the form of a customer service support system for physical stores. This system collects, analyzes, and notifies voice and image data to grasp the customer's emotional state in real time and use this information to improve customer service.

[0412] The terminal functions as a data collection device for acquiring audio and image data. Specifically, it uses a camera and microphone connected to the terminal to collect customer facial expressions and voice. The collected data is transmitted to the server in real time. Encryption technology is used during this transmission process to ensure the security of the data.

[0413] The server analyzes the received audio data using audio analysis software (e.g., AudioAnalyzer) to estimate the customer's emotional state. It also uses image analysis software (e.g., OpenCV) to recognize facial expressions from image data. These analysis results are further evaluated by a built-in emotion engine to gain a detailed understanding of the customer's current emotional state.

[0414] Based on the analyzed emotional information, the server generates a notification and presents the appropriate action to take to the store staff. This notification is sent in real time to devices held by staff in the physical store, such as tablets and smartphones. This allows store staff to instantly understand the customer's emotional state and respond flexibly and accurately.

[0415] For example, if a customer shows signs of negative emotions (such as dissatisfaction or stress), the server immediately analyzes this and sends an alert to the employee's device. Based on this information, the employee can follow up by offering polite assistance to the customer or suggesting ways to improve service. In this way, the present invention contributes to improving the quality of customer service and maximizing customer satisfaction.

[0416] An example of a prompt for a generative AI model is, "Think about designing real-time feedback necessary to analyze a customer's emotional state in real time and improve the quality of service." This prompt assigns the AI ​​model the task of designing emotion recognition and the corresponding feedback.

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

[0418] Step 1:

[0419] The terminal uses camera equipment and microphones placed inside the store to collect image and audio data of customers. The input is ambient sound and video, and the output is the acquired raw audio and image files. In this step, the terminal captures the data in real time and records the collected data in a buffer.

[0420] Step 2:

[0421] The terminal encrypts the collected audio and image data and sends it to the server. The input is the raw data obtained in step 1, and the output is encrypted data packets. In this step, the terminal uses an existing encryption library to protect data privacy and ensure the security of the transmitted data.

[0422] Step 3:

[0423] The server receives encrypted audio data, decrypts it, and analyzes it. The input is encrypted data, and the output is an estimated result of the emotional state derived from the audio. In this step, the server uses AudioAnalyzer to extract audio features and a generative AI model to estimate the emotional state.

[0424] Step 4:

[0425] The server receives encrypted image data, decrypts it, and analyzes it. The input is encrypted data, and the output is the result of facial recognition obtained from the image. In this step, the server uses OpenCV to perform face detection and extract facial features, which are then analyzed by the emotion engine.

[0426] Step 5:

[0427] The server integrates customer emotional state information based on the analysis results and generates a notification if action is required. The input is the emotional recognition data obtained in steps 3 and 4, and the output is an alert message for the store staff. In this step, the server uses a generated AI model to detect patterns of emotional state changes and incorporates appropriate information into the notification.

[0428] Step 6:

[0429] The server sends the generated notification to a terminal within the store (for example, a smartphone or tablet carried by an employee). The input is the notification message generated in step 5, and the output is the alert message received by the employee. In this step, the server sends the notification in real time using a communication protocol.

[0430] Step 7:

[0431] The user (store clerk) checks the received notification and takes action according to the customer's emotional state. The input is the received notification message, and the output is the user's action and its consequences. In this step, the store clerk adjusts their customer service and takes specific actions to improve the quality of service.

[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 consists of a system comprising multiple devices, which in particular uses voice data and image data to identify the emotional state of a user (an elderly person) and, if necessary, sends notifications to an external device. This system mainly consists of the following elements.

[0449] First, the device is installed in the user's environment and collects audio and image data during daily life. The device has a built-in microphone and camera, which acquire audio and visual information, respectively. The collected data is temporarily stored as digital data within the device.

[0450] The terminal then transmits this digital data to the server at fixed intervals or in real time. The server converts the received audio data into text using a speech recognition module and simultaneously runs an emotion analysis algorithm to estimate the user's emotional state. In parallel, it uses face detection and facial expression analysis algorithms on the image data to identify the user's facial expressions.

[0451] The server categorizes and records the user's emotional state based on the analysis results. In particular, if the server determines that the user is experiencing a specific emotional state (e.g., stress or loneliness), it generates a notification based on that information. This notification is sent to an external device (for example, a smartphone owned by a family member of the user), allowing the recipient to respond quickly.

[0452] To give a specific example, if the device detects sadness in the user's voice and the server analyzes it and determines that the user is experiencing "sadness," the server immediately notifies the user's family. Upon receiving the notification, the family can then take immediate follow-up action, which is expected to alleviate the user's feelings of loneliness.

[0453] In this way, the system of the present invention provides a new monitoring solution for an aging society by remotely monitoring the emotional state of the user and transmitting necessary information to an external party.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] The device collects ambient sound via a microphone. The microphone converts the user's voice into digital audio data in real time and stores it. It also uses a camera to capture the user's face and continuously capture image data. This data is stored in temporary storage.

[0457] Step 2:

[0458] The device transfers collected audio and image data to the server at regular intervals. The audio data is encrypted and securely transmitted along with the image data via a wireless or wired network connection.

[0459] Step 3:

[0460] The server passes the received audio data through a speech recognition module and converts it into text. Based on this text data, an emotion analysis algorithm evaluates the user's emotional state. Emotional components such as joy, anger, and sadness are extracted.

[0461] Step 4:

[0462] Simultaneously, the server performs face detection and expression analysis on the image data. It extracts facial feature data and identifies what kind of expression the user is making. Results such as smiling, surprised, or expressionless are output.

[0463] Step 5:

[0464] The server integrates the results of voice and facial expression analysis to estimate and classify the user's current emotional state. This includes evaluating whether the change is significant compared to past data.

[0465] Step 6:

[0466] If the server determines that a user's emotional state exceeds a pre-set threshold, it generates a notification based on this and sends it to an external device. This external device may include a smartphone belonging to a family member of the user.

[0467] Step 7:

[0468] The user (family member) who receives the notification can take the necessary action based on the alert. For example, they might contact their parent directly or engage in further communication.

[0469] In this way, the system works in conjunction with each step, enabling it to understand the emotional state of users in remote locations and provide support accordingly.

[0470] (Example 1)

[0471] 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."

[0472] In an aging society, there is a need to detect negative emotional states such as loneliness and stress in the elderly early on and to promptly inform their families and caregivers. However, conventional technologies have made it difficult to monitor emotional states in real time and efficiently notify accurate information. Therefore, an efficient method is needed to better ensure the safety and security of the elderly.

[0473] 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.

[0474] In this invention, the server includes means for interpreting acoustic information acquired by an audio input device to estimate a person's emotional state, means for interpreting visual information acquired by an image acquisition device to recognize facial expressions, means for generating a notification based on the interpreted emotional state and facial expressions of the person and transmitting it to an external device, and means for providing information so that an immediate response can be taken when the notification detects a negative emotional state. This makes it possible to monitor the emotional state of elderly people in real time and to quickly notify family members or caregivers as needed.

