System

Abstract

A system is provided.SOLUTION: A system including means for generating an appropriate response by converting a voice input into text using generative artificial intelligence technology and analyzing the text using a generative artificial intelligence model, means for converting the generated response into voice and notifying a user, means for providing a reminder function for managing a health condition or a schedule of the user, means for notifying the user of reminder content and recording and analyzing a response of the user, means for generating a list or a reminder for supporting daily life using generative artificial intelligence technology, and means for notifying the user of the generated list or reminder.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] As the aging of society progresses, there is a need to provide elderly people with support for dementia prevention, relief from loneliness, health management, and daily life. In particular, elderly people face social isolation, difficulties in health management, and the harshness of daily life due to the progression of dementia. The present invention aims to provide technology that enables elderly people to overcome these problems and enjoy a better living environment. [Means for solving the problem]

[0005] The present invention provides a system for supporting the elderly using the following means. It includes a means for converting voice input into text using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model. It also includes a means for converting the generated response into voice and notifying the user, and a means for providing a reminder function for managing the user's health status and schedule. It also includes a means for notifying the user of the reminder content and recording and analyzing the user's response. It also includes a means for generating lists and reminders to support daily life using generative artificial intelligence technology and a means for notifying the user of the generated lists and reminders. It also provides a means for using generative artificial intelligence technology to recognize the faces and names of family and friends for memory assistance and conveying this to the elderly, thereby contributing to the prevention of dementia. By combining these means, a system is constructed that enables the elderly to live their daily lives in good health and without feeling lonely.

[0006] "Generative AI technology" refers to AI technology that has the ability to engage in natural dialogue and generate sentences based on given data.

[0007] "Voice input" refers to a technology that captures a user's voice as digital data and uses it for subsequent processing.

[0008] "Convert to text" refers to the process of analyzing audio data and converting it into written information.

[0009] A "generative artificial intelligence model" refers to an algorithm that uses machine learning to generate meaningful responses or results from input data.

[0010] "Analysis" refers to the process of structuring and understanding input data.

[0011] "Response" refers to a reply or instruction generated based on the analysis results.

[0012] "Convert to voice" refers to the technology of converting text data back into voice data and outputting it as sound through a speaker.

[0013] "Health management" refers to the function of monitoring and recording the user's health status and providing appropriate advice and reminders as needed.

[0014] The "reminder function" refers to a function that aids memory by notifying the user of schedules and activities.

[0015] "Recording and analysis" refers to the process of storing user actions and responses in a database and extracting meaning from that data.

[0016] "Support for daily life" refers to functions that assist users with daily household chores, schedule management, and other tasks.

[0017] "List and reminder generation" refers to the process of automatically creating required lists and event notifications based on specified criteria.

[0018] "Memory aids" refer to technologies and features provided to help older adults with information they tend to forget.

[0019] "Recognizes the faces and names of family and friends" refers to a function that uses artificial intelligence technology to identify people around the user and makes it easier for the user to recognize people based on that data.

[0020] "Dementia prevention" refers to the range of support provided to prevent memory and cognitive decline.

[0021] "User" refers to the elderly person who uses this system.

[0022] "Terminal" refers to a device or equipment for using this system, including smartphones and dedicated devices.

[0023] "Server" refers to the central processing unit that analyzes data and runs generative AI models. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a 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.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0038] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] This invention is a system that utilizes generative artificial intelligence technology to support the health management and daily life of elderly people and to eliminate social isolation. This system consists of three main components: a server, a terminal, and a user. Below, we will explain how each component works together.

[0046] 1. Conversation partners and dementia prevention

[0047] overview

[0048] This feature aims to reduce feelings of loneliness and encourage sociability by interacting with users, and its memory support function also contributes to preventing dementia.

[0049] Specific processing

[0050] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server passes the text data to the generative AI model for analysis and generation of an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[0051] Specific examples

[0052] When a user asks, "What's the weather like today?", the device recognizes the speech, converts it into text, and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response such as "It's sunny today," and sends it to the device. The device then converts this into speech and relays it to the user.

