system

Abstract

We provide the system. [Solution] A speech recognition means that receives human voice input and converts the voice into text data, A natural language processing means that analyzes the aforementioned text data and generates a corresponding response, A speech synthesis means that outputs the aforementioned response as speech, A gesture recognition system that monitors user movements in real time and analyzes unusual movements, A log generation means that detects anomalies based on the results of the gesture recognition and generates a monitoring log, A log transmission means for transmitting the aforementioned monitoring log to an external source, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, there is an increasing need for a monitoring system to prevent the isolation of the elderly and enable them to live their daily lives with peace of mind. However, conventional monitoring systems give users an unnatural device feeling and are difficult to blend into daily life. In addition, the privacy issues in monitoring the daily lives of the elderly and the accompanying burden on their families have also become problems. Under such circumstances, there is a demand for a natural solution that allows the elderly to live with peace of mind and supports their communication.

Means for Solving the Problems

[0005] The present invention solves the aforementioned problems by providing a system comprising: speech recognition means for receiving human voice input and converting the voice into text data; natural language processing means for analyzing the text data and generating a corresponding response; speech synthesis means for outputting the response as voice; gesture recognition means for monitoring user movements in real time and analyzing unusual movements; log generation means for detecting anomalies based on the results of the gesture recognition and generating a monitoring log; and log transmission means for transmitting the monitoring log to an external party. By combining natural dialogue and motion recognition, this system blends into the daily lives of the elderly, enabling reliable monitoring while respecting their privacy.

[0006] "Speech recognition means" refers to technology that processes human speech input and converts it into text data.

[0007] "Natural language processing means" refers to technologies that analyze text data, understand the user's intent, and generate appropriate responses.

[0008] "Speech synthesis means" refers to a technology for reproducing a response generated from text data as speech.

[0009] "Gesture recognition means" refers to a technology that monitors a user's movements in real time using cameras or other means and analyzes any unusual movements.

[0010] A "log generation method" is a technology that detects anomalies and creates monitoring logs based on the results of gesture recognition and voice interaction.

[0011] The "log transmission method" is a technology for notifying external parties of the generated monitoring logs. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged 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.

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

[0017] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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.

[0018] In the following embodiments, a tagged communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The present invention aims to enable communication in a convenient manner by converting the voice of an elderly person into text using a terminal equipped with speech recognition means and natural language processing means. The terminal receives voice input from the user and performs natural language processing based on it. The server receives the converted text and sends the generated response back to the terminal. The terminal outputs voice from the text generated by the speech synthesis means and responds to the user.

[0034] Furthermore, gesture recognition is used to monitor the user's movements in real time and analyze any unusual movements as needed. Based on this movement data, the server detects anomalies and creates a monitoring log using a log generation mechanism. This log is sent to external family members via a log transmission mechanism, allowing family members in remote locations to understand the elderly person's situation.

[0035] For example, if a user asks, "What are my plans for today?", the device converts this audio into text and sends it to the server. The server consults the schedule management system, generates a response containing the appropriate information, and sends it back to the device. The device then communicates this information to the user via voice. Additionally, if the user becomes unsteady, the device's gesture recognition system detects this, and the server immediately generates a warning log to notify family members.

[0036] Thus, the present invention can provide a comprehensive monitoring system that enhances natural communication and security. Through interaction with the pet robot, users can avoid isolation and live their daily lives safely in a relaxed environment.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0040] Step 2:

[0041] The device passes the recorded audio to a speech recognition system, which converts the audio into text data. Advanced ASR technology is used for this conversion.

[0042] Step 3:

[0043] The terminal sends the converted text data to the server. The server uses natural language processing to analyze the text and understand the user's intent.

[0044] Step 4:

[0045] The server uses a generative AI model to generate response text based on the analysis results. This response includes an appropriate reply to the user's utterance.

[0046] Step 5:

[0047] The server sends the generated response text to the terminal. The terminal uses speech synthesis to convert the text into natural-sounding speech and delivers it to the user.

[0048] Step 6:

[0049] The device uses a camera to monitor user movements in real time. It also utilizes gesture recognition to analyze unusual movements.

[0050] Step 7:

[0051] The terminal sends the gesture recognition results to the server, which then determines whether or not there are any abnormalities based on the operation data.

[0052] Step 8:

[0053] If the server detects an anomaly, it generates a monitoring log using a log generation mechanism. This log includes user actions and speech content.

[0054] Step 9:

[0055] The server sends the generated monitoring logs to the family via a log transmission device. The family can then view these logs, for example, through a dedicated application.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] It is necessary to provide an environment in which users, including the elderly, can safely live their daily lives through natural voice-based dialogue and monitoring of their physical movements. Furthermore, there is a need for a system that allows family members, even from a distance, to easily understand the situation of elderly individuals. Existing technologies have limitations in the accuracy and speed of responses based on voice and gesture recognition, highlighting the need for more effective and reliable communication and monitoring methods.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes a speech recognition means that receives voice input and converts the voice into text information, a natural language processing means that analyzes the text information and generates a corresponding response, and an information recording means that detects abnormalities based on the results of the gesture recognition and generates a monitoring log. This enables natural dialogue with the user, rapid detection of operational abnormalities, and external sharing of that information.

[0061] "Speech recognition means" refers to a technology or device that receives speech input and converts that speech into text information.

[0062] "Natural language processing means" refers to a technology or device that analyzes text data, understands the user's intent, and generates an appropriate response.

[0063] "Speech synthesis means" refers to a technology or device that converts text information into speech and provides a voice response to the user.

[0064] "Gesture recognition means" refers to a technology or device that monitors a user's physical movements in real time and analyzes unusual movements.

[0065] "Information recording means" refers to a technology or device that detects anomalies based on the results of gesture recognition and records that information.

[0066] "Information transmission means" refers to a technology or device that transmits the generated monitoring log to an external recipient.

[0067] This invention is a system that integrates the functions of speech recognition, natural language processing, gesture recognition, information recording, and information transmission, and is mainly implemented via a server and terminals.

[0068] The device is equipped with a microphone to receive voice input, which is then converted into text information by a speech recognition system. This process utilizes commonly used speech recognition APIs. This text information is further analyzed by a natural language processing system to understand the user's intent and generate an appropriate response. At this stage, using a generative AI model can yield a more sophisticated response. Examples of natural language processing technologies include widely used natural language processing APIs.

[0069] The server generates a response based on the analyzed data and outputs that response to the terminal. The terminal then uses speech synthesis to convert this response into speech and provide it to the user. This speech synthesis uses a speech synthesis API that enables high-quality and realistic speech output.

[0070] The terminal also features gesture recognition capabilities to monitor the user's movements in real time. This recognition technology utilizes a general-purpose gesture recognition sensor. This sensor employs advanced analysis algorithms to detect unusual or abnormal movements. When an anomaly is detected, the information is sent to a server, and a log is generated. The generated log is recorded by an information recording device and transmitted to family members or others in remote locations via an information transmission device. This allows family members in distant locations to constantly monitor the elderly person's condition, providing peace of mind.

[0071] For example, if a user says, "Tell me today's news," the device converts the speech to text and sends it to the server. The server collects appropriate news information, generates a response, and returns it to the device. The device then conveys this information to the user as speech, achieving seamless information delivery.

[0072] An example of a prompt sentence to input into a generative AI model is, "What technologies are necessary to create a system that can have natural conversations with elderly people?"

[0073] This system allows users to live a safe and comfortable life, and since the information is shared externally as needed, it provides great convenience in terms of both communication and security.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device receives voice input from the user. When the user says, "What's the weather like today?", the device's microphone captures the voice data. The input is an analog voice signal. This voice data is converted into text data using a speech recognition system. Specifically, a speech recognition API is used to perform the data conversion, and the output is the text information "What's the weather like today?".

[0077] Step 2:

[0078] The device analyzes the converted text data using natural language processing. The input is the text "What's the weather like today?". A generative AI model is used to analyze the data and understand the user's intent. The data analysis results in the interpretation that the user is seeking weather information. This information is used in the next step.

[0079] Step 3:

[0080] The terminal sends a request to the server based on the data it has analyzed. The server receives this request and consults databases and APIs for collecting weather information. The input is a request for weather information, and the output is the latest weather information. Specifically, the server retrieves weather data from external sources, integrates it, and prepares a response.

[0081] Step 4:

[0082] The server generates an appropriate response and sends that data to the terminal. The input is text data containing weather information. After generating the response, the output will be a specific message such as "Today's weather is sunny and the temperature is 25 degrees." The server forwards this message to the terminal.

[0083] Step 5:

[0084] The terminal converts response data received from the server into speech. It uses speech synthesis to convert text into speech output. The input is the text data "Today's weather is sunny and the temperature is 25 degrees," and the output is synthesized speech. The terminal fulfills its role in providing information by conveying this speech to the user.

[0085] Step 6:

[0086] The terminal monitors user actions in real time using gesture recognition. If unusual actions are detected, the data is sent to the server. The input is user action information, and through processing, abnormal actions are identified, and an abnormal warning log is generated as output. The terminal checks for unusual actions and, if an abnormality is found, sends that information to the server.

[0087] Step 7:

[0088] The server generates a monitoring log using an information recording device based on the anomaly warning log, and notifies external family members or others using an information transmission device. The input is the anomaly warning log, and the output is a monitoring log containing the information. The server generates the monitoring log and sends it to a remote recipient, allowing a third party to understand the situation.

[0089] (Application Example 1)

[0090] 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."

[0091] To ensure that elderly people can live their daily lives with peace of mind, there is a need for systems that facilitate communication and quickly detect and notify external parties of abnormalities. However, conventional technologies have insufficient accuracy in voice recognition and anomaly detection, and often require operations that are unfamiliar to the elderly. As a result, there is a challenge in that the safety and smooth daily lives of the elderly have not been fully achieved.

[0092] 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.

[0093] In this invention, the server includes speech recognition means for converting the voice input of an elderly person into text data, natural language processing means for analyzing the text data and generating a response, and anomaly detection and log generation means based on gesture recognition. This enables natural dialogue using the voice of an elderly person, as well as rapid detection and notification of anomalies.

[0094] "Speech recognition means" refers to a device or system for converting human speech into digital speech data and processing this data as text data.

[0095] "Natural language processing means" refers to a technology for analyzing text data and generating appropriate responses for a specific purpose, and is a part of a computer program.

[0096] "Speech synthesis means" refers to a technology that generates and outputs speech based on text data, and is a means of conveying responses in a dialogue system to the user.

[0097] "Gesture recognition means" refers to a device or software that detects a user's physical movements and determines the type and abnormality of those movements.

[0098] A "log generation method" is a technology for creating, saving, or transmitting logs for record-keeping purposes based on data detected within a system.

[0099] "Log transmission means" refers to a technology or device that transmits generated logs to external parties or systems, and is used for notification purposes.

[0100] "Means for generating responses that support the safety and lives of the elderly" refers to the system component that generates and provides information related to the daily lives and safety of the elderly based on user input.

[0101] To implement this invention, collaboration between a terminal, a server, and a user is necessary. First, the terminal has built-in hardware such as a microphone, camera, and accelerometer to monitor the user's voice and movements in real time. This terminal uses speech recognition means to convert the user's voice into digital voice data, and then converts it into text data via the Google® Speech-to-Text API. Furthermore, it uses the Python NLTK library, a natural language processing means, to analyze the text data and generate responses that meet the needs of the elderly person.

[0102] The server uses a generative AI model to send the generated response to the terminal. For example, if the server receives the prompt "I want to know my schedule for tomorrow," it first searches the calendar database and constructs a response such as "You have a hospital appointment at 9am tomorrow." Then, it converts this response into speech data using a speech synthesis system and sends it back to the user.

[0103] Furthermore, the device uses gesture recognition to analyze data from cameras and sensors and monitor the user's physical movements. When unusual movements are detected, the server immediately analyzes the anomaly and generates a monitoring log. The generated log is sent to family members or care services via a log transmission system to ensure the safety of the elderly.

[0104] For example, if a user loses their balance while trying to stand up, the gesture recognition system detects this, and the server sends a warning message such as "Grandpa has lost his balance" to the family. This allows family members who are far away to be aware of the elderly person's situation.

[0105] Examples of prompt messages are as follows:

[0106] Voice: "I want to know what tomorrow's schedule is."

[0107] System: "Checking the calendar... I have a hospital appointment tomorrow at 9am."

[0108] In this way, advanced speech recognition and natural language processing technologies can be used to realize two-way interaction with the user and monitoring functions.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device acquires the user's voice through the microphone. Analog audio data is obtained as input, and this data is converted into digital data using an ADC (analog-to-digital converter). The resulting digital data is prepared for speech recognition processing in the next step.

[0112] Step 2:

[0113] The device uses the Google Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, which is then processed into text data by the API. The resulting text data represents the user's requests and questions.