[0475] A "sound input device" is a device used to acquire the user's acoustic information, and typically includes a microphone.

[0476] A "video acquisition device" is a device used to acquire a user's visual information, and typically includes a camera.

[0477] "Acoustic information" refers to digital sound data acquired by sound input devices, and primarily includes human speech and ambient sounds.

[0478] "Visual information" refers to digital data of images acquired by image acquisition devices, and primarily focuses on human faces, expressions, and body movements.

[0479] "Emotional state" refers to the type and intensity of a person's emotions, estimated through the analysis of acoustic and visual information.

[0480] A "notification" is a message generated based on an interpreted emotional state, intended to convey information to the relevant parties.

[0481] "External devices" refer to devices used to receive notifications, and typically include communication devices such as smartphones and tablets.

[0482] A "negative emotional state" refers to a state of exhibiting undesirable emotions such as stress or loneliness.

[0483] This invention provides a system that effectively monitors the emotional state of elderly individuals and promptly notifies relevant parties as needed. This system primarily consists of terminal devices, a server device, and external equipment.

[0484] The terminal is installed in the user's living environment and includes an audio input device and a video acquisition device. The audio input device is a microphone that collects the user's speech and background sounds. The video acquisition device is a camera that captures the user's face and facial expressions. These devices acquire the user's acoustic and visual information as digital data in their daily life.

[0485] The acquired acoustic and visual information is encrypted and periodically sent to the server. The server uses speech recognition software (e.g., a speech recognition API) to convert the acoustic information into text data. During this process, an emotion analysis algorithm is applied to estimate the user's emotional state. The server also uses libraries such as OpenCV and Dlib for image processing to perform face detection and facial expression analysis, identifying the user's facial expressions from the visual information.

[0486] The server classifies the user's emotional state based on the analysis results. If a negative emotional state is detected, the server immediately generates a notification and sends it to an external device (e.g., a family member's smartphone). The notification includes information about the user's state and recommended actions.

[0487] As a concrete example, if a user expresses feelings of loneliness in their daily life, the acoustic information is collected by the device and, based on emotion analysis, is determined to be "loneliness" by the server. Based on this information, the server sends a notification to the user's family to encourage support. This enables a quick and appropriate response.

[0488] An example of a prompt message might be, "Please describe the specific implementation method of a system that monitors the emotional state of elderly people using audio and image data."

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The device acquires acoustic and visual information from the user's daily life using an audio input device and a video acquisition device. Inputs include the user's speech and facial images, and outputs consist of digital audio and image data. Specifically, it records the user's voice with a microphone and captures images of the user's facial expressions and surroundings with a camera.

[0492] Step 2:

[0493] The device encrypts the collected acoustic and visual information. The input consists of the digital acoustic and image data acquired in step 1. The output is encrypted data. Specifically, it performs operations to protect the data using an encryption algorithm.

[0494] Step 3:

[0495] The terminal sends encrypted acoustic and visual information to the server. The input is the data encrypted in step 2, and the output is the data received on the server. Specifically, the operation involves transferring data using a secure communication protocol.

[0496] Step 4:

[0497] The server converts received acoustic information into text data using speech recognition software. The input is encrypted acoustic data sent from the terminal, and the output is text data. This process involves calling a speech recognition API to perform data analysis.

[0498] Step 5:

[0499] The server applies a sentiment analysis algorithm to estimate the user's emotional state based on the converted character data. The input is the text data obtained in step 4, and the output is information indicating the user's emotional state. Specifically, the server performs text analysis using a sentiment analysis library.

[0500] Step 6:

[0501] The server performs face detection and facial expression analysis using the transmitted image data. The input is encrypted image data sent from the terminal, and the output is information about the user's facial expressions. Specific operations include image processing using OpenCV and Dlib.

[0502] Step 7:

[0503] The server integrates emotional state and facial expression information to classify the user's emotional state. The input is the data obtained in steps 5 and 6, and the output is the emotional state category. Specifically, it aggregates this information and processes it to classify emotions according to the judgment criteria.

[0504] Step 8:

[0505] If a user is classified as being in a negative emotional state, the server generates a notification and sends it to an external device. The input is the emotional state information obtained in step 7, and the output is the notification and its transmission. Specifically, the system creates the message content and sends the notification to family members' smartphones or other devices via the network.

[0506] (Application Example 1)

[0507] 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."

[0508] In an aging society, there is a need to appropriately monitor and promptly support elderly people who are experiencing loneliness and stress. However, conventional technologies are not sufficient to identify emotional states in real time or to quickly notify family members in remote locations. Solving this problem and creating an environment where elderly people can live with peace of mind is essential.

[0509] 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.

[0510] In this invention, the server includes means for analyzing voice signals to estimate emotional states, means for analyzing facial expressions, and means for generating warnings and transmitting them to a communication device. This makes it possible to identify the emotional state of elderly people in real time and quickly inform family members or caregivers who are in remote locations.

[0511] A "collection device" is equipment installed to acquire audio and image signals, and includes microphones and cameras.

[0512] "Analysis" is the process of analyzing signals acquired by a data collection device and extracting detailed information about the target.

[0513] "Emotional state" refers to an individual's mental state and includes emotions such as joy, sadness, and stress.

[0514] "Facial expression" refers to the emotions and reactions that appear on a person's face, and includes things like smiles and frowns.

[0515] A "warning" is a notification message generated based on analyzed emotional states and facial expressions, and is sent to family members or caregivers as needed.

[0516] "Communication device" refers to a device used to transmit generated warnings to a remote location, and includes smartphones and communication networks via the internet.

[0517] An "application program" is software installed on smartphones or other devices to perform specific functions.

[0518] "Recommended actions" refer to specific actions or procedures suggested to users who have received a warning, with the aim of improving the situation.

[0519] The system for implementing this invention provides a solution for identifying the emotional state of elderly individuals and notifying their families and caregivers. The system mainly consists of the following elements:

[0520] Hardware configuration

[0521] 1. Data Collection Device: A terminal equipped with a microphone and camera will be used to acquire audio and image signals. This terminal will be installed in the living space of the elderly person to collect information about their daily life.

[0522] 2. Communication device: Used to send alerts to external devices such as smartphones and tablets. Data is transmitted via Wi-Fi or mobile networks.

[0523] Software Configuration

[0524] 1. Speech Analysis Module: This module utilizes the Google Cloud Speech-to-Text API to analyze speech signals, convert them to text, and estimate emotional states. The analyzed data is then subjected to sentiment analysis using the Hugging Face converter library.

[0525] 2. Image Analysis Module: Using the open-source libraries OpenCV and Dlib, this module analyzes facial expressions within image signals. This allows for the identification of the user's facial expressions and emotions.

[0526] 3. Notification Generation Module: Based on analyzed emotional states and facial expressions, it sends notifications to external devices using Firebase Cloud Messaging. These notifications also include recommendations for user actions.

[0527] Examples

[0528] For example, suppose an elderly person frequently mutters "I'm lonely." The device collects this audio, and if the server identifies "loneliness," a notification is immediately sent to the family's smartphone. Upon receiving this warning, the family can communicate with the elderly person via video call, such as Skype.

