system

Abstract

We provide the system. [Solution] A generation device that generates appropriate dialogue based on the characteristics of the user, A speech recognition device for converting user voice input into text data, An analytical device that performs emotional analysis to analyze the user's state, A user interface device that provides a visual interface that can be easily operated by the user, A display device for presenting information to a visual device, A speech synthesis device for presenting audio data through a visual device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, the number of elderly people who are concerned about the decline of cognitive function is increasing. In order to maintain and improve the cognitive function of such elderly people, appropriate communication opportunities are necessary, but it is not easy to constantly provide conversations according to individual elderly people. In particular, when targeting elderly people with diverse cognitive levels and characteristics, the establishment of a flexible and efficient support method is required.

Means for Solving the Problems

[0005] This invention provides an elderly support system equipped with a generation module that generates dialogues based on the user's characteristics, a speech recognition module that converts speech to text, and an analysis module that performs sentiment analysis. This system provides personalized conversation opportunities tailored to the individual cognitive level and interests of elderly individuals, thereby helping to maintain cognitive function. Furthermore, the adoption of a visual interface makes the system easy to use and compensates for low IT literacy. Therefore, through sustained dialogue, it is possible to prevent dementia and improve the quality of life.

[0006] "Users" refer to individual elderly people who use the system and receive personalized services based on their specific characteristics.

[0007] "Characteristics" refer to the user's cognitive level, interests, language use, etc., and are the basis for providing services tailored to these characteristics.

[0008] A "generation module" is a component of a system that has the function of generating appropriate dialogue content based on the characteristics of the user.

[0009] A "speech recognition module" is a component of a system that has the function of converting voice input uttered by the user into text data, forming the foundation for continuing and analyzing conversations.

[0010] "Sentiment analysis" is a process that detects and analyzes the emotions contained in the user's voice data and adjusts the content of the conversation based on the results.

[0011] A "user interface module" is a component of a system that provides a visual and interactive interface for users to interact with the system.

[0012] An "elderly support system" is a system designed to support the cognitive functions of elderly people and provide personalized communication. [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 the data processing device and 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, when an emotion engine is combined. [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, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention provides an elderly support system that assists the cognitive function of the elderly and stimulates conversation in daily life. This system operates as follows, while exchanging information between a server, a terminal, and a user.

[0035] The server first retrieves user characteristic information from a database. This characteristic information includes the user's cognitive level, past activity logs, interests, and spoken language characteristics. Based on this information, a generation module installed on the server generates dialogue content suitable for the user. The generated dialogue content is sent to the terminal in text format. For example, the server provides dialogue content to a user whose hobby is gardening, including the latest knowledge and questions related to gardening.

[0036] The terminal receives text-based dialogue from the server, converts it into audio data, and delivers it to the user. When the user speaks to the terminal, the terminal's speech recognition module converts the voice input into text data and sends that content back to the server. Simultaneously, an emotion analysis module analyzes the tone and patterns of the user's voice and evaluates the user's emotional state. For example, if the user responds with an energetic voice, the terminal can maintain the user's interest by offering more interesting topics for the next day's dialogue.

[0037] Users can access the user interface displayed on their device and adjust the topic and progress of the conversation with simple touch operations. For example, if a user wants to change the topic of conversation from gardening to cooking, they can do so by tapping the device screen. This allows users to enjoy the conversation at their own pace while stimulating their cognitive functions.

[0038] In this way, the elderly support system of the present invention provides a personalized conversational experience for each user, aiming to maintain cognitive function and improve the quality of life for the elderly. Through continuous data collection and analysis, the system can provide the most appropriate support for each individual user.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server receives user identification information and retrieves characteristic information from the user profile database, such as the user's cognitive level, interests, and past activity logs.

[0042] Step 2:

[0043] The server uses a generation module based on acquired characteristic information to generate the most suitable dialogue for the user. This dialogue is expressed in text format and adjusted to capture the user's interest.

[0044] Step 3:

[0045] The server sends the generated dialogue content to the terminal. It also sends related images and supplementary information as needed.

[0046] Step 4:

[0047] The terminal receives text-based dialogue content from the server, converts it into audio data using a speech synthesis module, and plays it back to the user.

[0048] Step 5:

[0049] The user responds to the device using voice. This response is recorded by the device's built-in microphone.

[0050] Step 6:

[0051] The terminal converts the recorded user voice input into text data using a speech recognition module. This text data is then prepared for the following analysis.

[0052] Step 7:

[0053] The terminal sends the converted text data to the server, and at the same time uses an emotion analysis module to analyze the user's emotional state from the voice data, and also sends the results to the server.

[0054] Step 8:

[0055] Based on the user's response text and sentiment analysis results, the server adjusts the direction and topic of the new conversation and prepares to generate the next dialogue.

[0056] Step 9:

[0057] Users can adjust the theme and difficulty level of the dialogue by manipulating the device's user interface. Based on this action, the next dialogue will be redesigned.

[0058] The goal is to support the cognitive function of older adults and provide them with opportunities to enjoy conversation spontaneously by repeating steps within this system.

[0059] (Example 1)

[0060] 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."

[0061] As people age, their cognitive function may decline, leading to a decrease in their quality of life. Therefore, there is a need to provide an appropriate environment that supports cognitive function in older adults. In particular, providing individualized communication tailored to each elderly person is expected to stimulate dialogue and maintain cognitive function. However, there is currently a lack of dialogue systems that take into account individual abilities and preferences.

[0062] 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.

[0063] In this invention, the server includes means for acquiring user characteristic information and generating personalized dialogue content based on said characteristics; means for converting the generated dialogue content into visual or audio format and providing it; and means for converting the user's voice input into text data. This makes it possible to provide dialogue tailored to each individual user, thereby realizing cognitive function support for the elderly.

[0064] "Characteristic information" refers to individualized information such as the user's cognitive level, hobbies and interests, and language characteristics.

[0065] "Generation means" refers to processes or modules for constructing personalized dialogue content based on characteristic information.

[0066] "Voice conversion means" refers to the process or technology of converting voice input into text data, including real-time conversion.

[0067] "Analysis means" refers to processes and modules used to analyze the user's emotional state and interests from acquired audio data.

[0068] A "visual interface" refers to a user interface designed for easy operation by the user, and includes functions for adjusting the theme and progress of a conversation.

[0069] This invention is a system aimed at supporting the cognitive function of the elderly and stimulating conversation in daily life. This system exchanges information between a server, a terminal, and a user, and provides dialogue using various modules.

[0070] The server first retrieves individual user characteristics from the database. This characteristics include the user's cognitive level, past activity logs, hobbies and interests, and spoken language characteristics. The server then uses a generative AI model to generate dialogue based on this characteristics. For example, natural language generation technology can be used for the generative AI model. A possible prompt might be, "The user's hobby is gardening. Please generate dialogue about the latest gardening techniques."

[0071] The terminal receives the generated text-based dialogue content from the server and converts it into speech data using speech synthesis software. The specific software could be a general-purpose speech synthesis technology. The converted speech data is then provided to the user as audio.

[0072] When a user speaks into the device, it uses speech recognition technology to convert the input into text data and sends it back to the server. The device also includes an emotion analysis module that analyzes the tone and patterns of the voice to evaluate the user's emotional state. If the user responds positively to the conversation, more interesting topics will be offered in the next conversation.

[0073] Furthermore, users can adjust the theme and progress of the conversation using the user interface displayed on their device. For example, if a user wants to change the theme of the conversation from gardening to cooking, they can do so through the device's interface.

[0074] In this way, the present invention can provide each elderly person with a personalized conversational experience, thereby maintaining cognitive function and improving their quality of life.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server accesses the database to retrieve user characteristic information. The input is the user's ID, and based on this, data such as cognitive level, past activity logs, hobbies and interests, and speech characteristics are extracted. The extracted information serves as foundational data for creating prompts for the generative AI model.

[0078] Step 2:

[0079] The server uses a generative AI model based on acquired characteristic information to generate prompt statements, and then creates individual dialogue content based on these prompts. Specifically, the data processing involves transforming the information into a format such as, "The user's hobby is gardening. Please generate dialogue content about the latest gardening techniques," and inputting it into the AI ​​model. The output is text data of dialogue content tailored to the user.

[0080] Step 3:

[0081] The server sends the generated text-formatted dialogue to the terminal. Here, the output from the server is text data, which the terminal uses for its next processing. This transmission process takes place over the network.

[0082] Step 4:

[0083] The terminal converts the received text data of the conversation into speech data using a speech synthesis module. Text data is taken as input, and the conversion results in an audio file (e.g., WAV format) that the user can listen to. Specifically, speech synthesis software is used.

[0084] Step 5:

[0085] The user speaks in response to the dialogue presented audibly from the terminal. The terminal converts this into text data using a speech recognition module. The input is the user's voice, and the output is the text data of that voice. The converted text is then sent to a server for further processing. Specifically, it is analyzed using speech recognition technology.

[0086] Step 6:

[0087] The terminal processes the user's voice input through an emotion analysis module, evaluating the emotional state based on the tone and patterns of the voice. This analysis outputs an index indicating the user's emotions from the input voice data. The results are sent to a server and used to adjust the content of subsequent conversations.

[0088] Step 7:

[0089] Users can change the topic and progression of the conversation using the terminal's user interface. Based on the input from the user interface, the terminal transmits the request to the server, which then adjusts the content of the next conversation accordingly. This feedback loop enhances the personalization of the conversation.

[0090] (Application Example 1)

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

[0092] In an aging society, there is a need to support the cognitive functions of the elderly and revitalize their daily conversations. Furthermore, improving the quality of life for users by providing individually optimized dialogue is a key challenge. In addition, there is a desire to enable on-site staff to provide support more efficiently by utilizing smart devices and enabling use in diverse environments.

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

[0094] In this invention, the server includes a generation device that generates appropriate dialogue based on the user's characteristics, a speech recognition device for converting the user's voice input into text data, an analysis device that performs sentiment analysis to analyze the user's state, a display device for presenting information to a visual device, and a speech synthesis device for presenting voice data through the visual device. This enables elderly people to enjoy natural and individually optimized dialogue, and in care settings, staff can provide appropriate conversations to users.

[0095] A "generator" is a device that has the function of generating appropriate dialogue based on the characteristics of the user.

[0096] A "speech recognition device" is a device that has the function of converting a user's voice input into text data.

[0097] An "analysis device" is a device that analyzes the tone and patterns of a user's voice and has the function of evaluating their emotional state.

[0098] A "user interface device" is a device that provides visual information that can be easily operated by the user.

[0099] A "display device" is a device that has the function of presenting information to a visual device.

[0100] A "speech synthesis device" is a device that has the function of presenting audio data through a visual device.

[0101] This system utilizes smart glasses as hardware and a server for backend processing. The server retrieves user characteristic information from a database, generates personalized dialogue content using a generator, and sends it to the terminal as text data. The terminal is the smart glasses, which converts voice input from the user into text data via a speech recognition device. A speech synthesis device also provides the text data received from the server as voice data tailored to the user. The data acquired by the speech recognition device is analyzed for emotion by an analysis device, and the content of the next dialogue is improved based on the user's emotional state. The user can adjust the theme and progress of the dialogue using visual information provided through the display device.

[0102] The software used includes Python libraries such as speech_recognition, pyttsx3, and TextBlob, which perform speech data recognition, text conversion, speech synthesis, and sentiment analysis. This system allows users to experience more natural and engaging conversations, ultimately contributing to the maintenance and activation of cognitive functions.

[0103] For example, if an elderly person is interested in gardening, the server might generate dialogue such as, "Are you interested in recent gardening topics? For example, there are new types of flowers." An example of a prompt might be, "The user is interested in gardening. Please suggest new gardening topics."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server retrieves user characteristic information from the database. This characteristic information includes the user's cognitive level, interests, and language characteristics. Based on this information, a generative AI model is used to generate personalized dialogue content for the user. The input here is the characteristic information, and the data processing involves analyzing this characteristic information to generate appropriate dialogue content. The output is the text data of the generated dialogue content.