[0114] Step 3:

[0115] The server receives text data and performs natural language processing using Python's NLTK library. The input is text data, and the server generates a corresponding response by analyzing the language structure. Generative AI models are utilized to consider the response from various angles. The data output in this process contains information that the system should respond with.

[0116] Step 4:

[0117] The server sends the generated response back to the terminal, where it is converted into audio data via a speech synthesis system. The input here is the response text data, which is then synthesized by the speech synthesis software to generate the audio data. The output is the audio data that the user can listen to.

[0118] Step 5:

[0119] The terminal outputs the generated audio data to the user through the speaker. The synthesized audio data is emitted by the speaker as a nominal tone, thereby providing the user with a response from the system.

[0120] Step 6:

[0121] The device uses a camera and accelerometer to monitor the user's gestures in real time. The input is motion data from the sensors, which is analyzed by a gesture recognition algorithm. If a unique gesture is detected, the information is sent to the next step.

[0122] Step 7:

[0123] The server receives gesture data and analyzes it for anomalies. The input is recognized gesture data, which is analyzed using an anomaly detection algorithm. If an anomaly is detected as a result of the analysis, the server generates a monitoring log based on this.

[0124] Step 8:

[0125] The server generates monitoring logs and sends them to external family members or caregivers via a log transmission system. The input is the generated log data, which is sent to recipients via email or a dedicated app. The output is the log message received by family members or caregivers, which accurately conveys the elderly person's condition.

[0126] 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.

[0127] The present invention includes a speech recognition means that receives voice input from a user and converts it into text data. The terminal sends this text data to a server, where it is analyzed using natural language processing means. Based on the analysis results, the server evaluates the user's intentions and emotions and recognizes the user's emotional state using an emotion engine. The server generates a response with information including the user's emotional state and sends it to the terminal. The terminal uses speech synthesis means to convey this response to the user as voice.

[0128] In addition, the device uses gesture recognition to monitor and analyze the user's physical movements, detecting unusual movements and emotional changes. The server combines the gesture recognition results with the emotional state determined by the emotion engine to identify anomalies and create a monitoring log. The monitoring log also includes the user's emotional information and is sent to family members and other relevant parties using a log transmission device. This allows family members to understand both the user's physical and emotional state.

[0129] For example, if a user says, "I feel lonely today," the device converts the audio into text and sends it to the server. The server uses voice analysis to recognize that the user is feeling lonely, and generates a gentle response appropriate to that state. The device communicates this response to the user and simultaneously continues to monitor the user's actions through gesture recognition. If the user sighs or makes other similar actions, the device detects this and sends the information to the server. Based on this, the server can update the monitoring log and notify family members to take measures to prevent the user from becoming emotionally isolated.

[0130] Thus, the present invention makes it possible to achieve natural communication with users and provide comprehensive monitoring services using a combination of voice, motion, and emotion data. Through interaction with the pet robot, users can gain a sense of security in their daily lives, and an environment can be created that reduces feelings of isolation among the elderly.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0134] Step 2:

[0135] The device inputs the recorded audio into a speech recognition system and converts the audio into text data.

[0136] Step 3:

[0137] The terminal sends the converted text data to the server. The server uses natural language processing to analyze this text data and interpret the user's intent.

[0138] Step 4:

[0139] The server runs an emotion engine and determines the user's emotional state from the analysis results. The emotion engine infers emotions by analyzing specific keywords and tones.

[0140] Step 5:

[0141] The server generates an appropriate response based on the user's intent and emotional state. The response is created using a generative AI model and is tailored to the user's emotions.

[0142] Step 6:

[0143] The server sends the generated response to the terminal. The terminal uses speech synthesis to output this response as audio and convey it to the user.

[0144] Step 7:

[0145] The device uses gesture recognition to monitor the user's body movements in real time via a camera. It analyzes unusual movements and movements that may influence emotions.

[0146] Step 8:

[0147] The device sends data obtained from emotion recognition and gesture recognition to the server. Based on this data, the server determines if the user is abnormal. At the same time, it generates a monitoring log.

[0148] Step 9:

[0149] The server sends monitoring logs, including emotional information, to the family via a log transmission system. The family can review the logs and understand the user's emotions and health status.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] There is a need for a system that can recognize human voice and movements in real time, detect emotions and abnormal behavior, and respond appropriately. Conventional technologies often perform voice recognition and gesture recognition separately, making integrated responses difficult. Furthermore, the lack of monitoring systems that can quickly reflect emotional changes has prevented effective support for human safety and security.

[0153] 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.

[0154] In this invention, the server includes an information processing device comprising: signal conversion means for receiving an acoustic signal and converting the acoustic signal into character data; language processing means for analyzing the character data and generating a related response; and motion recognition means for monitoring human movements in real time and analyzing unusual movements. This enables comprehensive recognition using both voice and motion, allowing for rapid and accurate detection and response to human emotional states and abnormal behavior, and providing a safe and comfortable living environment.

[0155] An "information processing device" is a device that receives and analyzes acoustic signals and operational data, and processes them as needed.

[0156] An "acoustic signal" is data that electrically represents human speech and other sounds.

[0157] A "signal conversion means" is a means that receives an acoustic signal as input and converts it into appropriate character data.

[0158] "Character data" refers to text-formatted data obtained as a result of converting an acoustic signal.

[0159] A "language processing means" is a means that has the function of analyzing character data and generating an appropriate response based on the results of that analysis.

[0160] A "motion recognition means" is a means that has the function of monitoring human body movements in real time and detecting and analyzing specific movements.

[0161] An "abnormality" refers to behavior or a state that differs from normal operation or circumstances, and indicates a potential problem or risk.

[0162] A "management record" is a collection of information generated based on motion recognition means and other sensor data, which records the state and abnormalities of the monitored object.

[0163] A "record transmission means" is a means that has the function of transmitting the generated management records to an external receiving device or related system.

[0164] This invention illustrates an embodiment of a system that recognizes human voice and actions and provides a corresponding response. The specific implementation method is described below.

[0165] First, when a user speaks into the device, the device receives an acoustic signal. This acoustic signal is then converted into text data by the device's built-in speech recognition technology. Speech recognition software is typically used in this process. For example, the "Google Speech-to-Text API" can be used.

[0166] Next, the data converted to text is sent from the terminal to the server. The server analyzes this text data using natural language processing (NLP) tools. By using software such as an emotion engine for analysis, it is possible to understand the user's emotional state and intentions.

[0167] Based on the analysis results, the server generates a relevant response. This response is constructed by a generative AI model within the server and takes into account the user's emotions and intentions. A concrete example of a prompt might be, "The user is feeling sad. What kind of comforting response should be provided?"

[0168] The generated response is sent from the server to the terminal. The terminal then uses speech synthesis to provide this response back to the user as an acoustic signal. For speech synthesis, software that generates speech from text, such as "Amazon Polly," is used.

[0169] Furthermore, the terminal uses gesture recognition to monitor user actions in real time. For example, if a user sighs, the terminal detects this as an unusual action and resends the information to the server. Gesture recognition devices such as "Microsoft® Kinect" are used for this action recognition.

[0170] The server combines transmitted behavioral information with emotional information obtained from voice analysis to determine if there are any abnormalities and generates management records. These records, including the user's emotional data, are sent as logs to external monitors, such as family members or related parties, enabling a comprehensive understanding of the person's emotional and physical state.

[0171] In this way, this invention enables natural communication with users and provides monitoring services, offering safety and peace of mind in daily life.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user speaks into the device. The device receives this voice as an acoustic signal. This acoustic signal is input data and is converted into text data using speech recognition technology. This data processing converts the acoustic signal into a parseable text format.

[0175] Step 2:

[0176] The terminal sends the converted text data to the server. The server receives this text data as input and performs analysis using natural language processing (NLP) software. The analysis involves data calculations to identify the user's intentions and emotional state. The analysis results provide the information necessary for response generation.

[0177] Step 3:

[0178] The server uses a generative AI model based on the analysis results to generate an appropriate response. This process utilizes a generative model that uses prompt statements. The generated response becomes output data and is sent from the server to the terminal.

[0179] Step 4:

[0180] The terminal converts the response received from the server into an acoustic signal using speech synthesis. In this process, the text-formatted response is used as input data, and data processing is performed to convert it into an acoustic signal as output data. Finally, the generated acoustic signal is transmitted to the user.

[0181] Step 5:

[0182] The terminal uses gesture recognition to monitor the user's physical movements in real time. For example, it detects actions such as the user sighing as unique actions. This action information is processed by the terminal as input data and sent to the server.

[0183] Step 6:

[0184] The server combines information obtained from gesture recognition with the previously analyzed emotional state to determine if there is an anomaly. Based on whether or not an anomaly is detected, it generates a management record and sends it externally as a monitoring log. This record is then sent to family members and other relevant parties as output data.

[0185] (Application Example 2)

[0186] 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."

[0187] In modern, real-world conversational environments, accurately understanding a user's emotional state from their voice and actions, and providing appropriate responses and guidance based on that understanding, is difficult. This issue is particularly important in physical stores, where providing optimal service tailored to the customer's emotions is crucial. However, conventional conversational systems often fail to capture subtle changes in facial expressions and voice, resulting in inappropriate responses. There is a need to address these challenges.

[0188] 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.

[0189] In this invention, the server includes emotion analysis means for evaluating the customer's emotional state and generating optimal response guidance; adaptive response means for sensing changes in the customer's emotions and providing corresponding guidance; and log generation means for detecting anomalies through voice and gesture recognition, generating monitoring logs, and transmitting them externally. This enables natural and appropriate conversational responses that take customer emotions into consideration in physical stores.

[0190] "Speech recognition means" refers to a device or process that has the function of receiving human speech input and converting it into text data.

[0191] "Natural language processing means" refers to technologies that analyze text data and generate corresponding responses.

[0192] "Speech synthesis means" refers to a technology for outputting the generated response as speech.

[0193] "Gesture recognition means" is a technology that monitors user movements in real time and analyzes unusual movements.

[0194] The "log generation method" is a technology that detects anomalies based on the results of gesture recognition and generates monitoring logs.

[0195] "Log transmission means" refers to technology for transmitting generated monitoring logs to an external source.

[0196] "Emotional analysis means" refers to technology that evaluates a customer's emotional state and generates the most appropriate response and guidance.

[0197] "Adaptive response methods" are technologies that detect changes in a customer's emotions and provide appropriate guidance accordingly.

[0198] This invention can be applied to dialogue systems in physical stores. In particular, it is useful for building systems that combine speech recognition and gesture recognition to understand the emotional state of customers and respond accordingly.

[0199] The system is implemented in a customer service robot, which captures customer voices with a microphone and converts them into text data using speech recognition (such as Google Cloud Speech-to-Text). The converted text data is sent to a server, where it is analyzed by natural language processing (such as OpenAI's GPT model) to determine the customer's intentions and emotions. Furthermore, emotion analysis (such as IBM Watson's Tone Analyzer) is used to evaluate the customer's emotional state.

[0200] Based on the evaluation results, an optimized response is generated by a speech synthesis system and presented to the customer as voice by the robot. In parallel, the customer's gestures are monitored by a camera mounted on the terminal, and the movements are analyzed by a gesture recognition system (such as TENSORFLOW® or OpenCV). Unusual movements are recorded by a log generation system and transmitted externally as a monitoring log. This allows the adaptive response system to detect changes in the customer's emotions and provide appropriate guidance.

[0201] For example, if a user says, "I'm not feeling very energetic today, so I'd like some recommendations for products that can help me relax," the system can understand their intention and respond with something like, "These relaxation products are very popular right now."

[0202] An example of a prompt message could be set as follows: "If a user says, 'I'm tired today, so I want to easily get the ingredients for dinner,' what kind of suggestion can be made?" In this way, the present invention provides a means to improve customer service in physical stores.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device captures the user's voice using its microphone. The input is the user's voice, which is then converted into text data using speech recognition technology (Google Cloud Speech-to-Text). The converted text data becomes the output.

[0206] Step 2:

[0207] The terminal sends the acquired text data to the server. The server uses natural language processing tools (OpenAI's GPT model) to analyze the text data and interpret the user's intentions and requests. Information generated based on this analysis is then output.

[0208] Step 3:

[0209] The server evaluates the user's emotional state using an emotion analysis tool (IBM Watson Tone Analyzer) based on the analysis results. The input is the result of natural language processing, and the evaluated emotion data is the output.

[0210] Step 4:

[0211] Based on emotional data, the server generates the optimal response. A speech synthesis system then generates this response as audio data and sends it to the terminal. The output is audio data.

[0212] Step 5:

[0213] The device plays the generated audio data as audio to the user. By receiving this audio, the user can receive guidance and suggestions.

[0214] Step 6:

[0215] The system monitors the user's gestures using the device's camera. Gesture recognition tools (TensorFlow, OpenCV) are used to detect unusual movements and changes in customer emotion. The input to this process is camera footage, and the output is analyzed motion data.