[0529] Examples of prompt statements

[0530] It analyzes the user's emotional state and generates prompts that detect whether their statements indicate loneliness or stress.

[0531] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0532] Step 1:

[0533] The terminal acquires the voice signals of elderly individuals through a collection device. The acquired voice data is temporarily stored as digital data on the spot. This data serves as input and is used for subsequent processing.

[0534] Step 2:

[0535] The terminal acquires image signals of elderly people's faces through a collection device. The acquired image data is also temporarily stored as digital data. This data serves as input and is used for face recognition and sentiment analysis.

[0536] Step 3:

[0537] The device sends audio data to the server at regular intervals. The server uses the Google Cloud Speech-to-Text API to convert this audio data into text. The input is audio data, and the output is the converted text. This text becomes the input for the generating AI model.

[0538] Step 4:

[0539] The server uses a generative AI model to analyze the emotional state from the converted text. The input is text data, and the output is the identified emotional state. Prompt sentences are also used to improve the accuracy of the emotion estimation.

[0540] Step 5:

[0541] The server uses OpenCV and Dlib to analyze facial expressions from transmitted image data. The input is image data, and the output is the identified facial expression. This allows for a more detailed understanding of the user's emotional state.

[0542] Step 6:

[0543] The server integrates the results of voice and image analysis to derive an overall emotional state. This analysis result forms the basis for subsequent notification generation.

[0544] Step 7:

[0545] The server uses Firebase Cloud Messaging to generate a notification message based on the analyzed sentiment state and send it to the communication device. The input is the analysis result, and the output is the sent notification message. This notification also includes a specified recommended action.

[0546] Step 8:

[0547] Users (such as family members or caregivers) receive the notifications sent and take follow-up actions as needed. Through this process, it becomes possible to provide an environment where elderly people can live with peace of mind.

[0548] 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.

[0549] This invention is an emotion recognition system that combines an emotion engine to collect user voice and image data and analyze that data to accurately understand the user's emotional state. This system mainly consists of a data collection device, a server, an emotion engine, and external devices.

[0550] First, the device functions as a data collection device, acquiring everyday voice and image data from the user. The device incorporates a high-performance microphone and camera, collecting voice data in real time and capturing the user's face. The data is then securely transmitted to a server at regular intervals. Encryption technology is used to prevent information leakage during data transmission.

[0551] The server analyzes the received audio and image data. The audio data is converted to text using speech recognition technology, and the emotional state of the user is analyzed from the tone and rhythm of their voice via an emotion engine. For the image data, face detection and facial expression recognition technology are used to identify specific facial expressions of the user.

[0552] The emotion engine is the central element of this system. This engine learns from emotion data collected and analyzed in the past and can predict changes and patterns in the user's emotional state by comparing it with current data. Furthermore, if a certain emotional state persists for a long period of time, it generates an alert and notifies the user and their family in an appropriate manner.

[0553] To give a specific example, if the emotion engine detects signs of stress at a very high frequency in the user's voice, the server will use the emotion engine's historical data analysis to determine whether the stress exceeds the normal range. When this information is notified to the user's family, they can take specific actions to alleviate the user's stress.

[0554] This invention enables highly accurate emotional monitoring of users, thereby providing support to help users live safely without feeling lonely.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The device collects audio and images in the user's living space. Specifically, a microphone built into the device collects the user's voice as digital data, and simultaneously, a camera captures the user's face as a still image or video. This data is temporarily stored in the device's storage.

[0558] Step 2:

[0559] The device sends the collected audio and image data to the server. The transmitted data is encrypted to ensure security. The data is sent in batches at predetermined intervals or streamed in real time.

[0560] Step 3:

[0561] The server analyzes the received data. For audio data, it is first converted to text via a speech recognition module. Then, an emotion engine analyzes the tone, pitch, and tempo to estimate the user's emotional state.

[0562] Step 4:

[0563] The server simultaneously analyzes the image data using facial recognition and expression analysis algorithms. It extracts facial features and obtains information to classify them into expression categories such as smiling, surprise, anger, and sadness.

[0564] Step 5:

[0565] The emotion engine integrates analysis results obtained from voice and facial expressions to determine the user's overall emotional state. It compares this to past datasets to evaluate whether the current emotional state is abnormal. It also tracks changes over time.

[0566] Step 6:

[0567] The server generates a notification when a user's emotional state exceeds a pre-set threshold or when an anomaly is detected. Based on the alert information from the emotion engine, the notification may include recommended actions.

[0568] Step 7:

[0569] A notification is sent to an external device and displayed to the user's family or related parties. Family members (notification recipients) can then take necessary actions based on the notification. Specific examples include calling or messaging the user, or adjusting visit plans.

[0570] This entire process allows for real-time and continuous monitoring of the user's emotional state, enabling the provision of support as needed.

[0571] (Example 2)

[0572] 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."

[0573] In modern society, continuously observing people's emotional states in their daily lives and providing timely feedback is extremely beneficial. However, current technology makes it difficult to efficiently detect and provide feedback on emotional changes and continuous emotional states. Furthermore, there is a lack of solutions to ensure the security of collected data while making effective recommendations to users.

[0574] 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.

[0575] In this invention, the server includes means for analyzing sound data collected by a voice acquisition device and estimating the user's emotional state, means for analyzing video data collected by a video acquisition device and recognizing expressions, and means for generating warnings based on the analyzed emotional state and expressions and transmitting them to an information output device. This makes it possible to observe the user's emotions with high accuracy while ensuring the security of the collected sound and video data, and to provide notifications and recommended actions as needed.

[0576] A "sound acquisition device" is a device for collecting sound data, and includes high-precision microphones and other such equipment.

[0577] An "image acquisition device" is a device for collecting video data, and includes high-precision cameras and other similar devices.

[0578] "Sound data" refers to digital signals that include the tone and pitch of a person's voice, and is used to analyze emotional states.

[0579] "Video data" refers to video recordings that include human facial expressions and movements, and is used for expression recognition through video analysis.

[0580] "Emotional state" refers to an individual's psychological or emotional state, estimated through the analysis of audio and video data.

[0581] "Expression" refers to emotional expressions through an individual's facial and body movements, as recognized through video analysis.

[0582] A "warning" is a notification or alert generated based on analysis results, which indicates an abnormal emotional state or long-term emotional change.

[0583] An "information output device" is a device used to transmit generated warnings and notifications, and includes, for example, smartphones and personal computers.

[0584] This emotion recognition system consists of a terminal, a server, and external devices. The specific hardware includes an audio acquisition device with a high-precision microphone and a video acquisition device with a high-resolution camera. The software utilizes an audio recognition engine, a facial expression recognition algorithm, and an emotion engine.

[0585] The device is responsible for continuously collecting the user's everyday audio and video data. This allows for real-time acquisition of information indicating the user's emotional state. Audio data collected by the audio acquisition device is converted into text by a speech recognition engine, and then the tone and rhythm of the voice are analyzed. Video data is recognized by an expression recognition algorithm, and the user's facial expressions are recognized from this data to estimate their emotional state.