[0107] Step 2:

[0108] The server sends the generated dialogue as text data to the terminal. The terminal receives this text data and converts it into speech data using a speech synthesis device. At this stage, the input is the text data received from the server, and the processing involves speech synthesis. The output is the speech data delivered to the user.

[0109] Step 3:

[0110] The user listens to audio data provided through the device (smart glasses). Then, when the user speaks a response, the device uses a speech recognition device to convert the voice input into text data. The input is the user's voice data, which is converted into text data through the conversion process. The output is the text data after speech recognition.

[0111] Step 4:

[0112] The server, upon receiving the text data, uses an analysis device to perform sentiment analysis and evaluate the user's emotional state. The input in this step is the converted text data, and data calculations are performed through sentiment analysis. The output is information about the user's emotional state.

[0113] Step 5:

[0114] The user operates the user interface device to obtain visual information from the terminal and to change the content and theme of the next dialogue. Input is the theme and dialogue progression instructions selected by the user, and the settings are changed as a result of the operation. Output is the updated dialogue settings information.

[0115] 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.

[0116] This invention provides a system for elderly care that recognizes the user's emotions and optimizes the dialogue based on those emotions. This system includes a server, a terminal, and an emotion engine as its main components.

[0117] The server first retrieves characteristic information such as cognitive level and interests from the database based on the user's identification information. Using this information, the generation module personalizes the conversation content to suit the user. Furthermore, the emotion engine analyzes the voice input and understands the user's emotional state in real time. This allows the generation module to dynamically adjust the conversation content considering the emotional state. For example, if the user is feeling anxious, the system will prioritize topics that provide reassurance.

[0118] The terminal converts the text-based dialogue received from the server into audio data and plays it back. When a user joins the conversation, the terminal converts the voice input into text data through a speech recognition module. This text data is sent to the server, and emotion analysis is performed by the emotion engine. For example, if the user is speaking in a cheerful voice, that positive emotional state is reflected in the next dialogue generation, increasing the user's motivation to continue the conversation.

[0119] Users can adjust the content and theme of the conversation through the terminal's user interface. For example, they can change the conversation theme to an area of ​​interest through the terminal's intuitive operation. This change is reflected in real time, and the server regenerates the new conversation content.

[0120] In this way, this system, which incorporates an emotion engine, recognizes the user's emotional state and dynamically customizes the conversation content based on that state, enabling more engaging and effective dialogue support for the elderly. Through daily conversations, it supports the improvement of cognitive function and quality of life for the elderly.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] Upon receiving user identification information, the server accesses a database to retrieve the user's cognitive level, interests, and past activity logs. Based on this information, the user's characteristics are profiled.

[0124] Step 2:

[0125] The server inputs the acquired profile data into a generation module to generate personalized dialogue tailored to the user. This dialogue is adjusted based on the user's interests and past statements.

[0126] Step 3:

[0127] The server sends the generated dialogue content to the terminal. It is provided as text data and configured for further processing by the terminal.

[0128] Step 4:

[0129] The terminal receives text-based dialogue from the server, converts it into speech using a speech synthesis module, and plays it back to the user. The speech output is clear and adjusted to be easily understood by the user.

[0130] Step 5:

[0131] The user verbally responds to the audio playing from the device. At this stage, audio data containing the user's emotions is collected.

[0132] Step 6:

[0133] The device converts the user's voice input into text data using a speech recognition module. Simultaneously, this voice data is sent to an emotion engine for real-time sentiment analysis.

[0134] Step 7:

[0135] The emotion engine analyzes emotional information obtained from the user's voice data and feeds the results back to the server. This information allows for a clear understanding of the user's current emotional state.

[0136] Step 8:

[0137] Based on feedback from the emotion engine, the server readjusts the generation module and updates the dialogue content as needed. For example, if the user expresses anxiety, dialogue that provides reassurance will be prioritized.

[0138] Step 9:

[0139] The user can operate the terminal's user interface to select the topic and difficulty level of the next conversation. The terminal receives this selection and requests the next dialogue content from the server.

[0140] By repeating this process, the system provides adaptive dialogue that allows elderly individuals to participate continuously, supporting the maintenance and improvement of cognitive function.

[0141] (Example 2)

[0142] 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".

[0143] The aim of this support system for the elderly is to effectively generate dialogues tailored to the user's emotions and characteristics, and to provide a method for improving cognitive function and quality of life through daily communication. Furthermore, it aims to enhance the user's willingness to converse by understanding their emotional state in real time and maintaining appropriate dialogue.

[0144] 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.

[0145] In this invention, the server includes means for acquiring characteristic information from a database based on user identification information, generation means for individualizing dialogue content based on the characteristic information, and means for analyzing voice input and evaluating the user's emotional state in real time. This enables the dynamic customization of dialogue that reflects the user's emotional state.

[0146] "Identification information" refers to information used to identify individual users.

[0147] "Characteristic information" refers to information necessary to generate personalized dialogue, such as the user's cognitive level, interests, and language characteristics.

[0148] "Generation means" refers to a function that personalizes dialogue content based on user characteristic information and provides appropriate conversation.

[0149] "Emotional state" is an indicator that shows the psychological and emotional state obtained from the user's voice and behavior.

[0150] "Means of converting to audio data" refers to a function that converts text-based dialogue content into audio and provides it to the user.

[0151] "Voice recognition means" refers to a function that converts voice input from the user into text data in real time.

[0152] A "user interface means" is a function that provides an interface that allows users to easily select or change dialogue themes.

[0153] This system, designed to support the elderly, consists primarily of a server, terminals, an emotion engine, and a speech recognition module.

[0154] The server uses user identification information to retrieve characteristic information such as cognitive level and interests from a database. This information is used to personalize the conversation content to suit the user by utilizing a generative AI model. The generated conversation content is analyzed by an emotion engine, and the emotional state is evaluated in real time. For example, if the user is feeling anxious, the server adjusts the conversation content to select topics that provide a sense of security.

[0155] The terminal converts the text-based dialogue received from the server into audio data and plays it back through the speaker. When a user joins the conversation, the terminal converts the voice input into text data using a speech recognition module and sends it to the server. This text data is also analyzed by an emotion engine, and the results are reflected in the generation of the next dialogue. For example, if the user is speaking with a smile, that positive emotion is reflected in the next dialogue, promoting the continuation of the conversation.

[0156] Users can adjust the content and theme of conversations using the terminal's user interface. For example, by changing the conversation theme to "hobbies" via the user interface, the server can generate new content and immediately reflect it in the conversation. In this way, the system supports the improvement of cognitive function and quality of life for the elderly through daily conversations.

[0157] The following are specific examples of prompt statements:

[0158] "Please generate conversational material that a 60-year-old woman can comfortably discuss. The theme is chatting at a bakery."

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server receives user identification information and accesses the database. It receives identification information as input and retrieves characteristic information such as the user's cognitive level and interests as output. This allows for the collection of basic personalized data about the user.

[0162] Step 2:

[0163] The server uses a generated AI model based on acquired characteristic information to personalize the conversation content. Using characteristic information as input, it outputs personalized conversation content tailored to the user. Specifically, it adjusts the difficulty of the conversation according to the user's cognitive level and selects topics based on their interests.

[0164] Step 3:

[0165] The terminal receives the dialogue content in text format sent from the server. It receives text data as input, converts it into audio data as output, and plays it through the speaker. Specifically, computer voice is generated using speech synthesis technology.

[0166] Step 4:

[0167] The user listens to the audio played from the device and responds to the conversation. The user's voice input is captured by the device, and audio data is obtained as concrete input.

[0168] Step 5:

[0169] The terminal processes the user's voice input through a speech recognition module to convert it into text data. It takes voice data as input and generates text data as output. Specifically, the speech recognition algorithm analyzes the speech and converts it into language. This text data is then sent to the server.

[0170] Step 6:

[0171] The server analyzes the received text data using an emotion engine. Using text data as input, it generates an evaluation of the user's emotional state as output. Specifically, emotion analysis technology is used to extract the emotional characteristics of the text.

[0172] Step 7:

[0173] The server dynamically adjusts the next dialogue based on the emotional state. It uses emotional evaluation as input and generates a new, adjusted dialogue as output. Specifically, if reassurance is needed, the dialogue content is restructured, such as by increasing the amount of encouragement.

[0174] Step 8:

[0175] Users operate the terminal's user interface to change the topic and content of the conversation. The specific input is the selection of a topic, and the new conversation topic is immediately reflected on the server as output. This allows users to engage in conversations tailored to their interests.

[0176] (Application Example 2)

[0177] 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."

[0178] The challenge lies in alleviating the individual psychological and emotional anxieties of the elderly and providing a more meaningful and reassuring communication environment. In particular, there is a need for methods to appropriately customize the content of conversations according to the diverse emotional states of the elderly, thereby improving their quality of life.

[0179] 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.

[0180] In this invention, the server includes generation means for generating appropriate dialogue based on the user's attributes, speech recognition means for converting the user's voice input into text information, and analysis means for performing emotion evaluation to analyze the user's state. This enables the dynamic adjustment of dialogue themes based on the user's emotional state, improving the quality of emotional support and communication for the elderly.

[0181] "User attributes" refer to characteristic information including the cognitive level, interests, and communication characteristics of the individuals targeted by the system.

[0182] A "generation mechanism" is a system for creating individually appropriate dialogue content based on the user's attribute information.

[0183] A "speech recognition system" is a mechanism that acquires the user's voice input and converts it into text information in real time.

[0184] The "analysis means" is a function that evaluates the user's emotional state based on the converted text information and uses the results to adjust the content of the dialogue.

[0185] A "user-operated device" is a device that provides a visual interface that users can easily operate and that assists in selecting dialogue content and themes.

[0186] A "setting method" is a method for dynamically adjusting and optimizing the theme and content of a conversation based on the user's emotional state.

[0187] The elderly support system of the present invention comprises a server, a terminal, and an emotion evaluation engine. The server has a generation means that generates dialogue content tailored to the user based on their characteristic information. Users communicate by voice via the terminal, which is equipped with a speech recognition means that converts speech into text information. The recognized text information is transferred to the server, where the emotional state is evaluated by an analysis means. This makes it possible to dynamically adjust the dialogue theme according to the user's state.

[0188] The hardware consists of smart glasses equipped with a microphone for capturing audio data and a speaker for playback. The software uses the SpeechRecognition library for speech recognition and the tone-analyzer library for sentiment evaluation.

[0189] For example, if an elderly person says, "I'm feeling a bit down today," the server will generate a relaxing conversation such as, "Shall we talk about your favorite hobby?" This conversation is then delivered to the user as audio via the device, increasing their willingness to continue the conversation.

[0190] An example of a prompt for a generative AI model is, "Please suggest relaxing topics to bring up when a user expresses anxiety." The AI's response, based on this prompt, enables more appropriate communication.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user inputs voice through the device's microphone. The input voice data is captured by the smart glasses' microphone and processed in real time.

[0194] Step 2:

[0195] The device uses speech recognition to convert this audio data into text information. Specifically, it analyzes the audio waveform data using the SpeechRecognition library and converts it into corresponding text. This conversion result is output as text data for use in subsequent processing.

[0196] Step 3:

[0197] The server receives the converted text information and uses analysis tools to evaluate the emotional state. In this process, the tone-analyzer library is used to analyze the emotional characteristics of the text and output evaluations such as positive or negative.

[0198] Step 4:

[0199] The server generates dialogue content using a generation mechanism based on the analyzed emotional state. Based on the user's emotional evaluation and attribute information, a generation AI model is used to generate relaxing dialogue content using appropriate prompt sentences.