[0053] 2. Health management

[0054] overview

[0055] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[0056] Specific processing

[0057] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The device will then send the input data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[0058] Specific examples

[0059] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[0060] 3. Daily life support

[0061] overview

[0062] This feature is intended to help users manage their daily schedules and assist with household chores.

[0063] Specific processing

[0064] Users can request the device to create schedules and lists, and the device sends the requests to the server, which then generates the appropriate reminders and lists. The generated information is returned to the device, which then notifies the user.

[0065] Specific examples

[0066] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[0067] Comprehensive System

[0068] The system provides services to users based on voice input and utilizes generative AI to generate responses and reminders. The entire process is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. The server analyzes and processes data, allowing appropriate assistance to be provided based on the user's health condition and lifestyle.

[0069] This will help seniors to improve their health management and quality of daily life without feeling socially isolated.

[0070] The processing flow will be explained below.

[0071] 1. Conversation partners and dementia prevention

[0072] Processing flow

[0073] Step 1:

[0074] The user speaks into the terminal.

[0075] Specific operation: The user speaks into the device's microphone.

[0076] Step 2:

[0077] The device converts the voice input into text.

[0078] How it works: The device's voice recognition software analyzes the voice waveform and generates a corresponding string of characters.

[0079] Step 3:

[0080] The terminal transmits the converted text data to the server.

[0081] Specific operation: Text data is transferred to the server via a communication protocol (e.g., HTTP or HTTPS).

[0082] Step 4:

[0083] The server inputs the text data into a generative AI model and analyzes it.

[0084] How it works: The generative AI model on the server receives the text data, analyzes it with an algorithm, and generates an appropriate response.

[0085] Step 5:

[0086] The server sends the generated response to the terminal.

[0087] Specific operation: The generated response text is sent back to the terminal via the communication protocol.

[0088] Step 6:

[0089] The device converts the response text into speech.

[0090] Specific operation: The speech synthesis engine in the device converts text data into speech data and outputs it from the speaker.

[0091] Step 7:

[0092] The user receives a voice response from the terminal.

[0093] Specific operation: The user hears the audio output from the device speaker.

[0094] 2. Health management

[0095] Processing flow

[0096] Step 1:

[0097] The device detects the reminder time.

[0098] What it does: The internal clock compares the set reminder time with the current time and prepares a notification if a reminder is needed.

[0099] Step 2:

[0100] The device will notify the user of the reminder.

[0101] Specific operation: The text-to-speech engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[0102] Step 3:

[0103] The user responds to the reminder.

[0104] Specific operation: The user responds by saying "confirm" or "complete" to the device using voice or touch input.

[0105] Step 4:

[0106] The terminal sends the user's response to the server.

[0107] Specific operation: Response data is sent to the server using a communication protocol.

[0108] Step 5:

[0109] The server records and analyzes the response.

[0110] Specific operations: The responses are stored in a health database and the information is analyzed using a data analysis algorithm.

[0111] Step 6:

[0112] The server notifies the terminal of the analysis results.

[0113] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[0114] Step 7:

[0115] The device notifies the user of the analysis results and advice.

[0116] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[0117] 3. Daily life support

[0118] Processing flow

[0119] Step 1:

[0120] The user inputs a request into the terminal.

[0121] What it does: Ask your device to create a specific reminder or list through voice command or touch input.

[0122] Step 2:

[0123] The terminal sends the request to the server.

[0124] Specific operation: The user's request is converted into text data and sent to the server using a communication protocol.

[0125] Step 3:

[0126] The server generates reminders and lists based on your request.

[0127] Specific operation: Analyzes the request and creates reminders and shopping lists using an automated generation algorithm.

[0128] Step 4:

[0129] The server returns the generated results to the terminal.

[0130] Specific operation: The generated list or reminder is sent to the device using a communication protocol.

[0131] Step 5:

[0132] The terminal displays or notifies the generated result.

[0133] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[0134] This allows users to easily perform many operations through voice input, providing smooth assistance in daily life.

[0135] Example 1

[0136] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] In modern society, the elderly face problems such as social isolation and difficulties in managing their health. Prevention of dementia and support for daily life are particularly important issues. However, existing technologies for solving these problems are insufficient in terms of usability and effectiveness. For example, conventional health management systems and schedule management applications are difficult for the elderly to operate and lack effective dementia prevention features. Therefore, there is a need for an effective system that comprehensively supports the elderly's health management, elimination of social isolation, and dementia prevention.