[0586] The server receives analytical data transmitted from the terminal and uses an emotion engine to compare it with past data. This allows for highly accurate prediction and analysis of changes and patterns in the user's emotional state. Furthermore, if a particular emotional state persists, the server generates a warning based on that information and notifies the information output device. Encryption technology plays a crucial role in maintaining data security throughout this process.

[0587] For example, if the emotion engine detects that a user's stress level is high, the server analyzes that information and, if necessary, sends a notification to the user's family. This allows the user's family to take appropriate action and reduce the user's mental burden.

[0588] Example prompt: "Detect signs of stress from the user's voice data and analyze whether the condition exceeds the normal range."

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The device collects audio and video data from the user. When the user expresses emotions verbally, the audio acquisition device records the sound in real time, and simultaneously, the video acquisition device records the user's facial expressions. The inputs in this process are the user's raw audio and video data. The outputs are the collected, unprocessed audio and video data.

[0592] Step 2:

[0593] The device sends the collected audio data to a speech recognition engine, which converts it into text data. In this process, the speech recognition engine analyzes sound waves, converts them into strings, and extracts the tone and rhythm of the user's voice. The input is audio data, and the output is the converted text data and emotional characteristics data.

[0594] Step 3:

[0595] The server receives text data and sentiment characteristic data sent from the terminal. The server feeds this data into the sentiment engine and estimates the user's emotional state by comparing it with past database data. The data input consists of text and sentiment characteristics, and the output is the result of the emotional state analysis.

[0596] Step 4:

[0597] The device processes the collected video data using an expression recognition algorithm to analyze the user's facial expressions. It extracts facial features from the video data and links them to emotion recognition. The input is video data, and the output is recognized expression data.

[0598] Step 5:

[0599] The server sends facial expression data to the emotion engine and integrates it with the analysis results of the sound data. In this step, the server analyzes the user's overall emotional state in detail and ensures data consistency. The input is the analysis results of the facial expression data and sound data, and the output is the overall emotion analysis result.

[0600] Step 6:

[0601] The server generates a warning based on the analysis results and sends it to the information output device. For example, if the results indicate prolonged stress, it will send a notification suggesting appropriate countermeasures to the user or their family. The input is the analysis results, and the output is a notification containing the generated warning and recommended actions.

[0602] (Application Example 2)

[0603] 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."

[0604] Currently, many brick-and-mortar stores require human staff to provide appropriate service based on customers' facial expressions and tone of voice in order to improve the quality of customer service. However, accurately understanding a customer's emotional state and providing appropriate service in real time is difficult. Furthermore, because it relies on human judgment, the quality of service can be inconsistent depending on the skills and experience of individual staff members. As a result, maintaining a consistent level of customer service across the entire store is a challenge.

[0605] 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.

[0606] In this invention, the server includes means for analyzing voice data to estimate a person's emotional state, means for analyzing image data to recognize facial expressions, means for generating a notification based on the analysis results and transmitting it to an external device, and means for prompting nearby people who receive the notification to respond based on the emotional information. This makes it possible to accurately grasp the emotional state of customers in real time and automatically notify store staff of the emotional information, thereby improving the quality of customer service.

[0607] A "collection device" is a device consisting of hardware and software for acquiring audio and image data.

[0608] "Audio data" refers to acoustic information, such as speech, collected from users.

[0609] "A person's emotional state" refers to their psychological state as inferred from things like their voice and facial expressions.

[0610] A "camera device" is a device intended for capturing still images or videos.

[0611] "Image data" is a collection of visual information acquired by a camera device.

[0612] "Facial expression" refers to the expression and changes in the face, and reflects a part of a person's emotions.

[0613] A "notification" is information generated based on analysis results and sent to the intended recipient.

[0614] An "external device" is a device that is connected outside the system and capable of receiving information.

[0615] "Emotional information" refers to detailed data about each person's emotional state obtained through analysis.

[0616] A "means to prompt action" refers to a mechanism that instructs and supports recipients to take appropriate action based on notifications from the system.

[0617] This invention is implemented in the form of a customer service support system for physical stores. This system collects, analyzes, and notifies voice and image data to grasp the customer's emotional state in real time and use this information to improve customer service.

[0618] The terminal functions as a data collection device for acquiring audio and image data. Specifically, it uses a camera and microphone connected to the terminal to collect customer facial expressions and voice. The collected data is transmitted to the server in real time. Encryption technology is used during this transmission process to ensure the security of the data.

[0619] The server analyzes the received audio data using audio analysis software (e.g., AudioAnalyzer) to estimate the customer's emotional state. It also uses image analysis software (e.g., OpenCV) to recognize facial expressions from image data. These analysis results are further evaluated by a built-in emotion engine to gain a detailed understanding of the customer's current emotional state.

[0620] Based on the analyzed emotional information, the server generates a notification and presents the appropriate action to take to the store staff. This notification is sent in real time to devices held by staff in the physical store, such as tablets and smartphones. This allows store staff to instantly understand the customer's emotional state and respond flexibly and accurately.

[0621] For example, if a customer shows signs of negative emotions (such as dissatisfaction or stress), the server immediately analyzes this and sends an alert to the employee's device. Based on this information, the employee can follow up by offering polite assistance to the customer or suggesting ways to improve service. In this way, the present invention contributes to improving the quality of customer service and maximizing customer satisfaction.

[0622] An example of a prompt for a generative AI model is, "Think about designing real-time feedback necessary to analyze a customer's emotional state in real time and improve the quality of service." This prompt assigns the AI ​​model the task of designing emotion recognition and the corresponding feedback.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The terminal uses camera equipment and microphones placed inside the store to collect image and audio data of customers. The input is ambient sound and video, and the output is the acquired raw audio and image files. In this step, the terminal captures the data in real time and records the collected data in a buffer.

[0626] Step 2:

[0627] The terminal encrypts the collected audio and image data and sends it to the server. The input is the raw data obtained in step 1, and the output is encrypted data packets. In this step, the terminal uses an existing encryption library to protect data privacy and ensure the security of the transmitted data.

[0628] Step 3:

[0629] The server receives encrypted audio data, decrypts it, and analyzes it. The input is encrypted data, and the output is an estimated result of the emotional state derived from the audio. In this step, the server uses AudioAnalyzer to extract audio features and a generative AI model to estimate the emotional state.

[0630] Step 4:

[0631] The server receives encrypted image data, decrypts it, and analyzes it. The input is encrypted data, and the output is the result of facial recognition obtained from the image. In this step, the server uses OpenCV to perform face detection and extract facial features, which are then analyzed by the emotion engine.

[0632] Step 5:

[0633] The server integrates customer emotional state information based on the analysis results and generates a notification if action is required. The input is the emotional recognition data obtained in steps 3 and 4, and the output is an alert message for the store staff. In this step, the server uses a generated AI model to detect patterns of emotional state changes and incorporates appropriate information into the notification.

[0634] Step 6:

[0635] The server sends the generated notification to a terminal within the store (for example, a smartphone or tablet carried by an employee). The input is the notification message generated in step 5, and the output is the alert message received by the employee. In this step, the server sends the notification in real time using a communication protocol.

