system

Abstract

We provide the system. [Solution] A motion detection means for detecting the user's actions, The motion detection means detects the user's actions and initiates a conversation, and the voice acquisition means acquires voice information. A data management system that digitizes acquired voice information and compares it with accumulated past conversation data, A response generation means that generates an appropriate response based on past conversation data, A response providing means that provides the generated response to the user, An abnormality notification means that notifies of an abnormality if no operation is detected for a predetermined period of time, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern aging societies, many elderly people are forced to live alone, and loneliness and decline in cognitive function have become major problems. Also, it is difficult for family members to constantly watch over the elderly, and there are also concerns about safety. There is a need to solve these problems and provide an environment in which the elderly can live with peace of mind.

Means for Solving the Problems

[0005] This invention is a system for supporting elderly people living alone, and includes motion detection means for detecting the user's movements. Furthermore, it includes voice acquisition means that initiates a conversation and acquires voice information when motion is detected. The acquired voice information is converted into data and managed by data management means that compares it with past conversation data. This allows the system to continuously provide the user with appropriate responses based on past conversation data via response generation means. In addition, if no motion is detected for a predetermined period of time, an abnormality notification means is used to notify the user of the abnormality, thereby enabling monitoring. This invention makes it possible to reduce feelings of loneliness, alleviate symptoms of dementia, and ensure safety.

[0006] "Motion detection means" refers to a function that senses the user's movements and allows the system to recognize that state.

[0007] "Voice acquisition means" refers to a function that picks up the user's voice and inputs that voice data into the system for analysis.

[0008] "Data management means" refers to a function that converts acquired audio information into text, compares it with past conversation content, and stores and manages it in a database.

[0009] The "response generation means" is a function that generates an appropriate and natural response based on data accumulated by the data management means.

[0010] "Response provision means" refers to a function that conveys the generated response to the user by voice or other means.

[0011] An "anomaly notification mechanism" is a function that transmits information to pre-configured contacts when an anomaly is detected under predetermined conditions.

[0012] "Communication means" refers to functions that make information ready for transmission and reception, and are used to transmit data via networks, etc. [Brief explanation of the drawing]

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

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

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

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. [[ID=1']]

[0018] In the following embodiments, the numbered 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.

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0034] The present invention is a system for monitoring and assisting elderly people in conversation, and includes motion detection means, voice acquisition means, data management means, response generation means, and abnormality notification means. The terminal has a built-in motion sensor for detecting user movements and automatically activates the system when a user passes nearby.

[0035] The device picks up the user's voice using a microphone and converts that voice into digital data using a voice acquisition system. This digital data is sent to a server, where it is analyzed as text data by a data management system on the server. Here, it is compared with previously stored conversation data to identify what the user has said in the past and the flow of those conversations. This algorithm is designed to take the user's conversation history into consideration while also being able to respond flexibly to new conversations.

[0036] The response generation mechanism generates natural-sounding responses based on analyzed conversation data. This allows for the inclusion of empathetic expressions that remind the user of what they have previously said. The generated responses are provided to the user as audio in real time via the terminal.

[0037] Furthermore, the device is designed to automatically power off to conserve energy if no user activity is detected for a certain period of time (e.g., 10 minutes or more). On the other hand, the anomaly notification system sends a notification to pre-configured family members via the server if it detects no activity for a predetermined period of time (e.g., 1 hour or more). This notification warns that the user's safety may have been compromised.

[0038] For example, if a user says, "I went for a walk yesterday," the server will remember that information and, the next time the topic of "walking" comes up, will respond naturally with, "You went yesterday too, didn't you?" In this way, the present invention aims to alleviate the psychological loneliness of the elderly and strengthen family support for monitoring them.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device uses a built-in motion sensor to detect user movement. When a user enters the sensor's range, the system automatically activates and switches to voice acquisition mode.

[0042] Step 2:

[0043] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The audio data is then processed by the audio acquisition method.

[0044] Step 3:

[0045] The terminal sends the converted audio data to the server. The server analyzes the audio data into text data using data management tools and compares it with past conversation records.

[0046] Step 4:

[0047] Based on the accumulated conversation data, the server identifies information related to the user's statements and applies an algorithm to construct an appropriate response.

[0048] Step 5:

[0049] The response generation mechanism runs on the server and generates an automated response that takes into account the user's past statements and the current context. This response is then sent from the server to the terminal.

[0050] Step 6:

[0051] The device uses speech synthesis technology to convey received responses to the user. This allows for natural conversation with the user, including appropriate responses and empathetic expressions.

[0052] Step 7:

[0053] The device continuously monitors sensor activity and automatically turns off the power and enters standby mode if no activity is detected for more than 10 minutes.

[0054] Step 8:

[0055] The server receives a notification that no activity has been detected for a certain period of time (for example, 1 hour). It then uses an anomaly notification mechanism to send an alert to pre-configured family contacts.

[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 alleviate the psychological loneliness experienced by the elderly and to create an environment where family members and caregivers can monitor them with peace of mind even from a distance. Furthermore, there is a need for an effective system that naturally recognizes the user's actions and speech and supports appropriate communication. In addition, notifications must be provided to enable a rapid response to any abnormal situations.

[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 sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses in natural language, a response provision device, and an anomaly notification device. This enables the rapid and natural detection of user actions to initiate a conversation, and the generation and provision of appropriate responses based on the accumulated conversation history. Furthermore, it enables prompt response by notifying of anomalies when necessary.

[0061] A "sensing device" is a device that uses sensor technology to detect user movements and plays a role in prompting the system to start up.

[0062] A "voice collection device" is a device equipped with a microphone that captures the user's speech and converts the voice information into digital data.

[0063] A "data processing device" is a device that has a data management function for converting acquired audio data into text data and comparing it with past conversation history.

[0064] A "response generation device" is a device that executes algorithms and models to generate appropriate responses based on accumulated conversation data.

[0065] "Natural language response generation means" refers to language processing technology that provides users with natural conversation based on the generated data.

[0066] A "response provider" is a device equipped with an audio output function to deliver generated responses to users in real time.

[0067] An "abnormality notification device" is a device that has the function of detecting abnormalities when the user's actions do not meet predetermined conditions and notifying family members or caregivers.

[0068] This invention is a system for monitoring and supporting conversations with the elderly. This system is implemented by combining a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses using natural language, a response provision device, and an abnormality notification device.

[0069] The terminal is equipped with a sensing device that uses sensor technology to detect the user's movements. When the user moves within the range of the sensing device, the sensing device starts the system.

[0070] Next, the device's built-in voice acquisition device captures the user's speech through a microphone. This speech is recorded in analog format and converted into digital data as voice information. To perform this conversion process, speech recognition technology is required to convert speech into text data.

[0071] Subsequently, the digitized audio data is transmitted to a server via the network. A data processing unit on the server analyzes the data and compares it with previously accumulated conversation history. The conversation database records past responses and their contexts, and new responses are formed based on this. A response generator uses a generative AI model to create appropriate natural language responses based on the spoken content. These responses are meticulously designed to facilitate effective communication with the user.

[0072] For example, if a user says, "I went for a walk yesterday," the system records that statement and generates a response such as, "You went for a walk yesterday too, didn't you?" the next time the topic of walks comes up. This allows for a more familiar and supportive conversation for the user.

[0073] The generated response is transmitted to the user via voice using the terminal's response provider. Because this response is provided in real time, the user can enjoy a smooth conversational experience.

[0074] Furthermore, the device is equipped with a function that automatically turns off the power if it does not detect any activity for a predetermined period of time (for example, 10 minutes or more). This helps to reduce energy consumption and enables efficient operation.

[0075] Furthermore, the server's anomaly detection system considers a situation where no operation is detected for a certain period of time (for example, more than one hour) as an anomaly and sends a notification to pre-registered contacts. This notification quickly informs family members and others that something unusual may be happening to the user.

[0076] An example of a prompt for this system would be to instruct the generation AI model with a message like, "When the user says, 'I went for a walk yesterday,' generate an appropriate response."

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

[0078] Step 1:

[0079] The terminal uses a sensing device to detect the user's movements. The input is the user's physical movement. The sensing device captures this input as a trigger and generates a system activation signal. This causes the system to transition to an operational state and prepare for voice collection.

[0080] Step 2:

[0081] After startup, the terminal uses a speech collection device to acquire the user's speech. The input is the user's voice. The speech collection device performs speech recognition to convert the analog speech into digital data and saves it in a digital file format. This digital data is then prepared for subsequent text conversion processing.

[0082] Step 3:

[0083] The terminal transmits the generated digital audio data to the server via the network. The input is the digital audio data from the terminal. The server uses specialized speech recognition software to convert the received data into text. The output is in text format, which is used for data analysis processing.

[0084] Step 4:

[0085] The server analyzes text data using a data processing unit and compares it with past conversation history. The input is the transcribed conversation content. The processing unit compares this with past data in the database to identify matching topics and relevant contexts. The output of this matching process is the contextual information necessary for response generation.

[0086] Step 5:

[0087] The server uses a response generator to produce an appropriate response based on past conversation history and matching results. The input is the matched context information. A generative AI model is used to apply algorithms to construct natural and responsive conversations. The output of this step is the response message to be provided to the user in voice.

[0088] Step 6:

[0089] The terminal provides the user with a generated response message as voice using a response provider. The input is the response message generated by the server. It is delivered to the user as synthesized speech through the speaker built into the terminal. Real-time interaction is completed at this point.

[0090] Step 7:

[0091] The terminal will power off if no user activity is detected for a certain period of time (e.g., 10 minutes or more). The input is time elapsed information obtained from the motion detection device. The output of this control process is a power-off signal to reduce energy consumption.

[0092] Step 8:

[0093] The server uses an anomaly notification device to send an anomaly notification if no operation is detected for a predetermined period of time (e.g., more than one hour). The input is the operation detection log. A message informing the user of the anomaly is sent to pre-registered contacts through the notification system. This allows for immediate confirmation of the user's safety status.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In physical stores, there is a challenge in providing personalized service and individual attention to each customer who visits. In particular, there is a need for product suggestions based on a customer's past purchase history and for quick support when a customer is unsure of what to buy. Furthermore, traditional systems have struggled to effectively utilize customer movements and conversations to achieve flexible and natural dialogue.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes motion detection means, including a human presence sensor for detecting user movements; data management means, which converts acquired voice information into digital data, transmits it to the server, and compares it with previously stored conversation history; and response generation means, which generates appropriate responses using a generation AI model based on the database. This enables the provision of personalized services to customers in physical stores and allows for optimal product suggestions based on past purchase history. Furthermore, if a customer is hesitant for a long time, immediate notification can be sent to store staff, enabling effective support.

[0099] A "motion detection means" is a means of using sensors to detect the user's movements. This allows the system to automatically detect when a user is near it.

[0100] A "voice acquisition method" is a means of collecting the voice spoken by the user and converting it into digital data. This enables the initiation of conversations and the acquisition of information in real time.

[0101] A "data management system" is a means of transmitting acquired digital data to a server and comparing it with previously stored conversation data. This allows for the generation of appropriate responses based on the user's past speech history.

[0102] A "response generation means" is a method for generating appropriate responses to users using a generation AI model based on a database. This enables natural responses that take past conversations into account.

[0103] A "response provisioning means" is a means of providing the generated response to the user in audio format. This allows the user to receive information in a way that is easy for them to understand.

[0104] An "anomaly notification mechanism" is a means of notifying a designated contact person of an anomaly if the user's actions are not detected for a predetermined period of time. This enhances user safety management.

[0105] A "proposal generation method" is a means of providing customers with optimal product suggestions based on their past purchase history. This can be used to promote sales in stores.

[0106] A "digital conversion method" is a means of converting audio data into text data using a speech recognition API. This makes it possible to format audio information in a way that is suitable for recording and analysis.

[0107] In this embodiment, the terminal is installed in the store and detects the presence of a user using a built-in motion sensor when the user approaches. The terminal then automatically enters conversation mode and collects the user's voice. This voice is converted into digital data using a speech recognition API and sent to a server for data management. The server uses a generative AI model to generate an appropriate response based on previously accumulated conversation data. This response, based on past purchase history and conversation content, is provided to the user via voice through the terminal.

[0108] When a user requests product suggestions, the suggestion generation system refers to their past purchase history to suggest the most suitable products. The server precisely converts the user's voice into text data using a speech recognition API, and then matches it against a database. For example, if a user says they are looking for a new product, the server will generate a response such as, "You previously purchased this product. How about this new product?"

[0109] An anomaly notification system is also in place; if there is no user activity within a specified time, staff will be notified via communication. This allows for smoother customer support at physical stores.

[0110] Examples of prompt messages include the following:

[0111] "Refer to the customer's purchase history database and use that knowledge to generate the best possible response to the current conversation. Ensure the response is personalized based on past data."

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

[0113] Step 1:

[0114] The terminal detects user movement. Using a motion sensor, the terminal automatically activates the system when a user approaches. The input is the motion detection signal from the sensor, and the output is the system activation signal. This activates the terminal from standby mode.