[0138] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0139] In this invention, the server includes: means for converting voice input into text data using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model; means for converting the generated response into voice and notifying the user; means for providing a reminder function for managing the user's health status and schedule; means for notifying the user of the reminder content and recording and analyzing the user's response; a terminal for outputting the generated response and instructions in voice; and means for communicating voice data and text data between the terminal and the server. This allows elderly people to easily and effectively receive support for health management and daily life while interacting with the terminal in natural language. Furthermore, adding a function for recognizing and communicating the faces and names of family and friends as a dementia prevention measure can promote emotional interaction, prevent social isolation, and contribute to maintaining cognitive function.

[0140] "Generative AI technology" is a technology that generates text and speech using artificial intelligence with advanced computing and data analysis capabilities.

[0141] "Voice input" means that a user gives instructions or questions to a terminal by voice.

[0142] "Character data" is data converted from voice input into text format.

[0143] A "generative artificial intelligence model" is an algorithm that uses artificial intelligence to analyze received data and generate appropriate responses or instructions.

[0144] A "response" is a reply or instruction that the generative AI model generates by analyzing the user's voice input.

[0145] "Conversion to speech" is the process of converting the generated text data back into speech.

[0146] "User notification" is the act of informing the user of a generated response or reminder in audio or visual form.

[0147] The "reminder function" is a function that memorizes the user's health condition and schedule, and notifies them at specific times or under specific conditions.

[0148] "Recording and analysis" is the process of storing and analyzing user responses and actions.

[0149] "Terminal" refers to any device that inputs and outputs audio.

[0150] A "server" is a computer system that processes data and performs various functions.

[0151] "Data communication" is a method of sending and receiving voice data and text data between a terminal and a server.

[0152] "Daily life support" refers to functions that support the user's daily life, such as schedule management and list creation.

[0153] A "memory aid" is a feature that supports a user's cognitive functions and helps them remember specific information.

[0154] "Facial recognition" is a technology that uses generative artificial intelligence techniques to identify facial features and identify people.

[0155] This invention is a system that uses generative artificial intelligence technology to manage the health of elderly people, support their daily lives, and alleviate social isolation. The system provides services to users based on voice input and utilizes a generative AI model to generate responses and reminders. The system is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. In addition, the server analyzes and processes data, so appropriate support is provided according to the user's health condition and lifestyle.

[0156] The primary hardware is a device (e.g., smartphone, smart speaker) with voice input and output, which allows users to give instructions and ask questions by voice. The server runs a generative AI model (e.g., OpenAI GPT-4) to analyze data and generate responses. Data is communicated between the device and the server via an internet connection.

[0157] Conversation partners and dementia prevention

[0158] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server uses a generative AI model to analyze the text, generate an appropriate response, and return it to the device. The device then converts the generated response into speech and relays it to the user. Here is an example:

[0159] When a user says, "What's the weather like today?", the device converts the speech into text and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response saying, "Today's weather is sunny," and sends it to the device. The device then converts this into speech and relays it to the user.

[0160] health care

[0161] The device will display or sound a reminder at the set time. The user will follow the reminder to perform a health check. When the user enters the results of the health check into the device, the device will send the data to the server. The server will analyze the data, generate any necessary advice or next actions, and notify the user via the device. Below is a specific example.

[0162] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[0163] Daily life support

[0164] The user can request the device to create a schedule or list. The device sends the request to the server, which then generates the appropriate reminder or list. The generated information is returned to the device and notified to the user. Here is an example:

[0165] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder and sends it back to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[0166] This system is designed to allow elderly people to receive support for health management and daily life while operating it naturally using voice commands, thereby improving the quality of their health management and daily life without causing them to feel socially isolated.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Conversation partners and dementia prevention

[0169] Processing Steps

[0170] Step 1:

[0171] The user speaks to the device. For example, they ask, "What's the weather like today?" This speech is the input.

[0172] Step 2:

[0173] The device collects the voice input and converts it into text using speech recognition software, such as Google Cloud Speech-to-Text, which then converts the voice data into text, providing an output for the next step.