[0216] Step 7:

[0217] The server determines if there is an anomaly based on the gesture analysis results and generates a monitoring log using the log generation mechanism. The monitoring log also includes emotional information and is sent to external parties using the log transmission mechanism. The output is the generated log data.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] The present invention aims to enable communication in a convenient manner by converting the voice of an elderly person into text using a terminal equipped with speech recognition means and natural language processing means. The terminal receives voice input from the user and performs natural language processing based on it. The server receives the converted text and sends the generated response back to the terminal. The terminal outputs voice from the text generated by the speech synthesis means and responds to the user.

[0235] Furthermore, gesture recognition is used to monitor the user's movements in real time and analyze any unusual movements as needed. Based on this movement data, the server detects anomalies and creates a monitoring log using a log generation mechanism. This log is sent to external family members via a log transmission mechanism, allowing family members in remote locations to understand the elderly person's situation.

[0236] For example, if a user asks, "What are my plans for today?", the device converts this audio into text and sends it to the server. The server consults the schedule management system, generates a response containing the appropriate information, and sends it back to the device. The device then communicates this information to the user via voice. Additionally, if the user becomes unsteady, the device's gesture recognition system detects this, and the server immediately generates a warning log to notify family members.

[0237] Thus, the present invention can provide a comprehensive monitoring system that enhances natural communication and security. Through interaction with the pet robot, users can avoid isolation and live their daily lives safely in a relaxed environment.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0241] Step 2:

[0242] The device passes the recorded audio to a speech recognition system, which converts the audio into text data. Advanced ASR technology is used for this conversion.

[0243] Step 3:

[0244] The terminal sends the converted text data to the server. The server uses natural language processing to analyze the text and understand the user's intent.

[0245] Step 4:

[0246] The server uses a generative AI model to generate response text based on the analysis results. This response includes an appropriate reply to the user's utterance.

[0247] Step 5:

[0248] The server sends the generated response text to the terminal. The terminal uses speech synthesis to convert the text into natural-sounding speech and delivers it to the user.

[0249] Step 6:

[0250] The device uses a camera to monitor user movements in real time. It also utilizes gesture recognition to analyze unusual movements.

[0251] Step 7:

[0252] The terminal sends the gesture recognition results to the server, which then determines whether or not there are any abnormalities based on the operation data.

[0253] Step 8:

[0254] If the server detects an anomaly, it generates a monitoring log using a log generation mechanism. This log includes user actions and speech content.

[0255] Step 9:

[0256] The server sends the generated monitoring logs to the family via a log transmission device. The family can then view these logs, for example, through a dedicated application.

[0257] (Example 1)

[0258] 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."

[0259] It is necessary to provide an environment in which users, including the elderly, can safely live their daily lives through natural voice-based dialogue and monitoring of their physical movements. Furthermore, there is a need for a system that allows family members, even from a distance, to easily understand the situation of elderly individuals. Existing technologies have limitations in the accuracy and speed of responses based on voice and gesture recognition, highlighting the need for more effective and reliable communication and monitoring methods.

[0260] 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.

[0261] In this invention, the server includes a speech recognition means that receives voice input and converts the voice into text information, a natural language processing means that analyzes the text information and generates a corresponding response, and an information recording means that detects abnormalities based on the results of the gesture recognition and generates a monitoring log. This enables natural dialogue with the user, rapid detection of operational abnormalities, and external sharing of that information.

[0262] "Speech recognition means" refers to a technology or device that receives speech input and converts that speech into text information.

[0263] "Natural language processing means" refers to a technology or device that analyzes text data, understands the user's intent, and generates an appropriate response.

[0264] "Speech synthesis means" refers to a technology or device that converts text information into speech and provides a voice response to the user.

[0265] "Gesture recognition means" refers to a technology or device that monitors a user's physical movements in real time and analyzes unusual movements.

[0266] "Information recording means" refers to a technology or device that detects anomalies based on the results of gesture recognition and records that information.

[0267] "Information transmission means" refers to a technology or device that transmits the generated monitoring log to an external recipient.

[0268] This invention is a system that integrates the functions of speech recognition, natural language processing, gesture recognition, information recording, and information transmission, and is mainly implemented via a server and terminals.

[0269] The device is equipped with a microphone to receive voice input, which is then converted into text information by a speech recognition system. This process utilizes commonly used speech recognition APIs. This text information is further analyzed by a natural language processing system to understand the user's intent and generate an appropriate response. At this stage, using a generative AI model can yield a more sophisticated response. Examples of natural language processing technologies include widely used natural language processing APIs.

[0270] The server generates a response based on the analyzed data and outputs that response to the terminal. The terminal then uses speech synthesis to convert this response into speech and provide it to the user. This speech synthesis uses a speech synthesis API that enables high-quality and realistic speech output.

[0271] The terminal also features gesture recognition capabilities to monitor the user's movements in real time. This recognition technology utilizes a general-purpose gesture recognition sensor. This sensor employs advanced analysis algorithms to detect unusual or abnormal movements. When an anomaly is detected, the information is sent to a server, and a log is generated. The generated log is recorded by an information recording device and transmitted to family members or others in remote locations via an information transmission device. This allows family members in distant locations to constantly monitor the elderly person's condition, providing peace of mind.

[0272] For example, if a user says, "Tell me today's news," the device converts the speech to text and sends it to the server. The server collects appropriate news information, generates a response, and returns it to the device. The device then conveys this information to the user as speech, achieving seamless information delivery.

[0273] An example of a prompt sentence to input into a generative AI model is, "What technologies are necessary to create a system that can have natural conversations with elderly people?"

[0274] This system allows users to live a safe and comfortable life, and since the information is shared externally as needed, it provides great convenience in terms of both communication and security.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The device receives voice input from the user. When the user says, "What's the weather like today?", the device's microphone captures the voice data. The input is an analog voice signal. This voice data is converted into text data using a speech recognition system. Specifically, a speech recognition API is used to perform the data conversion, and the output is the text information "What's the weather like today?".

[0278] Step 2:

[0279] The device analyzes the converted text data using natural language processing. The input is the text "What's the weather like today?". A generative AI model is used to analyze the data and understand the user's intent. The data analysis results in the interpretation that the user is seeking weather information. This information is used in the next step.

[0280] Step 3:

[0281] Based on the analyzed data, the terminal sends a request to the server. The server receives this request and refers to a database and an API for collecting weather information. The input is a request regarding weather information, and the output is the latest weather information. As a specific operation, the server obtains meteorological data from the outside, integrates it, and prepares a response.

[0282] Step 4:

[0283] The server generates an appropriate response and sends that data to the terminal. The input is text data containing weather information. After response generation, the output is a specific message such as "Today's weather is sunny and the temperature is 25 degrees." The server transfers this message to the terminal.

[0284] Step 5:

[0285] The terminal converts the response data received from the server into voice. Using voice synthesis means, the text is converted into voice output. The input is the text data "Today's weather is sunny and the temperature is 25 degrees," and the output is synthesized voice. The terminal plays the role of providing information by conveying this voice to the user.

[0286] Step 6:

[0287] The terminal monitors the user's actions in real time using gesture recognition means. If a specific action is detected, the data is sent to the server. The input is the user's action information, through which abnormal actions are identified, and an abnormal warning log is generated as the output. If the terminal confirms a specific action and there is an abnormality, it sends that information to the server.

[0288] Step 7:

[0289] The server generates a monitoring log using an information recording device based on the anomaly warning log, and notifies external family members or others using an information transmission device. The input is the anomaly warning log, and the output is a monitoring log containing the information. The server generates the monitoring log and sends it to a remote recipient, allowing a third party to understand the situation.

[0290] (Application Example 1)

[0291] 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."

[0292] To ensure that elderly people can live their daily lives with peace of mind, there is a need for systems that facilitate communication and quickly detect and notify external parties of abnormalities. However, conventional technologies have insufficient accuracy in voice recognition and anomaly detection, and often require operations that are unfamiliar to the elderly. As a result, there is a challenge in that the safety and smooth daily lives of the elderly have not been fully achieved.

[0293] 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.

[0294] In this invention, the server includes speech recognition means for converting the voice input of an elderly person into text data, natural language processing means for analyzing the text data and generating a response, and anomaly detection and log generation means based on gesture recognition. This enables natural dialogue using the voice of an elderly person, as well as rapid detection and notification of anomalies.

[0295] "Speech recognition means" refers to a device or system for converting human speech into digital speech data and processing this data as text data.

[0296] "Natural language processing means" refers to a technology for analyzing text data and generating appropriate responses for a specific purpose, and is a part of a computer program.

[0297] "Speech synthesis means" refers to a technology that generates and outputs speech based on text data, and is a means of conveying responses in a dialogue system to the user.

[0298] "Gesture recognition means" refers to a device or software that detects a user's physical movements and determines the type and abnormality of those movements.

[0299] A "log generation method" is a technology for creating, saving, or transmitting logs for record-keeping purposes based on data detected within a system.

[0300] "Log transmission means" refers to a technology or device that transmits generated logs to external parties or systems, and is used for notification purposes.

[0301] "Means for generating responses that support the safety and lives of the elderly" refers to the system component that generates and provides information related to the daily lives and safety of the elderly based on user input.

[0302] To implement this invention, collaboration between a terminal, a server, and a user is necessary. First, the terminal has built-in hardware such as a microphone, camera, and accelerometer to monitor the user's voice and movements in real time. This terminal uses speech recognition means to convert the user's voice into digital voice data, and then converts it into text data via the Google Speech-to-Text API. Furthermore, it uses the Python NLTK library, a natural language processing means, to analyze the text data and generate responses that meet the needs of the elderly person.

[0303] The server uses a generative AI model to send the generated response to the terminal. For example, if the server receives the prompt "I want to know my schedule for tomorrow," it first searches the calendar database and constructs a response such as "You have a hospital appointment at 9am tomorrow." Then, it converts this response into speech data using a speech synthesis system and sends it back to the user.

[0304] In addition, the terminal uses gesture recognition means to analyze data from cameras and sensors and monitor the user's body movements. When a specific movement is detected, the server immediately analyzes the abnormality and generates a monitoring log. The generated log is transmitted to family members and care services by the log transmission means to ensure the safety of the elderly.

[0305] As a specific example, when the user loses balance when trying to stand up, the gesture recognition means detects this, and the server sends a warning message such as "Grandpa is wobbling" to the family members. This enables family members who are in a distant location to grasp the situation of the elderly.

[0306] Examples of prompt sentences are as follows.

[0307] Voice: "I want to know tomorrow's schedule"

[0308] System: "Checking the calendar... You have a hospital appointment at 9 o'clock tomorrow"

[0309] In this way, it is possible to realize a two-way dialogue and monitoring function with the user by using advanced speech recognition and natural language processing technologies.

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] [[ID=​​​​​​​​​​​The device uses the Google Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, which is then processed into text data by the API. The resulting text data represents the user's requests and questions.

[0315] Step 3:

[0316] The server receives text data and performs natural language processing using Python's NLTK library. The input is text data, and the server generates a corresponding response by analyzing the language structure. Generative AI models are utilized to consider the response from various angles. The data output in this process contains information that the system should respond with.

[0317] Step 4:

[0318] The server sends the generated response back to the terminal, where it is converted into audio data via a speech synthesis system. The input here is the response text data, which is then synthesized by the speech synthesis software to generate the audio data. The output is the audio data that the user can listen to.

[0319] Step 5:

[0320] The terminal outputs the generated audio data to the user through the speaker. The synthesized audio data is emitted by the speaker as a nominal tone, thereby providing the user with a response from the system.

[0321] Step 6:

[0322] The device uses a camera and accelerometer to monitor the user's gestures in real time. The input is motion data from the sensors, which is analyzed by a gesture recognition algorithm. If a unique gesture is detected, the information is sent to the next step.

[0323] Step 7:

[0324] The server receives gesture data and analyzes it for anomalies. The input is recognized gesture data, which is analyzed using an anomaly detection algorithm. If an anomaly is detected as a result of the analysis, the server generates a monitoring log based on this.

[0325] Step 8:

[0326] The server generates monitoring logs and sends them to external family members or caregivers via a log transmission system. The input is the generated log data, which is sent to recipients via email or a dedicated app. The output is the log message received by family members or caregivers, which accurately conveys the elderly person's condition.

[0327] 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.

[0328] The present invention includes a speech recognition means that receives voice input from a user and converts it into text data. The terminal sends this text data to a server, where it is analyzed using natural language processing means. Based on the analysis results, the server evaluates the user's intentions and emotions and recognizes the user's emotional state using an emotion engine. The server generates a response with information including the user's emotional state and sends it to the terminal. The terminal uses speech synthesis means to convey this response to the user as voice.