[0200] Step 5:

[0201] The generated dialogue is sent from the server to the terminal. The terminal converts this text data into audio data and plays it back to the user through the smart glasses' speaker.

[0202] Step 6:

[0203] The user continues to participate in the dialogue based on the provided dialogue content. If there is any new input during this dialogue, the system restarts from step 1 and continues the dialogue.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention provides an elderly support system that assists the cognitive function of the elderly and stimulates conversation in daily life. This system operates as follows, while exchanging information between a server, a terminal, and a user.

[0221] The server first retrieves user characteristic information from a database. This characteristic information includes the user's cognitive level, past activity logs, interests, and spoken language characteristics. Based on this information, a generation module installed on the server generates dialogue content suitable for the user. The generated dialogue content is sent to the terminal in text format. For example, the server provides dialogue content to a user whose hobby is gardening, including the latest knowledge and questions related to gardening.

[0222] The terminal receives text-based dialogue from the server, converts it into audio data, and delivers it to the user. When the user speaks to the terminal, the terminal's speech recognition module converts the voice input into text data and sends that content back to the server. Simultaneously, an emotion analysis module analyzes the tone and patterns of the user's voice and evaluates the user's emotional state. For example, if the user responds with an energetic voice, the terminal can maintain the user's interest by offering more interesting topics for the next day's dialogue.

[0223] Users can access the user interface displayed on their device and adjust the topic and progress of the conversation with simple touch operations. For example, if a user wants to change the topic of conversation from gardening to cooking, they can do so by tapping the device screen. This allows users to enjoy the conversation at their own pace while stimulating their cognitive functions.

[0224] In this way, the elderly support system of the present invention provides a personalized conversational experience for each user, aiming to maintain cognitive function and improve the quality of life for the elderly. Through continuous data collection and analysis, the system can provide the most appropriate support for each individual user.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server receives user identification information and retrieves characteristic information from the user profile database, such as the user's cognitive level, interests, and past activity logs.

[0228] Step 2:

[0229] The server uses a generation module based on acquired characteristic information to generate the most suitable dialogue for the user. This dialogue is expressed in text format and adjusted to capture the user's interest.

[0230] Step 3:

[0231] The server sends the generated dialogue content to the terminal. It also sends related images and supplementary information as needed.

[0232] Step 4:

[0233] The terminal receives text-based dialogue content from the server, converts it into audio data using a speech synthesis module, and plays it back to the user.

[0234] Step 5:

[0235] The user responds to the device using voice. This response is recorded by the device's built-in microphone.

[0236] Step 6:

[0237] The terminal converts the recorded user voice input into text data using a speech recognition module. This text data is then prepared for the following analysis.

[0238] Step 7:

[0239] The terminal sends the converted text data to the server, and at the same time uses an emotion analysis module to analyze the user's emotional state from the voice data, and also sends the results to the server.

[0240] Step 8:

[0241] Based on the user's response text and sentiment analysis results, the server adjusts the direction and topic of the new conversation and prepares to generate the next dialogue.

[0242] Step 9:

[0243] Users can adjust the theme and difficulty level of the dialogue by manipulating the device's user interface. Based on this action, the next dialogue will be redesigned.

[0244] The goal is to support the cognitive function of older adults and provide them with opportunities to enjoy conversation spontaneously by repeating steps within this system.

[0245] (Example 1)

[0246] 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."

[0247] As people age, their cognitive function may decline, leading to a decrease in their quality of life. Therefore, there is a need to provide an appropriate environment that supports cognitive function in older adults. In particular, providing individualized communication tailored to each elderly person is expected to stimulate dialogue and maintain cognitive function. However, there is currently a lack of dialogue systems that take into account individual abilities and preferences.

[0248] 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.

[0249] In this invention, the server includes means for acquiring user characteristic information and generating personalized dialogue content based on said characteristics; means for converting the generated dialogue content into visual or audio format and providing it; and means for converting the user's voice input into text data. This makes it possible to provide dialogue tailored to each individual user, thereby realizing cognitive function support for the elderly.

[0250] "Characteristic information" refers to individualized information such as the user's cognitive level, hobbies and interests, and language characteristics.

[0251] "Generation means" refers to processes or modules for constructing personalized dialogue content based on characteristic information.

[0252] "Voice conversion means" refers to the process or technology of converting voice input into text data, including real-time conversion.

[0253] "Analysis means" refers to processes and modules used to analyze the user's emotional state and interests from acquired audio data.

[0254] A "visual interface" refers to a user interface designed for easy operation by the user, and includes functions for adjusting the theme and progress of a conversation.

[0255] This invention is a system aimed at supporting the cognitive function of the elderly and stimulating conversation in daily life. This system exchanges information between a server, a terminal, and a user, and provides dialogue using various modules.

[0256] The server first retrieves individual user characteristics from the database. This characteristics include the user's cognitive level, past activity logs, hobbies and interests, and spoken language characteristics. The server then uses a generative AI model to generate dialogue based on this characteristics. For example, natural language generation technology can be used for the generative AI model. A possible prompt might be, "The user's hobby is gardening. Please generate dialogue about the latest gardening techniques."

[0257] The terminal receives the generated text-based dialogue content from the server and converts it into speech data using speech synthesis software. The specific software could be a general-purpose speech synthesis technology. The converted speech data is then provided to the user as audio.

[0258] When a user speaks into the device, it uses speech recognition technology to convert the input into text data and sends it back to the server. The device also includes an emotion analysis module that analyzes the tone and patterns of the voice to evaluate the user's emotional state. If the user responds positively to the conversation, more interesting topics will be offered in the next conversation.

[0259] Furthermore, users can adjust the theme and progress of the conversation using the user interface displayed on their device. For example, if a user wants to change the theme of the conversation from gardening to cooking, they can do so through the device's interface.

[0260] In this way, the present invention can provide each elderly person with a personalized conversational experience, thereby maintaining cognitive function and improving their quality of life.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server accesses the database to retrieve user characteristic information. The input is the user's ID, and based on this, data such as cognitive level, past activity logs, hobbies and interests, and speech characteristics are extracted. The extracted information serves as foundational data for creating prompts for the generative AI model.

[0264] Step 2:

[0265] The server uses a generative AI model based on acquired characteristic information to generate prompt statements, and then creates individual dialogue content based on these prompts. Specifically, the data processing involves transforming the information into a format such as, "The user's hobby is gardening. Please generate dialogue content about the latest gardening techniques," and inputting it into the AI ​​model. The output is text data of dialogue content tailored to the user.

[0266] Step 3:

[0267] The server sends the generated text-formatted dialogue to the terminal. Here, the output from the server is text data, which the terminal uses for its next processing. This transmission process takes place over the network.

[0268] Step 4:

[0269] The terminal converts the received text data of the conversation into speech data using a speech synthesis module. Text data is taken as input, and the conversion results in an audio file (e.g., WAV format) that the user can listen to. Specifically, speech synthesis software is used.

[0270] Step 5:

[0271] The user speaks in response to the dialogue presented audibly from the terminal. The terminal converts this into text data using a speech recognition module. The input is the user's voice, and the output is the text data of that voice. The converted text is then sent to a server for further processing. Specifically, it is analyzed using speech recognition technology.

[0272] Step 6:

[0273] The terminal processes the user's voice input through an emotion analysis module, evaluating the emotional state based on the tone and patterns of the voice. This analysis outputs an index indicating the user's emotions from the input voice data. The results are sent to a server and used to adjust the content of subsequent conversations.

[0274] Step 7:

[0275] Users can change the topic and progression of the conversation using the terminal's user interface. Based on the input from the user interface, the terminal transmits the request to the server, which then adjusts the content of the next conversation accordingly. This feedback loop enhances the personalization of the conversation.

[0276] (Application Example 1)

[0277] 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."

[0278] In an aging society, there is a need to support the cognitive functions of the elderly and revitalize their daily conversations. Furthermore, improving the quality of life for users by providing individually optimized dialogue is a key challenge. In addition, there is a desire to enable on-site staff to provide support more efficiently by utilizing smart devices and enabling use in diverse environments.

[0279] 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.

[0280] In this invention, the server includes a generation device that generates an appropriate conversation based on the characteristics of the user, a voice recognition device for converting the user's voice input into text data, an analysis device that performs sentiment analysis to analyze the user's state, a display device for presenting information to the visual device, and a voice synthesis device for presenting voice data through the visual device. As a result, it becomes possible for the elderly to enjoy a natural and individually optimized conversation, and even in the caregiving scene, staff can provide an appropriate conversation to the user.

[0281] The "generation device" is a device having a function of generating an appropriate conversation based on the characteristics of the user.

[0282] The "voice recognition device" is a device having a function of converting the user's voice input into text data.

[0283] The "analysis device" is a device having a function of analyzing the tone and pattern of the user's voice and evaluating the emotional state.

[0284] The "user interface device" is a device for providing visual information that can be easily operated by the user.

[0285] The "display device" is a device having a function of presenting information to the visual device.

[0286] The "voice synthesis device" is a device having a function of presenting voice data through the visual device.

[0287] This system utilizes smart glasses as hardware and a server for backend processing. The server retrieves user characteristic information from a database, generates personalized dialogue content using a generator, and sends it to the terminal as text data. The terminal is the smart glasses, which converts voice input from the user into text data via a speech recognition device. A speech synthesis device also provides the text data received from the server as voice data tailored to the user. The data acquired by the speech recognition device is analyzed for emotion by an analysis device, and the content of the next dialogue is improved based on the user's emotional state. The user can adjust the theme and progress of the dialogue using visual information provided through the display device.

[0288] The software used includes Python libraries such as speech_recognition, pyttsx3, and TextBlob, which perform speech data recognition, text conversion, speech synthesis, and sentiment analysis. This system allows users to experience more natural and engaging conversations, ultimately contributing to the maintenance and activation of cognitive functions.

[0289] For example, if an elderly person is interested in gardening, the server might generate dialogue such as, "Are you interested in recent gardening topics? For example, there are new types of flowers." An example of a prompt might be, "The user is interested in gardening. Please suggest new gardening topics."

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The server retrieves user characteristic information from the database. This characteristic information includes the user's cognitive level, interests, and language characteristics. Based on this information, a generative AI model is used to generate personalized dialogue content for the user. The input here is the characteristic information, and the data processing involves analyzing this characteristic information to generate appropriate dialogue content. The output is the text data of the generated dialogue content.

[0293] Step 2:

[0294] The server sends the generated dialogue as text data to the terminal. The terminal receives this text data and converts it into speech data using a speech synthesis device. At this stage, the input is the text data received from the server, and the processing involves speech synthesis. The output is the speech data delivered to the user.

[0295] Step 3:

[0296] The user listens to audio data provided through the device (smart glasses). Then, when the user speaks a response, the device uses a speech recognition device to convert the voice input into text data. The input is the user's voice data, which is converted into text data through the conversion process. The output is the text data after speech recognition.

[0297] Step 4:

[0298] The server, upon receiving the text data, uses an analysis device to perform sentiment analysis and evaluate the user's emotional state. The input in this step is the converted text data, and data calculations are performed through sentiment analysis. The output is information about the user's emotional state.

[0299] Step 5:

[0300] The user operates the user interface device to obtain visual information from the terminal and to change the content and theme of the next dialogue. Input is the theme and dialogue progression instructions selected by the user, and the settings are changed as a result of the operation. Output is the updated dialogue settings information.

[0301] 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.

[0302] This invention provides a system for elderly care that recognizes the user's emotions and optimizes the dialogue based on those emotions. This system includes a server, a terminal, and an emotion engine as its main components.