[0174] Step 3:

[0175] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[0176] Step 4:

[0177] The server passes the received text data to a generative AI model. An example of a generative AI model used here is OpenAI GPT-4. The generative AI model analyzes the text data and generates an appropriate response. This analysis and response generation corresponds to data processing and data calculation. The input is the received text data, and the output is the generated response text.

[0178] Step 5:

[0179] The server generates a response text and sends it back to the terminal. The input is the response text and the output is what is sent to the terminal.

[0180] Step 6:

[0181] The device converts the generated response text into speech using speech synthesis software, such as Google Cloud Text-to-Speech. The input is the response text, and the output is synthesized speech.

[0182] Step 7:

[0183] The device plays the synthesized voice to the user, who receives a response such as "The weather is sunny today." At this point, all processing is complete.

[0184] health care

[0185] Processing Steps

[0186] Step 1:

[0187] The device displays or sounds a reminder at the set time. For example, it may say, "It's time to take your medicine." This notification is an input.

[0188] Step 2:

[0189] The user performs a health check or takes an action (e.g., taking medicine) according to the reminder. The user's action corresponds to the input, and the result of the action becomes the output for the next step.

[0190] Step 3:

[0191] The user inputs a result, such as "I drank it," into the terminal by voice or text. This creates input data. The input is the user's response, and the output is text data.

[0192] Step 4:

[0193] The terminal sends the input data to the server. This communication also takes place over the Internet. The input is the user's response data, and the output is the data sent to the server.

[0194] Step 5:

[0195] The data received by the server is analyzed using a data analysis tool. Python's Pandas and NumPy are used here. Reminder settings and advice for the next day are generated based on the data analysis. This analysis corresponds to data processing. The input is the user's response data, and the output is the analysis results (reminder settings and advice).

[0196] Step 6:

[0197] The server generates the analysis results and sends them back to the terminal. The input is the analysis results and the output is the data sent to the terminal.

[0198] Step 7:

[0199] The device then notifies the user of any reminders or advice for the next day, for example, "Take your next dose of medicine at 8:00 AM tomorrow." At this point, all processing is complete.

[0200] Daily life support

[0201] Processing Steps

[0202] Step 1:

[0203] The user asks the device to create an appointment or list. For example, they might say, "Set a cleaning reminder for tomorrow at 10:00 AM." This voice corresponds to input.

[0204] Step 2:

[0205] The device records the speech and uses voice recognition software to convert it into text data, which is where Google Cloud Speech-to-Text comes in. Once the audio data is converted into text data, an output is generated for the next step.

[0206] Step 3:

[0207] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[0208] Step 4:

[0209] The server parses the received request text and creates a reminder using reminder generation software. As an example, the Google Calendar API is used. Parsing and reminder creation fall under data processing and data calculation. The input is the request text, and the output is the generated reminder.

[0210] Step 5:

[0211] The server sends the generated reminder back to the device. The input is the generated reminder and the output is the transmission to the device.

[0212] Step 6:

[0213] The device will notify the user of the reminder at the specified time, for example, "It's 10:00 AM. Time to clean." At this point, all processing is complete.

[0214] (Application example 1)

[0215] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] In addition to existing systems aimed at managing the health of the elderly, supporting their daily lives, and eliminating social isolation, there is a lack of means to reduce the burden on workers in factories and improve their work efficiency. In particular, there is a demand for systems that integrate functions such as voice input and analysis of work instructions, schedule management, and memory assistance.

[0217] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0218] In this invention, the server includes means for receiving work instructions in the factory by voice, converting them into text and sending them to the server, where the server analyzes them using a generative AI model and generates a response, means for converting the generated response into voice and notifying workers, and means for managing workers' work schedules and break times and providing reminders. This enables the reception of work instructions and response generation based on voice input, management of work schedules, and efficient work support through reminders.

[0219] "Generative AI technology" is a technology that converts voice input into text and generates appropriate responses by analyzing that text.

[0220] A "generative AI model" is an artificial intelligence model that generates appropriate responses and reminders based on input data.

[0221] "Health status" refers to information or status regarding the user's physical health.