[0115] Step 2:

[0116] The device acquires the user's voice. Using the microphone, it digitizes the voice data emitted by the user and sends this data to the server. The input is the user's voice, and the output is digital voice data. This makes the voice information available for remote processing.

[0117] Step 3:

[0118] The server converts digital audio data into text data. It uses a speech recognition API (e.g., Google® Cloud Speech-to-Text API) to convert the audio data into text format. The input is digital audio data, and the output is text data. This text data serves as the basis for further processing.

[0119] Step 4:

[0120] The server compares text data with past conversation data. Using data management tools, it prepares to generate appropriate responses based on past conversation history. The input is the current text data, and the output is the result of the comparison with past data. This allows the server to understand the user's context.

[0121] Step 5:

[0122] The server generates responses using a generative AI model. Based on the prompt, the generative AI model interprets the text data and generates a natural-sounding response. The input is the prompt and the matching result, and the output is the generated response. This ensures that a personalized and appropriate response is prepared for the user.

[0123] Step 6:

[0124] The terminal provides the generated response to the user in audio format. Using a response delivery mechanism, the terminal outputs the response as audio that the user can hear. The input is response data from the server, and the output is an audio response. This allows the user to receive immediate feedback.

[0125] Step 7:

[0126] The server detects anomalies and sends notifications as needed. Through the anomaly notification system, if no user activity is detected for a predetermined period of time, an anomaly is notified to a designated contact. The input is activity detection information, and the output is an anomaly notification signal. This ensures user safety.

[0127] 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.

[0128] This invention is an advanced conversation and monitoring support system that incorporates emotion recognition for the user. In addition to motion detection means, voice acquisition means, data management means, response generation means, and anomaly notification means, this system includes an emotion engine that recognizes the user's emotions.

[0129] The terminal first detects user movement using a motion sensor and activates the system when it determines that the user has entered a range. When the user speaks, the terminal uses a microphone to capture the voice, and a voice acquisition device converts this into digital data. The converted voice data is sent to a server, where a data management device converts it back into text.

[0130] The server compares the text data analyzed by the data management system with past conversation history. This process enables contextual understanding based on past conversations. Next, the emotion engine identifies the user's emotions from the speech. The emotion engine evaluates the features of the analyzed speech and identifies emotions such as joy, sadness, and anger.

[0131] The response generation mechanism prepares a response that matches the user's emotions based on the emotional information identified by the emotion engine. The server selects the optimal response and sends it to the terminal. The terminal provides the received response to the user, enabling a natural conversation that takes emotions into consideration.

[0132] Furthermore, the device has a function that automatically turns off the power if no activity is detected for a certain period of time (e.g., 10 minutes or more) to ensure energy efficiency. On the other hand, if no activity is detected for a predetermined period of time, the abnormality notification system sends a warning to pre-configured family members or related parties via the server. This ensures the user's safety and enables a quick response.

[0133] For example, if a user speaks in an emotionally sad voice saying, "I'm feeling a little lonely today," the emotion engine recognizes the "sadness," and the response generation means selects a response in a gentle tone, such as, "I hope something cheers you up. Shall we continue talking?" In this way, the present invention aims to enhance the user's psychological support and provide a richer communication experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device uses a motion sensor to detect user movement. When a user enters the sensor's range, the device activates its system and switches to voice acquisition mode.

[0137] Step 2:

[0138] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The voice acquisition device performs this conversion.

[0139] Step 3:

[0140] The acquired audio data is immediately sent to the server, where the data management system converts the audio data into text data. This text data is then compared with past conversation history.

[0141] Step 4:

[0142] The server uses an emotion engine to analyze the user's emotional state from text data. It identifies emotions such as joy and sadness from the intonation and content of the voice.

[0143] Step 5:

[0144] The response generation mechanism generates an appropriate response based on the matching results and analyzed emotions. The generated response is designed to take the user's emotions into consideration and to facilitate a smooth conversation.

[0145] Step 6:

[0146] The server sends the generated response to the terminal, which then uses speech synthesis technology to provide the response to the user. The response is expressed in a way that reflects the user's emotions.

[0147] Step 7:

[0148] The device continuously monitors user activity, and if no activity is detected for more than 10 minutes, it automatically powers off and enters standby mode.

[0149] Step 8:

[0150] If no motion is detected by the sensor for a certain period of time or longer, the server uses an anomaly notification mechanism to send a warning to pre-configured contacts, such as family members, and reports the situation.

[0151] (Example 2)

[0152] 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".

[0153] In modern society, the increasing feelings of loneliness and stress experienced by individuals are a significant problem, but appropriate dialogue support systems for addressing these emotions are limited. Conventional technologies struggle to accurately recognize individual user emotions and provide corresponding responses, resulting in a lack of improved user experience. Furthermore, anomaly detection and appropriate response methods to ensure user safety are insufficient.

[0154] 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.

[0155] In this invention, the server includes motion detection means for detecting user actions, voice acquisition means for acquiring voice data, and emotion analysis means for analyzing emotions. This makes it possible to provide a dialogue system that builds deeper human relationships by accurately recognizing the emotions of individual users and providing natural responses based on those emotions. Furthermore, it can respond immediately to abnormal situations, thereby enhancing user safety.

[0156] "Motion detection means" refers to technology that uses sensors to detect physical movements occurring around the user.

[0157] "Voice acquisition means" refers to technology for collecting and digitizing the voice emitted by a user.

[0158] "Data management means" refers to technology used to appropriately process acquired digital audio data and compare it with past dialogue history.

[0159] "Sentiment analysis techniques" are technologies that evaluate the features of audio data to identify emotions such as joy, sadness, and anger.

[0160] "Response generation means" refers to technology for automatically generating appropriate responses to the user based on analysis results.

[0161] A "response delivery means" is a technology that transmits the generated response to the user and provides a natural dialogue.

[0162] An "anomaly warning mechanism" is a technology that notifies pre-configured contacts of an anomaly if no operation is detected within the system for a certain period of time or longer.

[0163] "Control means" refers to technologies for managing various functions of a system and adjusting power supply under specific conditions.

[0164] "Communication means" refers to technologies for transmitting abnormality or response information to users or related organizations in remote locations.

[0165] This invention is an advanced conversation support system that understands the user's emotions and provides dialogue accordingly. The system includes motion detection means, voice acquisition means, data management means, emotion analysis means, response generation means, response provision means, abnormality warning means, and control means.

[0166] Hardware and software configuration

[0167] The device utilizes infrared sensors and camera modules as motion detection means. This allows the system to activate when it detects that a user has entered the sensor's range. A microphone is used to acquire the user's voice, converting the analog audio into a digital signal. The converted digital audio data is then sent to a server either within the device or in the cloud.

[0168] The server converts received audio data into text using data management tools and compares it with past conversation history. Natural language processing techniques are used to deepen the understanding of context. For sentiment analysis, machine learning models are utilized to identify emotions from the audio waveform and text content.

[0169] The response generation mechanism generates several emotion-appropriate dialogue phrases based on a generation AI model. The server then selects an appropriate response and sends it to the terminal via the response delivery mechanism. The terminal then communicates the response to the user via voice through its speaker.

[0170] Furthermore, if no activity is detected for a certain period of time, the device will automatically switch to power-saving mode. In addition, an abnormality warning system will notify the user's family and related parties via communication if no activity is detected.

[0171] Specific example

[0172] For example, if a user says, "I'm feeling a little lonely today," the system receives the audio and, through emotion analysis, determines that the user is feeling "sad." Based on this result, the response generation system generates a gentle response such as, "I hope something cheers you up. Shall we continue talking?" In this way, the system provides a conversational experience aimed at providing psychological support to the user.

[0173] Example of a prompt

[0174] "Generate examples of appropriate dialogue for when a user is feeling lonely."

[0175] "Please provide examples of conversational responses based on the results of emotion recognition."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The terminal detects user movement using a motion sensor or camera. The system activates when the user enters the sensor range. At this stage, the input is the user's physical movement, and the output is the system activation signal.

[0179] Step 2:

[0180] When a user speaks, the device uses its built-in microphone to capture the voice. Since the captured voice is an analog signal, it is converted into a digital signal. The input is the user's voice data, and the output is the digitized voice data.

[0181] Step 3:

[0182] The digitized audio data is sent from the terminal to the server. The server uses data management tools to convert the audio data into text. In this step, the input is digital audio data, and the output is data in text format.

[0183] Step 4:

[0184] The server performs contextual analysis by comparing the transcribed data with past conversation history. This process uses natural language processing techniques, with text data as input and parsed data containing contextual information as output.

[0185] Step 5:

[0186] The server uses sentiment analysis tools to identify the user's emotions from the analyzed text data and voice features. The input is the analyzed data, and the output is the identified emotion information.

[0187] Step 6:

[0188] The response generation mechanism generates multiple response candidates based on emotional information. This process uses a generative AI model, where the input is emotional information and the output is a group of response candidates.

[0189] Step 7:

[0190] The server selects the best response from the generated candidate responses. The selection criteria reflect past conversation history and the user's sentiment. The input is the set of candidate responses, and the output is the selected response.

[0191] Step 8:

[0192] The selected response is sent to the terminal, which then communicates it to the user via voice through its speaker. The input is the selected response, and the output is the voice response to the user.

[0193] Step 9:

[0194] If no activity is detected for a certain period of time, the device will use an abnormality warning mechanism to send a warning message to a designated contact. The input is activity detection information, and the output is the warning message.

[0195] (Application Example 2)

[0196] 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."

[0197] Modern commercial facilities require personalized service for customers, but traditional customer service systems struggle to improve customer satisfaction because they cannot respond in a way that takes emotions and feelings into account. Furthermore, while rapid response to emergencies is crucial, the inability to assess urgency based on emotional changes results in inadequate responses.

[0198] 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.

[0199] In this invention, the server includes detection means for detecting movement, acquisition means for acquiring voice information, and management means for digitizing and managing the voice information. This makes it possible to recognize emotions from the customer's movements and voice, and to provide appropriate responses and anomaly notifications according to the situation.

[0200] "Detection means" refers to a device or mechanism for sensing a user's movements and determining their presence or actions.

[0201] "Acquisition means" refers to a mechanism for collecting voice information emitted by the user and further incorporating it as digital data.

[0202] "Management measures" refer to procedures for appropriately processing and managing data by saving acquired data and comparing it with past records.

[0203] "Generation method" refers to the process of creating appropriate and emotionally considerate responses for users based on managed data.

[0204] "Means of delivery" refers to the method of communicating the generated response to the user and providing an interactive experience.

[0205] A "notification method" is a communication protocol used to quickly send information to designated contacts when an anomaly is detected.

[0206] "Emotion recognition means" refers to technology that analyzes a user's voice and actions to determine and understand their emotional state.

[0207] This system includes detection means for sensing user actions, acquisition means for collecting voice information, and management means for processing data. The server processes the data in the following steps: First, hardware such as a microphone for voice acquisition and sensors for analyzing emotions are used. This captures the user's voice and actions in detail.

[0208] Audio data is digitized by the acquisition method, and the management method uses it to compare it with accumulated historical records. During this process, Google Speech-to-Text API is used for speech recognition, and Google Cloud Natural Language or Microsoft® Azure® Text Analytics are used for sentiment analysis. This process evaluates the features of the audio, identifying emotions such as joy, sadness, and anger.

[0209] The emotion recognition means uses that information to generate an appropriate response, which the delivery means then communicates to the user. This entire process enables dynamic responses and personalized service tailored to the customer's emotions in a physical store setting.

[0210] For example, if a customer in a store spends a long time browsing products but is unresponsive when spoken to, the emotion recognition system might determine that the customer is "tired." The robot could then gently ask, "You seem tired; is there anything I can help you with?" This is an example of natural conversation supported by a generative AI model.

[0211] An example of a prompt message would be, "Please suggest to the assistant how to provide customer service on a tiring day." This example allows for flexible responses tailored to the customer's situation.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] When a user enters the system's range, the terminal's motion detection system senses the user's movement. This detection triggers the system to activate. The input is the user's physical movement, and the output is the system's activation signal. This action prepares the system to proceed to the next step.

[0215] Step 2:

[0216] The device collects voice data using voice acquisition methods when the user speaks. The input is the user's voice information, which is converted into digital data. The output is the digitized voice data. This makes the voice data in an analyzable format.

[0217] Step 3:

[0218] The server uses a management system to compare digitized audio data with past records. Inputs are digital audio data and past conversation records, while output is information indicating the context in which the current conversation is taking place. Data processing creates a foundation for providing appropriate responses.

[0219] Step 4:

[0220] The server's emotion recognition system analyzes and identifies the user's emotions from the audio data. The input is the contextual information and audio features obtained in the previous step, and the output is the emotion (joy, sadness, anger, etc.) that the user is judged to be experiencing. Through data processing, the user's psychological state is clarified.

[0221] Step 5:

[0222] A response generation mechanism based on this mechanism creates an appropriate response within the server that corresponds to the user's emotions. The input is the user's emotional information and current context information, and the output is the specific response content to the user. The generated response enables natural conversation.