[0329] In addition, the device uses gesture recognition to monitor and analyze the user's physical movements, detecting unusual movements and emotional changes. The server combines the gesture recognition results with the emotional state determined by the emotion engine to identify anomalies and create a monitoring log. The monitoring log also includes the user's emotional information and is sent to family members and other relevant parties using a log transmission device. This allows family members to understand both the user's physical and emotional state.

[0330] For example, if a user says, "I feel lonely today," the device converts the audio into text and sends it to the server. The server uses voice analysis to recognize that the user is feeling lonely, and generates a gentle response appropriate to that state. The device communicates this response to the user and simultaneously continues to monitor the user's actions through gesture recognition. If the user sighs or makes other similar actions, the device detects this and sends the information to the server. Based on this, the server can update the monitoring log and notify family members to take measures to prevent the user from becoming emotionally isolated.

[0331] Thus, the present invention makes it possible to achieve natural communication with users and provide comprehensive monitoring services using a combination of voice, motion, and emotion data. Through interaction with the pet robot, users can gain a sense of security in their daily lives, and an environment can be created that reduces feelings of isolation among the elderly.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0335] Step 2:

[0336] The device inputs the recorded audio into a speech recognition system and converts the audio into text data.

[0337] Step 3:

[0338] The terminal sends the converted text data to the server. The server uses natural language processing to analyze this text data and interpret the user's intent.

[0339] Step 4:

[0340] The server runs an emotion engine and determines the user's emotional state from the analysis results. The emotion engine infers emotions by analyzing specific keywords and tones.

[0341] Step 5:

[0342] The server generates an appropriate response based on the user's intent and emotional state. The response is created using a generative AI model and is tailored to the user's emotions.

[0343] Step 6:

[0344] The server sends the generated response to the terminal. The terminal uses speech synthesis to output this response as audio and convey it to the user.

[0345] Step 7:

[0346] The device uses gesture recognition to monitor the user's body movements in real time via a camera. It analyzes unusual movements and movements that may influence emotions.

[0347] Step 8:

[0348] The device sends data obtained from emotion recognition and gesture recognition to the server. Based on this data, the server determines if the user is abnormal. At the same time, it generates a monitoring log.

[0349] Step 9:

[0350] The server sends monitoring logs, including emotional information, to the family via a log transmission system. The family can review the logs and understand the user's emotions and health status.

[0351] (Example 2)

[0352] 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".

[0353] There is a need for a system that can recognize human voice and movements in real time, detect emotions and abnormal behavior, and respond appropriately. Conventional technologies often perform voice recognition and gesture recognition separately, making integrated responses difficult. Furthermore, the lack of monitoring systems that can quickly reflect emotional changes has prevented effective support for human safety and security.

[0354] 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.

[0355] In this invention, the server includes an information processing device comprising: signal conversion means for receiving an acoustic signal and converting the acoustic signal into character data; language processing means for analyzing the character data and generating a related response; and motion recognition means for monitoring human movements in real time and analyzing unusual movements. This enables comprehensive recognition using both voice and motion, allowing for rapid and accurate detection and response to human emotional states and abnormal behavior, and providing a safe and comfortable living environment.

[0356] An "information processing device" is a device that receives and analyzes acoustic signals and operational data, and processes them as needed.

[0357] An "acoustic signal" is data that electrically represents human speech and other sounds.

[0358] A "signal conversion means" is a means that receives an acoustic signal as input and converts it into appropriate character data.

[0359] "Character data" refers to text-formatted data obtained as a result of converting an acoustic signal.

[0360] A "language processing means" is a means that has the function of analyzing character data and generating an appropriate response based on the results of that analysis.

[0361] A "motion recognition means" is a means that has the function of monitoring human body movements in real time and detecting and analyzing specific movements.

[0362] An "abnormality" refers to behavior or a state that differs from normal operation or circumstances, and indicates a potential problem or risk.

[0363] A "management record" is a collection of information generated based on motion recognition means and other sensor data, which records the state and abnormalities of the monitored object.

[0364] A "record transmission means" is a means that has the function of transmitting the generated management records to an external receiving device or related system.

[0365] This invention illustrates an embodiment of a system that recognizes human voice and actions and provides a corresponding response. The specific implementation method is described below.

[0366] First, when a user speaks into the device, the device receives an acoustic signal. This acoustic signal is then converted into text data by the device's built-in speech recognition technology. Speech recognition software is typically used in this process. For example, the "Google Speech-to-Text API" can be used.

[0367] Next, the data converted to text is sent from the terminal to the server. The server analyzes this text data using natural language processing (NLP) tools. By using software such as an emotion engine for analysis, it is possible to understand the user's emotional state and intentions.

[0368] Based on the analysis results, the server generates a relevant response. This response is constructed by a generative AI model within the server and takes into account the user's emotions and intentions. A concrete example of a prompt might be, "The user is feeling sad. What kind of comforting response should be provided?"

[0369] The generated response is sent from the server to the terminal. The terminal then uses speech synthesis to provide this response back to the user as an acoustic signal. For speech synthesis, software that generates speech from text, such as "Amazon Polly," is used.

[0370] Furthermore, the terminal uses gesture recognition to monitor user actions in real time. For example, if a user sighs, the terminal detects this as an unusual action and resends the information to the server. Gesture recognition devices such as "Microsoft Kinect" are used for this action recognition.

[0371] The server combines transmitted behavioral information with emotional information obtained from voice analysis to determine if there are any abnormalities and generates management records. These records, including the user's emotional data, are sent as logs to external monitors, such as family members or related parties, enabling a comprehensive understanding of the person's emotional and physical state.

[0372] In this way, this invention enables natural communication with users and provides monitoring services, offering safety and peace of mind in daily life.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The user speaks into the device. The device receives this voice as an acoustic signal. This acoustic signal is input data and is converted into text data using speech recognition technology. This data processing converts the acoustic signal into a parseable text format.

[0376] Step 2:

[0377] The terminal sends the converted text data to the server. The server receives this text data as input and performs analysis using natural language processing (NLP) software. The analysis involves data calculations to identify the user's intentions and emotional state. The analysis results provide the information necessary for response generation.

[0378] Step 3:

[0379] The server uses a generative AI model based on the analysis results to generate an appropriate response. This process utilizes a generative model that uses prompt statements. The generated response becomes output data and is sent from the server to the terminal.

[0380] Step 4:

[0381] The terminal converts the response received from the server into an acoustic signal using speech synthesis. In this process, the text-formatted response is used as input data, and data processing is performed to convert it into an acoustic signal as output data. Finally, the generated acoustic signal is transmitted to the user.

[0382] Step 5:

[0383] The terminal uses gesture recognition to monitor the user's physical movements in real time. For example, it detects actions such as the user sighing as unique actions. This action information is processed by the terminal as input data and sent to the server.

[0384] Step 6:

[0385] The server combines information obtained from gesture recognition with the previously analyzed emotional state to determine if there is an anomaly. Based on whether or not an anomaly is detected, it generates a management record and sends it externally as a monitoring log. This record is then sent to family members and other relevant parties as output data.

[0386] (Application Example 2)

[0387] 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 as the "terminal".

[0388] In modern, real-world conversational environments, accurately understanding a user's emotional state from their voice and actions, and providing appropriate responses and guidance based on that understanding, is difficult. This issue is particularly important in physical stores, where providing optimal service tailored to the customer's emotions is crucial. However, conventional conversational systems often fail to capture subtle changes in facial expressions and voice, resulting in inappropriate responses. There is a need to address these challenges.

[0389] 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.

[0390] In this invention, the server includes emotion analysis means for evaluating the customer's emotional state and generating optimal response guidance; adaptive response means for sensing changes in the customer's emotions and providing corresponding guidance; and log generation means for detecting anomalies through voice and gesture recognition, generating monitoring logs, and transmitting them externally. This enables natural and appropriate conversational responses that take customer emotions into consideration in physical stores.

[0391] "Speech recognition means" refers to a device or process that has the function of receiving human speech input and converting it into text data.

[0392] "Natural language processing means" refers to technologies that analyze text data and generate corresponding responses.

[0393] "Speech synthesis means" refers to a technology for outputting the generated response as speech.

[0394] "Gesture recognition means" is a technology that monitors user movements in real time and analyzes unusual movements.

[0395] The "log generation method" is a technology that detects anomalies based on the results of gesture recognition and generates monitoring logs.

[0396] "Log transmission means" refers to technology for transmitting generated monitoring logs to an external source.

[0397] "Emotional analysis means" refers to technology that evaluates a customer's emotional state and generates the most appropriate response and guidance.

[0398] "Adaptive response methods" are technologies that detect changes in a customer's emotions and provide appropriate guidance accordingly.

[0399] This invention can be applied to dialogue systems in physical stores. In particular, it is useful for building systems that combine speech recognition and gesture recognition to understand the emotional state of customers and respond accordingly.

[0400] The system is implemented in a customer service robot, which captures customer voices with a microphone and converts them into text data using speech recognition technology (such as Google Cloud Speech-to-Text). The converted text data is sent to a server, where it is analyzed using natural language processing technology (such as OpenAI's GPT model) to determine the customer's intentions and emotions. Furthermore, emotion analysis technology (such as IBM Watson Tone Analyzer) is used to evaluate the customer's emotional state.

[0401] Based on the evaluation results, an optimized response is generated by a speech synthesis system and presented to the customer as voice by the robot. In parallel, the customer's gestures are monitored by a camera mounted on the terminal, and the movements are analyzed by a gesture recognition system (TensorFlow, OpenCV, etc.). Unusual movements are recorded by a log generation system and transmitted externally as a monitoring log. This allows the adaptive response system to detect changes in the customer's emotions and provide appropriate guidance.

[0402] For example, if a user says, "I'm not feeling very energetic today, so I'd like some recommendations for products that can help me relax," the system can understand their intention and respond with something like, "These relaxation products are very popular right now."

[0403] An example of a prompt message could be set as follows: "If a user says, 'I'm tired today, so I want to easily get the ingredients for dinner,' what kind of suggestion can be made?" In this way, the present invention provides a means to improve customer service in physical stores.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The device captures the user's voice using its microphone. The input is the user's voice, which is then converted into text data using speech recognition technology (Google Cloud Speech-to-Text). The converted text data becomes the output.

[0407] Step 2:

[0408] The terminal sends the acquired text data to the server. The server uses natural language processing tools (OpenAI's GPT model) to analyze the text data and interpret the user's intentions and requests. Information generated based on this analysis is then output.

[0409] Step 3:

[0410] The server evaluates the user's emotional state using an emotion analysis tool (IBM Watson Tone Analyzer) based on the analysis results. The input is the result of natural language processing, and the evaluated emotion data is the output.

[0411] Step 4:

[0412] Based on emotional data, the server generates the optimal response. A speech synthesis system then generates this response as audio data and sends it to the terminal. The output is audio data.

[0413] Step 5:

[0414] The device plays the generated audio data as audio to the user. By receiving this audio, the user can receive guidance and suggestions.

[0415] Step 6:

[0416] The system monitors the user's gestures using the device's camera. Gesture recognition tools (TensorFlow, OpenCV) are used to detect unusual movements and changes in customer emotion. The input to this process is camera footage, and the output is analyzed motion data.

[0417] Step 7:

[0418] The server determines if there is an anomaly based on the gesture analysis results and generates a monitoring log using the log generation mechanism. The monitoring log also includes emotional information and is sent to external parties using the log transmission mechanism. The output is the generated log data.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] The present invention aims to enable communication in a convenient manner by converting the voice of an elderly person into text using a terminal equipped with speech recognition means and natural language processing means. The terminal receives voice input from the user and performs natural language processing based on it. The server receives the converted text and sends the generated response back to the terminal. The terminal outputs voice from the text generated by the speech synthesis means and responds to the user.

[0436] Furthermore, gesture recognition is used to monitor the user's movements in real time and analyze any unusual movements as needed. Based on this movement data, the server detects anomalies and creates a monitoring log using a log generation mechanism. This log is sent to external family members via a log transmission mechanism, allowing family members in remote locations to understand the elderly person's situation.

[0437] For example, if a user asks, "What are my plans for today?", the device converts this audio into text and sends it to the server. The server consults the schedule management system, generates a response containing the appropriate information, and sends it back to the device. The device then communicates this information to the user via voice. Additionally, if the user becomes unsteady, the device's gesture recognition system detects this, and the server immediately generates a warning log to notify family members.

[0438] Thus, the present invention can provide a comprehensive monitoring system that enhances natural communication and security. Through interaction with the pet robot, users can avoid isolation and live their daily lives safely in a relaxed environment.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0442] Step 2:

[0443] The device passes the recorded audio to a speech recognition system, which converts the audio into text data. Advanced ASR technology is used for this conversion.

[0444] Step 3:

[0445] The terminal sends the converted text data to the server. The server uses natural language processing to analyze the text and understand the user's intent.