[0222] A "reminder" is a notification function that prompts a user to take action at a specific time or in a specific situation.

[0223] "Server" means a cloud-based or on-premise computer system for analyzing data and generating responses.

[0224] A "factory robot" is a robot that assists workers in a factory by receiving and responding to work instructions, managing schedules, and providing reminders.

[0225] "Voice input" is a method by which a user verbally inputs instructions or information.

[0226] "Text conversion" is the process of converting speech input into text data.

[0227] "Analysis" is the process of generating responses and instructions based on input data.

[0228] "Notification" is the act of informing a user of generated information or reminders.

[0229] "Work instructions" refers to specific instructions and procedures required when performing work within a factory.

[0230] "Schedule management" is a function for managing a user's time and schedule.

[0231] "Memory aid" is a function that helps the user's memory.

[0232] MODE FOR CARRYING OUT THE INVENTION

[0233] The embodiment of the present invention is a system that uses a factory robot to assist workers in giving work instructions, manage schedules, and provide reminders. The system program and its processing method will be described below.

[0234] System Configuration

[0235] The system includes a terminal for voice input and text output, a server that runs the generative artificial intelligence model, a means of communicating data between the terminal and the server, and a microphone and speaker for receiving and transmitting work instructions within the factory.

[0236] Hardware

[0237] Terminal: This is a robot equipped with a microphone for voice recognition and a speaker for audio playback.

[0238] Server: A cloud-based data analysis server (e.g., Google Cloud Platform or Amazon Web Services) is used.

[0239] software

[0240] Speech recognition: Uses the speech_recognition library.

[0241] Speech synthesis: Uses the pyttsx3 library.

[0242] Generative AI model: Use the transformers library to utilize a GPT-3 based generative AI model.

[0243] Program processing and data flow

[0244] The system mainly works through the following processes:

[0245] 1. Handling voice input:

[0246] When a user speaks into the microphone, the server receives the audio and converts it into text using the speech_recognition library.

[0247] 2. Text analysis and response generation:

[0248] The converted input data is parsed by the server using the transformers library, and a generative AI model (GPT-3) generates an appropriate response based on the input.

[0249] 3. Vocalization and notification of responses:

[0250] The generated text response is sent from the server to the terminal, where it is converted into speech using the pyttsx3 library and communicated to the user (worker) through the speaker.

[0251] Specific examples

[0252] Case 1: Receiving a work order and generating a response

[0253] Worker: "Prepare the parts for the next process."

[0254] The device converts the speech into text and sends it to the server.

[0255] The server uses a generative AI model to analyze the instructions and generate a response such as, "The parts to be used in the next process have been retrieved from the warehouse."

[0256] The terminal converts the response into voice and transmits it to the worker.

[0257] Case 2: Reminder Notification

[0258] The server manages the workers' schedules and sets reminders.

[0259] Terminal: "It's lunchtime. Let's take a break."

[0260] Usage example 1: "Preparing parts for the next process"

[0261] Use case 2: "Set a reminder for your lunch break"

[0262] This embodiment enables efficient work support by receiving work instructions and generating responses based on voice input, managing work schedules, and sending reminders.

[0263] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0264] Step 1:

[0265] The user gives voice input (work instructions or requests) to the terminal. The terminal receives the voice through a microphone. The input is the user's voice, and the output is voice data.

[0266] Step 2:

[0267] The device converts voice data into text data using the speech_recognition library. The input is voice data and the output is text data. Specifically, the voice recognition engine analyzes the voice signal and generates a corresponding string of characters.

[0268] Step 3:

[0269] The terminal sends the converted text data to the server. The input is text data, and the output is text data sent to the server. Specifically, data communication is performed using a network protocol.

[0270] Step 4:

[0271] The server analyzes the received text data using the transformers library. As a result of the analysis, a generative AI model (GPT-3) generates a response text. The input is text data, and the output is response text data. Specifically, the generative AI model executes a process to generate a response text based on the text data.

[0272] Step 5:

[0273] The server sends the generated response text data to the terminal. The input is the response text data, and the output is the text data sent to the terminal. Specifically, data communication is again performed using a network protocol.