[0303] The server first retrieves characteristic information such as cognitive level and interests from the database based on the user's identification information. Using this information, the generation module personalizes the conversation content to suit the user. Furthermore, the emotion engine analyzes the voice input and understands the user's emotional state in real time. This allows the generation module to dynamically adjust the conversation content considering the emotional state. For example, if the user is feeling anxious, the system will prioritize topics that provide reassurance.

[0304] The terminal converts the text-based dialogue received from the server into audio data and plays it back. When a user joins the conversation, the terminal converts the voice input into text data through a speech recognition module. This text data is sent to the server, and emotion analysis is performed by the emotion engine. For example, if the user is speaking in a cheerful voice, that positive emotional state is reflected in the next dialogue generation, increasing the user's motivation to continue the conversation.

[0305] The user can adjust the conversation content and theme through the user interface of the terminal. For example, it is possible to change the conversation theme to an area of interest through intuitive operations on the terminal. This change is reflected in real time, and the server regenerates new conversation content.

[0306] In this way, the system incorporating the emotion engine can recognize the user's emotional state and dynamically customize the conversation content based on it, enabling more attractive and effective conversation support for the elderly. It supports the improvement of the cognitive function and the quality of life of the elderly through daily conversations.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] When the server receives the user's identification information, it accesses the database to obtain the user's cognitive level, interests, and past activity logs. Based on this information, the user's characteristics are profiled.

[0310] Step 2:

[0311] The server inputs the obtained profile data into the generation module to generate personalized conversation content tailored to the user. This conversation content is adjusted based on the user's interests and past statements.

[0312] Step 3:

[0313] The server sends the generated conversation content to the terminal. At this time, it is provided as text data and is set to be further processed by the terminal.

[0314] Step 4:

[0315] The terminal receives text-based dialogue from the server, converts it into speech using a speech synthesis module, and plays it back to the user. The speech output is clear and adjusted to be easily understood by the user.

[0316] Step 5:

[0317] The user verbally responds to the audio playing from the device. At this stage, audio data containing the user's emotions is collected.

[0318] Step 6:

[0319] The device converts the user's voice input into text data using a speech recognition module. Simultaneously, this voice data is sent to an emotion engine for real-time sentiment analysis.

[0320] Step 7:

[0321] The emotion engine analyzes emotional information obtained from the user's voice data and feeds the results back to the server. This information allows for a clear understanding of the user's current emotional state.

[0322] Step 8:

[0323] Based on feedback from the emotion engine, the server readjusts the generation module and updates the dialogue content as needed. For example, if the user expresses anxiety, dialogue that provides reassurance will be prioritized.

[0324] Step 9:

[0325] The user can operate the terminal's user interface to select the topic and difficulty level of the next conversation. The terminal receives this selection and requests the next dialogue content from the server.

[0326] By repeating this process, the system provides adaptive dialogue that allows elderly individuals to participate continuously, supporting the maintenance and improvement of cognitive function.

[0327] (Example 2)

[0328] 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".

[0329] The aim of this support system for the elderly is to effectively generate dialogues tailored to the user's emotions and characteristics, and to provide a method for improving cognitive function and quality of life through daily communication. Furthermore, it aims to enhance the user's willingness to converse by understanding their emotional state in real time and maintaining appropriate dialogue.

[0330] 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.

[0331] In this invention, the server includes means for acquiring characteristic information from a database based on user identification information, generation means for individualizing dialogue content based on the characteristic information, and means for analyzing voice input and evaluating the user's emotional state in real time. This enables the dynamic customization of dialogue that reflects the user's emotional state.

[0332] "Identification information" refers to information used to identify individual users.

[0333] "Characteristic information" refers to information necessary to generate personalized dialogue, such as the user's cognitive level, interests, and language characteristics.

[0334] "Generation means" refers to a function that personalizes dialogue content based on user characteristic information and provides appropriate conversation.

[0335] "Emotional state" is an indicator that shows the psychological and emotional state obtained from the user's voice and behavior.

[0336] "Means of converting to audio data" refers to a function that converts text-based dialogue content into audio and provides it to the user.

[0337] "Voice recognition means" refers to a function that converts voice input from the user into text data in real time.

[0338] A "user interface means" is a function that provides an interface that allows users to easily select or change dialogue themes.

[0339] This system, designed to support the elderly, consists primarily of a server, terminals, an emotion engine, and a speech recognition module.

[0340] The server uses user identification information to retrieve characteristic information such as cognitive level and interests from a database. This information is used to personalize the conversation content to suit the user by utilizing a generative AI model. The generated conversation content is analyzed by an emotion engine, and the emotional state is evaluated in real time. For example, if the user is feeling anxious, the server adjusts the conversation content to select topics that provide a sense of security.

[0341] The terminal converts the text-based dialogue received from the server into audio data and plays it back through the speaker. When a user joins the conversation, the terminal converts the voice input into text data using a speech recognition module and sends it to the server. This text data is also analyzed by an emotion engine, and the results are reflected in the generation of the next dialogue. For example, if the user is speaking with a smile, that positive emotion is reflected in the next dialogue, promoting the continuation of the conversation.

[0342] Users can adjust the content and theme of conversations using the terminal's user interface. For example, by changing the conversation theme to "hobbies" via the user interface, the server can generate new content and immediately reflect it in the conversation. In this way, the system supports the improvement of cognitive function and quality of life for the elderly through daily conversations.

[0343] The following are specific examples of prompt statements:

[0344] "Please generate conversational material that a 60-year-old woman can comfortably discuss. The theme is chatting at a bakery."

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server receives user identification information and accesses the database. It receives identification information as input and retrieves characteristic information such as the user's cognitive level and interests as output. This allows for the collection of basic personalized data about the user.

[0348] Step 2:

[0349] The server uses a generated AI model based on acquired characteristic information to personalize the conversation content. Using characteristic information as input, it outputs personalized conversation content tailored to the user. Specifically, it adjusts the difficulty of the conversation according to the user's cognitive level and selects topics based on their interests.

[0350] Step 3:

[0351] The terminal receives the dialogue content in text format sent from the server. It receives text data as input, converts it into audio data as output, and plays it through the speaker. Specifically, computer voice is generated using speech synthesis technology.

[0352] Step 4:

[0353] The user listens to the audio played from the device and responds to the conversation. The user's voice input is captured by the device, and audio data is obtained as concrete input.

[0354] Step 5:

[0355] The terminal processes the user's voice input through a speech recognition module to convert it into text data. It takes voice data as input and generates text data as output. Specifically, the speech recognition algorithm analyzes the speech and converts it into language. This text data is then sent to the server.

[0356] Step 6:

[0357] The server analyzes the received text data using an emotion engine. Using text data as input, it generates an evaluation of the user's emotional state as output. Specifically, emotion analysis technology is used to extract the emotional characteristics of the text.

[0358] Step 7:

[0359] The server dynamically adjusts the next dialogue based on the emotional state. It uses emotional evaluation as input and generates a new, adjusted dialogue as output. Specifically, if reassurance is needed, the dialogue content is restructured, such as by increasing the amount of encouragement.

[0360] Step 8:

[0361] Users operate the terminal's user interface to change the topic and content of the conversation. The specific input is the selection of a topic, and the new conversation topic is immediately reflected on the server as output. This allows users to engage in conversations tailored to their interests.

[0362] (Application Example 2)

[0363] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0364] The challenge lies in alleviating the individual psychological and emotional anxieties of the elderly and providing a more meaningful and reassuring communication environment. In particular, there is a need for methods to appropriately customize the content of conversations according to the diverse emotional states of the elderly, thereby improving their quality of life.

[0365] 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.

[0366] In this invention, the server includes generation means for generating appropriate dialogue based on the user's attributes, speech recognition means for converting the user's voice input into text information, and analysis means for performing emotion evaluation to analyze the user's state. This enables the dynamic adjustment of dialogue themes based on the user's emotional state, improving the quality of emotional support and communication for the elderly.

[0367] "User attributes" refer to characteristic information including the cognitive level, interests, and communication characteristics of the individuals targeted by the system.

[0368] A "generation mechanism" is a system for creating individually appropriate dialogue content based on the user's attribute information.

[0369] A "speech recognition system" is a mechanism that acquires the user's voice input and converts it into text information in real time.

[0370] The "analysis means" is a function that evaluates the user's emotional state based on the converted text information and uses the results to adjust the content of the dialogue.

[0371] A "user-operated device" is a device that provides a visual interface that users can easily operate and that assists in selecting dialogue content and themes.

[0372] A "setting method" is a method for dynamically adjusting and optimizing the theme and content of a conversation based on the user's emotional state.

[0373] The elderly support system of the present invention comprises a server, a terminal, and an emotion evaluation engine. The server has a generation means that generates dialogue content tailored to the user based on their characteristic information. Users communicate by voice via the terminal, which is equipped with a speech recognition means that converts speech into text information. The recognized text information is transferred to the server, where the emotional state is evaluated by an analysis means. This makes it possible to dynamically adjust the dialogue theme according to the user's state.

[0374] The hardware consists of smart glasses equipped with a microphone for capturing audio data and a speaker for playback. The software uses the SpeechRecognition library for speech recognition and the tone-analyzer library for sentiment evaluation.

[0375] For example, if an elderly person says, "I'm feeling a bit down today," the server will generate a relaxing conversation such as, "Shall we talk about your favorite hobby?" This conversation is then delivered to the user as audio via the device, increasing their willingness to continue the conversation.

[0376] An example of a prompt for a generative AI model is, "Please suggest relaxing topics to bring up when a user expresses anxiety." The AI's response, based on this prompt, enables more appropriate communication.

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The user inputs voice through the device's microphone. The input voice data is captured by the smart glasses' microphone and processed in real time.

[0380] Step 2:

[0381] The device uses speech recognition to convert this audio data into text information. Specifically, it analyzes the audio waveform data using the SpeechRecognition library and converts it into corresponding text. This conversion result is output as text data for use in subsequent processing.

[0382] Step 3:

[0383] The server receives the converted text information and uses analysis tools to evaluate the emotional state. In this process, the tone-analyzer library is used to analyze the emotional characteristics of the text and output evaluations such as positive or negative.

[0384] Step 4:

[0385] The server generates dialogue content using a generation mechanism based on the analyzed emotional state. Based on the user's emotional evaluation and attribute information, a generation AI model is used to generate relaxing dialogue content using appropriate prompt sentences.

[0386] Step 5:

[0387] The generated dialogue is sent from the server to the terminal. The terminal converts this text data into audio data and plays it back to the user through the smart glasses' speaker.

[0388] Step 6:

[0389] The user continues to participate in the dialogue based on the provided dialogue content. If there is any new input during this dialogue, the system restarts from step 1 and continues the dialogue.

[0390] 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.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0392] 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.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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".

[0406] This invention provides an elderly support system that assists the cognitive function of the elderly and stimulates conversation in daily life. This system operates as follows, while exchanging information between a server, a terminal, and a user.

[0407] The server first retrieves user characteristic information from a database. This characteristic information includes the user's cognitive level, past activity logs, interests, and spoken language characteristics. Based on this information, a generation module installed on the server generates dialogue content suitable for the user. The generated dialogue content is sent to the terminal in text format. For example, the server provides dialogue content to a user whose hobby is gardening, including the latest knowledge and questions related to gardening.

[0408] The terminal receives text-based dialogue from the server, converts it into audio data, and delivers it to the user. When the user speaks to the terminal, the terminal's speech recognition module converts the voice input into text data and sends that content back to the server. Simultaneously, an emotion analysis module analyzes the tone and patterns of the user's voice and evaluates the user's emotional state. For example, if the user responds with an energetic voice, the terminal can maintain the user's interest by offering more interesting topics for the next day's dialogue.

[0409] Users can access the user interface displayed on their device and adjust the topic and progress of the conversation with simple touch operations. For example, if a user wants to change the topic of conversation from gardening to cooking, they can do so by tapping the device screen. This allows users to enjoy the conversation at their own pace while stimulating their cognitive functions.