[0223] Step 6:

[0224] The terminal uses the provided means to present the generated response to the user. The input is the response content sent from the server, and the output is information displayed via audio or on the screen. The interaction with the user is completed upon the provision of this information.

[0225] Step 7:

[0226] If an anomaly is detected and no user activity is detected for a specified period (e.g., 10 minutes or more), the server's notification system will send an alert to the configured contact. The input is data indicating inactivity from the motion detection sensor, and the output is an alert message. This operation ensures user safety.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] The present invention is a system for monitoring and assisting elderly people in conversation, and includes motion detection means, voice acquisition means, data management means, response generation means, and abnormality notification means. The terminal has a built-in motion sensor for detecting user movements and automatically activates the system when a user passes nearby.

[0244] The device picks up the user's voice using a microphone and converts that voice into digital data using a voice acquisition system. This digital data is sent to a server, where it is analyzed as text data by a data management system on the server. Here, it is compared with previously stored conversation data to identify what the user has said in the past and the flow of those conversations. This algorithm is designed to take the user's conversation history into consideration while also being able to respond flexibly to new conversations.

[0245] The response generation mechanism generates natural-sounding responses based on analyzed conversation data. This allows for the inclusion of empathetic expressions that remind the user of what they have previously said. The generated responses are provided to the user as audio in real time via the terminal.

[0246] Furthermore, the device is designed to automatically power off to conserve energy if no user activity is detected for a certain period of time (e.g., 10 minutes or more). On the other hand, the anomaly notification system sends a notification to pre-configured family members via the server if it detects no activity for a predetermined period of time (e.g., 1 hour or more). This notification warns that the user's safety may have been compromised.

[0247] For example, if a user says, "I went for a walk yesterday," the server will remember that information and, the next time the topic of "walking" comes up, will respond naturally with, "You went yesterday too, didn't you?" In this way, the present invention aims to alleviate the psychological loneliness of the elderly and strengthen family support for monitoring them.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The device uses a built-in motion sensor to detect user movement. When a user enters the sensor's range, the system automatically activates and switches to voice acquisition mode.

[0251] Step 2:

[0252] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The audio data is then processed by the audio acquisition method.

[0253] Step 3:

[0254] The terminal sends the converted audio data to the server. The server analyzes the audio data into text data using data management tools and compares it with past conversation records.

[0255] Step 4:

[0256] Based on the accumulated conversation data, the server identifies information related to the user's statements and applies an algorithm to construct an appropriate response.

[0257] Step 5:

[0258] The response generation mechanism runs on the server and generates an automated response that takes into account the user's past statements and the current context. This response is then sent from the server to the terminal.

[0259] Step 6:

[0260] The device uses speech synthesis technology to convey received responses to the user. This allows for natural conversation with the user, including appropriate responses and empathetic expressions.

[0261] Step 7:

[0262] The device continuously monitors sensor activity and automatically turns off the power and enters standby mode if no activity is detected for more than 10 minutes.

[0263] Step 8:

[0264] The server receives a notification that no activity has been detected for a certain period of time (for example, 1 hour). It then uses an anomaly notification mechanism to send an alert to pre-configured family contacts.

[0265] (Example 1)

[0266] 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."

[0267] It is necessary to alleviate the psychological loneliness experienced by the elderly and to create an environment where family members and caregivers can monitor them with peace of mind even from a distance. Furthermore, there is a need for an effective system that naturally recognizes the user's actions and speech and supports appropriate communication. In addition, notifications must be provided to enable a rapid response to any abnormal situations.

[0268] 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.

[0269] In this invention, the server includes a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses in natural language, a response provision device, and an anomaly notification device. This enables the rapid and natural detection of user actions to initiate a conversation, and the generation and provision of appropriate responses based on the accumulated conversation history. Furthermore, it enables prompt response by notifying of anomalies when necessary.

[0270] A "sensing device" is a device that uses sensor technology to detect user movements and plays a role in prompting the system to start up.

[0271] A "voice collection device" is a device equipped with a microphone that captures the user's speech and converts the voice information into digital data.

[0272] A "data processing device" is a device that has a data management function for converting acquired audio data into text data and comparing it with past conversation history.

[0273] A "response generation device" is a device that executes algorithms and models to generate appropriate responses based on accumulated conversation data.

[0274] "Natural language response generation means" refers to language processing technology that provides users with natural conversation based on the generated data.

[0275] A "response provider" is a device equipped with an audio output function to deliver generated responses to users in real time.

[0276] An "abnormality notification device" is a device that has the function of detecting abnormalities when the user's actions do not meet predetermined conditions and notifying family members or caregivers.

[0277] This invention is a system for monitoring and supporting conversations with the elderly. This system is implemented by combining a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses using natural language, a response provision device, and an abnormality notification device.

[0278] The terminal is equipped with a sensing device that uses sensor technology to detect the user's movements. When the user moves within the range of the sensing device, the sensing device starts the system.

[0279] Next, the device's built-in voice acquisition device captures the user's speech through a microphone. This speech is recorded in analog format and converted into digital data as voice information. To perform this conversion process, speech recognition technology is required to convert speech into text data.

[0280] Subsequently, the digitized voice data is transmitted to the server via the network. The data processing device on the server analyzes the data and compares it with the conversation history accumulated in the past. The conversation database records past response contents and their contexts, and based on this, new responses are formed. The response generation device uses a generation AI model to create an appropriate natural language response based on the utterance content. This response is carefully designed to achieve effective communication with the user.

[0281] For example, when the user says "I went for a walk yesterday", the system records the utterance and generates a response such as "I went yesterday too" when the topic of the next walk comes up. This allows the user to feel a sense of familiarity and enables a supportive conversation to be realized.

[0282] The generated response is transmitted to the user in voice using the response providing device of the terminal. Since this response provision is performed in real time, the user can enjoy a smooth conversation experience. <​​​​​​​​​​​​​​​​ Step 1:

[0288] The terminal uses a sensing device to detect the user's movements. The input is the user's physical movement. The sensing device captures this input as a trigger and generates a system activation signal. This causes the system to transition to an operational state and prepare for voice collection.

[0289] Step 2:

[0290] After startup, the terminal uses a speech collection device to acquire the user's speech. The input is the user's voice. The speech collection device performs speech recognition to convert the analog speech into digital data and saves it in a digital file format. This digital data is then prepared for subsequent text conversion processing.

[0291] Step 3:

[0292] The terminal transmits the generated digital audio data to the server via the network. The input is the digital audio data from the terminal. The server uses specialized speech recognition software to convert the received data into text. The output is in text format, which is used for data analysis processing.

[0293] Step 4:

[0294] The server analyzes text data using a data processing unit and compares it with past conversation history. The input is the transcribed conversation content. The processing unit compares this with past data in the database to identify matching topics and relevant contexts. The output of this matching process is the contextual information necessary for response generation.

[0295] Step 5:

[0296] The server uses a response generator to produce an appropriate response based on past conversation history and matching results. The input is the matched context information. A generative AI model is used to apply algorithms to construct natural and responsive conversations. The output of this step is the response message to be provided to the user in voice.

[0297] Step 6:

[0298] The terminal provides the user with a generated response message as voice using a response provider. The input is the response message generated by the server. It is delivered to the user as synthesized speech through the speaker built into the terminal. Real-time interaction is completed at this point.

[0299] Step 7:

[0300] The terminal will power off if no user activity is detected for a certain period of time (e.g., 10 minutes or more). The input is time elapsed information obtained from the motion detection device. The output of this control process is a power-off signal to reduce energy consumption.

[0301] Step 8:

[0302] The server uses an anomaly notification device to send an anomaly notification if no operation is detected for a predetermined period of time (e.g., more than one hour). The input is the operation detection log. A message informing the user of the anomaly is sent to pre-registered contacts through the notification system. This allows for immediate confirmation of the user's safety status.

[0303] (Application Example 1)

[0304] 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."

[0305] In a physical store, there is a problem that it is difficult to provide personalized customer service by individually responding to each customer who visits. In particular, there is a need for product recommendations based on a customer's past purchase history and prompt support when the customer is undecided. Furthermore, it has been difficult for conventional systems to effectively utilize a customer's movements and conversations to achieve flexible and natural conversations.

[0306] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes motion detection means including a human sensor for detecting the motion of a user, data management means for converting the acquired voice information into digital data, transmitting it to the server, and collating it with the conversation history accumulated in the past, and response generation means for generating an appropriate response using a generated AI model based on a database. As a result, it becomes possible to provide individual services to customers in a physical store, and it becomes possible to make optimal product recommendations based on past purchase history. Also, when a customer is undecided for a long time, effective support can be realized by immediately notifying a store clerk.

[0308] The "motion detection means" is means for using a sensor to sense the motion of a user. Thereby, it is possible to automatically detect that the user is near the system.

[0309] The "voice acquisition means" is means for collecting the voice uttered by a user and converting it into digital data. Thereby, it becomes possible to start a conversation and acquire information in real time.

[0310] The "data management means" is means for transmitting the acquired digital data to the server and collating it with the conversation data accumulated in the past. Thereby, an appropriate response can be generated by referring to the past speech history of the user.

[0311] A "response generation means" is a method for generating appropriate responses to users using a generation AI model based on a database. This enables natural responses that take past conversations into account.

[0312] A "response provisioning means" is a means of providing the generated response to the user in audio format. This allows the user to receive information in a way that is easy for them to understand.

[0313] An "anomaly notification mechanism" is a means of notifying a designated contact person of an anomaly if the user's actions are not detected for a predetermined period of time. This enhances user safety management.

[0314] A "proposal generation method" is a means of providing customers with optimal product suggestions based on their past purchase history. This can be used to promote sales in stores.

[0315] A "digital conversion method" is a means of converting audio data into text data using a speech recognition API. This makes it possible to format audio information in a way that is suitable for recording and analysis.

[0316] In this embodiment, the terminal is installed in the store and detects the presence of a user using a built-in motion sensor when the user approaches. The terminal then automatically enters conversation mode and collects the user's voice. This voice is converted into digital data using a speech recognition API and sent to a server for data management. The server uses a generative AI model to generate an appropriate response based on previously accumulated conversation data. This response, based on past purchase history and conversation content, is provided to the user via voice through the terminal.

[0317] When a user requests product suggestions, the suggestion generation system refers to their past purchase history to suggest the most suitable products. The server precisely converts the user's voice into text data using a speech recognition API, and then matches it against a database. For example, if a user says they are looking for a new product, the server will generate a response such as, "You previously purchased this product. How about this new product?"

[0318] An anomaly notification system is also in place; if there is no user activity within a specified time, staff will be notified via communication. This allows for smoother customer support at physical stores.

[0319] Examples of prompt messages include the following:

[0320] "Refer to the customer's purchase history database and use that knowledge to generate the best possible response to the current conversation. Ensure the response is personalized based on past data."

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The terminal detects user movement. Using a motion sensor, the terminal automatically activates the system when a user approaches. The input is the motion detection signal from the sensor, and the output is the system activation signal. This activates the terminal from standby mode.

[0324] Step 2:

[0325] The device acquires the user's voice. Using the microphone, it digitizes the voice data emitted by the user and sends this data to the server. The input is the user's voice, and the output is digital voice data. This makes the voice information available for remote processing.

[0326] Step 3:

[0327] The server converts digital audio data into text data. It uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert the audio data into text format. The input is digital audio data, and the output is text data. This text data serves as the basis for further processing.

[0328] Step 4:

[0329] The server compares text data with past conversation data. Using data management tools, it prepares to generate appropriate responses based on past conversation history. The input is the current text data, and the output is the result of the comparison with past data. This allows the server to understand the user's context.

[0330] Step 5:

[0331] The server generates responses using a generative AI model. Based on the prompt, the generative AI model interprets the text data and generates a natural-sounding response. The input is the prompt and the matching result, and the output is the generated response. This ensures that a personalized and appropriate response is prepared for the user.

[0332] Step 6:

[0333] The terminal provides the generated response to the user in audio format. Using a response delivery mechanism, the terminal outputs the response as audio that the user can hear. The input is response data from the server, and the output is an audio response. This allows the user to receive immediate feedback.

[0334] Step 7:

[0335] The server detects anomalies and sends notifications as needed. Through the anomaly notification system, if no user activity is detected for a predetermined period of time, an anomaly is notified to a designated contact. The input is activity detection information, and the output is an anomaly notification signal. This ensures user safety.

[0336] 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.

[0337] This invention is an advanced conversation and monitoring support system that incorporates emotion recognition for the user. In addition to motion detection means, voice acquisition means, data management means, response generation means, and anomaly notification means, this system includes an emotion engine that recognizes the user's emotions.

[0338] The terminal first detects user movement using a motion sensor and activates the system when it determines that the user has entered a range. When the user speaks, the terminal uses a microphone to capture the voice, and a voice acquisition device converts this into digital data. The converted voice data is sent to a server, where a data management device converts it back into text.

[0339] The server compares the text data analyzed by the data management system with past conversation history. This process enables contextual understanding based on past conversations. Next, the emotion engine identifies the user's emotions from the speech. The emotion engine evaluates the features of the analyzed speech and identifies emotions such as joy, sadness, and anger.