[0274] Step 6:

[0275] The terminal converts the received response text data into voice data using the pyttsx3 library. The input is the response text data, and the output is voice data. Specifically, the speech synthesis engine runs a process to generate voice signals based on the text.

[0276] Step 7:

[0277] The audio data is transmitted to the user through a speaker. The input is the audio data, and the output is the audio message that the user hears. Specifically, the audio playback device converts the audio signal into physical sound and plays it back.

[0278] Step 8:

[0279] The server manages the user's schedule and health status and generates the necessary reminders. The input is the user's schedule data and health check data, and the output is the reminder text data. Specifically, the server retrieves information from the database and runs the reminder generation algorithm.

[0280] Step 9:

[0281] The generated reminder is notified to the user via the device. The input is the text data of the reminder, and the output is a voice reminder notified to the user from the device. The specific operation involves the processes of voice synthesis and voice playback.

[0282] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0283] This invention is a system that combines generative AI and an emotion engine to provide comprehensive support for elderly people to live their lives with peace of mind, aiming to manage their health, alleviate social isolation, and support their daily lives.

[0284] This system consists of three main components: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it provides responses and support that correspond to the user's emotional state.

[0285] 1. Conversation partners and dementia prevention

[0286] overview

[0287] This feature reduces feelings of loneliness and encourages sociability through interaction with users. It also contributes to preventing dementia through its memory support function.

[0288] Specific processing

[0289] When a user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion engine. The text and emotion data are sent to the server, where a generative AI model generates an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[0290] Specific examples

[0291] When a user asks, "What's the weather like today?", the emotion engine analyzes the user's voice and identifies "interest" and "joy." The device converts the speech into text and sends it to the server. The server uses a generative AI model to generate a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this into speech and relays it to the user.

[0292] 2. Health management

[0293] overview

[0294] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[0295] Specific processing

[0296] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The emotion engine will analyze the user's emotions, which will then be sent as data. The device will then send this data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[0297] Specific examples

[0298] When the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion engine analyzes the user's sense of relief along with that information and sends the data to the server. The server records the data and sets an appropriate medication reminder for the next day. At the same time, the device provides advice such as, "It looks like you're taking your medicine with peace of mind. Keep up the good work."

[0299] 3. Daily life support

[0300] overview

[0301] This feature is intended to help users manage their daily schedules and assist with household chores.

[0302] Specific processing

[0303] Users can request the device to create schedules and lists. The device sends the request to the server, where the emotion engine analyzes the user's emotions and sends the data. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[0304] Specific examples

[0305] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the emotion engine analyzes the user's "proactivity," and the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[0306] Emotion engine integration

[0307] The emotion engine analyzes voice input and identifies the user's emotional state. This data is fed back to the generative AI model and reminder support functions to individually optimize responses and support content. This allows seniors to receive close support that is adapted to their emotional state.

[0308] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[0309] The processing flow will be explained below.

[0310] 1. Conversation partners and dementia prevention

[0311] Processing flow

[0312] Step 1:

[0313] The user speaks into the terminal.

[0314] Specific operation: The user speaks into the device's microphone.

[0315] Step 2:

[0316] The device converts the voice input into text.

[0317] How it works: Speech recognition software analyzes the audio waveform and converts it into a string of characters.

[0318] Step 3:

[0319] The terminal transmits the text data to the server.

[0320] Specific operation: Sends text data to the server using a communication protocol (HTTP / HTTPS).

[0321] Step 4:

[0322] The server inputs the text data into a generative AI model and analyzes it.

[0323] How it works: The generative AI model analyzes the text data and generates an appropriate response.

[0324] Step 5:

[0325] The server sends the generated response to the terminal.

[0326] Specific operation: The generated response text is sent to the terminal using a communication protocol.

[0327] Step 6:

[0328] The device converts the response text into speech.

[0329] Specific operation: The speech synthesis engine converts text data into speech and outputs it from the speaker.

[0330] Step 7:

[0331] The user receives a voice response from the terminal.

[0332] Specific operation: Listen to the audio output from the device's speaker.

[0333] 2. Health management

[0334] Processing flow

[0335] Step 1:

[0336] The device detects the reminder time.