[0446] Step 4:

[0447] The server uses a generative AI model to generate response text based on the analysis results. This response includes an appropriate reply to the user's utterance.

[0448] Step 5:

[0449] The server sends the generated response text to the terminal. The terminal uses speech synthesis to convert the text into natural-sounding speech and delivers it to the user.

[0450] Step 6:

[0451] The device uses a camera to monitor user movements in real time. It also utilizes gesture recognition to analyze unusual movements.

[0452] Step 7:

[0453] The terminal sends the gesture recognition results to the server, which then determines whether or not there are any abnormalities based on the operation data.

[0454] Step 8:

[0455] If the server detects an anomaly, it generates a monitoring log using a log generation mechanism. This log includes user actions and speech content.

[0456] Step 9:

[0457] The server sends the generated monitoring logs to the family via a log transmission device. The family can then view these logs, for example, through a dedicated application.

[0458] (Example 1)

[0459] 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."

[0460] It is necessary to provide an environment in which users, including the elderly, can safely live their daily lives through natural voice-based dialogue and monitoring of their physical movements. Furthermore, there is a need for a system that allows family members, even from a distance, to easily understand the situation of elderly individuals. Existing technologies have limitations in the accuracy and speed of responses based on voice and gesture recognition, highlighting the need for more effective and reliable communication and monitoring methods.

[0461] 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.

[0462] In this invention, the server includes a speech recognition means that receives voice input and converts the voice into text information, a natural language processing means that analyzes the text information and generates a corresponding response, and an information recording means that detects abnormalities based on the results of the gesture recognition and generates a monitoring log. This enables natural dialogue with the user, rapid detection of operational abnormalities, and external sharing of that information.

[0463] "Speech recognition means" refers to a technology or device that receives speech input and converts that speech into text information.

[0464] "Natural language processing means" refers to a technology or device that analyzes text data, understands the user's intent, and generates an appropriate response.

[0465] "Speech synthesis means" refers to a technology or device that converts text information into speech and provides a voice response to the user.

[0466] "Gesture recognition means" refers to a technology or device that monitors a user's physical movements in real time and analyzes unusual movements.

[0467] "Information recording means" refers to a technology or device that detects anomalies based on the results of gesture recognition and records that information.

[0468] "Information transmission means" refers to a technology or device that transmits the generated monitoring log to an external recipient.

[0469] This invention is a system that integrates the functions of speech recognition, natural language processing, gesture recognition, information recording, and information transmission, and is mainly implemented via a server and terminals.

[0470] The device is equipped with a microphone to receive voice input, which is then converted into text information by a speech recognition system. This process utilizes commonly used speech recognition APIs. This text information is further analyzed by a natural language processing system to understand the user's intent and generate an appropriate response. At this stage, using a generative AI model can yield a more sophisticated response. Examples of natural language processing technologies include widely used natural language processing APIs.

[0471] The server generates a response based on the analyzed data and outputs that response to the terminal. The terminal then uses speech synthesis to convert this response into speech and provide it to the user. This speech synthesis uses a speech synthesis API that enables high-quality and realistic speech output.

[0472] The terminal also features gesture recognition capabilities to monitor the user's movements in real time. This recognition technology utilizes a general-purpose gesture recognition sensor. This sensor employs advanced analysis algorithms to detect unusual or abnormal movements. When an anomaly is detected, the information is sent to a server, and a log is generated. The generated log is recorded by an information recording device and transmitted to family members or others in remote locations via an information transmission device. This allows family members in distant locations to constantly monitor the elderly person's condition, providing peace of mind.

[0473] For example, if a user says, "Tell me today's news," the device converts the speech to text and sends it to the server. The server collects appropriate news information, generates a response, and returns it to the device. The device then conveys this information to the user as speech, achieving seamless information delivery.

[0474] An example of a prompt sentence to input into a generative AI model is, "What technologies are necessary to create a system that can have natural conversations with elderly people?"

[0475] This system allows users to live a safe and comfortable life, and since the information is shared externally as needed, it provides great convenience in terms of both communication and security.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device receives voice input from the user. When the user says, "What's the weather like today?", the device's microphone captures the voice data. The input is an analog voice signal. This voice data is converted into text data using a speech recognition system. Specifically, a speech recognition API is used to perform the data conversion, and the output is the text information "What's the weather like today?".

[0479] Step 2:

[0480] The device analyzes the converted text data using natural language processing. The input is the text "What's the weather like today?". A generative AI model is used to analyze the data and understand the user's intent. The data analysis results in the interpretation that the user is seeking weather information. This information is used in the next step.

[0481] Step 3:

[0482] The terminal sends a request to the server based on the data it has analyzed. The server receives this request and consults databases and APIs for collecting weather information. The input is a request for weather information, and the output is the latest weather information. Specifically, the server retrieves weather data from external sources, integrates it, and prepares a response.

[0483] Step 4:

[0484] The server generates an appropriate response and sends that data to the terminal. The input is text data containing weather information. After generating the response, the output will be a specific message such as "Today's weather is sunny and the temperature is 25 degrees." The server forwards this message to the terminal.

[0485] Step 5:

[0486] The terminal converts response data received from the server into speech. It uses speech synthesis to convert text into speech output. The input is the text data "Today's weather is sunny and the temperature is 25 degrees," and the output is synthesized speech. The terminal fulfills its role in providing information by conveying this speech to the user.

[0487] Step 6:

[0488] The terminal monitors user actions in real time using gesture recognition. If unusual actions are detected, the data is sent to the server. The input is user action information, and through processing, abnormal actions are identified, and an abnormal warning log is generated as output. The terminal checks for unusual actions and, if an abnormality is found, sends that information to the server.

[0489] Step 7:

[0490] The server generates a monitoring log using an information recording device based on the anomaly warning log, and notifies external family members or others using an information transmission device. The input is the anomaly warning log, and the output is a monitoring log containing the information. The server generates the monitoring log and sends it to a remote recipient, allowing a third party to understand the situation.

[0491] (Application Example 1)

[0492] 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."

[0493] To ensure that elderly people can live their daily lives with peace of mind, there is a need for systems that facilitate communication and quickly detect and notify external parties of abnormalities. However, conventional technologies have insufficient accuracy in voice recognition and anomaly detection, and often require operations that are unfamiliar to the elderly. As a result, there is a challenge in that the safety and smooth daily lives of the elderly have not been fully achieved.

[0494] 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.

[0495] In this invention, the server includes speech recognition means for converting the voice input of an elderly person into text data, natural language processing means for analyzing the text data and generating a response, and anomaly detection and log generation means based on gesture recognition. This enables natural dialogue using the voice of an elderly person, as well as rapid detection and notification of anomalies.

[0496] "Speech recognition means" refers to a device or system for converting human speech into digital speech data and processing this data as text data.

[0497] "Natural language processing means" refers to a technology for analyzing text data and generating appropriate responses for a specific purpose, and is a part of a computer program.

[0498] "Speech synthesis means" refers to a technology that generates and outputs speech based on text data, and is a means of conveying responses in a dialogue system to the user.

[0499] "Gesture recognition means" refers to a device or software that detects a user's physical movements and determines the type and abnormality of those movements.

[0500] A "log generation method" is a technology for creating, saving, or transmitting logs for record-keeping purposes based on data detected within a system.

[0501] "Log transmission means" refers to a technology or device that transmits generated logs to external parties or systems, and is used for notification purposes.

[0502] "Means for generating responses that support the safety and lives of the elderly" refers to the system component that generates and provides information related to the daily lives and safety of the elderly based on user input.

[0503] To implement this invention, collaboration between a terminal, a server, and a user is necessary. First, the terminal has built-in hardware such as a microphone, camera, and accelerometer to monitor the user's voice and movements in real time. This terminal uses speech recognition means to convert the user's voice into digital voice data, and then converts it into text data via the Google Speech-to-Text API. Furthermore, it uses the Python NLTK library, a natural language processing means, to analyze the text data and generate responses that meet the needs of the elderly person.

[0504] The server uses a generative AI model to send the generated response to the terminal. For example, if the server receives the prompt "I want to know my schedule for tomorrow," it first searches the calendar database and constructs a response such as "You have a hospital appointment at 9am tomorrow." Then, it converts this response into speech data using a speech synthesis system and sends it back to the user.

[0505] Furthermore, the device uses gesture recognition to analyze data from cameras and sensors and monitor the user's physical movements. When unusual movements are detected, the server immediately analyzes the anomaly and generates a monitoring log. The generated log is sent to family members or care services via a log transmission system to ensure the safety of the elderly.

[0506] For example, if a user loses their balance while trying to stand up, the gesture recognition system detects this, and the server sends a warning message such as "Grandpa has lost his balance" to the family. This allows family members who are far away to be aware of the elderly person's situation.

[0507] Examples of prompt messages are as follows:

[0508] Voice: "I want to know what tomorrow's schedule is."

[0509] System: "Checking the calendar... I have a hospital appointment tomorrow at 9am."

[0510] In this way, advanced speech recognition and natural language processing technologies can be used to realize two-way interaction with the user and monitoring functions.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The device acquires the user's voice through the microphone. Analog audio data is obtained as input, and this data is converted into digital data using an ADC (analog-to-digital converter). The resulting digital data is prepared for speech recognition processing in the next step.

[0514] Step 2:

[0515] The device uses the Google Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, which is then processed into text data by the API. The resulting text data represents the user's requests and questions.

[0516] Step 3:

[0517] The server receives text data and performs natural language processing using Python's NLTK library. The input is text data, and the server generates a corresponding response by analyzing the language structure. Generative AI models are utilized to consider the response from various angles. The data output in this process contains information that the system should respond with.

[0518] Step 4:

[0519] The server sends the generated response back to the terminal, where it is converted into audio data via a speech synthesis system. The input here is the response text data, which is then synthesized by the speech synthesis software to generate the audio data. The output is the audio data that the user can listen to.

[0520] Step 5:

[0521] The terminal outputs the generated audio data to the user through the speaker. The synthesized audio data is emitted by the speaker as a nominal tone, thereby providing the user with a response from the system.

[0522] Step 6:

[0523] The device uses a camera and accelerometer to monitor the user's gestures in real time. The input is motion data from the sensors, which is analyzed by a gesture recognition algorithm. If a unique gesture is detected, the information is sent to the next step.

[0524] Step 7:

[0525] The server receives gesture data and analyzes it for anomalies. The input is recognized gesture data, which is analyzed using an anomaly detection algorithm. If an anomaly is detected as a result of the analysis, the server generates a monitoring log based on this.

[0526] Step 8:

[0527] The server generates monitoring logs and sends them to external family members or caregivers via a log transmission system. The input is the generated log data, which is sent to recipients via email or a dedicated app. The output is the log message received by family members or caregivers, which accurately conveys the elderly person's condition.

[0528] 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.

[0529] The present invention includes a speech recognition means that receives voice input from a user and converts it into text data. The terminal sends this text data to a server, where it is analyzed using natural language processing means. Based on the analysis results, the server evaluates the user's intentions and emotions and recognizes the user's emotional state using an emotion engine. The server generates a response with information including the user's emotional state and sends it to the terminal. The terminal uses speech synthesis means to convey this response to the user as voice.

[0530] In addition, the device uses gesture recognition to monitor and analyze the user's physical movements, detecting unusual movements and emotional changes. The server combines the gesture recognition results with the emotional state determined by the emotion engine to identify anomalies and create a monitoring log. The monitoring log also includes the user's emotional information and is sent to family members and other relevant parties using a log transmission device. This allows family members to understand both the user's physical and emotional state.

[0531] For example, if a user says, "I feel lonely today," the device converts the audio into text and sends it to the server. The server uses voice analysis to recognize that the user is feeling lonely, and generates a gentle response appropriate to that state. The device communicates this response to the user and simultaneously continues to monitor the user's actions through gesture recognition. If the user sighs or makes other similar actions, the device detects this and sends the information to the server. Based on this, the server can update the monitoring log and notify family members to take measures to prevent the user from becoming emotionally isolated.

[0532] Thus, the present invention makes it possible to achieve natural communication with users and provide comprehensive monitoring services using a combination of voice, motion, and emotion data. Through interaction with the pet robot, users can gain a sense of security in their daily lives, and an environment can be created that reduces feelings of isolation among the elderly.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0536] Step 2:

[0537] The device inputs the recorded audio into a speech recognition system and converts the audio into text data.

[0538] Step 3:

[0539] The terminal sends the converted text data to the server. The server uses natural language processing to analyze this text data and interpret the user's intent.

[0540] Step 4:

[0541] The server runs an emotion engine and determines the user's emotional state from the analysis results. The emotion engine infers emotions by analyzing specific keywords and tones.

[0542] Step 5:

[0543] The server generates an appropriate response based on the user's intent and emotional state. The response is created using a generative AI model and is tailored to the user's emotions.