[0340] The response generation mechanism prepares a response that matches the user's emotions based on the emotional information identified by the emotion engine. The server selects the optimal response and sends it to the terminal. The terminal provides the received response to the user, enabling a natural conversation that takes emotions into consideration.

[0341] Furthermore, the device has a function that automatically turns off the power if no activity is detected for a certain period of time (e.g., 10 minutes or more) to ensure energy efficiency. On the other hand, if no activity is detected for a predetermined period of time, the abnormality notification system sends a warning to pre-configured family members or related parties via the server. This ensures the user's safety and enables a quick response.

[0342] For example, if a user speaks in an emotionally sad voice saying, "I'm feeling a little lonely today," the emotion engine recognizes the "sadness," and the response generation means selects a response in a gentle tone, such as, "I hope something cheers you up. Shall we continue talking?" In this way, the present invention aims to enhance the user's psychological support and provide a richer communication experience.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The device uses a motion sensor to detect user movement. When a user enters the sensor's range, the device activates its system and switches to voice acquisition mode.

[0346] Step 2:

[0347] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The voice acquisition device performs this conversion.

[0348] Step 3:

[0349] The acquired audio data is immediately sent to the server, where the data management system converts the audio data into text data. This text data is then compared with past conversation history.

[0350] Step 4:

[0351] The server uses an emotion engine to analyze the user's emotional state from text data. It identifies emotions such as joy and sadness from the intonation and content of the voice.

[0352] Step 5:

[0353] The response generation mechanism generates an appropriate response based on the matching results and analyzed emotions. The generated response is designed to take the user's emotions into consideration and to facilitate a smooth conversation.

[0354] Step 6:

[0355] The server sends the generated response to the terminal, which then uses speech synthesis technology to provide the response to the user. The response is expressed in a way that reflects the user's emotions.

[0356] Step 7:

[0357] The device continuously monitors user activity, and if no activity is detected for more than 10 minutes, it automatically powers off and enters standby mode.

[0358] Step 8:

[0359] If no motion is detected by the sensor for a certain period of time or longer, the server uses an anomaly notification mechanism to send a warning to pre-configured contacts, such as family members, and reports the situation.

[0360] (Example 2)

[0361] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0362] In modern society, the increasing feelings of loneliness and stress experienced by individuals are a significant problem, but appropriate dialogue support systems for addressing these emotions are limited. Conventional technologies struggle to accurately recognize individual user emotions and provide corresponding responses, resulting in a lack of improved user experience. Furthermore, anomaly detection and appropriate response methods to ensure user safety are insufficient.

[0363] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0364] In this invention, the server includes motion detection means for detecting user actions, voice acquisition means for acquiring voice data, and emotion analysis means for analyzing emotions. This makes it possible to provide a dialogue system that builds deeper human relationships by accurately recognizing the emotions of individual users and providing natural responses based on those emotions. Furthermore, it can respond immediately to abnormal situations, thereby enhancing user safety.

[0365] "Motion detection means" refers to technology that uses sensors to detect physical movements occurring around the user.

[0366] "Voice acquisition means" refers to technology for collecting and digitizing the voice emitted by a user.

[0367] "Data management means" refers to technology used to appropriately process acquired digital audio data and compare it with past dialogue history.

[0368] "Sentiment analysis techniques" are technologies that evaluate the features of audio data to identify emotions such as joy, sadness, and anger.

[0369] "Response generation means" refers to technology for automatically generating appropriate responses to the user based on analysis results.

[0370] A "response delivery means" is a technology that transmits the generated response to the user and provides a natural dialogue.

[0371] An "anomaly warning mechanism" is a technology that notifies pre-configured contacts of an anomaly if no operation is detected within the system for a certain period of time or longer.

[0372] "Control means" refers to technologies for managing various functions of a system and adjusting power supply under specific conditions.

[0373] "Communication means" refers to technologies for transmitting abnormality or response information to users or related organizations in remote locations.

[0374] This invention is an advanced conversation support system that understands the user's emotions and provides dialogue accordingly. The system includes motion detection means, voice acquisition means, data management means, emotion analysis means, response generation means, response provision means, abnormality warning means, and control means.

[0375] Hardware and software configuration

[0376] The device utilizes infrared sensors and camera modules as motion detection means. This allows the system to activate when it detects that a user has entered the sensor's range. A microphone is used to acquire the user's voice, converting the analog audio into a digital signal. The converted digital audio data is then sent to a server either within the device or in the cloud.

[0377] The server converts received audio data into text using data management tools and compares it with past conversation history. Natural language processing techniques are used to deepen the understanding of context. For sentiment analysis, machine learning models are utilized to identify emotions from the audio waveform and text content.

[0378] The response generation mechanism generates several emotion-appropriate dialogue phrases based on a generation AI model. The server then selects an appropriate response and sends it to the terminal via the response delivery mechanism. The terminal then communicates the response to the user via voice through its speaker.

[0379] Furthermore, if no activity is detected for a certain period of time, the device will automatically switch to power-saving mode. In addition, an abnormality warning system will notify the user's family and related parties via communication if no activity is detected.

[0380] Specific example

[0381] For example, if a user says, "I'm feeling a little lonely today," the system receives the audio and, through emotion analysis, determines that the user is feeling "sad." Based on this result, the response generation system generates a gentle response such as, "I hope something cheers you up. Shall we continue talking?" In this way, the system provides a conversational experience aimed at providing psychological support to the user.

[0382] Example of a prompt

[0383] "Generate examples of appropriate dialogue for when a user is feeling lonely."

[0384] "Please provide examples of conversational responses based on the results of emotion recognition."

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] The terminal detects user movement using a motion sensor or camera. The system activates when the user enters the sensor range. At this stage, the input is the user's physical movement, and the output is the system activation signal.

[0388] Step 2:

[0389] When a user speaks, the device uses its built-in microphone to capture the voice. Since the captured voice is an analog signal, it is converted into a digital signal. The input is the user's voice data, and the output is the digitized voice data.

[0390] Step 3:

[0391] The digitized audio data is sent from the terminal to the server. The server uses data management tools to convert the audio data into text. In this step, the input is digital audio data, and the output is data in text format.

[0392] Step 4:

[0393] The server performs contextual analysis by comparing the transcribed data with past conversation history. This process uses natural language processing techniques, with text data as input and parsed data containing contextual information as output.

[0394] Step 5:

[0395] The server uses sentiment analysis tools to identify the user's emotions from the analyzed text data and voice features. The input is the analyzed data, and the output is the identified emotion information.

[0396] Step 6:

[0397] The response generation mechanism generates multiple response candidates based on emotional information. This process uses a generative AI model, where the input is emotional information and the output is a group of response candidates.

[0398] Step 7:

[0399] The server selects the best response from the generated candidate responses. The selection criteria reflect past conversation history and the user's sentiment. The input is the set of candidate responses, and the output is the selected response.

[0400] Step 8:

[0401] The selected response is sent to the terminal, which then communicates it to the user via voice through its speaker. The input is the selected response, and the output is the voice response to the user.

[0402] Step 9:

[0403] If no activity is detected for a certain period of time, the device will use an abnormality warning mechanism to send a warning message to a designated contact. The input is activity detection information, and the output is the warning message.

[0404] (Application Example 2)

[0405] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0406] Modern commercial facilities require personalized service for customers, but traditional customer service systems struggle to improve customer satisfaction because they cannot respond in a way that takes emotions and feelings into account. Furthermore, while rapid response to emergencies is crucial, the inability to assess urgency based on emotional changes results in inadequate responses.

[0407] 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.

[0408] In this invention, the server includes detection means for detecting movement, acquisition means for acquiring voice information, and management means for digitizing and managing the voice information. This makes it possible to recognize emotions from the customer's movements and voice, and to provide appropriate responses and anomaly notifications according to the situation.

[0409] "Detection means" refers to a device or mechanism for sensing a user's movements and determining their presence or actions.

[0410] "Acquisition means" refers to a mechanism for collecting voice information emitted by the user and further incorporating it as digital data.

[0411] "Management measures" refer to procedures for appropriately processing and managing data by saving acquired data and comparing it with past records.

[0412] "Generation method" refers to the process of creating appropriate and emotionally considerate responses for users based on managed data.

[0413] "Means of delivery" refers to the method of communicating the generated response to the user and providing an interactive experience.

[0414] A "notification method" is a communication protocol used to quickly send information to designated contacts when an anomaly is detected.

[0415] "Emotion recognition means" refers to technology that analyzes a user's voice and actions to determine and understand their emotional state.

[0416] This system includes detection means for sensing user actions, acquisition means for collecting voice information, and management means for processing data. The server processes the data in the following steps: First, hardware such as a microphone for voice acquisition and sensors for analyzing emotions are used. This captures the user's voice and actions in detail.

[0417] The audio data is digitized by the acquisition method, and the management method uses it to compare it with accumulated historical records. During this process, Google Speech-to-Text API is used for speech recognition, and Google Cloud Natural Language or Microsoft Azure Text Analytics are used for sentiment analysis. This process evaluates the audio features and identifies emotions such as joy, sadness, and anger.

[0418] The emotion recognition means uses that information to generate an appropriate response, which the delivery means then communicates to the user. This entire process enables dynamic responses and personalized service tailored to the customer's emotions in a physical store setting.

[0419] For example, if a customer in a store spends a long time browsing products but is unresponsive when spoken to, the emotion recognition system might determine that the customer is "tired." The robot could then gently ask, "You seem tired; is there anything I can help you with?" This is an example of natural conversation supported by a generative AI model.

[0420] An example of a prompt message would be, "Please suggest to the assistant how to provide customer service on a tiring day." This example allows for flexible responses tailored to the customer's situation.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] When a user enters the system's range, the terminal's motion detection system senses the user's movement. This detection triggers the system to activate. The input is the user's physical movement, and the output is the system's activation signal. This action prepares the system to proceed to the next step.

[0424] Step 2:

[0425] The device collects voice data using voice acquisition methods when the user speaks. The input is the user's voice information, which is converted into digital data. The output is the digitized voice data. This makes the voice data in an analyzable format.

[0426] Step 3:

[0427] The server uses a management system to compare digitized audio data with past records. Inputs are digital audio data and past conversation records, while output is information indicating the context in which the current conversation is taking place. Data processing creates a foundation for providing appropriate responses.

[0428] Step 4:

[0429] The server's emotion recognition system analyzes and identifies the user's emotions from the audio data. The input is the contextual information and audio features obtained in the previous step, and the output is the emotion (joy, sadness, anger, etc.) that the user is judged to be experiencing. Through data processing, the user's psychological state is clarified.

[0430] Step 5:

[0431] A response generation mechanism based on this mechanism creates an appropriate response within the server that corresponds to the user's emotions. The input is the user's emotional information and current context information, and the output is the specific response content to the user. The generated response enables natural conversation.

[0432] Step 6:

[0433] The terminal uses the provided means to present the generated response to the user. The input is the response content sent from the server, and the output is information displayed via audio or on the screen. The interaction with the user is completed upon the provision of this information.

[0434] Step 7:

[0435] If an anomaly is detected and no user activity is detected for a specified period (e.g., 10 minutes or more), the server's notification system will send an alert to the configured contact. The input is data indicating inactivity from the motion detection sensor, and the output is an alert message. This operation ensures user safety.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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".

[0452] The present invention is a system for monitoring and assisting elderly people in conversation, and includes motion detection means, voice acquisition means, data management means, response generation means, and abnormality notification means. The terminal has a built-in motion sensor for detecting user movements and automatically activates the system when a user passes nearby.

[0453] The device picks up the user's voice using a microphone and converts that voice into digital data using a voice acquisition system. This digital data is sent to a server, where it is analyzed as text data by a data management system on the server. Here, it is compared with previously stored conversation data to identify what the user has said in the past and the flow of those conversations. This algorithm is designed to take the user's conversation history into consideration while also being able to respond flexibly to new conversations.

[0454] The response generation mechanism generates natural-sounding responses based on analyzed conversation data. This allows for the inclusion of empathetic expressions that remind the user of what they have previously said. The generated responses are provided to the user as audio in real time via the terminal.

[0455] Furthermore, the device is designed to automatically power off to conserve energy if no user activity is detected for a certain period of time (e.g., 10 minutes or more). On the other hand, the anomaly notification system sends a notification to pre-configured family members via the server if it detects no activity for a predetermined period of time (e.g., 1 hour or more). This notification warns that the user's safety may have been compromised.

[0456] For example, if a user says, "I went for a walk yesterday," the server will remember that information and, the next time the topic of "walking" comes up, will respond naturally with, "You went yesterday too, didn't you?" In this way, the present invention aims to alleviate the psychological loneliness of the elderly and strengthen family support for monitoring them.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The device uses a built-in motion sensor to detect user movement. When a user enters the sensor's range, the system automatically activates and switches to voice acquisition mode.

[0460] Step 2:

[0461] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The audio data is then processed by the audio acquisition method.