[0636] Step 7:

[0637] The user (store clerk) checks the received notification and takes action according to the customer's emotional state. The input is the received notification message, and the output is the user's action and its consequences. In this step, the store clerk adjusts their customer service and takes specific actions to improve the quality of service.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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".

[0655] This invention consists of a system comprising multiple devices, which in particular uses voice data and image data to identify the emotional state of a user (an elderly person) and, if necessary, sends notifications to an external device. This system mainly consists of the following elements.

[0656] First, the device is installed in the user's environment and collects audio and image data during daily life. The device has a built-in microphone and camera, which acquire audio and visual information, respectively. The collected data is temporarily stored as digital data within the device.

[0657] The terminal then transmits this digital data to the server at fixed intervals or in real time. The server converts the received audio data into text using a speech recognition module and simultaneously runs an emotion analysis algorithm to estimate the user's emotional state. In parallel, it uses face detection and facial expression analysis algorithms on the image data to identify the user's facial expressions.

[0658] The server categorizes and records the user's emotional state based on the analysis results. In particular, if the server determines that the user is experiencing a specific emotional state (e.g., stress or loneliness), it generates a notification based on that information. This notification is sent to an external device (for example, a smartphone owned by a family member of the user), allowing the recipient to respond quickly.

[0659] To give a specific example, if the device detects sadness in the user's voice and the server analyzes it and determines that the user is experiencing "sadness," the server immediately notifies the user's family. Upon receiving the notification, the family can then take immediate follow-up action, which is expected to alleviate the user's feelings of loneliness.

[0660] In this way, the system of the present invention provides a new monitoring solution for an aging society by remotely monitoring the emotional state of the user and transmitting necessary information to an external party.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The device collects ambient sound via a microphone. The microphone converts the user's voice into digital audio data in real time and stores it. It also uses a camera to capture the user's face and continuously capture image data. This data is stored in temporary storage.

[0664] Step 2:

[0665] The device transfers collected audio and image data to the server at regular intervals. The audio data is encrypted and securely transmitted along with the image data via a wireless or wired network connection.

[0666] Step 3:

[0667] The server passes the received audio data through a speech recognition module and converts it into text. Based on this text data, an emotion analysis algorithm evaluates the user's emotional state. Emotional components such as joy, anger, and sadness are extracted.

[0668] Step 4:

[0669] Simultaneously, the server performs face detection and expression analysis on the image data. It extracts facial feature data and identifies what kind of expression the user is making. Results such as smiling, surprised, or expressionless are output.

[0670] Step 5:

[0671] The server integrates the results of voice and facial expression analysis to estimate and classify the user's current emotional state. This includes evaluating whether the change is significant compared to past data.

[0672] Step 6:

[0673] If the server determines that a user's emotional state exceeds a pre-set threshold, it generates a notification based on this and sends it to an external device. This external device may include a smartphone belonging to a family member of the user.

[0674] Step 7:

[0675] The user (family member) who receives the notification can take the necessary action based on the alert. For example, they might contact their parent directly or engage in further communication.

[0676] In this way, the system works in conjunction with each step, enabling it to understand the emotional state of users in remote locations and provide support accordingly.

[0677] (Example 1)

[0678] 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".

[0679] In an aging society, there is a need to detect negative emotional states such as loneliness and stress in the elderly early on and to promptly inform their families and caregivers. However, conventional technologies have made it difficult to monitor emotional states in real time and efficiently notify accurate information. Therefore, an efficient method is needed to better ensure the safety and security of the elderly.

[0680] 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.

[0681] In this invention, the server includes means for interpreting acoustic information acquired by an audio input device to estimate a person's emotional state, means for interpreting visual information acquired by an image acquisition device to recognize facial expressions, means for generating a notification based on the interpreted emotional state and facial expressions of the person and transmitting it to an external device, and means for providing information so that an immediate response can be taken when the notification detects a negative emotional state. This makes it possible to monitor the emotional state of elderly people in real time and to quickly notify family members or caregivers as needed.

[0682] A "sound input device" is a device used to acquire the user's acoustic information, and typically includes a microphone.

[0683] A "video acquisition device" is a device used to acquire a user's visual information, and typically includes a camera.

[0684] "Acoustic information" refers to digital sound data acquired by sound input devices, and primarily includes human speech and ambient sounds.

[0685] "Visual information" refers to digital data of images acquired by image acquisition devices, and primarily focuses on human faces, expressions, and body movements.

[0686] "Emotional state" refers to the type and intensity of a person's emotions, estimated through the analysis of acoustic and visual information.

[0687] A "notification" is a message generated based on an interpreted emotional state, intended to convey information to the relevant parties.

[0688] "External devices" refer to devices used to receive notifications, and typically include communication devices such as smartphones and tablets.

[0689] A "negative emotional state" refers to a state of exhibiting undesirable emotions such as stress or loneliness.

[0690] This invention provides a system that effectively monitors the emotional state of elderly individuals and promptly notifies relevant parties as needed. This system primarily consists of terminal devices, a server device, and external equipment.

[0691] The terminal is installed in the user's living environment and includes an audio input device and a video acquisition device. The audio input device is a microphone that collects the user's speech and background sounds. The video acquisition device is a camera that captures the user's face and facial expressions. These devices acquire the user's acoustic and visual information as digital data in their daily life.

[0692] The acquired acoustic and visual information is encrypted and periodically sent to the server. The server uses speech recognition software (e.g., a speech recognition API) to convert the acoustic information into text data. During this process, an emotion analysis algorithm is applied to estimate the user's emotional state. The server also uses libraries such as OpenCV and Dlib for image processing to perform face detection and facial expression analysis, identifying the user's facial expressions from the visual information.

[0693] The server classifies the user's emotional state based on the analysis results. If a negative emotional state is detected, the server immediately generates a notification and sends it to an external device (e.g., a family member's smartphone). The notification includes information about the user's state and recommended actions.

[0694] As a concrete example, if a user expresses feelings of loneliness in their daily life, the acoustic information is collected by the device and, based on emotion analysis, is determined to be "loneliness" by the server. Based on this information, the server sends a notification to the user's family to encourage support. This enables a quick and appropriate response.

[0695] An example of a prompt message might be, "Please describe the specific implementation method of a system that monitors the emotional state of elderly people using audio and image data."

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] The device acquires acoustic and visual information from the user's daily life using an audio input device and a video acquisition device. Inputs include the user's speech and facial images, and outputs consist of digital audio and image data. Specifically, it records the user's voice with a microphone and captures images of the user's facial expressions and surroundings with a camera.

[0699] Step 2:

[0700] The device encrypts the collected acoustic and visual information. The input consists of the digital acoustic and image data acquired in step 1. The output is encrypted data. Specifically, it performs operations to protect the data using an encryption algorithm.

[0701] Step 3:

[0702] The terminal sends encrypted acoustic and visual information to the server. The input is the data encrypted in step 2, and the output is the data received on the server. Specifically, the operation involves transferring data using a secure communication protocol.

[0703] Step 4:

[0704] The server converts received acoustic information into text data using speech recognition software. The input is encrypted acoustic data sent from the terminal, and the output is text data. This process involves calling a speech recognition API to perform data analysis.