[0410] In this way, the elderly support system of the present invention provides a personalized conversational experience for each user, aiming to maintain cognitive function and improve the quality of life for the elderly. Through continuous data collection and analysis, the system can provide the most appropriate support for each individual user.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The server receives user identification information and retrieves characteristic information from the user profile database, such as the user's cognitive level, interests, and past activity logs.

[0414] Step 2:

[0415] The server uses a generation module based on acquired characteristic information to generate the most suitable dialogue for the user. This dialogue is expressed in text format and adjusted to capture the user's interest.

[0416] Step 3:

[0417] The server sends the generated dialogue content to the terminal. It also sends related images and supplementary information as needed.

[0418] Step 4:

[0419] The terminal receives text-based dialogue content from the server, converts it into audio data using a speech synthesis module, and plays it back to the user.

[0420] Step 5:

[0421] The user responds to the device using voice. This response is recorded by the device's built-in microphone.

[0422] Step 6:

[0423] The terminal converts the recorded user voice input into text data using a speech recognition module. This text data is then prepared for the following analysis.

[0424] Step 7:

[0425] The terminal sends the converted text data to the server, and at the same time uses an emotion analysis module to analyze the user's emotional state from the voice data, and also sends the results to the server.

[0426] Step 8:

[0427] Based on the user's response text and sentiment analysis results, the server adjusts the direction and topic of the new conversation and prepares to generate the next dialogue.

[0428] Step 9:

[0429] Users can adjust the theme and difficulty level of the dialogue by manipulating the device's user interface. Based on this action, the next dialogue will be redesigned.

[0430] The goal is to support the cognitive function of older adults and provide them with opportunities to enjoy conversation spontaneously by repeating steps within this system.

[0431] (Example 1)

[0432] 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."

[0433] As people age, their cognitive function may decline, leading to a decrease in their quality of life. Therefore, there is a need to provide an appropriate environment that supports cognitive function in older adults. In particular, providing individualized communication tailored to each elderly person is expected to stimulate dialogue and maintain cognitive function. However, there is currently a lack of dialogue systems that take into account individual abilities and preferences.

[0434] 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.

[0435] In this invention, the server includes means for acquiring user characteristic information and generating personalized dialogue content based on said characteristics; means for converting the generated dialogue content into visual or audio format and providing it; and means for converting the user's voice input into text data. This makes it possible to provide dialogue tailored to each individual user, thereby realizing cognitive function support for the elderly.

[0436] "Characteristic information" refers to individualized information such as the user's cognitive level, hobbies and interests, and language characteristics.

[0437] "Generation means" refers to processes or modules for constructing personalized dialogue content based on characteristic information.

[0438] "Voice conversion means" refers to the process or technology of converting voice input into text data, including real-time conversion.

[0439] "Analysis means" refers to processes and modules used to analyze the user's emotional state and interests from acquired audio data.

[0440] A "visual interface" refers to a user interface designed for easy operation by the user, and includes functions for adjusting the theme and progress of a conversation.

[0441] This invention is a system aimed at supporting the cognitive function of the elderly and stimulating conversation in daily life. This system exchanges information between a server, a terminal, and a user, and provides dialogue using various modules.

[0442] The server first retrieves individual user characteristics from the database. This characteristics include the user's cognitive level, past activity logs, hobbies and interests, and spoken language characteristics. The server then uses a generative AI model to generate dialogue based on this characteristics. For example, natural language generation technology can be used for the generative AI model. A possible prompt might be, "The user's hobby is gardening. Please generate dialogue about the latest gardening techniques."

[0443] The terminal receives the generated text-based dialogue content from the server and converts it into speech data using speech synthesis software. The specific software could be a general-purpose speech synthesis technology. The converted speech data is then provided to the user as audio.

[0444] When a user speaks into the device, it uses speech recognition technology to convert the input into text data and sends it back to the server. The device also includes an emotion analysis module that analyzes the tone and patterns of the voice to evaluate the user's emotional state. If the user responds positively to the conversation, more interesting topics will be offered in the next conversation.

[0445] Furthermore, users can adjust the theme and progress of the conversation using the user interface displayed on their device. For example, if a user wants to change the theme of the conversation from gardening to cooking, they can do so through the device's interface.

[0446] In this way, the present invention can provide each elderly person with a personalized conversational experience, thereby maintaining cognitive function and improving their quality of life.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The server accesses the database to retrieve user characteristic information. The input is the user's ID, and based on this, data such as cognitive level, past activity logs, hobbies and interests, and speech characteristics are extracted. The extracted information serves as foundational data for creating prompts for the generative AI model.

[0450] Step 2:

[0451] The server uses a generative AI model based on acquired characteristic information to generate prompt statements, and then creates individual dialogue content based on these prompts. Specifically, the data processing involves transforming the information into a format such as, "The user's hobby is gardening. Please generate dialogue content about the latest gardening techniques," and inputting it into the AI ​​model. The output is text data of dialogue content tailored to the user.

[0452] Step 3:

[0453] The server sends the generated text-formatted dialogue to the terminal. Here, the output from the server is text data, which the terminal uses for its next processing. This transmission process takes place over the network.

[0454] Step 4:

[0455] The terminal converts the received text data of the conversation into speech data using a speech synthesis module. Text data is taken as input, and the conversion results in an audio file (e.g., WAV format) that the user can listen to. Specifically, speech synthesis software is used.

[0456] Step 5:

[0457] The user speaks in response to the dialogue presented audibly from the terminal. The terminal converts this into text data using a speech recognition module. The input is the user's voice, and the output is the text data of that voice. The converted text is then sent to a server for further processing. Specifically, it is analyzed using speech recognition technology.

[0458] Step 6:

[0459] The terminal processes the user's voice input through an emotion analysis module, evaluating the emotional state based on the tone and patterns of the voice. This analysis outputs an index indicating the user's emotions from the input voice data. The results are sent to a server and used to adjust the content of subsequent conversations.

[0460] Step 7:

[0461] Users can change the topic and progression of the conversation using the terminal's user interface. Based on the input from the user interface, the terminal transmits the request to the server, which then adjusts the content of the next conversation accordingly. This feedback loop enhances the personalization of the conversation.

[0462] (Application Example 1)

[0463] 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."

[0464] In an aging society, there is a need to support the cognitive functions of the elderly and revitalize their daily conversations. Furthermore, improving the quality of life for users by providing individually optimized dialogue is a key challenge. In addition, there is a desire to enable on-site staff to provide support more efficiently by utilizing smart devices and enabling use in diverse environments.

[0465] 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.

[0466] In this invention, the server includes a generation device that generates appropriate dialogue based on the user's characteristics, a speech recognition device for converting the user's voice input into text data, an analysis device that performs sentiment analysis to analyze the user's state, a display device for presenting information to a visual device, and a speech synthesis device for presenting voice data through the visual device. This enables elderly people to enjoy natural and individually optimized dialogue, and in care settings, staff can provide appropriate conversations to users.

[0467] A "generator" is a device that has the function of generating appropriate dialogue based on the characteristics of the user.

[0468] A "speech recognition device" is a device that has the function of converting a user's voice input into text data.

[0469] An "analysis device" is a device that analyzes the tone and patterns of a user's voice and has the function of evaluating their emotional state.

[0470] A "user interface device" is a device that provides visual information that can be easily operated by the user.

[0471] A "display device" is a device that has the function of presenting information to a visual device.

[0472] A "speech synthesis device" is a device that has the function of presenting audio data through a visual device.

[0473] This system utilizes smart glasses as hardware and a server for backend processing. The server retrieves user characteristic information from a database, generates personalized dialogue content using a generator, and sends it to the terminal as text data. The terminal is the smart glasses, which converts voice input from the user into text data via a speech recognition device. A speech synthesis device also provides the text data received from the server as voice data tailored to the user. The data acquired by the speech recognition device is analyzed for emotion by an analysis device, and the content of the next dialogue is improved based on the user's emotional state. The user can adjust the theme and progress of the dialogue using visual information provided through the display device.

[0474] The software used includes Python libraries such as speech_recognition, pyttsx3, and TextBlob, which perform speech data recognition, text conversion, speech synthesis, and sentiment analysis. This system allows users to experience more natural and engaging conversations, ultimately contributing to the maintenance and activation of cognitive functions.

[0475] For example, if an elderly person is interested in gardening, the server might generate dialogue such as, "Are you interested in recent gardening topics? For example, there are new types of flowers." An example of a prompt might be, "The user is interested in gardening. Please suggest new gardening topics."

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server retrieves user characteristic information from the database. This characteristic information includes the user's cognitive level, interests, and language characteristics. Based on this information, a generative AI model is used to generate personalized dialogue content for the user. The input here is the characteristic information, and the data processing involves analyzing this characteristic information to generate appropriate dialogue content. The output is the text data of the generated dialogue content.

[0479] Step 2:

[0480] The server sends the generated dialogue as text data to the terminal. The terminal receives this text data and converts it into speech data using a speech synthesis device. At this stage, the input is the text data received from the server, and the processing involves speech synthesis. The output is the speech data delivered to the user.

[0481] Step 3:

[0482] The user listens to audio data provided through the device (smart glasses). Then, when the user speaks a response, the device uses a speech recognition device to convert the voice input into text data. The input is the user's voice data, which is converted into text data through the conversion process. The output is the text data after speech recognition.

[0483] Step 4:

[0484] The server, upon receiving the text data, uses an analysis device to perform sentiment analysis and evaluate the user's emotional state. The input in this step is the converted text data, and data calculations are performed through sentiment analysis. The output is information about the user's emotional state.

[0485] Step 5:

[0486] The user operates the user interface device to obtain visual information from the terminal and to change the content and theme of the next dialogue. Input is the theme and dialogue progression instructions selected by the user, and the settings are changed as a result of the operation. Output is the updated dialogue settings information.

[0487] 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.

[0488] This invention provides a system for elderly care that recognizes the user's emotions and optimizes the dialogue based on those emotions. This system includes a server, a terminal, and an emotion engine as its main components.

[0489] The server first retrieves characteristic information such as cognitive level and interests from the database based on the user's identification information. Using this information, the generation module personalizes the conversation content to suit the user. Furthermore, the emotion engine analyzes the voice input and understands the user's emotional state in real time. This allows the generation module to dynamically adjust the conversation content considering the emotional state. For example, if the user is feeling anxious, the system will prioritize topics that provide reassurance.

[0490] The terminal converts the text-based dialogue received from the server into audio data and plays it back. When a user joins the conversation, the terminal converts the voice input into text data through a speech recognition module. This text data is sent to the server, and emotion analysis is performed by the emotion engine. For example, if the user is speaking in a cheerful voice, that positive emotional state is reflected in the next dialogue generation, increasing the user's motivation to continue the conversation.

[0491] Users can adjust the content and theme of the conversation through the terminal's user interface. For example, they can change the conversation theme to an area of ​​interest through the terminal's intuitive operation. This change is reflected in real time, and the server regenerates the new conversation content.

[0492] In this way, this system, which incorporates an emotion engine, recognizes the user's emotional state and dynamically customizes the conversation content based on that state, enabling more engaging and effective dialogue support for the elderly. Through daily conversations, it supports the improvement of cognitive function and quality of life for the elderly.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] Upon receiving user identification information, the server accesses a database to retrieve the user's cognitive level, interests, and past activity logs. Based on this information, the user's characteristics are profiled.

[0496] Step 2:

[0497] The server inputs the acquired profile data into a generation module to generate personalized dialogue tailored to the user. This dialogue is adjusted based on the user's interests and past statements.

[0498] Step 3:

[0499] The server sends the generated dialogue content to the terminal. It is provided as text data and configured for further processing by the terminal.