[0462] Step 3:

[0463] The terminal sends the converted audio data to the server. The server analyzes the audio data into text data using data management tools and compares it with past conversation records.

[0464] Step 4:

[0465] Based on the accumulated conversation data, the server identifies information related to the user's statements and applies an algorithm to construct an appropriate response.

[0466] Step 5:

[0467] The response generation mechanism runs on the server and generates an automated response that takes into account the user's past statements and the current context. This response is then sent from the server to the terminal.

[0468] Step 6:

[0469] The device uses speech synthesis technology to convey received responses to the user. This allows for natural conversation with the user, including appropriate responses and empathetic expressions.

[0470] Step 7:

[0471] The device continuously monitors sensor activity and automatically turns off the power and enters standby mode if no activity is detected for more than 10 minutes.

[0472] Step 8:

[0473] The server receives a notification that no activity has been detected for a certain period of time (for example, 1 hour). It then uses an anomaly notification mechanism to send an alert to pre-configured family contacts.

[0474] (Example 1)

[0475] 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."

[0476] It is necessary to alleviate the psychological loneliness experienced by the elderly and to create an environment where family members and caregivers can monitor them with peace of mind even from a distance. Furthermore, there is a need for an effective system that naturally recognizes the user's actions and speech and supports appropriate communication. In addition, notifications must be provided to enable a rapid response to any abnormal situations.

[0477] 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.

[0478] In this invention, the server includes a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses in natural language, a response provision device, and an anomaly notification device. This enables the rapid and natural detection of user actions to initiate a conversation, and the generation and provision of appropriate responses based on the accumulated conversation history. Furthermore, it enables prompt response by notifying of anomalies when necessary.

[0479] A "sensing device" is a device that uses sensor technology to detect user movements and plays a role in prompting the system to start up.

[0480] A "voice collection device" is a device equipped with a microphone that captures the user's speech and converts the voice information into digital data.

[0481] A "data processing device" is a device that has a data management function for converting acquired audio data into text data and comparing it with past conversation history.

[0482] A "response generation device" is a device that executes algorithms and models to generate appropriate responses based on accumulated conversation data.

[0483] "Natural language response generation means" refers to language processing technology that provides users with natural conversation based on the generated data.

[0484] A "response provider" is a device equipped with an audio output function to deliver generated responses to users in real time.

[0485] An "abnormality notification device" is a device that has the function of detecting abnormalities when the user's actions do not meet predetermined conditions and notifying family members or caregivers.

[0486] This invention is a system for monitoring and supporting conversations with the elderly. This system is implemented by combining a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses using natural language, a response provision device, and an abnormality notification device.

[0487] The terminal is equipped with a sensing device that uses sensor technology to detect the user's movements. When the user moves within the range of the sensing device, the sensing device starts the system.

[0488] Next, the device's built-in voice acquisition device captures the user's speech through a microphone. This speech is recorded in analog format and converted into digital data as voice information. To perform this conversion process, speech recognition technology is required to convert speech into text data.

[0489] Subsequently, the digitized audio data is transmitted to a server via the network. A data processing unit on the server analyzes the data and compares it with previously accumulated conversation history. The conversation database records past responses and their contexts, and new responses are formed based on this. A response generator uses a generative AI model to create appropriate natural language responses based on the spoken content. These responses are meticulously designed to facilitate effective communication with the user.

[0490] For example, if a user says, "I went for a walk yesterday," the system records that statement and generates a response such as, "You went for a walk yesterday too, didn't you?" the next time the topic of walks comes up. This allows for a more familiar and supportive conversation for the user.

[0491] The generated response is transmitted to the user via voice using the terminal's response provider. Because this response is provided in real time, the user can enjoy a smooth conversational experience.

[0492] Furthermore, the device is equipped with a function that automatically turns off the power if it does not detect any activity for a predetermined period of time (for example, 10 minutes or more). This helps to reduce energy consumption and enables efficient operation.

[0493] Furthermore, the server's anomaly detection system considers a situation where no operation is detected for a certain period of time (for example, more than one hour) as an anomaly and sends a notification to pre-registered contacts. This notification quickly informs family members and others that something unusual may be happening to the user.

[0494] An example of a prompt for this system would be to instruct the generation AI model with a message like, "When the user says, 'I went for a walk yesterday,' generate an appropriate response."

[0495] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0496] Step 1:

[0497] The terminal uses a sensing device to detect the user's movements. The input is the user's physical movement. The sensing device captures this input as a trigger and generates a system activation signal. This causes the system to transition to an operational state and prepare for voice collection.

[0498] Step 2:

[0499] After startup, the terminal uses a speech collection device to acquire the user's speech. The input is the user's voice. The speech collection device performs speech recognition to convert the analog speech into digital data and saves it in a digital file format. This digital data is then prepared for subsequent text conversion processing.

[0500] Step 3:

[0501] The terminal transmits the generated digital audio data to the server via the network. The input is the digital audio data from the terminal. The server uses specialized speech recognition software to convert the received data into text. The output is in text format, which is used for data analysis processing.

[0502] Step 4:

[0503] The server analyzes text data using a data processing unit and compares it with past conversation history. The input is the transcribed conversation content. The processing unit compares this with past data in the database to identify matching topics and relevant contexts. The output of this matching process is the contextual information necessary for response generation.

[0504] Step 5:

[0505] The server uses a response generator to produce an appropriate response based on past conversation history and matching results. The input is the matched context information. A generative AI model is used to apply algorithms to construct natural and responsive conversations. The output of this step is the response message to be provided to the user in voice.

[0506] Step 6:

[0507] The terminal provides the user with a generated response message as voice using a response provider. The input is the response message generated by the server. It is delivered to the user as synthesized speech through the speaker built into the terminal. Real-time interaction is completed at this point.

[0508] Step 7:

[0509] The terminal will power off if no user activity is detected for a certain period of time (e.g., 10 minutes or more). The input is time elapsed information obtained from the motion detection device. The output of this control process is a power-off signal to reduce energy consumption.

[0510] Step 8:

[0511] The server uses an anomaly notification device to send an anomaly notification if no operation is detected for a predetermined period of time (e.g., more than one hour). The input is the operation detection log. A message informing the user of the anomaly is sent to pre-registered contacts through the notification system. This allows for immediate confirmation of the user's safety status.

[0512] (Application Example 1)

[0513] 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."

[0514] In physical stores, there is a challenge in providing personalized service and individual attention to each customer who visits. In particular, there is a need for product suggestions based on a customer's past purchase history and for quick support when a customer is unsure of what to buy. Furthermore, traditional systems have struggled to effectively utilize customer movements and conversations to achieve flexible and natural dialogue.

[0515] 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.

[0516] In this invention, the server includes motion detection means, including a human presence sensor for detecting user movements; data management means, which converts acquired voice information into digital data, transmits it to the server, and compares it with previously stored conversation history; and response generation means, which generates appropriate responses using a generation AI model based on the database. This enables the provision of personalized services to customers in physical stores and allows for optimal product suggestions based on past purchase history. Furthermore, if a customer is hesitant for a long time, immediate notification can be sent to store staff, enabling effective support.

[0517] A "motion detection means" is a means of using sensors to detect the user's movements. This allows the system to automatically detect when a user is near it.

[0518] A "voice acquisition method" is a means of collecting the voice spoken by the user and converting it into digital data. This enables the initiation of conversations and the acquisition of information in real time.

[0519] A "data management system" is a means of transmitting acquired digital data to a server and comparing it with previously stored conversation data. This allows for the generation of appropriate responses based on the user's past speech history.

[0520] A "response generation means" is a method for generating appropriate responses to users using a generation AI model based on a database. This enables natural responses that take past conversations into account.

[0521] A "response provisioning means" is a means of providing the generated response to the user in audio format. This allows the user to receive information in a way that is easy for them to understand.

[0522] An "anomaly notification mechanism" is a means of notifying a designated contact person of an anomaly if the user's actions are not detected for a predetermined period of time. This enhances user safety management.

[0523] A "proposal generation method" is a means of providing customers with optimal product suggestions based on their past purchase history. This can be used to promote sales in stores.

[0524] A "digital conversion method" is a means of converting audio data into text data using a speech recognition API. This makes it possible to format audio information in a way that is suitable for recording and analysis.

[0525] In this embodiment, the terminal is installed in the store and detects the presence of a user using a built-in motion sensor when the user approaches. The terminal then automatically enters conversation mode and collects the user's voice. This voice is converted into digital data using a speech recognition API and sent to a server for data management. The server uses a generative AI model to generate an appropriate response based on previously accumulated conversation data. This response, based on past purchase history and conversation content, is provided to the user via voice through the terminal.

[0526] When a user requests product suggestions, the suggestion generation system refers to their past purchase history to suggest the most suitable products. The server precisely converts the user's voice into text data using a speech recognition API, and then matches it against a database. For example, if a user says they are looking for a new product, the server will generate a response such as, "You previously purchased this product. How about this new product?"

[0527] An anomaly notification system is also in place; if there is no user activity within a specified time, staff will be notified via communication. This allows for smoother customer support at physical stores.

[0528] Examples of prompt messages include the following:

[0529] "Refer to the customer's purchase history database and use that knowledge to generate the best possible response to the current conversation. Ensure the response is personalized based on past data."

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The terminal detects user movement. Using a motion sensor, the terminal automatically activates the system when a user approaches. The input is the motion detection signal from the sensor, and the output is the system activation signal. This activates the terminal from standby mode.

[0533] Step 2:

[0534] The device acquires the user's voice. Using the microphone, it digitizes the voice data emitted by the user and sends this data to the server. The input is the user's voice, and the output is digital voice data. This makes the voice information available for remote processing.

[0535] Step 3:

[0536] The server converts digital audio data into text data. It uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert the audio data into text format. The input is digital audio data, and the output is text data. This text data serves as the basis for further processing.

[0537] Step 4:

[0538] The server compares text data with past conversation data. Using data management tools, it prepares to generate appropriate responses based on past conversation history. The input is the current text data, and the output is the result of the comparison with past data. This allows the server to understand the user's context.

[0539] Step 5:

[0540] The server generates responses using a generative AI model. Based on the prompt, the generative AI model interprets the text data and generates a natural-sounding response. The input is the prompt and the matching result, and the output is the generated response. This ensures that a personalized and appropriate response is prepared for the user.

[0541] Step 6:

[0542] The terminal provides the generated response to the user in audio format. Using a response delivery mechanism, the terminal outputs the response as audio that the user can hear. The input is response data from the server, and the output is an audio response. This allows the user to receive immediate feedback.

[0543] Step 7:

[0544] The server detects anomalies and sends notifications as needed. Through the anomaly notification system, if no user activity is detected for a predetermined period of time, an anomaly is notified to a designated contact. The input is activity detection information, and the output is an anomaly notification signal. This ensures user safety.

[0545] 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.

[0546] This invention is an advanced conversation and monitoring support system that incorporates emotion recognition for the user. In addition to motion detection means, voice acquisition means, data management means, response generation means, and anomaly notification means, this system includes an emotion engine that recognizes the user's emotions.

[0547] The terminal first detects user movement using a motion sensor and activates the system when it determines that the user has entered a range. When the user speaks, the terminal uses a microphone to capture the voice, and a voice acquisition device converts this into digital data. The converted voice data is sent to a server, where a data management device converts it back into text.

[0548] The server compares the text data analyzed by the data management system with past conversation history. This process enables contextual understanding based on past conversations. Next, the emotion engine identifies the user's emotions from the speech. The emotion engine evaluates the features of the analyzed speech and identifies emotions such as joy, sadness, and anger.

[0549] The response generation mechanism prepares a response that matches the user's emotions based on the emotional information identified by the emotion engine. The server selects the optimal response and sends it to the terminal. The terminal provides the received response to the user, enabling a natural conversation that takes emotions into consideration.

[0550] Furthermore, the device has a function that automatically turns off the power if no activity is detected for a certain period of time (e.g., 10 minutes or more) to ensure energy efficiency. On the other hand, if no activity is detected for a predetermined period of time, the abnormality notification system sends a warning to pre-configured family members or related parties via the server. This ensures the user's safety and enables a quick response.

[0551] For example, if a user speaks in an emotionally sad voice saying, "I'm feeling a little lonely today," the emotion engine recognizes the "sadness," and the response generation means selects a response in a gentle tone, such as, "I hope something cheers you up. Shall we continue talking?" In this way, the present invention aims to enhance the user's psychological support and provide a richer communication experience.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The device uses a motion sensor to detect user movement. When a user enters the sensor's range, the device activates its system and switches to voice acquisition mode.

[0555] Step 2:

[0556] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The voice acquisition device performs this conversion.

[0557] Step 3:

[0558] The acquired audio data is immediately sent to the server, where the data management system converts the audio data into text data. This text data is then compared with past conversation history.

[0559] Step 4:

[0560] The server uses an emotion engine to analyze the user's emotional state from text data. It identifies emotions such as joy and sadness from the intonation and content of the voice.

[0561] Step 5:

[0562] The response generation mechanism generates an appropriate response based on the matching results and analyzed emotions. The generated response is designed to take the user's emotions into consideration and to facilitate a smooth conversation.