[0705] Step 5:

[0706] The server applies a sentiment analysis algorithm to estimate the user's emotional state based on the converted character data. The input is the text data obtained in step 4, and the output is information indicating the user's emotional state. Specifically, the server performs text analysis using a sentiment analysis library.

[0707] Step 6:

[0708] The server performs face detection and facial expression analysis using the transmitted image data. The input is encrypted image data sent from the terminal, and the output is information about the user's facial expressions. Specific operations include image processing using OpenCV and Dlib.

[0709] Step 7:

[0710] The server integrates emotional state and facial expression information to classify the user's emotional state. The input is the data obtained in steps 5 and 6, and the output is the emotional state category. Specifically, it aggregates this information and processes it to classify emotions according to the judgment criteria.

[0711] Step 8:

[0712] If a user is classified as being in a negative emotional state, the server generates a notification and sends it to an external device. The input is the emotional state information obtained in step 7, and the output is the notification and its transmission. Specifically, the system creates the message content and sends the notification to family members' smartphones or other devices via the network.

[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 an aging society, there is a need to appropriately monitor and promptly support elderly people who are experiencing loneliness and stress. However, conventional technologies are not sufficient to identify emotional states in real time or to quickly notify family members in remote locations. Solving this problem and creating an environment where elderly people can live with peace of mind is essential.

[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 means for analyzing voice signals to estimate emotional states, means for analyzing facial expressions, and means for generating warnings and transmitting them to a communication device. This makes it possible to identify the emotional state of elderly people in real time and quickly inform family members or caregivers who are in remote locations.

[0718] A "collection device" is equipment installed to acquire audio and image signals, and includes microphones and cameras.

[0719] "Analysis" is the process of analyzing signals acquired by a data collection device and extracting detailed information about the target.

[0720] "Emotional state" refers to an individual's mental state and includes emotions such as joy, sadness, and stress.

[0721] "Facial expression" refers to the emotions and reactions that appear on a person's face, and includes things like smiles and frowns.

[0722] A "warning" is a notification message generated based on analyzed emotional states and facial expressions, and is sent to family members or caregivers as needed.

[0723] "Communication device" refers to a device used to transmit generated warnings to a remote location, and includes smartphones and communication networks via the internet.

[0724] An "application program" is software installed on smartphones or other devices to perform specific functions.

[0725] "Recommended actions" refer to specific actions or procedures suggested to users who have received a warning, with the aim of improving the situation.

[0726] The system for implementing this invention provides a solution for identifying the emotional state of elderly individuals and notifying their families and caregivers. The system mainly consists of the following elements:

[0727] Hardware configuration

[0728] 1. Data Collection Device: A terminal equipped with a microphone and camera will be used to acquire audio and image signals. This terminal will be installed in the living space of the elderly person to collect information about their daily life.

[0729] 2. Communication device: Used to send alerts to external devices such as smartphones and tablets. Data is transmitted via Wi-Fi or mobile networks.

[0730] Software Configuration

[0731] 1. Speech Analysis Module: This module utilizes the Google Cloud Speech-to-Text API to analyze speech signals, convert them to text, and estimate emotional states. The analyzed data is then subjected to sentiment analysis using the Hugging Face converter library.

[0732] 2. Image Analysis Module: Using the open-source libraries OpenCV and Dlib, this module analyzes facial expressions within image signals. This allows for the identification of the user's facial expressions and emotions.

[0733] 3. Notification Generation Module: Based on analyzed emotional states and facial expressions, it sends notifications to external devices using Firebase Cloud Messaging. These notifications also include recommendations for user actions.

[0734] Examples

[0735] For example, suppose an elderly person frequently mutters "I'm lonely." The device collects this audio, and if the server identifies "loneliness," a notification is immediately sent to the family's smartphone. Upon receiving this warning, the family can communicate with the elderly person via video call, such as Skype.

[0736] Examples of prompt statements

[0737] It analyzes the user's emotional state and generates prompts that detect whether their statements indicate loneliness or stress.

[0738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0739] Step 1:

[0740] The terminal acquires the voice signals of elderly individuals through a collection device. The acquired voice data is temporarily stored as digital data on the spot. This data serves as input and is used for subsequent processing.

[0741] Step 2:

[0742] The terminal acquires image signals of elderly people's faces through a collection device. The acquired image data is also temporarily stored as digital data. This data serves as input and is used for face recognition and sentiment analysis.

[0743] Step 3:

[0744] The device sends audio data to the server at regular intervals. The server uses the Google Cloud Speech-to-Text API to convert this audio data into text. The input is audio data, and the output is the converted text. This text becomes the input for the generating AI model.

[0745] Step 4:

[0746] The server uses a generative AI model to analyze the emotional state from the converted text. The input is text data, and the output is the identified emotional state. Prompt sentences are also used to improve the accuracy of the emotion estimation.

[0747] Step 5:

[0748] The server uses OpenCV and Dlib to analyze facial expressions from transmitted image data. The input is image data, and the output is the identified facial expression. This allows for a more detailed understanding of the user's emotional state.

[0749] Step 6:

[0750] The server integrates the results of voice and image analysis to derive an overall emotional state. This analysis result forms the basis for subsequent notification generation.

[0751] Step 7:

[0752] The server uses Firebase Cloud Messaging to generate a notification message based on the analyzed sentiment state and send it to the communication device. The input is the analysis result, and the output is the sent notification message. This notification also includes a specified recommended action.

[0753] Step 8:

[0754] Users (such as family members or caregivers) receive the notifications sent and take follow-up actions as needed. Through this process, it becomes possible to provide an environment where elderly people can live with peace of mind.

[0755] 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.

[0756] This invention is an emotion recognition system that combines an emotion engine to collect user voice and image data and analyze that data to accurately understand the user's emotional state. This system mainly consists of a data collection device, a server, an emotion engine, and external devices.

[0757] First, the device functions as a data collection device, acquiring everyday voice and image data from the user. The device incorporates a high-performance microphone and camera, collecting voice data in real time and capturing the user's face. The data is then securely transmitted to a server at regular intervals. Encryption technology is used to prevent information leakage during data transmission.

[0758] The server analyzes the received audio and image data. The audio data is converted to text using speech recognition technology, and the emotional state of the user is analyzed from the tone and rhythm of their voice via an emotion engine. For the image data, face detection and facial expression recognition technology are used to identify specific facial expressions of the user.

[0759] The emotion engine is the central element of this system. This engine learns from emotion data collected and analyzed in the past and can predict changes and patterns in the user's emotional state by comparing it with current data. Furthermore, if a certain emotional state persists for a long period of time, it generates an alert and notifies the user and their family in an appropriate manner.

[0760] To give a specific example, if the emotion engine detects signs of stress at a very high frequency in the user's voice, the server will use the emotion engine's historical data analysis to determine whether the stress exceeds the normal range. When this information is notified to the user's family, they can take specific actions to alleviate the user's stress.

[0761] This invention enables highly accurate emotional monitoring of users, thereby providing support to help users live safely without feeling lonely.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The device collects audio and images in the user's living space. Specifically, a microphone built into the device collects the user's voice as digital data, and simultaneously, a camera captures the user's face as a still image or video. This data is temporarily stored in the device's storage.