[0500] Step 4:

[0501] The terminal receives text-based dialogue from the server, converts it into speech using a speech synthesis module, and plays it back to the user. The speech output is clear and adjusted to be easily understood by the user.

[0502] Step 5:

[0503] The user verbally responds to the audio playing from the device. At this stage, audio data containing the user's emotions is collected.

[0504] Step 6:

[0505] The device converts the user's voice input into text data using a speech recognition module. Simultaneously, this voice data is sent to an emotion engine for real-time sentiment analysis.

[0506] Step 7:

[0507] The emotion engine analyzes emotional information obtained from the user's voice data and feeds the results back to the server. This information allows for a clear understanding of the user's current emotional state.

[0508] Step 8:

[0509] Based on feedback from the emotion engine, the server readjusts the generation module and updates the dialogue content as needed. For example, if the user expresses anxiety, dialogue that provides reassurance will be prioritized.

[0510] Step 9:

[0511] The user can operate the terminal's user interface to select the topic and difficulty level of the next conversation. The terminal receives this selection and requests the next dialogue content from the server.

[0512] By repeating this process, the system provides adaptive dialogue that allows elderly individuals to participate continuously, supporting the maintenance and improvement of cognitive function.

[0513] (Example 2)

[0514] 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."

[0515] The aim of this support system for the elderly is to effectively generate dialogues tailored to the user's emotions and characteristics, and to provide a method for improving cognitive function and quality of life through daily communication. Furthermore, it aims to enhance the user's willingness to converse by understanding their emotional state in real time and maintaining appropriate dialogue.

[0516] 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.

[0517] In this invention, the server includes means for acquiring characteristic information from a database based on user identification information, generation means for individualizing dialogue content based on the characteristic information, and means for analyzing voice input and evaluating the user's emotional state in real time. This enables the dynamic customization of dialogue that reflects the user's emotional state.

[0518] "Identification information" refers to information used to identify individual users.

[0519] "Characteristic information" refers to information necessary to generate personalized dialogue, such as the user's cognitive level, interests, and language characteristics.

[0520] "Generation means" refers to a function that personalizes dialogue content based on user characteristic information and provides appropriate conversation.

[0521] "Emotional state" is an indicator that shows the psychological and emotional state obtained from the user's voice and behavior.

[0522] "Means of converting to audio data" refers to a function that converts text-based dialogue content into audio and provides it to the user.

[0523] "Voice recognition means" refers to a function that converts voice input from the user into text data in real time.

[0524] A "user interface means" is a function that provides an interface that allows users to easily select or change dialogue themes.

[0525] This system, designed to support the elderly, consists primarily of a server, terminals, an emotion engine, and a speech recognition module.

[0526] The server uses user identification information to retrieve characteristic information such as cognitive level and interests from a database. This information is used to personalize the conversation content to suit the user by utilizing a generative AI model. The generated conversation content is analyzed by an emotion engine, and the emotional state is evaluated in real time. For example, if the user is feeling anxious, the server adjusts the conversation content to select topics that provide a sense of security.

[0527] The terminal converts the text-based dialogue received from the server into audio data and plays it back through the speaker. When a user joins the conversation, the terminal converts the voice input into text data using a speech recognition module and sends it to the server. This text data is also analyzed by an emotion engine, and the results are reflected in the generation of the next dialogue. For example, if the user is speaking with a smile, that positive emotion is reflected in the next dialogue, promoting the continuation of the conversation.

[0528] Users can adjust the content and theme of conversations using the terminal's user interface. For example, by changing the conversation theme to "hobbies" via the user interface, the server can generate new content and immediately reflect it in the conversation. In this way, the system supports the improvement of cognitive function and quality of life for the elderly through daily conversations.

[0529] The following are specific examples of prompt statements:

[0530] "Please generate conversational material that a 60-year-old woman can comfortably discuss. The theme is chatting at a bakery."

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The server receives user identification information and accesses the database. It receives identification information as input and retrieves characteristic information such as the user's cognitive level and interests as output. This allows for the collection of basic personalized data about the user.

[0534] Step 2:

[0535] The server uses a generated AI model based on acquired characteristic information to personalize the conversation content. Using characteristic information as input, it outputs personalized conversation content tailored to the user. Specifically, it adjusts the difficulty of the conversation according to the user's cognitive level and selects topics based on their interests.

[0536] Step 3:

[0537] The terminal receives the dialogue content in text format sent from the server. It receives text data as input, converts it into audio data as output, and plays it through the speaker. Specifically, computer voice is generated using speech synthesis technology.

[0538] Step 4:

[0539] The user listens to the audio played from the device and responds to the conversation. The user's voice input is captured by the device, and audio data is obtained as concrete input.

[0540] Step 5:

[0541] The terminal processes the user's voice input through a speech recognition module to convert it into text data. It takes voice data as input and generates text data as output. Specifically, the speech recognition algorithm analyzes the speech and converts it into language. This text data is then sent to the server.

[0542] Step 6:

[0543] The server analyzes the received text data using an emotion engine. Using text data as input, it generates an evaluation of the user's emotional state as output. Specifically, emotion analysis technology is used to extract the emotional characteristics of the text.

[0544] Step 7:

[0545] The server dynamically adjusts the next dialogue based on the emotional state. It uses emotional evaluation as input and generates a new, adjusted dialogue as output. Specifically, if reassurance is needed, the dialogue content is restructured, such as by increasing the amount of encouragement.

[0546] Step 8:

[0547] Users operate the terminal's user interface to change the topic and content of the conversation. The specific input is the selection of a topic, and the new conversation topic is immediately reflected on the server as output. This allows users to engage in conversations tailored to their interests.

[0548] (Application Example 2)

[0549] 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."

[0550] The challenge lies in alleviating the individual psychological and emotional anxieties of the elderly and providing a more meaningful and reassuring communication environment. In particular, there is a need for methods to appropriately customize the content of conversations according to the diverse emotional states of the elderly, thereby improving their quality of life.

[0551] 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.

[0552] In this invention, the server includes generation means for generating appropriate dialogue based on the user's attributes, speech recognition means for converting the user's voice input into text information, and analysis means for performing emotion evaluation to analyze the user's state. This enables the dynamic adjustment of dialogue themes based on the user's emotional state, improving the quality of emotional support and communication for the elderly.

[0553] "User attributes" refer to characteristic information including the cognitive level, interests, and communication characteristics of the individuals targeted by the system.

[0554] A "generation mechanism" is a system for creating individually appropriate dialogue content based on the user's attribute information.

[0555] A "speech recognition system" is a mechanism that acquires the user's voice input and converts it into text information in real time.

[0556] The "analysis means" is a function that evaluates the user's emotional state based on the converted text information and uses the results to adjust the content of the dialogue.

[0557] A "user-operated device" is a device that provides a visual interface that users can easily operate and that assists in selecting dialogue content and themes.

[0558] A "setting method" is a method for dynamically adjusting and optimizing the theme and content of a conversation based on the user's emotional state.

[0559] The elderly support system of the present invention comprises a server, a terminal, and an emotion evaluation engine. The server has a generation means that generates dialogue content tailored to the user based on their characteristic information. Users communicate by voice via the terminal, which is equipped with a speech recognition means that converts speech into text information. The recognized text information is transferred to the server, where the emotional state is evaluated by an analysis means. This makes it possible to dynamically adjust the dialogue theme according to the user's state.

[0560] The hardware consists of smart glasses equipped with a microphone for capturing audio data and a speaker for playback. The software uses the SpeechRecognition library for speech recognition and the tone-analyzer library for sentiment evaluation.

[0561] For example, if an elderly person says, "I'm feeling a bit down today," the server will generate a relaxing conversation such as, "Shall we talk about your favorite hobby?" This conversation is then delivered to the user as audio via the device, increasing their willingness to continue the conversation.

[0562] An example of a prompt for a generative AI model is, "Please suggest relaxing topics to bring up when a user expresses anxiety." The AI's response, based on this prompt, enables more appropriate communication.

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0564] Step 1:

[0565] The user inputs voice through the device's microphone. The input voice data is captured by the smart glasses' microphone and processed in real time.

[0566] Step 2:

[0567] The device uses speech recognition to convert this audio data into text information. Specifically, it analyzes the audio waveform data using the SpeechRecognition library and converts it into corresponding text. This conversion result is output as text data for use in subsequent processing.

[0568] Step 3:

[0569] The server receives the converted text information and uses analysis tools to evaluate the emotional state. In this process, the tone-analyzer library is used to analyze the emotional characteristics of the text and output evaluations such as positive or negative.

[0570] Step 4:

[0571] The server generates dialogue content using a generation mechanism based on the analyzed emotional state. Based on the user's emotional evaluation and attribute information, a generation AI model is used to generate relaxing dialogue content using appropriate prompt sentences.

[0572] Step 5:

[0573] The generated dialogue is sent from the server to the terminal. The terminal converts this text data into audio data and plays it back to the user through the smart glasses' speaker.

[0574] Step 6:

[0575] The user continues to participate in the dialogue based on the provided dialogue content. If there is any new input during this dialogue, the system restarts from step 1 and continues the dialogue.

[0576] 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.

[0577] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0578] 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.

[0579] [Fourth Embodiment]

[0580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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".

[0593] This invention provides an elderly support system that assists the cognitive function of the elderly and stimulates conversation in daily life. This system operates as follows, while exchanging information between a server, a terminal, and a user.

[0594] The server first retrieves user characteristic information from a database. This characteristic information includes the user's cognitive level, past activity logs, interests, and spoken language characteristics. Based on this information, a generation module installed on the server generates dialogue content suitable for the user. The generated dialogue content is sent to the terminal in text format. For example, the server provides dialogue content to a user whose hobby is gardening, including the latest knowledge and questions related to gardening.

[0595] The terminal receives text-based dialogue from the server, converts it into audio data, and delivers it to the user. When the user speaks to the terminal, the terminal's speech recognition module converts the voice input into text data and sends that content back to the server. Simultaneously, an emotion analysis module analyzes the tone and patterns of the user's voice and evaluates the user's emotional state. For example, if the user responds with an energetic voice, the terminal can maintain the user's interest by offering more interesting topics for the next day's dialogue.

[0596] Users can access the user interface displayed on their device and adjust the topic and progress of the conversation with simple touch operations. For example, if a user wants to change the topic of conversation from gardening to cooking, they can do so by tapping the device screen. This allows users to enjoy the conversation at their own pace while stimulating their cognitive functions.

[0597] In this way, the elderly support system of the present invention provides a personalized conversational experience for each user, aiming to maintain cognitive function and improve the quality of life for the elderly. Through continuous data collection and analysis, the system can provide the most appropriate support for each individual user.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The server receives user identification information and retrieves characteristic information from the user profile database, such as the user's cognitive level, interests, and past activity logs.

[0601] Step 2:

[0602] The server uses a generation module based on acquired characteristic information to generate the most suitable dialogue for the user. This dialogue is expressed in text format and adjusted to capture the user's interest.

[0603] Step 3:

[0604] The server sends the generated dialogue content to the terminal. It also sends related images and supplementary information as needed.

[0605] Step 4:

[0606] The terminal receives text-based dialogue content from the server, converts it into audio data using a speech synthesis module, and plays it back to the user.

[0607] Step 5:

[0608] The user responds to the device using voice. This response is recorded by the device's built-in microphone.

[0609] Step 6:

[0610] The terminal converts the recorded user voice input into text data using a speech recognition module. This text data is then prepared for the following analysis.

[0611] Step 7:

[0612] The terminal sends the converted text data to the server, and at the same time uses an emotion analysis module to analyze the user's emotional state from the voice data, and also sends the results to the server.

[0613] Step 8:

[0614] Based on the user's response text and sentiment analysis results, the server adjusts the direction and topic of the new conversation and prepares to generate the next dialogue.