[0563] Step 6:

[0564] The server sends the generated response to the terminal, which then uses speech synthesis technology to provide the response to the user. The response is expressed in a way that reflects the user's emotions.

[0565] Step 7:

[0566] The device continuously monitors user activity, and if no activity is detected for more than 10 minutes, it automatically powers off and enters standby mode.

[0567] Step 8:

[0568] If no motion is detected by the sensor for a certain period of time or longer, the server uses an anomaly notification mechanism to send a warning to pre-configured contacts, such as family members, and reports the situation.

[0569] (Example 2)

[0570] 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."

[0571] In modern society, the increasing feelings of loneliness and stress experienced by individuals are a significant problem, but appropriate dialogue support systems for addressing these emotions are limited. Conventional technologies struggle to accurately recognize individual user emotions and provide corresponding responses, resulting in a lack of improved user experience. Furthermore, anomaly detection and appropriate response methods to ensure user safety are insufficient.

[0572] 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.

[0573] In this invention, the server includes motion detection means for detecting user actions, voice acquisition means for acquiring voice data, and emotion analysis means for analyzing emotions. This makes it possible to provide a dialogue system that builds deeper human relationships by accurately recognizing the emotions of individual users and providing natural responses based on those emotions. Furthermore, it can respond immediately to abnormal situations, thereby enhancing user safety.

[0574] "Motion detection means" refers to technology that uses sensors to detect physical movements occurring around the user.

[0575] "Voice acquisition means" refers to technology for collecting and digitizing the voice emitted by a user.

[0576] "Data management means" refers to technology used to appropriately process acquired digital audio data and compare it with past dialogue history.

[0577] "Sentiment analysis techniques" are technologies that evaluate the features of audio data to identify emotions such as joy, sadness, and anger.

[0578] "Response generation means" refers to technology for automatically generating appropriate responses to the user based on analysis results.

[0579] A "response delivery means" is a technology that transmits the generated response to the user and provides a natural dialogue.

[0580] An "anomaly warning mechanism" is a technology that notifies pre-configured contacts of an anomaly if no operation is detected within the system for a certain period of time or longer.

[0581] "Control means" refers to technologies for managing various functions of a system and adjusting power supply under specific conditions.

[0582] "Communication means" refers to technologies for transmitting abnormality or response information to users or related organizations in remote locations.

[0583] This invention is an advanced conversation support system that understands the user's emotions and provides dialogue accordingly. The system includes motion detection means, voice acquisition means, data management means, emotion analysis means, response generation means, response provision means, abnormality warning means, and control means.

[0584] Hardware and software configuration

[0585] The device utilizes infrared sensors and camera modules as motion detection means. This allows the system to activate when it detects that a user has entered the sensor's range. A microphone is used to acquire the user's voice, converting the analog audio into a digital signal. The converted digital audio data is then sent to a server either within the device or in the cloud.

[0586] The server converts received audio data into text using data management tools and compares it with past conversation history. Natural language processing techniques are used to deepen the understanding of context. For sentiment analysis, machine learning models are utilized to identify emotions from the audio waveform and text content.

[0587] The response generation mechanism generates several emotion-appropriate dialogue phrases based on a generation AI model. The server then selects an appropriate response and sends it to the terminal via the response delivery mechanism. The terminal then communicates the response to the user via voice through its speaker.

[0588] Furthermore, if no activity is detected for a certain period of time, the device will automatically switch to power-saving mode. In addition, an abnormality warning system will notify the user's family and related parties via communication if no activity is detected.

[0589] Specific example

[0590] For example, if a user says, "I'm feeling a little lonely today," the system receives the audio and, through emotion analysis, determines that the user is feeling "sad." Based on this result, the response generation system generates a gentle response such as, "I hope something cheers you up. Shall we continue talking?" In this way, the system provides a conversational experience aimed at providing psychological support to the user.

[0591] Example of a prompt

[0592] "Generate examples of appropriate dialogue for when a user is feeling lonely."

[0593] "Please provide examples of conversational responses based on the results of emotion recognition."

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] The terminal detects user movement using a motion sensor or camera. The system activates when the user enters the sensor range. At this stage, the input is the user's physical movement, and the output is the system activation signal.

[0597] Step 2:

[0598] When a user speaks, the device uses its built-in microphone to capture the voice. Since the captured voice is an analog signal, it is converted into a digital signal. The input is the user's voice data, and the output is the digitized voice data.

[0599] Step 3:

[0600] The digitized audio data is sent from the terminal to the server. The server uses data management tools to convert the audio data into text. In this step, the input is digital audio data, and the output is data in text format.

[0601] Step 4:

[0602] The server performs contextual analysis by comparing the transcribed data with past conversation history. This process uses natural language processing techniques, with text data as input and parsed data containing contextual information as output.

[0603] Step 5:

[0604] The server uses sentiment analysis tools to identify the user's emotions from the analyzed text data and voice features. The input is the analyzed data, and the output is the identified emotion information.

[0605] Step 6:

[0606] The response generation mechanism generates multiple response candidates based on emotional information. This process uses a generative AI model, where the input is emotional information and the output is a group of response candidates.

[0607] Step 7:

[0608] The server selects the best response from the generated candidate responses. The selection criteria reflect past conversation history and the user's sentiment. The input is the set of candidate responses, and the output is the selected response.

[0609] Step 8:

[0610] The selected response is sent to the terminal, which then communicates it to the user via voice through its speaker. The input is the selected response, and the output is the voice response to the user.

[0611] Step 9:

[0612] If no activity is detected for a certain period of time, the device will use an abnormality warning mechanism to send a warning message to a designated contact. The input is activity detection information, and the output is the warning message.

[0613] (Application Example 2)

[0614] 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."

[0615] Modern commercial facilities require personalized service for customers, but traditional customer service systems struggle to improve customer satisfaction because they cannot respond in a way that takes emotions and feelings into account. Furthermore, while rapid response to emergencies is crucial, the inability to assess urgency based on emotional changes results in inadequate responses.

[0616] 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.

[0617] In this invention, the server includes detection means for detecting movement, acquisition means for acquiring voice information, and management means for digitizing and managing the voice information. This makes it possible to recognize emotions from the customer's movements and voice, and to provide appropriate responses and anomaly notifications according to the situation.

[0618] "Detection means" refers to a device or mechanism for sensing a user's movements and determining their presence or actions.

[0619] "Acquisition means" refers to a mechanism for collecting voice information emitted by the user and further incorporating it as digital data.

[0620] "Management measures" refer to procedures for appropriately processing and managing data by saving acquired data and comparing it with past records.

[0621] "Generation method" refers to the process of creating appropriate and emotionally considerate responses for users based on managed data.

[0622] "Means of delivery" refers to the method of communicating the generated response to the user and providing an interactive experience.

[0623] A "notification method" is a communication protocol used to quickly send information to designated contacts when an anomaly is detected.

[0624] "Emotion recognition means" refers to technology that analyzes a user's voice and actions to determine and understand their emotional state.

[0625] This system includes detection means for sensing user actions, acquisition means for collecting voice information, and management means for processing data. The server processes the data in the following steps: First, hardware such as a microphone for voice acquisition and sensors for analyzing emotions are used. This captures the user's voice and actions in detail.

[0626] The audio data is digitized by the acquisition method, and the management method uses it to compare it with accumulated historical records. During this process, Google Speech-to-Text API is used for speech recognition, and Google Cloud Natural Language or Microsoft Azure Text Analytics are used for sentiment analysis. This process evaluates the audio features and identifies emotions such as joy, sadness, and anger.

[0627] The emotion recognition means uses that information to generate an appropriate response, which the delivery means then communicates to the user. This entire process enables dynamic responses and personalized service tailored to the customer's emotions in a physical store setting.

[0628] For example, if a customer in a store spends a long time browsing products but is unresponsive when spoken to, the emotion recognition system might determine that the customer is "tired." The robot could then gently ask, "You seem tired; is there anything I can help you with?" This is an example of natural conversation supported by a generative AI model.

[0629] An example of a prompt message would be, "Please suggest to the assistant how to provide customer service on a tiring day." This example allows for flexible responses tailored to the customer's situation.

[0630] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0631] Step 1:

[0632] When a user enters the system's range, the terminal's motion detection system senses the user's movement. This detection triggers the system to activate. The input is the user's physical movement, and the output is the system's activation signal. This action prepares the system to proceed to the next step.

[0633] Step 2:

[0634] The device collects voice data using voice acquisition methods when the user speaks. The input is the user's voice information, which is converted into digital data. The output is the digitized voice data. This makes the voice data in an analyzable format.

[0635] Step 3:

[0636] The server uses a management system to compare digitized audio data with past records. Inputs are digital audio data and past conversation records, while output is information indicating the context in which the current conversation is taking place. Data processing creates a foundation for providing appropriate responses.

[0637] Step 4:

[0638] The server's emotion recognition system analyzes and identifies the user's emotions from the audio data. The input is the contextual information and audio features obtained in the previous step, and the output is the emotion (joy, sadness, anger, etc.) that the user is judged to be experiencing. Through data processing, the user's psychological state is clarified.

[0639] Step 5:

[0640] A response generation mechanism based on this mechanism creates an appropriate response within the server that corresponds to the user's emotions. The input is the user's emotional information and current context information, and the output is the specific response content to the user. The generated response enables natural conversation.

[0641] Step 6:

[0642] The terminal uses the provided means to present the generated response to the user. The input is the response content sent from the server, and the output is information displayed via audio or on the screen. The interaction with the user is completed upon the provision of this information.

[0643] Step 7:

[0644] If an anomaly is detected and no user activity is detected for a specified period (e.g., 10 minutes or more), the server's notification system will send an alert to the configured contact. The input is data indicating inactivity from the motion detection sensor, and the output is an alert message. This operation ensures user safety.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] The present invention is a system for monitoring and assisting elderly people in conversation, and includes motion detection means, voice acquisition means, data management means, response generation means, and abnormality notification means. The terminal has a built-in motion sensor for detecting user movements and automatically activates the system when a user passes nearby.

[0663] The device picks up the user's voice using a microphone and converts that voice into digital data using a voice acquisition system. This digital data is sent to a server, where it is analyzed as text data by a data management system on the server. Here, it is compared with previously stored conversation data to identify what the user has said in the past and the flow of those conversations. This algorithm is designed to take the user's conversation history into consideration while also being able to respond flexibly to new conversations.

[0664] The response generation mechanism generates natural-sounding responses based on analyzed conversation data. This allows for the inclusion of empathetic expressions that remind the user of what they have previously said. The generated responses are provided to the user as audio in real time via the terminal.

[0665] Furthermore, the device is designed to automatically power off to conserve energy if no user activity is detected for a certain period of time (e.g., 10 minutes or more). On the other hand, the anomaly notification system sends a notification to pre-configured family members via the server if it detects no activity for a predetermined period of time (e.g., 1 hour or more). This notification warns that the user's safety may have been compromised.

[0666] For example, if a user says, "I went for a walk yesterday," the server will remember that information and, the next time the topic of "walking" comes up, will respond naturally with, "You went yesterday too, didn't you?" In this way, the present invention aims to alleviate the psychological loneliness of the elderly and strengthen family support for monitoring them.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The device uses a built-in motion sensor to detect user movement. When a user enters the sensor's range, the system automatically activates and switches to voice acquisition mode.

[0670] Step 2:

[0671] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The audio data is then processed by the audio acquisition method.

[0672] Step 3:

[0673] The terminal sends the converted audio data to the server. The server analyzes the audio data into text data using data management tools and compares it with past conversation records.

[0674] Step 4:

[0675] Based on the accumulated conversation data, the server identifies information related to the user's statements and applies an algorithm to construct an appropriate response.

[0676] Step 5:

[0677] The response generation mechanism runs on the server and generates an automated response that takes into account the user's past statements and the current context. This response is then sent from the server to the terminal.

[0678] Step 6:

[0679] The device uses speech synthesis technology to convey received responses to the user. This allows for natural conversation with the user, including appropriate responses and empathetic expressions.

[0680] Step 7:

[0681] The device continuously monitors sensor activity and automatically turns off the power and enters standby mode if no activity is detected for more than 10 minutes.

[0682] Step 8:

[0683] The server receives a notification that no activity has been detected for a certain period of time (for example, 1 hour). It then uses an anomaly notification mechanism to send an alert to pre-configured family contacts.

[0684] (Example 1)

[0685] 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".

[0686] It is necessary to alleviate the psychological loneliness experienced by the elderly and to create an environment where family members and caregivers can monitor them with peace of mind even from a distance. Furthermore, there is a need for an effective system that naturally recognizes the user's actions and speech and supports appropriate communication. In addition, notifications must be provided to enable a rapid response to any abnormal situations.

[0687] 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.

[0688] In this invention, the server includes a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses in natural language, a response provision device, and an anomaly notification device. This enables the rapid and natural detection of user actions to initiate a conversation, and the generation and provision of appropriate responses based on the accumulated conversation history. Furthermore, it enables prompt response by notifying of anomalies when necessary.