[0765] Step 2:

[0766] The device sends the collected audio and image data to the server. The transmitted data is encrypted to ensure security. The data is sent in batches at predetermined intervals or streamed in real time.

[0767] Step 3:

[0768] The server analyzes the received data. For audio data, it is first converted to text via a speech recognition module. Then, an emotion engine analyzes the tone, pitch, and tempo to estimate the user's emotional state.

[0769] Step 4:

[0770] The server simultaneously analyzes the image data using facial recognition and expression analysis algorithms. It extracts facial features and obtains information to classify them into expression categories such as smiling, surprise, anger, and sadness.

[0771] Step 5:

[0772] The emotion engine integrates analysis results obtained from voice and facial expressions to determine the user's overall emotional state. It compares this to past datasets to evaluate whether the current emotional state is abnormal. It also tracks changes over time.

[0773] Step 6:

[0774] The server generates a notification when a user's emotional state exceeds a pre-set threshold or when an anomaly is detected. Based on the alert information from the emotion engine, the notification may include recommended actions.

[0775] Step 7:

[0776] A notification is sent to an external device and displayed to the user's family or related parties. Family members (notification recipients) can then take necessary actions based on the notification. Specific examples include calling or messaging the user, or adjusting visit plans.

[0777] This entire process allows for real-time and continuous monitoring of the user's emotional state, enabling the provision of support as needed.

[0778] (Example 2)

[0779] 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".

[0780] In modern society, continuously observing people's emotional states in their daily lives and providing timely feedback is extremely beneficial. However, current technology makes it difficult to efficiently detect and provide feedback on emotional changes and continuous emotional states. Furthermore, there is a lack of solutions to ensure the security of collected data while making effective recommendations to users.

[0781] 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.

[0782] In this invention, the server includes means for analyzing sound data collected by a voice acquisition device and estimating the user's emotional state, means for analyzing video data collected by a video acquisition device and recognizing expressions, and means for generating warnings based on the analyzed emotional state and expressions and transmitting them to an information output device. This makes it possible to observe the user's emotions with high accuracy while ensuring the security of the collected sound and video data, and to provide notifications and recommended actions as needed.

[0783] A "sound acquisition device" is a device for collecting sound data, and includes high-precision microphones and other such equipment.

[0784] An "image acquisition device" is a device for collecting video data, and includes high-precision cameras and other similar devices.

[0785] "Sound data" refers to digital signals that include the tone and pitch of a person's voice, and is used to analyze emotional states.

[0786] "Video data" refers to video recordings that include human facial expressions and movements, and is used for expression recognition through video analysis.

[0787] "Emotional state" refers to an individual's psychological or emotional state, estimated through the analysis of audio and video data.

[0788] "Expression" refers to emotional expressions through an individual's facial and body movements, as recognized through video analysis.

[0789] A "warning" is a notification or alert generated based on analysis results, which indicates an abnormal emotional state or long-term emotional change.

[0790] An "information output device" is a device used to transmit generated warnings and notifications, and includes, for example, smartphones and personal computers.

[0791] This emotion recognition system consists of a terminal, a server, and external devices. The specific hardware includes an audio acquisition device with a high-precision microphone and a video acquisition device with a high-resolution camera. The software utilizes an audio recognition engine, a facial expression recognition algorithm, and an emotion engine.

[0792] The device is responsible for continuously collecting the user's everyday audio and video data. This allows for real-time acquisition of information indicating the user's emotional state. Audio data collected by the audio acquisition device is converted into text by a speech recognition engine, and then the tone and rhythm of the voice are analyzed. Video data is recognized by an expression recognition algorithm, and the user's facial expressions are recognized from this data to estimate their emotional state.

[0793] The server receives analytical data transmitted from the terminal and uses an emotion engine to compare it with past data. This allows for highly accurate prediction and analysis of changes and patterns in the user's emotional state. Furthermore, if a particular emotional state persists, the server generates a warning based on that information and notifies the information output device. Encryption technology plays a crucial role in maintaining data security throughout this process.

[0794] For example, if the emotion engine detects that a user's stress level is high, the server analyzes that information and, if necessary, sends a notification to the user's family. This allows the user's family to take appropriate action and reduce the user's mental burden.

[0795] Example prompt: "Detect signs of stress from the user's voice data and analyze whether the condition exceeds the normal range."

[0796] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0797] Step 1:

[0798] The device collects audio and video data from the user. When the user expresses emotions verbally, the audio acquisition device records the sound in real time, and simultaneously, the video acquisition device records the user's facial expressions. The inputs in this process are the user's raw audio and video data. The outputs are the collected, unprocessed audio and video data.

[0799] Step 2:

[0800] The device sends the collected audio data to a speech recognition engine, which converts it into text data. In this process, the speech recognition engine analyzes sound waves, converts them into strings, and extracts the tone and rhythm of the user's voice. The input is audio data, and the output is the converted text data and emotional characteristics data.

[0801] Step 3:

[0802] The server receives text data and sentiment characteristic data sent from the terminal. The server feeds this data into the sentiment engine and estimates the user's emotional state by comparing it with past database data. The data input consists of text and sentiment characteristics, and the output is the result of the emotional state analysis.

[0803] Step 4:

[0804] The device processes the collected video data using an expression recognition algorithm to analyze the user's facial expressions. It extracts facial features from the video data and links them to emotion recognition. The input is video data, and the output is recognized expression data.

[0805] Step 5:

[0806] The server sends facial expression data to the emotion engine and integrates it with the analysis results of the sound data. In this step, the server analyzes the user's overall emotional state in detail and ensures data consistency. The input is the analysis results of the facial expression data and sound data, and the output is the overall emotion analysis result.

[0807] Step 6:

[0808] The server generates a warning based on the analysis results and sends it to the information output device. For example, if the results indicate prolonged stress, it will send a notification suggesting appropriate countermeasures to the user or their family. The input is the analysis results, and the output is a notification containing the generated warning and recommended actions.

[0809] (Application Example 2)

[0810] 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".

[0811] Currently, many brick-and-mortar stores require human staff to provide appropriate service based on customers' facial expressions and tone of voice in order to improve the quality of customer service. However, accurately understanding a customer's emotional state and providing appropriate service in real time is difficult. Furthermore, because it relies on human judgment, the quality of service can be inconsistent depending on the skills and experience of individual staff members. As a result, maintaining a consistent level of customer service across the entire store is a challenge.

[0812] 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.

[0813] In this invention, the server includes means for analyzing voice data to estimate a person's emotional state, means for analyzing image data to recognize facial expressions, means for generating a notification based on the analysis results and transmitting it to an external device, and means for prompting nearby people who receive the notification to respond based on the emotional information. This makes it possible to accurately grasp the emotional state of customers in real time and automatically notify store staff of the emotional information, thereby improving the quality of customer service.

[0814] A "collection device" is a device consisting of hardware and software for acquiring audio and image data.

[0815] "Audio data" refers to acoustic information, such as speech, collected from users.

[0816] "A person's emotional state" refers to their psychological state as inferred from things like their voice and facial expressions.