[0615] Step 9:

[0616] Users can adjust the theme and difficulty level of the dialogue by manipulating the device's user interface. Based on this action, the next dialogue will be redesigned.

[0617] The goal is to support the cognitive function of older adults and provide them with opportunities to enjoy conversation spontaneously by repeating steps within this system.

[0618] (Example 1)

[0619] 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".

[0620] As people age, their cognitive function may decline, leading to a decrease in their quality of life. Therefore, there is a need to provide an appropriate environment that supports cognitive function in older adults. In particular, providing individualized communication tailored to each elderly person is expected to stimulate dialogue and maintain cognitive function. However, there is currently a lack of dialogue systems that take into account individual abilities and preferences.

[0621] 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.

[0622] In this invention, the server includes means for acquiring user characteristic information and generating personalized dialogue content based on said characteristics; means for converting the generated dialogue content into visual or audio format and providing it; and means for converting the user's voice input into text data. This makes it possible to provide dialogue tailored to each individual user, thereby realizing cognitive function support for the elderly.

[0623] "Characteristic information" refers to individualized information such as the user's cognitive level, hobbies and interests, and language characteristics.

[0624] "Generation means" refers to processes or modules for constructing personalized dialogue content based on characteristic information.

[0625] "Voice conversion means" refers to the process or technology of converting voice input into text data, including real-time conversion.

[0626] "Analysis means" refers to processes and modules used to analyze the user's emotional state and interests from acquired audio data.

[0627] A "visual interface" refers to a user interface designed for easy operation by the user, and includes functions for adjusting the theme and progress of a conversation.

[0628] This invention is a system aimed at supporting the cognitive function of the elderly and stimulating conversation in daily life. This system exchanges information between a server, a terminal, and a user, and provides dialogue using various modules.

[0629] The server first retrieves individual user characteristics from the database. This characteristics include the user's cognitive level, past activity logs, hobbies and interests, and spoken language characteristics. The server then uses a generative AI model to generate dialogue based on this characteristics. For example, natural language generation technology can be used for the generative AI model. A possible prompt might be, "The user's hobby is gardening. Please generate dialogue about the latest gardening techniques."

[0630] The terminal receives the generated text-based dialogue content from the server and converts it into speech data using speech synthesis software. The specific software could be a general-purpose speech synthesis technology. The converted speech data is then provided to the user as audio.

[0631] When a user speaks into the device, it uses speech recognition technology to convert the input into text data and sends it back to the server. The device also includes an emotion analysis module that analyzes the tone and patterns of the voice to evaluate the user's emotional state. If the user responds positively to the conversation, more interesting topics will be offered in the next conversation.

[0632] Furthermore, users can adjust the theme and progress of the conversation using the user interface displayed on their device. For example, if a user wants to change the theme of the conversation from gardening to cooking, they can do so through the device's interface.

[0633] In this way, the present invention can provide each elderly person with a personalized conversational experience, thereby maintaining cognitive function and improving their quality of life.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The server accesses the database to retrieve user characteristic information. The input is the user's ID, and based on this, data such as cognitive level, past activity logs, hobbies and interests, and speech characteristics are extracted. The extracted information serves as foundational data for creating prompts for the generative AI model.

[0637] Step 2:

[0638] The server uses a generative AI model based on acquired characteristic information to generate prompt statements, and then creates individual dialogue content based on these prompts. Specifically, the data processing involves transforming the information into a format such as, "The user's hobby is gardening. Please generate dialogue content about the latest gardening techniques," and inputting it into the AI ​​model. The output is text data of dialogue content tailored to the user.

[0639] Step 3:

[0640] The server sends the generated text-formatted dialogue to the terminal. Here, the output from the server is text data, which the terminal uses for its next processing. This transmission process takes place over the network.

[0641] Step 4:

[0642] The terminal converts the received text data of the conversation into speech data using a speech synthesis module. Text data is taken as input, and the conversion results in an audio file (e.g., WAV format) that the user can listen to. Specifically, speech synthesis software is used.

[0643] Step 5:

[0644] The user speaks in response to the dialogue presented audibly from the terminal. The terminal converts this into text data using a speech recognition module. The input is the user's voice, and the output is the text data of that voice. The converted text is then sent to a server for further processing. Specifically, it is analyzed using speech recognition technology.

[0645] Step 6:

[0646] The terminal processes the user's voice input through an emotion analysis module, evaluating the emotional state based on the tone and patterns of the voice. This analysis outputs an index indicating the user's emotions from the input voice data. The results are sent to a server and used to adjust the content of subsequent conversations.

[0647] Step 7:

[0648] Users can change the topic and progression of the conversation using the terminal's user interface. Based on the input from the user interface, the terminal transmits the request to the server, which then adjusts the content of the next conversation accordingly. This feedback loop enhances the personalization of the conversation.

[0649] (Application Example 1)

[0650] 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".

[0651] In an aging society, there is a need to support the cognitive functions of the elderly and revitalize their daily conversations. Furthermore, improving the quality of life for users by providing individually optimized dialogue is a key challenge. In addition, there is a desire to enable on-site staff to provide support more efficiently by utilizing smart devices and enabling use in diverse environments.

[0652] 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.

[0653] In this invention, the server includes a generation device that generates appropriate dialogue based on the user's characteristics, a speech recognition device for converting the user's voice input into text data, an analysis device that performs sentiment analysis to analyze the user's state, a display device for presenting information to a visual device, and a speech synthesis device for presenting voice data through the visual device. This enables elderly people to enjoy natural and individually optimized dialogue, and in care settings, staff can provide appropriate conversations to users.

[0654] A "generator" is a device that has the function of generating appropriate dialogue based on the characteristics of the user.

[0655] A "speech recognition device" is a device that has the function of converting a user's voice input into text data.

[0656] An "analysis device" is a device that analyzes the tone and patterns of a user's voice and has the function of evaluating their emotional state.

[0657] A "user interface device" is a device that provides visual information that can be easily operated by the user.

[0658] A "display device" is a device that has the function of presenting information to a visual device.

[0659] A "speech synthesis device" is a device that has the function of presenting audio data through a visual device.

[0660] This system utilizes smart glasses as hardware and a server for backend processing. The server retrieves user characteristic information from a database, generates personalized dialogue content using a generator, and sends it to the terminal as text data. The terminal is the smart glasses, which converts voice input from the user into text data via a speech recognition device. A speech synthesis device also provides the text data received from the server as voice data tailored to the user. The data acquired by the speech recognition device is analyzed for emotion by an analysis device, and the content of the next dialogue is improved based on the user's emotional state. The user can adjust the theme and progress of the dialogue using visual information provided through the display device.

[0661] The software used includes Python libraries such as speech_recognition, pyttsx3, and TextBlob, which perform speech data recognition, text conversion, speech synthesis, and sentiment analysis. This system allows users to experience more natural and engaging conversations, ultimately contributing to the maintenance and activation of cognitive functions.

[0662] For example, if an elderly person is interested in gardening, the server might generate dialogue such as, "Are you interested in recent gardening topics? For example, there are new types of flowers." An example of a prompt might be, "The user is interested in gardening. Please suggest new gardening topics."

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The server retrieves user characteristic information from the database. This characteristic information includes the user's cognitive level, interests, and language characteristics. Based on this information, a generative AI model is used to generate personalized dialogue content for the user. The input here is the characteristic information, and the data processing involves analyzing this characteristic information to generate appropriate dialogue content. The output is the text data of the generated dialogue content.

[0666] Step 2:

[0667] The server sends the generated dialogue as text data to the terminal. The terminal receives this text data and converts it into speech data using a speech synthesis device. At this stage, the input is the text data received from the server, and the processing involves speech synthesis. The output is the speech data delivered to the user.

[0668] Step 3:

[0669] The user listens to audio data provided through the device (smart glasses). Then, when the user speaks a response, the device uses a speech recognition device to convert the voice input into text data. The input is the user's voice data, which is converted into text data through the conversion process. The output is the text data after speech recognition.

[0670] Step 4:

[0671] The server, upon receiving the text data, uses an analysis device to perform sentiment analysis and evaluate the user's emotional state. The input in this step is the converted text data, and data calculations are performed through sentiment analysis. The output is information about the user's emotional state.

[0672] Step 5:

[0673] The user operates the user interface device to obtain visual information from the terminal and to change the content and theme of the next dialogue. Input is the theme and dialogue progression instructions selected by the user, and the settings are changed as a result of the operation. Output is the updated dialogue settings information.

[0674] 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.

[0675] This invention provides a system for elderly care that recognizes the user's emotions and optimizes the dialogue based on those emotions. This system includes a server, a terminal, and an emotion engine as its main components.

[0676] The server first retrieves characteristic information such as cognitive level and interests from the database based on the user's identification information. Using this information, the generation module personalizes the conversation content to suit the user. Furthermore, the emotion engine analyzes the voice input and understands the user's emotional state in real time. This allows the generation module to dynamically adjust the conversation content considering the emotional state. For example, if the user is feeling anxious, the system will prioritize topics that provide reassurance.

[0677] The terminal converts the text-based dialogue received from the server into audio data and plays it back. When a user joins the conversation, the terminal converts the voice input into text data through a speech recognition module. This text data is sent to the server, and emotion analysis is performed by the emotion engine. For example, if the user is speaking in a cheerful voice, that positive emotional state is reflected in the next dialogue generation, increasing the user's motivation to continue the conversation.

[0678] Users can adjust the content and theme of the conversation through the terminal's user interface. For example, they can change the conversation theme to an area of ​​interest through the terminal's intuitive operation. This change is reflected in real time, and the server regenerates the new conversation content.

[0679] In this way, this system, which incorporates an emotion engine, recognizes the user's emotional state and dynamically customizes the conversation content based on that state, enabling more engaging and effective dialogue support for the elderly. Through daily conversations, it supports the improvement of cognitive function and quality of life for the elderly.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] Upon receiving user identification information, the server accesses a database to retrieve the user's cognitive level, interests, and past activity logs. Based on this information, the user's characteristics are profiled.

[0683] Step 2:

[0684] The server inputs the acquired profile data into a generation module to generate personalized dialogue tailored to the user. This dialogue is adjusted based on the user's interests and past statements.

[0685] Step 3:

[0686] The server sends the generated dialogue content to the terminal. It is provided as text data and configured for further processing by the terminal.

[0687] Step 4:

[0688] The terminal receives text-based dialogue from the server, converts it into speech using a speech synthesis module, and plays it back to the user. The speech output is clear and adjusted to be easily understood by the user.

[0689] Step 5:

[0690] The user verbally responds to the audio playing from the device. At this stage, audio data containing the user's emotions is collected.

[0691] Step 6:

[0692] The device converts the user's voice input into text data using a speech recognition module. Simultaneously, this voice data is sent to an emotion engine for real-time sentiment analysis.

[0693] Step 7:

[0694] The emotion engine analyzes emotional information obtained from the user's voice data and feeds the results back to the server. This information allows for a clear understanding of the user's current emotional state.

[0695] Step 8:

[0696] Based on feedback from the emotion engine, the server readjusts the generation module and updates the dialogue content as needed. For example, if the user expresses anxiety, dialogue that provides reassurance will be prioritized.

[0697] Step 9:

[0698] The user can operate the terminal's user interface to select the topic and difficulty level of the next conversation. The terminal receives this selection and requests the next dialogue content from the server.

[0699] By repeating this process, the system provides adaptive dialogue that allows elderly individuals to participate continuously, supporting the maintenance and improvement of cognitive function.

[0700] (Example 2)

[0701] 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".

[0702] The aim of this support system for the elderly is to effectively generate dialogues tailored to the user's emotions and characteristics, and to provide a method for improving cognitive function and quality of life through daily communication. Furthermore, it aims to enhance the user's willingness to converse by understanding their emotional state in real time and maintaining appropriate dialogue.