[0689] A "sensing device" is a device that uses sensor technology to detect user movements and plays a role in prompting the system to start up.

[0690] A "voice collection device" is a device equipped with a microphone that captures the user's speech and converts the voice information into digital data.

[0691] A "data processing device" is a device that has a data management function for converting acquired audio data into text data and comparing it with past conversation history.

[0692] A "response generation device" is a device that executes algorithms and models to generate appropriate responses based on accumulated conversation data.

[0693] "Natural language response generation means" refers to language processing technology that provides users with natural conversation based on the generated data.

[0694] A "response provider" is a device equipped with an audio output function to deliver generated responses to users in real time.

[0695] An "abnormality notification device" is a device that has the function of detecting abnormalities when the user's actions do not meet predetermined conditions and notifying family members or caregivers.

[0696] This invention is a system for monitoring and supporting conversations with the elderly. This system is implemented by combining a sensing device, a voice collection device, a data processing device, a response generation device, a means for generating responses using natural language, a response provision device, and an abnormality notification device.

[0697] The terminal is equipped with a sensing device that uses sensor technology to detect the user's movements. When the user moves within the range of the sensing device, the sensing device starts the system.

[0698] Next, the device's built-in voice acquisition device captures the user's speech through a microphone. This speech is recorded in analog format and converted into digital data as voice information. To perform this conversion process, speech recognition technology is required to convert speech into text data.

[0699] Subsequently, the digitized audio data is transmitted to a server via the network. A data processing unit on the server analyzes the data and compares it with previously accumulated conversation history. The conversation database records past responses and their contexts, and new responses are formed based on this. A response generator uses a generative AI model to create appropriate natural language responses based on the spoken content. These responses are meticulously designed to facilitate effective communication with the user.

[0700] For example, if a user says, "I went for a walk yesterday," the system records that statement and generates a response such as, "You went for a walk yesterday too, didn't you?" the next time the topic of walks comes up. This allows for a more familiar and supportive conversation for the user.

[0701] The generated response is transmitted to the user via voice using the terminal's response provider. Because this response is provided in real time, the user can enjoy a smooth conversational experience.

[0702] Furthermore, the device is equipped with a function that automatically turns off the power if it does not detect any activity for a predetermined period of time (for example, 10 minutes or more). This helps to reduce energy consumption and enables efficient operation.

[0703] Furthermore, the server's anomaly detection system considers a situation where no operation is detected for a certain period of time (for example, more than one hour) as an anomaly and sends a notification to pre-registered contacts. This notification quickly informs family members and others that something unusual may be happening to the user.

[0704] An example of a prompt for this system would be to instruct the generation AI model with a message like, "When the user says, 'I went for a walk yesterday,' generate an appropriate response."

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] The terminal uses a sensing device to detect the user's movements. The input is the user's physical movement. The sensing device captures this input as a trigger and generates a system activation signal. This causes the system to transition to an operational state and prepare for voice collection.

[0708] Step 2:

[0709] After startup, the terminal uses a speech collection device to acquire the user's speech. The input is the user's voice. The speech collection device performs speech recognition to convert the analog speech into digital data and saves it in a digital file format. This digital data is then prepared for subsequent text conversion processing.

[0710] Step 3:

[0711] The terminal transmits the generated digital audio data to the server via the network. The input is the digital audio data from the terminal. The server uses specialized speech recognition software to convert the received data into text. The output is in text format, which is used for data analysis processing.

[0712] Step 4:

[0713] The server analyzes text data using a data processing unit and compares it with past conversation history. The input is the transcribed conversation content. The processing unit compares this with past data in the database to identify matching topics and relevant contexts. The output of this matching process is the contextual information necessary for response generation.

[0714] Step 5:

[0715] The server uses a response generator to produce an appropriate response based on past conversation history and matching results. The input is the matched context information. A generative AI model is used to apply algorithms to construct natural and responsive conversations. The output of this step is the response message to be provided to the user in voice.

[0716] Step 6:

[0717] The terminal provides the user with a generated response message as voice using a response provider. The input is the response message generated by the server. It is delivered to the user as synthesized speech through the speaker built into the terminal. Real-time interaction is completed at this point.

[0718] Step 7:

[0719] The terminal will power off if no user activity is detected for a certain period of time (e.g., 10 minutes or more). The input is time elapsed information obtained from the motion detection device. The output of this control process is a power-off signal to reduce energy consumption.

[0720] Step 8:

[0721] The server uses an anomaly notification device to send an anomaly notification if no operation is detected for a predetermined period of time (e.g., more than one hour). The input is the operation detection log. A message informing the user of the anomaly is sent to pre-registered contacts through the notification system. This allows for immediate confirmation of the user's safety status.

[0722] (Application Example 1)

[0723] 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".

[0724] In physical stores, there is a challenge in providing personalized service and individual attention to each customer who visits. In particular, there is a need for product suggestions based on a customer's past purchase history and for quick support when a customer is unsure of what to buy. Furthermore, traditional systems have struggled to effectively utilize customer movements and conversations to achieve flexible and natural dialogue.

[0725] 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.

[0726] In this invention, the server includes motion detection means, including a human presence sensor for detecting user movements; data management means, which converts acquired voice information into digital data, transmits it to the server, and compares it with previously stored conversation history; and response generation means, which generates appropriate responses using a generation AI model based on the database. This enables the provision of personalized services to customers in physical stores and allows for optimal product suggestions based on past purchase history. Furthermore, if a customer is hesitant for a long time, immediate notification can be sent to store staff, enabling effective support.

[0727] A "motion detection means" is a means of using sensors to detect the user's movements. This allows the system to automatically detect when a user is near it.

[0728] A "voice acquisition method" is a means of collecting the voice spoken by the user and converting it into digital data. This enables the initiation of conversations and the acquisition of information in real time.

[0729] A "data management system" is a means of transmitting acquired digital data to a server and comparing it with previously stored conversation data. This allows for the generation of appropriate responses based on the user's past speech history.

[0730] A "response generation means" is a method for generating appropriate responses to users using a generation AI model based on a database. This enables natural responses that take past conversations into account.

[0731] A "response provisioning means" is a means of providing the generated response to the user in audio format. This allows the user to receive information in a way that is easy for them to understand.

[0732] An "anomaly notification mechanism" is a means of notifying a designated contact person of an anomaly if the user's actions are not detected for a predetermined period of time. This enhances user safety management.

[0733] A "proposal generation method" is a means of providing customers with optimal product suggestions based on their past purchase history. This can be used to promote sales in stores.

[0734] A "digital conversion method" is a means of converting audio data into text data using a speech recognition API. This makes it possible to format audio information in a way that is suitable for recording and analysis.

[0735] In this embodiment, the terminal is installed in the store and detects the presence of a user using a built-in motion sensor when the user approaches. The terminal then automatically enters conversation mode and collects the user's voice. This voice is converted into digital data using a speech recognition API and sent to a server for data management. The server uses a generative AI model to generate an appropriate response based on previously accumulated conversation data. This response, based on past purchase history and conversation content, is provided to the user via voice through the terminal.

[0736] When a user requests product suggestions, the suggestion generation system refers to their past purchase history to suggest the most suitable products. The server precisely converts the user's voice into text data using a speech recognition API, and then matches it against a database. For example, if a user says they are looking for a new product, the server will generate a response such as, "You previously purchased this product. How about this new product?"

[0737] An anomaly notification system is also in place; if there is no user activity within a specified time, staff will be notified via communication. This allows for smoother customer support at physical stores.

[0738] Examples of prompt messages include the following:

[0739] "Refer to the customer's purchase history database and use that knowledge to generate the best possible response to the current conversation. Ensure the response is personalized based on past data."

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The terminal detects user movement. Using a motion sensor, the terminal automatically activates the system when a user approaches. The input is the motion detection signal from the sensor, and the output is the system activation signal. This activates the terminal from standby mode.

[0743] Step 2:

[0744] The device acquires the user's voice. Using the microphone, it digitizes the voice data emitted by the user and sends this data to the server. The input is the user's voice, and the output is digital voice data. This makes the voice information available for remote processing.

[0745] Step 3:

[0746] The server converts digital audio data into text data. It uses a speech recognition API (e.g., Google Cloud Speech-to-Text API) to convert the audio data into text format. The input is digital audio data, and the output is text data. This text data serves as the basis for further processing.

[0747] Step 4:

[0748] The server compares text data with past conversation data. Using data management tools, it prepares to generate appropriate responses based on past conversation history. The input is the current text data, and the output is the result of the comparison with past data. This allows the server to understand the user's context.

[0749] Step 5:

[0750] The server generates responses using a generative AI model. Based on the prompt, the generative AI model interprets the text data and generates a natural-sounding response. The input is the prompt and the matching result, and the output is the generated response. This ensures that a personalized and appropriate response is prepared for the user.

[0751] Step 6:

[0752] The terminal provides the generated response to the user in audio format. Using a response delivery mechanism, the terminal outputs the response as audio that the user can hear. The input is response data from the server, and the output is an audio response. This allows the user to receive immediate feedback.

[0753] Step 7:

[0754] The server detects anomalies and sends notifications as needed. Through the anomaly notification system, if no user activity is detected for a predetermined period of time, an anomaly is notified to a designated contact. The input is activity detection information, and the output is an anomaly notification signal. This ensures user safety.

[0755] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0756] This invention is an advanced conversation and monitoring support system that incorporates emotion recognition for the user. In addition to motion detection means, voice acquisition means, data management means, response generation means, and anomaly notification means, this system includes an emotion engine that recognizes the user's emotions.

[0757] The terminal first detects user movement using a motion sensor and activates the system when it determines that the user has entered a range. When the user speaks, the terminal uses a microphone to capture the voice, and a voice acquisition device converts this into digital data. The converted voice data is sent to a server, where a data management device converts it back into text.

[0758] The server compares the text data analyzed by the data management system with past conversation history. This process enables contextual understanding based on past conversations. Next, the emotion engine identifies the user's emotions from the speech. The emotion engine evaluates the features of the analyzed speech and identifies emotions such as joy, sadness, and anger.

[0759] The response generation mechanism prepares a response that matches the user's emotions based on the emotional information identified by the emotion engine. The server selects the optimal response and sends it to the terminal. The terminal provides the received response to the user, enabling a natural conversation that takes emotions into consideration.

[0760] Furthermore, the device has a function that automatically turns off the power if no activity is detected for a certain period of time (e.g., 10 minutes or more) to ensure energy efficiency. On the other hand, if no activity is detected for a predetermined period of time, the abnormality notification system sends a warning to pre-configured family members or related parties via the server. This ensures the user's safety and enables a quick response.

[0761] For example, if a user speaks in an emotionally sad voice saying, "I'm feeling a little lonely today," the emotion engine recognizes the "sadness," and the response generation means selects a response in a gentle tone, such as, "I hope something cheers you up. Shall we continue talking?" In this way, the present invention aims to enhance the user's psychological support and provide a richer communication experience.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The device uses a motion sensor to detect user movement. When a user enters the sensor's range, the device activates its system and switches to voice acquisition mode.

[0765] Step 2:

[0766] When a user speaks into the device, the device's microphone receives the sound and converts it into digital audio data. The voice acquisition device performs this conversion.

[0767] Step 3:

[0768] The acquired audio data is immediately sent to the server, where the data management system converts the audio data into text data. This text data is then compared with past conversation history.

[0769] Step 4:

[0770] The server uses an emotion engine to analyze the user's emotional state from text data. It identifies emotions such as joy and sadness from the intonation and content of the voice.

[0771] Step 5:

[0772] The response generation mechanism generates an appropriate response based on the matching results and analyzed emotions. The generated response is designed to take the user's emotions into consideration and to facilitate a smooth conversation.

[0773] Step 6:

[0774] The server sends the generated response to the terminal, which then uses speech synthesis technology to provide the response to the user. The response is expressed in a way that reflects the user's emotions.

[0775] Step 7:

[0776] The device continuously monitors user activity, and if no activity is detected for more than 10 minutes, it automatically powers off and enters standby mode.

[0777] Step 8:

[0778] If no motion is detected by the sensor for a certain period of time or longer, the server uses an anomaly notification mechanism to send a warning to pre-configured contacts, such as family members, and reports the situation.

[0779] (Example 2)

[0780] 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".

[0781] In modern society, the increasing feelings of loneliness and stress experienced by individuals are a significant problem, but appropriate dialogue support systems for addressing these emotions are limited. Conventional technologies struggle to accurately recognize individual user emotions and provide corresponding responses, resulting in a lack of improved user experience. Furthermore, anomaly detection and appropriate response methods to ensure user safety are insufficient.

[0782] 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.

[0783] In this invention, the server includes motion detection means for detecting user actions, voice acquisition means for acquiring voice data, and emotion analysis means for analyzing emotions. This makes it possible to provide a dialogue system that builds deeper human relationships by accurately recognizing the emotions of individual users and providing natural responses based on those emotions. Furthermore, it can respond immediately to abnormal situations, thereby enhancing user safety.

[0784] "Motion detection means" refers to technology that uses sensors to detect physical movements occurring around the user.