[0817] A "camera device" is a device intended for capturing still images or videos.

[0818] "Image data" is a collection of visual information acquired by a camera device.

[0819] "Facial expression" refers to the expression and changes in the face, and reflects a part of a person's emotions.

[0820] A "notification" is information generated based on analysis results and sent to the intended recipient.

[0821] An "external device" is a device that is connected outside the system and capable of receiving information.

[0822] "Emotional information" refers to detailed data about each person's emotional state obtained through analysis.

[0823] A "means to prompt action" refers to a mechanism that instructs and supports recipients to take appropriate action based on notifications from the system.

[0824] This invention is implemented in the form of a customer service support system for physical stores. This system collects, analyzes, and notifies voice and image data to grasp the customer's emotional state in real time and use this information to improve customer service.

[0825] The terminal functions as a data collection device for acquiring audio and image data. Specifically, it uses a camera and microphone connected to the terminal to collect customer facial expressions and voice. The collected data is transmitted to the server in real time. Encryption technology is used during this transmission process to ensure the security of the data.

[0826] The server analyzes the received audio data using audio analysis software (e.g., AudioAnalyzer) to estimate the customer's emotional state. It also uses image analysis software (e.g., OpenCV) to recognize facial expressions from image data. These analysis results are further evaluated by a built-in emotion engine to gain a detailed understanding of the customer's current emotional state.

[0827] Based on the analyzed emotional information, the server generates a notification and presents the appropriate action to take to the store staff. This notification is sent in real time to devices held by staff in the physical store, such as tablets and smartphones. This allows store staff to instantly understand the customer's emotional state and respond flexibly and accurately.

[0828] For example, if a customer shows signs of negative emotions (such as dissatisfaction or stress), the server immediately analyzes this and sends an alert to the employee's device. Based on this information, the employee can follow up by offering polite assistance to the customer or suggesting ways to improve service. In this way, the present invention contributes to improving the quality of customer service and maximizing customer satisfaction.

[0829] An example of a prompt for a generative AI model is, "Think about designing real-time feedback necessary to analyze a customer's emotional state in real time and improve the quality of service." This prompt assigns the AI ​​model the task of designing emotion recognition and the corresponding feedback.

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] The terminal uses camera equipment and microphones placed inside the store to collect image and audio data of customers. The input is ambient sound and video, and the output is the acquired raw audio and image files. In this step, the terminal captures the data in real time and records the collected data in a buffer.

[0833] Step 2:

[0834] The terminal encrypts the collected audio and image data and sends it to the server. The input is the raw data obtained in step 1, and the output is encrypted data packets. In this step, the terminal uses an existing encryption library to protect data privacy and ensure the security of the transmitted data.

[0835] Step 3:

[0836] The server receives encrypted audio data, decrypts it, and analyzes it. The input is encrypted data, and the output is an estimated result of the emotional state derived from the audio. In this step, the server uses AudioAnalyzer to extract audio features and a generative AI model to estimate the emotional state.

[0837] Step 4:

[0838] The server receives encrypted image data, decrypts it, and analyzes it. The input is encrypted data, and the output is the result of facial recognition obtained from the image. In this step, the server uses OpenCV to perform face detection and extract facial features, which are then analyzed by the emotion engine.

[0839] Step 5:

[0840] The server integrates customer emotional state information based on the analysis results and generates a notification if action is required. The input is the emotional recognition data obtained in steps 3 and 4, and the output is an alert message for the store staff. In this step, the server uses a generated AI model to detect patterns of emotional state changes and incorporates appropriate information into the notification.

[0841] Step 6:

[0842] The server sends the generated notification to a terminal within the store (for example, a smartphone or tablet carried by an employee). The input is the notification message generated in step 5, and the output is the alert message received by the employee. In this step, the server sends the notification in real time using a communication protocol.

[0843] Step 7:

[0844] The user (store clerk) checks the received notification and takes action according to the customer's emotional state. The input is the received notification message, and the output is the user's action and its consequences. In this step, the store clerk adjusts their customer service and takes specific actions to improve the quality of service.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means of estimating a person's emotional state by analyzing audio data acquired by a collection device,

[0869] A means of recognizing facial expressions by analyzing image data acquired by a camera device,

[0870] A means for generating a notification based on the analyzed emotional state and facial expressions of a person and sending it to an external device,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the aforementioned audio data and image data are encrypted and transmitted.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the notification includes a recommended action for the user.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A means of interpreting acoustic information acquired by an audio input device to estimate a person's emotional state,

[0879] A means of interpreting visual information acquired by an image acquisition device to recognize facial expressions,

[0880] A means for generating a report based on the interpreted emotional state and facial expressions of a person and transmitting it to an external device,

[0881] A means of providing information so that a response can be taken immediately when a report detects a negative emotional state,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the aforementioned acoustic information and visual information are encrypted and communicated.

[0885] (Claim 3)

[0886] The system according to claim 1, which incorporates recommended actions for a person into the aforementioned notification.

[0887] "Application Example 1"

[0888] (Claim 1)

[0889] A means of estimating a person's emotional state by analyzing audio signals acquired by a collection device,

[0890] A means of recognizing facial expressions by analyzing image signals acquired by a video device,

[0891] A means for generating a warning based on the analyzed emotional state and facial expressions of a person and transmitting it to a communication device,

[0892] A means of transmitting warnings to the user's close relatives using an application program installed on a smartphone,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, wherein the aforementioned audio signal and image signal are encrypted and transmitted in order to protect them.

[0896] (Claim 3)

[0897] The system according to claim 1, wherein the warning includes recommended actions for the user.

[0898] "Example 2 of combining an emotion engine"

[0899] (Claim 1)

[0900] A means for analyzing sound data collected by a voice acquisition device and estimating the user's emotional state,

[0901] A means for analyzing video data collected by a video acquisition device and recognizing expressions,

[0902] A means for generating a warning based on the analyzed emotional state and expression and transmitting it to an information output device,

[0903] A means of conducting long-term observation by predicting changes in emotional states and detecting consecutive specific emotional states,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein the aforementioned sound data and video data are transmitted using encryption technology to protect them.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the warning includes recommended actions for the user and related parties.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] A means of estimating a person's emotional state by analyzing audio data acquired by a collection device,

[0912] A means of recognizing facial expressions by analyzing image data acquired by a camera device,

[0913] A means for generating a notification based on the analyzed emotional state and facial expressions of a person and sending it to an external device,

[0914] A means to encourage those around who receive the notification to respond based on emotional information,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the aforementioned audio data and image data are encrypted and transmitted.

[0918] (Claim 3)

[0919] The system according to claim 1, wherein the notification includes recommended actions and suggestions for actions to be taken by the user. [Explanation of symbols]

[0920] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of estimating a person's emotional state by analyzing audio data acquired by a collection device, A means of recognizing facial expressions by analyzing image data acquired by a camera device, A means for generating a notification based on the analyzed emotional state and facial expressions of a person and sending it to an external device, A system that includes this.

2. The system according to claim 1, wherein the aforementioned audio data and image data are encrypted and transmitted.

3. The system according to claim 1, wherein the notification includes a recommended action for the user.

Images