[0703] 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.

[0704] In this invention, the server includes means for acquiring characteristic information from a database based on user identification information, generation means for individualizing dialogue content based on the characteristic information, and means for analyzing voice input and evaluating the user's emotional state in real time. This enables the dynamic customization of dialogue that reflects the user's emotional state.

[0705] "Identification information" refers to information used to identify individual users.

[0706] "Characteristic information" refers to information necessary to generate personalized dialogue, such as the user's cognitive level, interests, and language characteristics.

[0707] "Generation means" refers to a function that personalizes dialogue content based on user characteristic information and provides appropriate conversation.

[0708] "Emotional state" is an indicator that shows the psychological and emotional state obtained from the user's voice and behavior.

[0709] "Means of converting to audio data" refers to a function that converts text-based dialogue content into audio and provides it to the user.

[0710] "Voice recognition means" refers to a function that converts voice input from the user into text data in real time.

[0711] A "user interface means" is a function that provides an interface that allows users to easily select or change dialogue themes.

[0712] This system, designed to support the elderly, consists primarily of a server, terminals, an emotion engine, and a speech recognition module.

[0713] The server uses user identification information to retrieve characteristic information such as cognitive level and interests from a database. This information is used to personalize the conversation content to suit the user by utilizing a generative AI model. The generated conversation content is analyzed by an emotion engine, and the emotional state is evaluated in real time. For example, if the user is feeling anxious, the server adjusts the conversation content to select topics that provide a sense of security.

[0714] The terminal converts the text-based dialogue received from the server into audio data and plays it back through the speaker. When a user joins the conversation, the terminal converts the voice input into text data using a speech recognition module and sends it to the server. This text data is also analyzed by an emotion engine, and the results are reflected in the generation of the next dialogue. For example, if the user is speaking with a smile, that positive emotion is reflected in the next dialogue, promoting the continuation of the conversation.

[0715] Users can adjust the content and theme of conversations using the terminal's user interface. For example, by changing the conversation theme to "hobbies" via the user interface, the server can generate new content and immediately reflect it in the conversation. In this way, the system supports the improvement of cognitive function and quality of life for the elderly through daily conversations.

[0716] The following are specific examples of prompt statements:

[0717] "Please generate conversational material that a 60-year-old woman can comfortably discuss. The theme is chatting at a bakery."

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The server receives user identification information and accesses the database. It receives identification information as input and retrieves characteristic information such as the user's cognitive level and interests as output. This allows for the collection of basic personalized data about the user.

[0721] Step 2:

[0722] The server uses a generated AI model based on acquired characteristic information to personalize the conversation content. Using characteristic information as input, it outputs personalized conversation content tailored to the user. Specifically, it adjusts the difficulty of the conversation according to the user's cognitive level and selects topics based on their interests.

[0723] Step 3:

[0724] The terminal receives the dialogue content in text format sent from the server. It receives text data as input, converts it into audio data as output, and plays it through the speaker. Specifically, computer voice is generated using speech synthesis technology.

[0725] Step 4:

[0726] The user listens to the audio played from the device and responds to the conversation. The user's voice input is captured by the device, and audio data is obtained as concrete input.

[0727] Step 5:

[0728] The terminal processes the user's voice input through a speech recognition module to convert it into text data. It takes voice data as input and generates text data as output. Specifically, the speech recognition algorithm analyzes the speech and converts it into language. This text data is then sent to the server.

[0729] Step 6:

[0730] The server analyzes the received text data using an emotion engine. Using text data as input, it generates an evaluation of the user's emotional state as output. Specifically, emotion analysis technology is used to extract the emotional characteristics of the text.

[0731] Step 7:

[0732] The server dynamically adjusts the next dialogue based on the emotional state. It uses emotional evaluation as input and generates a new, adjusted dialogue as output. Specifically, if reassurance is needed, the dialogue content is restructured, such as by increasing the amount of encouragement.

[0733] Step 8:

[0734] Users operate the terminal's user interface to change the topic and content of the conversation. The specific input is the selection of a topic, and the new conversation topic is immediately reflected on the server as output. This allows users to engage in conversations tailored to their interests.

[0735] (Application Example 2)

[0736] 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".

[0737] The challenge lies in alleviating the individual psychological and emotional anxieties of the elderly and providing a more meaningful and reassuring communication environment. In particular, there is a need for methods to appropriately customize the content of conversations according to the diverse emotional states of the elderly, thereby improving their quality of life.

[0738] 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.

[0739] In this invention, the server includes generation means for generating appropriate dialogue based on the user's attributes, speech recognition means for converting the user's voice input into text information, and analysis means for performing emotion evaluation to analyze the user's state. This enables the dynamic adjustment of dialogue themes based on the user's emotional state, improving the quality of emotional support and communication for the elderly.

[0740] "User attributes" refer to characteristic information including the cognitive level, interests, and communication characteristics of the individuals targeted by the system.

[0741] A "generation mechanism" is a system for creating individually appropriate dialogue content based on the user's attribute information.

[0742] A "speech recognition system" is a mechanism that acquires the user's voice input and converts it into text information in real time.

[0743] The "analysis means" is a function that evaluates the user's emotional state based on the converted text information and uses the results to adjust the content of the dialogue.

[0744] A "user-operated device" is a device that provides a visual interface that users can easily operate and that assists in selecting dialogue content and themes.

[0745] A "setting method" is a method for dynamically adjusting and optimizing the theme and content of a conversation based on the user's emotional state.

[0746] The elderly support system of the present invention comprises a server, a terminal, and an emotion evaluation engine. The server has a generation means that generates dialogue content tailored to the user based on their characteristic information. Users communicate by voice via the terminal, which is equipped with a speech recognition means that converts speech into text information. The recognized text information is transferred to the server, where the emotional state is evaluated by an analysis means. This makes it possible to dynamically adjust the dialogue theme according to the user's state.

[0747] The hardware consists of smart glasses equipped with a microphone for capturing audio data and a speaker for playback. The software uses the SpeechRecognition library for speech recognition and the tone-analyzer library for sentiment evaluation.

[0748] For example, if an elderly person says, "I'm feeling a bit down today," the server will generate a relaxing conversation such as, "Shall we talk about your favorite hobby?" This conversation is then delivered to the user as audio via the device, increasing their willingness to continue the conversation.

[0749] An example of a prompt for a generative AI model is, "Please suggest relaxing topics to bring up when a user expresses anxiety." The AI's response, based on this prompt, enables more appropriate communication.

[0750] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0751] Step 1:

[0752] The user inputs voice through the device's microphone. The input voice data is captured by the smart glasses' microphone and processed in real time.

[0753] Step 2:

[0754] The device uses speech recognition to convert this audio data into text information. Specifically, it analyzes the audio waveform data using the SpeechRecognition library and converts it into corresponding text. This conversion result is output as text data for use in subsequent processing.

[0755] Step 3:

[0756] The server receives the converted text information and uses analysis tools to evaluate the emotional state. In this process, the tone-analyzer library is used to analyze the emotional characteristics of the text and output evaluations such as positive or negative.

[0757] Step 4:

[0758] The server generates dialogue content using a generation mechanism based on the analyzed emotional state. Based on the user's emotional evaluation and attribute information, a generation AI model is used to generate relaxing dialogue content using appropriate prompt sentences.

[0759] Step 5:

[0760] The generated dialogue is sent from the server to the terminal. The terminal converts this text data into audio data and plays it back to the user through the smart glasses' speaker.

[0761] Step 6:

[0762] The user continues to participate in the dialogue based on the provided dialogue content. If there is any new input during this dialogue, the system restarts from step 1 and continues the dialogue.

[0763] 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.

[0764] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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."

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] A generation module that generates appropriate dialogues based on user characteristics,

[0787] A speech recognition module for converting user voice input into text data,

[0788] An analysis module that performs sentiment analysis to analyze the user's state,

[0789] A user interface module that provides a visual interface that users can easily operate,

[0790] A support system for the elderly, including...

[0791] (Claim 2)

[0792] The elderly support system according to claim 1, characterized in that the generation module adjusts the content of the dialogue according to the user's cognitive level, interests, and language characteristics.

[0793] (Claim 3)

[0794] The elderly support system according to claim 1, characterized in that the speech recognition module converts the user's voice input into text data in real time and provides the analysis module with the conversion result.

[0795] "Example 1"

[0796] (Claim 1)

[0797] A means for acquiring user characteristic information and generating personalized dialogue content based on said characteristics,

[0798] Means for converting the generated dialogue content into visual or auditory form and providing it,

[0799] A means of converting user voice input into text data,

[0800] A means for analyzing the user's emotional state based on the voice input,

[0801] A means of providing a visual interface that allows users to adjust the theme and progress of the dialogue,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, characterized in that the generation means adjusts the content of the dialogue according to the user's cognitive level, hobbies and interests, and language characteristics.

[0805] (Claim 3)

[0806] The system according to claim 1, characterized in that the voice conversion means converts the user's voice input into text data in real time and provides the analysis means with the conversion result.

[0807] "Application Example 1"

[0808] (Claim 1)

[0809] A generation device that generates appropriate dialogue based on the characteristics of the user,

[0810] A speech recognition device for converting user voice input into text data,

[0811] An analytical device that performs emotional analysis to analyze the user's state,

[0812] A user interface device that provides a visual interface that can be easily operated by the user,

[0813] A display device for presenting information to a visual device,

[0814] A speech synthesis device for presenting audio data through a visual device,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, characterized in that the generating device adjusts the content of the dialogue according to the user's cognitive level, interests, and language characteristics.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the speech recognition device converts the user's voice input into text data in real time and provides the analysis device with the conversion result.

[0820] "Example 2 of combining an emotion engine"

[0821] (Claim 1)

[0822] A means of obtaining characteristic information from a database based on user identification information,

[0823] A generation means for personalizing the dialogue content based on the aforementioned characteristic information,

[0824] A method for analyzing voice input to evaluate the user's emotional state in real time,

[0825] Means for dynamically adjusting the content of the conversation according to the aforementioned emotional state,

[0826] A means for converting the adjusted dialogue content into audio data and playing it back,

[0827] A speech recognition means that recognizes the user's voice input and converts it into text data,

[0828] A user interface means for users to select and change dialogue themes,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, characterized in that the generation means adaptively changes the content of the dialogue in consideration of the emotional state of the user.

[0832] (Claim 3)

[0833] The system according to claim 1, characterized in that the speech recognition means converts the user's voice input into text data in real time and performs sentiment analysis using the conversion result.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] A generation means for generating appropriate dialogue based on user attributes,

[0837] A speech recognition means for converting user voice input into text information,

[0838] An analytical means for performing emotional evaluation to analyze the user's state,

[0839] A user operation means that provides a visual interface that can be easily operated by the user,

[0840] A setting means for dynamically adjusting the dialogue theme based on emotional state,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, characterized in that the generation means adjusts the content of the dialogue according to the user's cognitive level, interests, and communication characteristics.

[0844] (Claim 3)

[0845] The system according to claim 1, characterized in that the speech recognition means converts the user's voice input into text information in real time and provides the analysis means with the conversion result. [Explanation of Symbols]

[0846] 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 generation device that generates appropriate dialogue based on the user's characteristics, A speech recognition device for converting user voice input into text data, An analytical device that performs emotional analysis to analyze the user's state, A user interface device that provides a visual interface that can be easily operated by the user, A display device for presenting information to a visual device, A speech synthesis device for presenting audio data through a visual device, A system that includes this.

2. The system according to claim 1, characterized in that the generating device adjusts the content of the dialogue according to the user's cognitive level, interests, and language characteristics.

3. The system according to claim 1, characterized in that the speech recognition device converts the user's voice input into text data in real time and provides the analysis device with the conversion result.

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