[0785] "Voice acquisition means" refers to technology for collecting and digitizing the voice emitted by a user.

[0786] "Data management means" refers to technology used to appropriately process acquired digital audio data and compare it with past dialogue history.

[0787] "Sentiment analysis techniques" are technologies that evaluate the features of audio data to identify emotions such as joy, sadness, and anger.

[0788] "Response generation means" refers to technology for automatically generating appropriate responses to the user based on analysis results.

[0789] A "response delivery means" is a technology that transmits the generated response to the user and provides a natural dialogue.

[0790] An "anomaly warning mechanism" is a technology that notifies pre-configured contacts of an anomaly if no operation is detected within the system for a certain period of time or longer.

[0791] "Control means" refers to technologies for managing various functions of a system and adjusting power supply under specific conditions.

[0792] "Communication means" refers to technologies for transmitting abnormality or response information to users or related organizations in remote locations.

[0793] This invention is an advanced conversation support system that understands the user's emotions and provides dialogue accordingly. The system includes motion detection means, voice acquisition means, data management means, emotion analysis means, response generation means, response provision means, abnormality warning means, and control means.

[0794] Hardware and software configuration

[0795] The device utilizes infrared sensors and camera modules as motion detection means. This allows the system to activate when it detects that a user has entered the sensor's range. A microphone is used to acquire the user's voice, converting the analog audio into a digital signal. The converted digital audio data is then sent to a server either within the device or in the cloud.

[0796] The server converts received audio data into text using data management tools and compares it with past conversation history. Natural language processing techniques are used to deepen the understanding of context. For sentiment analysis, machine learning models are utilized to identify emotions from the audio waveform and text content.

[0797] The response generation mechanism generates several emotion-appropriate dialogue phrases based on a generation AI model. The server then selects an appropriate response and sends it to the terminal via the response delivery mechanism. The terminal then communicates the response to the user via voice through its speaker.

[0798] Furthermore, if no activity is detected for a certain period of time, the device will automatically switch to power-saving mode. In addition, an abnormality warning system will notify the user's family and related parties via communication if no activity is detected.

[0799] Specific example

[0800] For example, if a user says, "I'm feeling a little lonely today," the system receives the audio and, through emotion analysis, determines that the user is feeling "sad." Based on this result, the response generation system generates a gentle response such as, "I hope something cheers you up. Shall we continue talking?" In this way, the system provides a conversational experience aimed at providing psychological support to the user.

[0801] Example of a prompt

[0802] "Generate examples of appropriate dialogue for when a user is feeling lonely."

[0803] "Please provide examples of conversational responses based on the results of emotion recognition."

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] The terminal detects user movement using a motion sensor or camera. The system activates when the user enters the sensor range. At this stage, the input is the user's physical movement, and the output is the system activation signal.

[0807] Step 2:

[0808] When a user speaks, the device uses its built-in microphone to capture the voice. Since the captured voice is an analog signal, it is converted into a digital signal. The input is the user's voice data, and the output is the digitized voice data.

[0809] Step 3:

[0810] The digitized audio data is sent from the terminal to the server. The server uses data management tools to convert the audio data into text. In this step, the input is digital audio data, and the output is data in text format.

[0811] Step 4:

[0812] The server performs contextual analysis by comparing the transcribed data with past conversation history. This process uses natural language processing techniques, with text data as input and parsed data containing contextual information as output.

[0813] Step 5:

[0814] The server uses sentiment analysis tools to identify the user's emotions from the analyzed text data and voice features. The input is the analyzed data, and the output is the identified emotion information.

[0815] Step 6:

[0816] The response generation mechanism generates multiple response candidates based on emotional information. This process uses a generative AI model, where the input is emotional information and the output is a group of response candidates.

[0817] Step 7:

[0818] The server selects the best response from the generated candidate responses. The selection criteria reflect past conversation history and the user's sentiment. The input is the set of candidate responses, and the output is the selected response.

[0819] Step 8:

[0820] The selected response is sent to the terminal, which then communicates it to the user via voice through its speaker. The input is the selected response, and the output is the voice response to the user.

[0821] Step 9:

[0822] If no activity is detected for a certain period of time, the device will use an abnormality warning mechanism to send a warning message to a designated contact. The input is activity detection information, and the output is the warning message.

[0823] (Application Example 2)

[0824] 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".

[0825] Modern commercial facilities require personalized service for customers, but traditional customer service systems struggle to improve customer satisfaction because they cannot respond in a way that takes emotions and feelings into account. Furthermore, while rapid response to emergencies is crucial, the inability to assess urgency based on emotional changes results in inadequate responses.

[0826] 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.

[0827] In this invention, the server includes detection means for detecting movement, acquisition means for acquiring voice information, and management means for digitizing and managing the voice information. This makes it possible to recognize emotions from the customer's movements and voice, and to provide appropriate responses and anomaly notifications according to the situation.

[0828] "Detection means" refers to a device or mechanism for sensing a user's movements and determining their presence or actions.

[0829] "Acquisition means" refers to a mechanism for collecting voice information emitted by the user and further incorporating it as digital data.

[0830] "Management measures" refer to procedures for appropriately processing and managing data by saving acquired data and comparing it with past records.

[0831] "Generation method" refers to the process of creating appropriate and emotionally considerate responses for users based on managed data.

[0832] "Means of delivery" refers to the method of communicating the generated response to the user and providing an interactive experience.

[0833] A "notification method" is a communication protocol used to quickly send information to designated contacts when an anomaly is detected.

[0834] "Emotion recognition means" refers to technology that analyzes a user's voice and actions to determine and understand their emotional state.

[0835] This system includes detection means for sensing user actions, acquisition means for collecting voice information, and management means for processing data. The server processes the data in the following steps: First, hardware such as a microphone for voice acquisition and sensors for analyzing emotions are used. This captures the user's voice and actions in detail.

[0836] The audio data is digitized by the acquisition method, and the management method uses it to compare it with accumulated historical records. During this process, Google Speech-to-Text API is used for speech recognition, and Google Cloud Natural Language or Microsoft Azure Text Analytics are used for sentiment analysis. This process evaluates the audio features and identifies emotions such as joy, sadness, and anger.

[0837] The emotion recognition means uses that information to generate an appropriate response, which the delivery means then communicates to the user. This entire process enables dynamic responses and personalized service tailored to the customer's emotions in a physical store setting.

[0838] For example, if a customer in a store spends a long time browsing products but is unresponsive when spoken to, the emotion recognition system might determine that the customer is "tired." The robot could then gently ask, "You seem tired; is there anything I can help you with?" This is an example of natural conversation supported by a generative AI model.

[0839] An example of a prompt message would be, "Please suggest to the assistant how to provide customer service on a tiring day." This example allows for flexible responses tailored to the customer's situation.

[0840] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0841] Step 1:

[0842] When a user enters the system's range, the terminal's motion detection system senses the user's movement. This detection triggers the system to activate. The input is the user's physical movement, and the output is the system's activation signal. This action prepares the system to proceed to the next step.

[0843] Step 2:

[0844] The device collects voice data using voice acquisition methods when the user speaks. The input is the user's voice information, which is converted into digital data. The output is the digitized voice data. This makes the voice data in an analyzable format.

[0845] Step 3:

[0846] The server uses a management system to compare digitized audio data with past records. Inputs are digital audio data and past conversation records, while output is information indicating the context in which the current conversation is taking place. Data processing creates a foundation for providing appropriate responses.

[0847] Step 4:

[0848] The server's emotion recognition system analyzes and identifies the user's emotions from the audio data. The input is the contextual information and audio features obtained in the previous step, and the output is the emotion (joy, sadness, anger, etc.) that the user is judged to be experiencing. Through data processing, the user's psychological state is clarified.

[0849] Step 5:

[0850] A response generation mechanism based on this mechanism creates an appropriate response within the server that corresponds to the user's emotions. The input is the user's emotional information and current context information, and the output is the specific response content to the user. The generated response enables natural conversation.

[0851] Step 6:

[0852] The terminal uses the provided means to present the generated response to the user. The input is the response content sent from the server, and the output is information displayed via audio or on the screen. The interaction with the user is completed upon the provision of this information.

[0853] Step 7:

[0854] If an anomaly is detected and no user activity is detected for a specified period (e.g., 10 minutes or more), the server's notification system will send an alert to the configured contact. The input is data indicating inactivity from the motion detection sensor, and the output is an alert message. This operation ensures user safety.

[0855] 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.

[0856] 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.

[0857] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] A motion detection means for detecting the user's actions,

[0879] The motion detection means detects the user's actions and initiates a conversation, and the voice acquisition means acquires voice information.

[0880] A data management system that digitizes acquired voice information and compares it with accumulated past conversation data,

[0881] A response generation means that generates an appropriate response based on past conversation data,

[0882] A response providing means that provides the generated response to the user,

[0883] An abnormality notification means that notifies of an abnormality if no operation is detected for a predetermined period of time,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising a control means for turning off the power when the motion detection means does not detect any user activity for 10 minutes or more.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the abnormality notification means comprises a communication means for notifying a predetermined contact of the abnormality.

[0889] "Example 1"

[0890] (Claim 1)

[0891] A sensing device that detects the user's movements,

[0892] A voice collection device that initiates a conversation and acquires voice information when the user's actions are detected by the sensing device,

[0893] A data processing device that converts acquired voice information into data and compares it with stored past conversation data,

[0894] A response generation means for constructing natural language conversations using a response generation device that generates appropriate responses based on past conversation data,

[0895] A response provider that provides the generated response to the user in real time,

[0896] An abnormality notification device that notifies of an abnormality if no operation is detected for a predetermined period of time,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, further comprising an energy management device that turns off the power when the sensing device does not detect any user activity for 10 minutes or more.

[0900] (Claim 3)

[0901] The system according to claim 1, wherein the abnormality notification device includes a communication device that notifies a predetermined contact of the abnormality.

[0902] "Application Example 1"

[0903] (Claim 1)

[0904] Motion detection means including a human presence sensor for detecting user movements,

[0905] The motion detection means initiates a conversation and acquires voice information when a user approaches, and

[0906] A data management means that converts acquired voice information into digital data, sends it to a server, and compares it with previously stored conversation history,

[0907] A response generation means that generates an appropriate response using a generative AI model based on a database,

[0908] A response provisioning means that provides the generated response to the user in voice,

[0909] An anomaly notification means that includes a communication means for notifying a designated contact of an anomaly if user activity is not detected for a predetermined period of time,

[0910] A suggestion generation method that proposes products based on past purchase history,

[0911] A digital conversion method that uses a speech recognition API to convert speech data into text data,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, further comprising a control means for turning off the power to conserve energy when no operation is detected for a set period of time or longer.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising a function to report an anomaly to staff using a communication means that notifies a designated contact of the anomaly.

[0917] "Example 2 of combining an emotion engine"

[0918] (Claim 1)

[0919] A motion detection means for detecting user actions,

[0920] The motion detection means initiates a dialogue and acquires voice data when it detects a user's action, and

[0921] A data management system that digitizes acquired voice data and compares it with accumulated past dialogue information,

[0922] A sentiment analysis method that identifies emotions by analyzing the characteristics of speech based on past dialogue information,

[0923] A response generation means that generates an appropriate response based on identified emotional information,

[0924] A response-providing means that provides the generated response to the user and realizes dialogue that takes human emotions into consideration,

[0925] An abnormality warning means that notifies of an abnormality if no operation is detected for a predetermined period of time,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising a control means for shutting off power when the motion detection means does not detect any user activity for 10 minutes or more.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the abnormality warning means includes a communication means for reporting the abnormality to a predetermined contact.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] A detection means for detecting user actions,

[0934] The detection means initiates a conversation and acquires voice information when the user's actions are detected,

[0935] A management system that digitizes the acquired audio information and compares it with accumulated past records,

[0936] A generation means that generates an appropriate response based on past records,

[0937] A means of providing the generated response to the user,

[0938] A notification means that notifies of an abnormality if no operation is detected for a predetermined period of time,

[0939] An emotion recognition means that recognizes the user's emotions and adjusts the response based on the recognized emotions,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, which dynamically changes the customer service style based on the emotions of the user identified by the emotion recognition means.

[0943] (Claim 3)

[0944] The system according to claim 1, wherein the notification means comprises a communication means for notifying a predetermined contact of an abnormality, and has a function for determining urgency based on the identification of emotions. [Explanation of symbols]

[0945] 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 motion detection means for detecting the user's actions, The motion detection means detects the user's actions and initiates a conversation, and the voice acquisition means acquires voice information. A data management system that digitizes acquired voice information and compares it with accumulated past conversation data, A response generation means that generates an appropriate response based on past conversation data, A response providing means that provides the generated response to the user, An abnormality notification means that notifies of an abnormality if no operation is detected for a predetermined period of time, A system that includes this.

2. The system according to claim 1, further comprising a control means for turning off the power when the motion detection means does not detect any user activity for 10 minutes or more.

3. The system according to claim 1, wherein the abnormality notification means includes a communication means for notifying a predetermined contact of the abnormality.

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