[0544] Step 6:

[0545] The server sends the generated response to the terminal. The terminal uses speech synthesis to output this response as audio and convey it to the user.

[0546] Step 7:

[0547] The device uses gesture recognition to monitor the user's body movements in real time via a camera. It analyzes unusual movements and movements that may influence emotions.

[0548] Step 8:

[0549] The device sends data obtained from emotion recognition and gesture recognition to the server. Based on this data, the server determines if the user is abnormal. At the same time, it generates a monitoring log.

[0550] Step 9:

[0551] The server sends monitoring logs, including emotional information, to the family via a log transmission system. The family can review the logs and understand the user's emotions and health status.

[0552] (Example 2)

[0553] 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."

[0554] There is a need for a system that can recognize human voice and movements in real time, detect emotions and abnormal behavior, and respond appropriately. Conventional technologies often perform voice recognition and gesture recognition separately, making integrated responses difficult. Furthermore, the lack of monitoring systems that can quickly reflect emotional changes has prevented effective support for human safety and security.

[0555] 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.

[0556] In this invention, the server includes an information processing device comprising: signal conversion means for receiving an acoustic signal and converting the acoustic signal into character data; language processing means for analyzing the character data and generating a related response; and motion recognition means for monitoring human movements in real time and analyzing unusual movements. This enables comprehensive recognition using both voice and motion, allowing for rapid and accurate detection and response to human emotional states and abnormal behavior, and providing a safe and comfortable living environment.

[0557] An "information processing device" is a device that receives and analyzes acoustic signals and operational data, and processes them as needed.

[0558] An "acoustic signal" is data that electrically represents human speech and other sounds.

[0559] A "signal conversion means" is a means that receives an acoustic signal as input and converts it into appropriate character data.

[0560] "Character data" refers to text-formatted data obtained as a result of converting an acoustic signal.

[0561] A "language processing means" is a means that has the function of analyzing character data and generating an appropriate response based on the results of that analysis.

[0562] A "motion recognition means" is a means that has the function of monitoring human body movements in real time and detecting and analyzing specific movements.

[0563] An "abnormality" refers to behavior or a state that differs from normal operation or circumstances, and indicates a potential problem or risk.

[0564] A "management record" is a collection of information generated based on motion recognition means and other sensor data, which records the state and abnormalities of the monitored object.

[0565] A "record transmission means" is a means that has the function of transmitting the generated management records to an external receiving device or related system.

[0566] This invention illustrates an embodiment of a system that recognizes human voice and actions and provides a corresponding response. The specific implementation method is described below.

[0567] First, when a user speaks into the device, the device receives an acoustic signal. This acoustic signal is then converted into text data by the device's built-in speech recognition technology. Speech recognition software is typically used in this process. For example, the "Google Speech-to-Text API" can be used.

[0568] Next, the data converted to text is sent from the terminal to the server. The server analyzes this text data using natural language processing (NLP) tools. By using software such as an emotion engine for analysis, it is possible to understand the user's emotional state and intentions.

[0569] Based on the analysis results, the server generates a relevant response. This response is constructed by a generative AI model within the server and takes into account the user's emotions and intentions. A concrete example of a prompt might be, "The user is feeling sad. What kind of comforting response should be provided?"

[0570] The generated response is sent from the server to the terminal. The terminal then uses speech synthesis to provide this response back to the user as an acoustic signal. For speech synthesis, software that generates speech from text, such as "Amazon Polly," is used.

[0571] Furthermore, the terminal uses gesture recognition to monitor user actions in real time. For example, if a user sighs, the terminal detects this as an unusual action and resends the information to the server. Gesture recognition devices such as "Microsoft Kinect" are used for this action recognition.

[0572] The server combines transmitted behavioral information with emotional information obtained from voice analysis to determine if there are any abnormalities and generates management records. These records, including the user's emotional data, are sent as logs to external monitors, such as family members or related parties, enabling a comprehensive understanding of the person's emotional and physical state.

[0573] In this way, this invention enables natural communication with users and provides monitoring services, offering safety and peace of mind in daily life.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The user speaks into the device. The device receives this voice as an acoustic signal. This acoustic signal is input data and is converted into text data using speech recognition technology. This data processing converts the acoustic signal into a parseable text format.

[0577] Step 2:

[0578] The terminal sends the converted text data to the server. The server receives this text data as input and performs analysis using natural language processing (NLP) software. The analysis involves data calculations to identify the user's intentions and emotional state. The analysis results provide the information necessary for response generation.

[0579] Step 3:

[0580] The server uses a generative AI model based on the analysis results to generate an appropriate response. This process utilizes a generative model that uses prompt statements. The generated response becomes output data and is sent from the server to the terminal.

[0581] Step 4:

[0582] The terminal converts the response received from the server into an acoustic signal using speech synthesis. In this process, the text-formatted response is used as input data, and data processing is performed to convert it into an acoustic signal as output data. Finally, the generated acoustic signal is transmitted to the user.

[0583] Step 5:

[0584] The terminal uses gesture recognition to monitor the user's physical movements in real time. For example, it detects actions such as the user sighing as unique actions. This action information is processed by the terminal as input data and sent to the server.

[0585] Step 6:

[0586] The server combines information obtained from gesture recognition with the previously analyzed emotional state to determine if there is an anomaly. Based on whether or not an anomaly is detected, it generates a management record and sends it externally as a monitoring log. This record is then sent to family members and other relevant parties as output data.

[0587] (Application Example 2)

[0588] 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."

[0589] In modern, real-world conversational environments, accurately understanding a user's emotional state from their voice and actions, and providing appropriate responses and guidance based on that understanding, is difficult. This issue is particularly important in physical stores, where providing optimal service tailored to the customer's emotions is crucial. However, conventional conversational systems often fail to capture subtle changes in facial expressions and voice, resulting in inappropriate responses. There is a need to address these challenges.

[0590] 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.

[0591] In this invention, the server includes emotion analysis means for evaluating the customer's emotional state and generating optimal response guidance; adaptive response means for sensing changes in the customer's emotions and providing corresponding guidance; and log generation means for detecting anomalies through voice and gesture recognition, generating monitoring logs, and transmitting them externally. This enables natural and appropriate conversational responses that take customer emotions into consideration in physical stores.

[0592] "Speech recognition means" refers to a device or process that has the function of receiving human speech input and converting it into text data.

[0593] "Natural language processing means" refers to technologies that analyze text data and generate corresponding responses.

[0594] "Speech synthesis means" refers to a technology for outputting the generated response as speech.

[0595] "Gesture recognition means" is a technology that monitors user movements in real time and analyzes unusual movements.

[0596] The "log generation method" is a technology that detects anomalies based on the results of gesture recognition and generates monitoring logs.

[0597] "Log transmission means" refers to technology for transmitting generated monitoring logs to an external source.

[0598] "Emotional analysis means" refers to technology that evaluates a customer's emotional state and generates the most appropriate response and guidance.

[0599] "Adaptive response methods" are technologies that detect changes in a customer's emotions and provide appropriate guidance accordingly.

[0600] This invention can be applied to dialogue systems in physical stores. In particular, it is useful for building systems that combine speech recognition and gesture recognition to understand the emotional state of customers and respond accordingly.

[0601] The system is implemented in a customer service robot, which captures customer voices with a microphone and converts them into text data using speech recognition technology (such as Google Cloud Speech-to-Text). The converted text data is sent to a server, where it is analyzed using natural language processing technology (such as OpenAI's GPT model) to determine the customer's intentions and emotions. Furthermore, emotion analysis technology (such as IBM Watson Tone Analyzer) is used to evaluate the customer's emotional state.

[0602] Based on the evaluation results, an optimized response is generated by a speech synthesis system and presented to the customer as voice by the robot. In parallel, the customer's gestures are monitored by a camera mounted on the terminal, and the movements are analyzed by a gesture recognition system (TensorFlow, OpenCV, etc.). Unusual movements are recorded by a log generation system and transmitted externally as a monitoring log. This allows the adaptive response system to detect changes in the customer's emotions and provide appropriate guidance.

[0603] For example, if a user says, "I'm not feeling very energetic today, so I'd like some recommendations for products that can help me relax," the system can understand their intention and respond with something like, "These relaxation products are very popular right now."

[0604] An example of a prompt message could be set as follows: "If a user says, 'I'm tired today, so I want to easily get the ingredients for dinner,' what kind of suggestion can be made?" In this way, the present invention provides a means to improve customer service in physical stores.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The device captures the user's voice using its microphone. The input is the user's voice, which is then converted into text data using speech recognition technology (Google Cloud Speech-to-Text). The converted text data becomes the output.

[0608] Step 2:

[0609] The terminal sends the acquired text data to the server. The server uses natural language processing tools (OpenAI's GPT model) to analyze the text data and interpret the user's intentions and requests. Information generated based on this analysis is then output.

[0610] Step 3:

[0611] The server evaluates the user's emotional state using an emotion analysis tool (IBM Watson Tone Analyzer) based on the analysis results. The input is the result of natural language processing, and the evaluated emotion data is the output.

[0612] Step 4:

[0613] Based on emotional data, the server generates the optimal response. A speech synthesis system then generates this response as audio data and sends it to the terminal. The output is audio data.

[0614] Step 5:

[0615] The device plays the generated audio data as audio to the user. By receiving this audio, the user can receive guidance and suggestions.

[0616] Step 6:

[0617] The system monitors the user's gestures using the device's camera. Gesture recognition tools (TensorFlow, OpenCV) are used to detect unusual movements and changes in customer emotion. The input to this process is camera footage, and the output is analyzed motion data.

[0618] Step 7:

[0619] The server determines if there is an anomaly based on the gesture analysis results and generates a monitoring log using the log generation mechanism. The monitoring log also includes emotional information and is sent to external parties using the log transmission mechanism. The output is the generated log data.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] The present invention aims to enable communication in a convenient manner by converting the voice of an elderly person into text using a terminal equipped with speech recognition means and natural language processing means. The terminal receives voice input from the user and performs natural language processing based on it. The server receives the converted text and sends the generated response back to the terminal. The terminal outputs voice from the text generated by the speech synthesis means and responds to the user.

[0638] Furthermore, gesture recognition is used to monitor the user's movements in real time and analyze any unusual movements as needed. Based on this movement data, the server detects anomalies and creates a monitoring log using a log generation mechanism. This log is sent to external family members via a log transmission mechanism, allowing family members in remote locations to understand the elderly person's situation.

[0639] For example, if a user asks, "What are my plans for today?", the device converts this audio into text and sends it to the server. The server consults the schedule management system, generates a response containing the appropriate information, and sends it back to the device. The device then communicates this information to the user via voice. Additionally, if the user becomes unsteady, the device's gesture recognition system detects this, and the server immediately generates a warning log to notify family members.

[0640] Thus, the present invention can provide a comprehensive monitoring system that enhances natural communication and security. Through interaction with the pet robot, users can avoid isolation and live their daily lives safely in a relaxed environment.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0644] Step 2:

[0645] The device passes the recorded audio to a speech recognition system, which converts the audio into text data. Advanced ASR technology is used for this conversion.

[0646] Step 3:

[0647] The terminal sends the converted text data to the server. The server uses natural language processing to analyze the text and understand the user's intent.

[0648] Step 4:

[0649] The server uses a generative AI model to generate response text based on the analysis results. This response includes an appropriate reply to the user's utterance.

[0650] Step 5:

[0651] The server sends the generated response text to the terminal. The terminal uses speech synthesis to convert the text into natural-sounding speech and delivers it to the user.

[0652] Step 6:

[0653] The device uses a camera to monitor user movements in real time. It also utilizes gesture recognition to analyze unusual movements.

[0654] Step 7:

[0655] The terminal sends the gesture recognition results to the server, which then determines whether or not there are any abnormalities based on the operation data.

[0656] Step 8:

[0657] If the server detects an anomaly, it generates a monitoring log using a log generation mechanism. This log includes user actions and speech content.

[0658] Step 9:

[0659] The server sends the generated monitoring logs to the family via a log transmission device. The family can then view these logs, for example, through a dedicated application.

[0660] (Example 1)

[0661] 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".

[0662] It is necessary to provide an environment in which users, including the elderly, can safely live their daily lives through natural voice-based dialogue and monitoring of their physical movements. Furthermore, there is a need for a system that allows family members, even from a distance, to easily understand the situation of elderly individuals. Existing technologies have limitations in the accuracy and speed of responses based on voice and gesture recognition, highlighting the need for more effective and reliable communication and monitoring methods.

[0663] 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.

[0664] In this invention, the server includes a speech recognition means that receives voice input and converts the voice into text information, a natural language processing means that analyzes the text information and generates a corresponding response, and an information recording means that detects abnormalities based on the results of the gesture recognition and generates a monitoring log. This enables natural dialogue with the user, rapid detection of operational abnormalities, and external sharing of that information.

[0665] "Speech recognition means" refers to a technology or device that receives speech input and converts that speech into text information.

[0666] "Natural language processing means" refers to a technology or device that analyzes text data, understands the user's intent, and generates an appropriate response.

[0667] "Speech synthesis means" refers to a technology or device that converts text information into speech and provides a voice response to the user.

[0668] "Gesture recognition means" refers to a technology or device that monitors a user's physical movements in real time and analyzes unusual movements.

[0669] "Information recording means" refers to a technology or device that detects anomalies based on the results of gesture recognition and records that information.

[0670] "Information transmission means" refers to a technology or device that transmits the generated monitoring log to an external recipient.

[0671] This invention is a system that integrates the functions of speech recognition, natural language processing, gesture recognition, information recording, and information transmission, and is mainly implemented via a server and terminals.

[0672] The device is equipped with a microphone to receive voice input, which is then converted into text information by a speech recognition system. This process utilizes commonly used speech recognition APIs. This text information is further analyzed by a natural language processing system to understand the user's intent and generate an appropriate response. At this stage, using a generative AI model can yield a more sophisticated response. Examples of natural language processing technologies include widely used natural language processing APIs.

[0673] The server generates a response based on the analyzed data and outputs that response to the terminal. The terminal then uses speech synthesis to convert this response into speech and provide it to the user. This speech synthesis uses a speech synthesis API that enables high-quality and realistic speech output.

[0674] The terminal also features gesture recognition capabilities to monitor the user's movements in real time. This recognition technology utilizes a general-purpose gesture recognition sensor. This sensor employs advanced analysis algorithms to detect unusual or abnormal movements. When an anomaly is detected, the information is sent to a server, and a log is generated. The generated log is recorded by an information recording device and transmitted to family members or others in remote locations via an information transmission device. This allows family members in distant locations to constantly monitor the elderly person's condition, providing peace of mind.

[0675] For example, if a user says, "Tell me today's news," the device converts the speech to text and sends it to the server. The server collects appropriate news information, generates a response, and returns it to the device. The device then conveys this information to the user as speech, achieving seamless information delivery.

[0676] An example of a prompt sentence to input into a generative AI model is, "What technologies are necessary to create a system that can have natural conversations with elderly people?"

[0677] This system allows users to live a safe and comfortable life, and since the information is shared externally as needed, it provides great convenience in terms of both communication and security.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The device receives voice input from the user. When the user says, "What's the weather like today?", the device's microphone captures the voice data. The input is an analog voice signal. This voice data is converted into text data using a speech recognition system. Specifically, a speech recognition API is used to perform the data conversion, and the output is the text information "What's the weather like today?".

[0681] Step 2:

[0682] The device analyzes the converted text data using natural language processing. The input is the text "What's the weather like today?". A generative AI model is used to analyze the data and understand the user's intent. The data analysis results in the interpretation that the user is seeking weather information. This information is used in the next step.

[0683] Step 3:

[0684] The terminal sends a request to the server based on the data it has analyzed. The server receives this request and consults databases and APIs for collecting weather information. The input is a request for weather information, and the output is the latest weather information. Specifically, the server retrieves weather data from external sources, integrates it, and prepares a response.

[0685] Step 4:

[0686] The server generates an appropriate response and sends that data to the terminal. The input is text data containing weather information. After generating the response, the output will be a specific message such as "Today's weather is sunny and the temperature is 25 degrees." The server forwards this message to the terminal.

[0687] Step 5:

[0688] The terminal converts response data received from the server into speech. It uses speech synthesis to convert text into speech output. The input is the text data "Today's weather is sunny and the temperature is 25 degrees," and the output is synthesized speech. The terminal fulfills its role in providing information by conveying this speech to the user.

[0689] Step 6:

[0690] The terminal monitors user actions in real time using gesture recognition. If unusual actions are detected, the data is sent to the server. The input is user action information, and through processing, abnormal actions are identified, and an abnormal warning log is generated as output. The terminal checks for unusual actions and, if an abnormality is found, sends that information to the server.

[0691] Step 7:

[0692] The server generates a monitoring log using an information recording device based on the anomaly warning log, and notifies external family members or others using an information transmission device. The input is the anomaly warning log, and the output is a monitoring log containing the information. The server generates the monitoring log and sends it to a remote recipient, allowing a third party to understand the situation.

[0693] (Application Example 1)

[0694] 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".

[0695] To ensure that elderly people can live their daily lives with peace of mind, there is a need for systems that facilitate communication and quickly detect and notify external parties of abnormalities. However, conventional technologies have insufficient accuracy in voice recognition and anomaly detection, and often require operations that are unfamiliar to the elderly. As a result, there is a challenge in that the safety and smooth daily lives of the elderly have not been fully achieved.

[0696] 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.

[0697] In this invention, the server includes speech recognition means for converting the voice input of an elderly person into text data, natural language processing means for analyzing the text data and generating a response, and anomaly detection and log generation means based on gesture recognition. This enables natural dialogue using the voice of an elderly person, as well as rapid detection and notification of anomalies.

[0698] "Speech recognition means" refers to a device or system for converting human speech into digital speech data and processing this data as text data.

[0699] "Natural language processing means" refers to a technology for analyzing text data and generating appropriate responses for a specific purpose, and is a part of a computer program.

[0700] "Speech synthesis means" refers to a technology that generates and outputs speech based on text data, and is a means of conveying responses in a dialogue system to the user.

[0701] "Gesture recognition means" refers to a device or software that detects a user's physical movements and determines the type and abnormality of those movements.

[0702] A "log generation method" is a technology for creating, saving, or transmitting logs for record-keeping purposes based on data detected within a system.

[0703] "Log transmission means" refers to a technology or device that transmits generated logs to external parties or systems, and is used for notification purposes.

[0704] "Means for generating responses that support the safety and lives of the elderly" refers to the system component that generates and provides information related to the daily lives and safety of the elderly based on user input.

[0705] To implement this invention, collaboration between a terminal, a server, and a user is necessary. First, the terminal has built-in hardware such as a microphone, camera, and accelerometer to monitor the user's voice and movements in real time. This terminal uses speech recognition means to convert the user's voice into digital voice data, and then converts it into text data via the Google Speech-to-Text API. Furthermore, it uses the Python NLTK library, a natural language processing means, to analyze the text data and generate responses that meet the needs of the elderly person.

[0706] The server uses a generative AI model to send the generated response to the terminal. For example, if the server receives the prompt "I want to know my schedule for tomorrow," it first searches the calendar database and constructs a response such as "You have a hospital appointment at 9am tomorrow." Then, it converts this response into speech data using a speech synthesis system and sends it back to the user.

[0707] Furthermore, the device uses gesture recognition to analyze data from cameras and sensors and monitor the user's physical movements. When unusual movements are detected, the server immediately analyzes the anomaly and generates a monitoring log. The generated log is sent to family members or care services via a log transmission system to ensure the safety of the elderly.

[0708] For example, if a user loses their balance while trying to stand up, the gesture recognition system detects this, and the server sends a warning message such as "Grandpa has lost his balance" to the family. This allows family members who are far away to be aware of the elderly person's situation.

[0709] Examples of prompt messages are as follows:

[0710] Voice: "I want to know what tomorrow's schedule is."

[0711] System: "Checking the calendar... I have a hospital appointment tomorrow at 9am."

[0712] In this way, advanced speech recognition and natural language processing technologies can be used to realize two-way interaction with the user and monitoring functions.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The device acquires the user's voice through the microphone. Analog audio data is obtained as input, and this data is converted into digital data using an ADC (analog-to-digital converter). The resulting digital data is prepared for speech recognition processing in the next step.

[0716] Step 2:

[0717] The device uses the Google Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, which is then processed into text data by the API. The resulting text data represents the user's requests and questions.

[0718] Step 3:

[0719] The server receives text data and performs natural language processing using Python's NLTK library. The input is text data, and the server generates a corresponding response by analyzing the language structure. Generative AI models are utilized to consider the response from various angles. The data output in this process contains information that the system should respond with.

[0720] Step 4:

[0721] The server sends the generated response back to the terminal, where it is converted into audio data via a speech synthesis system. The input here is the response text data, which is then synthesized by the speech synthesis software to generate the audio data. The output is the audio data that the user can listen to.

[0722] Step 5:

[0723] The terminal outputs the generated audio data to the user through the speaker. The synthesized audio data is emitted by the speaker as a nominal tone, thereby providing the user with a response from the system.

[0724] Step 6:

[0725] The device uses a camera and accelerometer to monitor the user's gestures in real time. The input is motion data from the sensors, which is analyzed by a gesture recognition algorithm. If a unique gesture is detected, the information is sent to the next step.

[0726] Step 7:

[0727] The server receives gesture data and analyzes it for anomalies. The input is recognized gesture data, which is analyzed using an anomaly detection algorithm. If an anomaly is detected as a result of the analysis, the server generates a monitoring log based on this.

[0728] Step 8:

[0729] The server generates monitoring logs and sends them to external family members or caregivers via a log transmission system. The input is the generated log data, which is sent to recipients via email or a dedicated app. The output is the log message received by family members or caregivers, which accurately conveys the elderly person's condition.

[0730] 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.

[0731] The present invention includes a speech recognition means that receives voice input from a user and converts it into text data. The terminal sends this text data to a server, where it is analyzed using natural language processing means. Based on the analysis results, the server evaluates the user's intentions and emotions and recognizes the user's emotional state using an emotion engine. The server generates a response with information including the user's emotional state and sends it to the terminal. The terminal uses speech synthesis means to convey this response to the user as voice.

[0732] In addition, the device uses gesture recognition to monitor and analyze the user's physical movements, detecting unusual movements and emotional changes. The server combines the gesture recognition results with the emotional state determined by the emotion engine to identify anomalies and create a monitoring log. The monitoring log also includes the user's emotional information and is sent to family members and other relevant parties using a log transmission device. This allows family members to understand both the user's physical and emotional state.

[0733] For example, if a user says, "I feel lonely today," the device converts the audio into text and sends it to the server. The server uses voice analysis to recognize that the user is feeling lonely, and generates a gentle response appropriate to that state. The device communicates this response to the user and simultaneously continues to monitor the user's actions through gesture recognition. If the user sighs or makes other similar actions, the device detects this and sends the information to the server. Based on this, the server can update the monitoring log and notify family members to take measures to prevent the user from becoming emotionally isolated.

[0734] Thus, the present invention makes it possible to achieve natural communication with users and provide comprehensive monitoring services using a combination of voice, motion, and emotion data. Through interaction with the pet robot, users can gain a sense of security in their daily lives, and an environment can be created that reduces feelings of isolation among the elderly.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user speaks to the pet robot. The device uses its built-in microphone to record the user's voice.

[0738] Step 2:

[0739] The device inputs the recorded audio into a speech recognition system and converts the audio into text data.

[0740] Step 3:

[0741] The terminal sends the converted text data to the server. The server uses natural language processing to analyze this text data and interpret the user's intent.

[0742] Step 4:

[0743] The server runs an emotion engine and determines the user's emotional state from the analysis results. The emotion engine infers emotions by analyzing specific keywords and tones.

[0744] Step 5:

[0745] The server generates an appropriate response based on the user's intent and emotional state. The response is created using a generative AI model and is tailored to the user's emotions.

[0746] Step 6:

[0747] The server sends the generated response to the terminal. The terminal uses speech synthesis to output this response as audio and convey it to the user.

[0748] Step 7:

[0749] The device uses gesture recognition to monitor the user's body movements in real time via a camera. It analyzes unusual movements and movements that may influence emotions.

[0750] Step 8:

[0751] The device sends data obtained from emotion recognition and gesture recognition to the server. Based on this data, the server determines if the user is abnormal. At the same time, it generates a monitoring log.

[0752] Step 9:

[0753] The server sends monitoring logs, including emotional information, to the family via a log transmission system. The family can review the logs and understand the user's emotions and health status.

[0754] (Example 2)

[0755] 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".

[0756] There is a need for a system that can recognize human voice and movements in real time, detect emotions and abnormal behavior, and respond appropriately. Conventional technologies often perform voice recognition and gesture recognition separately, making integrated responses difficult. Furthermore, the lack of monitoring systems that can quickly reflect emotional changes has prevented effective support for human safety and security.

[0757] 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.

[0758] In this invention, the server includes an information processing device comprising: signal conversion means for receiving an acoustic signal and converting the acoustic signal into character data; language processing means for analyzing the character data and generating a related response; and motion recognition means for monitoring human movements in real time and analyzing unusual movements. This enables comprehensive recognition using both voice and motion, allowing for rapid and accurate detection and response to human emotional states and abnormal behavior, and providing a safe and comfortable living environment.

[0759] An "information processing device" is a device that receives and analyzes acoustic signals and operational data, and processes them as needed.

[0760] An "acoustic signal" is data that electrically represents human speech and other sounds.

[0761] A "signal conversion means" is a means that receives an acoustic signal as input and converts it into appropriate character data.

[0762] "Character data" refers to text-formatted data obtained as a result of converting an acoustic signal.

[0763] A "language processing means" is a means that has the function of analyzing character data and generating an appropriate response based on the results of that analysis.

[0764] A "motion recognition means" is a means that has the function of monitoring human body movements in real time and detecting and analyzing specific movements.

[0765] An "abnormality" refers to behavior or a state that differs from normal operation or circumstances, and indicates a potential problem or risk.

[0766] A "management record" is a collection of information generated based on motion recognition means and other sensor data, which records the state and abnormalities of the monitored object.

[0767] A "record transmission means" is a means that has the function of transmitting the generated management records to an external receiving device or related system.

[0768] This invention illustrates an embodiment of a system that recognizes human voice and actions and provides a corresponding response. The specific implementation method is described below.

[0769] First, when a user speaks into the device, the device receives an acoustic signal. This acoustic signal is then converted into text data by the device's built-in speech recognition technology. Speech recognition software is typically used in this process. For example, the "Google Speech-to-Text API" can be used.

[0770] Next, the data converted to text is sent from the terminal to the server. The server analyzes this text data using natural language processing (NLP) tools. By using software such as an emotion engine for analysis, it is possible to understand the user's emotional state and intentions.

[0771] Based on the analysis results, the server generates a relevant response. This response is constructed by a generative AI model within the server and takes into account the user's emotions and intentions. A concrete example of a prompt might be, "The user is feeling sad. What kind of comforting response should be provided?"

[0772] The generated response is sent from the server to the terminal. The terminal then uses speech synthesis to provide this response back to the user as an acoustic signal. For speech synthesis, software that generates speech from text, such as "Amazon Polly," is used.

[0773] Furthermore, the terminal uses gesture recognition to monitor user actions in real time. For example, if a user sighs, the terminal detects this as an unusual action and resends the information to the server. Gesture recognition devices such as "Microsoft Kinect" are used for this action recognition.

[0774] The server combines transmitted behavioral information with emotional information obtained from voice analysis to determine if there are any abnormalities and generates management records. These records, including the user's emotional data, are sent as logs to external monitors, such as family members or related parties, enabling a comprehensive understanding of the person's emotional and physical state.

[0775] In this way, this invention enables natural communication with users and provides monitoring services, offering safety and peace of mind in daily life.

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The user speaks into the device. The device receives this voice as an acoustic signal. This acoustic signal is input data and is converted into text data using speech recognition technology. This data processing converts the acoustic signal into a parseable text format.

[0779] Step 2:

[0780] The terminal sends the converted text data to the server. The server receives this text data as input and performs analysis using natural language processing (NLP) software. The analysis involves data calculations to identify the user's intentions and emotional state. The analysis results provide the information necessary for response generation.

[0781] Step 3:

[0782] The server uses a generative AI model based on the analysis results to generate an appropriate response. This process utilizes a generative model that uses prompt statements. The generated response becomes output data and is sent from the server to the terminal.

[0783] Step 4:

[0784] The terminal converts the response received from the server into an acoustic signal using speech synthesis. In this process, the text-formatted response is used as input data, and data processing is performed to convert it into an acoustic signal as output data. Finally, the generated acoustic signal is transmitted to the user.

[0785] Step 5:

[0786] The terminal uses gesture recognition to monitor the user's physical movements in real time. For example, it detects actions such as the user sighing as unique actions. This action information is processed by the terminal as input data and sent to the server.

[0787] Step 6:

[0788] The server combines information obtained from gesture recognition with the previously analyzed emotional state to determine if there is an anomaly. Based on whether or not an anomaly is detected, it generates a management record and sends it externally as a monitoring log. This record is then sent to family members and other relevant parties as output data.

[0789] (Application Example 2)

[0790] 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".

[0791] In modern, real-world conversational environments, accurately understanding a user's emotional state from their voice and actions, and providing appropriate responses and guidance based on that understanding, is difficult. This issue is particularly important in physical stores, where providing optimal service tailored to the customer's emotions is crucial. However, conventional conversational systems often fail to capture subtle changes in facial expressions and voice, resulting in inappropriate responses. There is a need to address these challenges.

[0792] 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.

[0793] In this invention, the server includes emotion analysis means for evaluating the customer's emotional state and generating optimal response guidance; adaptive response means for sensing changes in the customer's emotions and providing corresponding guidance; and log generation means for detecting anomalies through voice and gesture recognition, generating monitoring logs, and transmitting them externally. This enables natural and appropriate conversational responses that take customer emotions into consideration in physical stores.

[0794] "Speech recognition means" refers to a device or process that has the function of receiving human speech input and converting it into text data.

[0795] "Natural language processing means" refers to technologies that analyze text data and generate corresponding responses.

[0796] "Speech synthesis means" refers to a technology for outputting the generated response as speech.

[0797] "Gesture recognition means" is a technology that monitors user movements in real time and analyzes unusual movements.

[0798] The "log generation method" is a technology that detects anomalies based on the results of gesture recognition and generates monitoring logs.

[0799] "Log transmission means" refers to technology for transmitting generated monitoring logs to an external source.

[0800] "Emotional analysis means" refers to technology that evaluates a customer's emotional state and generates the most appropriate response and guidance.

[0801] "Adaptive response methods" are technologies that detect changes in a customer's emotions and provide appropriate guidance accordingly.

[0802] This invention can be applied to dialogue systems in physical stores. In particular, it is useful for building systems that combine speech recognition and gesture recognition to understand the emotional state of customers and respond accordingly.

[0803] The system is implemented in a customer service robot, which captures customer voices with a microphone and converts them into text data using speech recognition technology (such as Google Cloud Speech-to-Text). The converted text data is sent to a server, where it is analyzed using natural language processing technology (such as OpenAI's GPT model) to determine the customer's intentions and emotions. Furthermore, emotion analysis technology (such as IBM Watson Tone Analyzer) is used to evaluate the customer's emotional state.

[0804] Based on the evaluation results, an optimized response is generated by a speech synthesis system and presented to the customer as voice by the robot. In parallel, the customer's gestures are monitored by a camera mounted on the terminal, and the movements are analyzed by a gesture recognition system (TensorFlow, OpenCV, etc.). Unusual movements are recorded by a log generation system and transmitted externally as a monitoring log. This allows the adaptive response system to detect changes in the customer's emotions and provide appropriate guidance.

[0805] For example, if a user says, "I'm not feeling very energetic today, so I'd like some recommendations for products that can help me relax," the system can understand their intention and respond with something like, "These relaxation products are very popular right now."

[0806] An example of a prompt message could be set as follows: "If a user says, 'I'm tired today, so I want to easily get the ingredients for dinner,' what kind of suggestion can be made?" In this way, the present invention provides a means to improve customer service in physical stores.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The device captures the user's voice using its microphone. The input is the user's voice, which is then converted into text data using speech recognition technology (Google Cloud Speech-to-Text). The converted text data becomes the output.

[0810] Step 2:

[0811] The terminal sends the acquired text data to the server. The server uses natural language processing tools (OpenAI's GPT model) to analyze the text data and interpret the user's intentions and requests. Information generated based on this analysis is then output.

[0812] Step 3:

[0813] The server evaluates the user's emotional state using an emotion analysis tool (IBM Watson Tone Analyzer) based on the analysis results. The input is the result of natural language processing, and the evaluated emotion data is the output.

[0814] Step 4:

[0815] Based on emotional data, the server generates the optimal response. A speech synthesis system then generates this response as audio data and sends it to the terminal. The output is audio data.

[0816] Step 5:

[0817] The device plays the generated audio data as audio to the user. By receiving this audio, the user can receive guidance and suggestions.

[0818] Step 6:

[0819] The system monitors the user's gestures using the device's camera. Gesture recognition tools (TensorFlow, OpenCV) are used to detect unusual movements and changes in customer emotion. The input to this process is camera footage, and the output is analyzed motion data.

[0820] Step 7:

[0821] The server determines if there is an anomaly based on the gesture analysis results and generates a monitoring log using the log generation mechanism. The monitoring log also includes emotional information and is sent to external parties using the log transmission mechanism. The output is the generated log data.

[0822] 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.

[0823] 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.

[0824] 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 robot 414.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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 as being incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A speech recognition means that receives human voice input and converts the voice into text data,

[0846] A natural language processing means that analyzes the aforementioned text data and generates a corresponding response,

[0847] A speech synthesis means that outputs the aforementioned response as speech,

[0848] A gesture recognition system that monitors user movements in real time and analyzes unusual movements,

[0849] A log generation means that detects anomalies based on the results of the gesture recognition and generates a monitoring log,

[0850] A log transmission means for transmitting the aforementioned monitoring log to an external source,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the speech recognition means uses a generative model to engage in natural dialogue with the user.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the gesture recognition means has means for identifying the user's physical movements and controlling physical actions based thereon.

[0856] "Example 1"

[0857] (Claim 1)

[0858] A speech recognition means that receives voice input and converts the voice into text information,

[0859] A natural language processing means that analyzes the aforementioned character information and generates a corresponding response,

[0860] A speech synthesis means that outputs the aforementioned response as speech,

[0861] A gesture recognition system that monitors the user's movements in real time and analyzes unusual movements,

[0862] Information recording means that detects anomalies based on the results of the gesture recognition and generates a monitoring log,

[0863] Information transmission means for transmitting the aforementioned monitoring log to an external party,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, wherein the speech recognition means uses a generative model to engage in natural dialogue with the user.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the gesture recognition means has means for identifying the user's physical movements and controlling a physical response based thereon.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A speech recognition means that receives human voice input and converts the voice into text data,

[0872] A natural language processing means that analyzes the aforementioned text data and generates a corresponding response,

[0873] A speech synthesis means that outputs the aforementioned response as speech,

[0874] A gesture recognition system that monitors user movements in real time and analyzes unusual movements,

[0875] A log generation means that detects anomalies based on the results of the gesture recognition and generates a monitoring log,

[0876] A log transmission means for transmitting the aforementioned monitoring log to an external source,

[0877] A means of generating responses that support the safety and lives of the elderly,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the speech recognition means uses a generative model to engage in natural dialogue with the user and provides security notifications.

[0881] (Claim 3)

[0882] The system according to claim 1, wherein the gesture recognition means has means for identifying the user's physical movements, controlling physical actions based on those movements, and providing external notifications in the event of an abnormality.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] The information processing device includes signal conversion means that receives an acoustic signal and converts the acoustic signal into character data,

[0886] Language processing means for analyzing the aforementioned character data and generating related responses,

[0887] A signal generation means that outputs the aforementioned response as an acoustic signal,

[0888] A motion recognition means that monitors human movements in real time and analyzes unusual movements,

[0889] A record generation means that detects anomalies based on the results of the aforementioned motion recognition and generates a management record,

[0890] A record transmission means for transmitting the aforementioned management record to an external party,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the signal conversion means uses a generative model to realize natural interaction with humans.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the motion recognition means has means for identifying human body movements and controlling physical operations based thereon.

[0896] "Application example 2 of combining emotional engines"

[0897] (Claim 1)

[0898] A speech recognition means that receives human voice input and converts the voice into text data,

[0899] A natural language processing means that analyzes the aforementioned text data and generates a corresponding response,

[0900] A speech synthesis means that outputs the aforementioned response as speech,

[0901] A gesture recognition system that monitors user movements in real time and analyzes unusual movements,

[0902] A log generation means that detects anomalies based on the results of the gesture recognition and generates a monitoring log,

[0903] A log transmission means for transmitting the aforementioned monitoring log to an external source,

[0904] An emotion analysis tool that evaluates the customer's emotional state and generates the optimal response and guidance,

[0905] Adaptive response methods that sense and respond to changes in customer emotions during real-world interactions,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, wherein the speech recognition means uses a generative model to engage in natural conversation with a customer.

[0909] (Claim 3)

[0910] The system according to claim 1, wherein the gesture recognition means has means for identifying the customer's physical movements and controlling physical actions based thereon. [Explanation of Symbols]

[0911] 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 speech recognition means that receives human voice input and converts the voice into text data, A natural language processing means that analyzes the aforementioned text data and generates a corresponding response, A speech synthesis means that outputs the aforementioned response as speech, A gesture recognition system that monitors user movements in real time and analyzes unusual movements, A log generation means that detects anomalies based on the results of the gesture recognition and generates a monitoring log, A log transmission means for transmitting the aforementioned monitoring log to an external source, A system that includes this.

2. The system according to claim 1, wherein the speech recognition means uses a generative model to engage in natural dialogue with the user.

3. The system according to claim 1, wherein the gesture recognition means has means for identifying the user's body movements and controlling physical actions based thereon.

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