[0337] What it does: The internal clock compares the set reminder time with the current time and prepares a notification when the time comes.

[0338] Step 2:

[0339] The device will notify the user of the reminder.

[0340] Specific operation: The speech synthesis engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[0341] Step 3:

[0342] The user responds to the reminder.

[0343] Specific action: Respond by voice or touch input, for example, "Confirm," "Done," etc.

[0344] Step 4:

[0345] The terminal inputs the user's response into the emotion engine.

[0346] Specific operation: The emotion engine analyzes the user's response and generates emotion data.

[0347] Step 5:

[0348] The terminal transmits the response and emotion data to the server.

[0349] Specific operation: Response data and emotion data are sent to the server using a communication protocol.

[0350] Step 6:

[0351] The server records and analyzes the responses and emotional data.

[0352] Specific operation: Response and emotion data is stored in a health database, and the information is analyzed using data analysis algorithms.

[0353] Step 7:

[0354] The server notifies the terminal of the analysis results.

[0355] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[0356] Step 8:

[0357] The device notifies the user of the analysis results and advice.

[0358] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[0359] 3. Daily life support

[0360] Processing flow

[0361] Step 1:

[0362] The user inputs a request into the terminal.

[0363] What it does: Use voice commands or touch input to ask your device to create a specific reminder or list.

[0364] Step 2:

[0365] The device inputs the request into an emotion engine and analyzes the emotion.

[0366] Specific operation: Analyze the user's emotions along with the request content and generate text and emotion data.

[0367] Step 3:

[0368] The terminal transmits the request content and emotion data to the server.

[0369] Specific operation: Text and emotion data are sent to the server using a communication protocol.

[0370] Step 4:

[0371] The server generates reminders and lists based on the request.

[0372] What it does: Analyzes requests and creates reminders and lists using an automated generation algorithm.

[0373] Step 5:

[0374] The server returns the generated results to the terminal.

[0375] Specific operation: The generated lists and reminders are sent to the device using a communication protocol.

[0376] Step 6:

[0377] The terminal displays or notifies the generated result.

[0378] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[0379] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[0380] Example 2

[0381] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0382] Elderly people often need health management, support for daily life, and communication tailored to their emotional state, but there is a lack of an integrated system to effectively do this. In particular, systems with health reminders, daily schedule management, and emotional response functions are becoming increasingly important in modern society, but are difficult to realize.

[0383] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0384] In this invention, the server includes means for converting voice input into text using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model, means for converting the generated response into voice and notifying the user, means for providing a reminder function for managing the user's health status and schedule, means for notifying the user of the reminder content and recording and analyzing the user's response, means for generating lists and reminders to support daily activities using generative artificial intelligence technology, means for notifying the user of the generated lists and reminders, and means for analyzing the user's emotional state using an emotion analysis engine and individually optimizing the response and reminder content, thereby enabling health management, daily life support, and emotionally appropriate communication for the elderly.

[0385] "Generative AI technology" refers to AI that converts a user's voice input into text data and generates an appropriate response based on that text data.

[0386] A "generative artificial intelligence model" is an algorithm or system that analyzes voice or text data and generates appropriate responses or actions based on that data.

[0387] An "emotion analysis engine" is software that analyzes a user's voice data to identify their emotional state.

[0388] The "reminder function" is a function that manages the user's health status and schedule and notifies them at specified times.

[0389] A "list" is a list that summarizes the user's requests and schedules.

[0390] "Server" means a computing system for running generative artificial intelligence models and analyzing and processing data sent from terminals.

[0391] A "terminal" is a device that inputs and outputs audio.

[0392] "Users" refer to the elderly who use the system and those receiving their care.

[0393] A "response" is a reply or instruction generated by a generative artificial intelligence model in response to input from a user.

[0394] "Support for daily activities" refers to the function of managing the user's schedule and assisting with housework.

[0395] This invention is a system that aims to provide comprehensive support for elderly people to live their lives with peace of mind. This system combines generative artificial intelligence technology and an emotion analysis engine to manage the health of elderly people, eliminate social isolation, and support their daily lives. This system consists of three main components: a server, a terminal, and a user.

[0396] (Conversation partners and dementia prevention)

[0397] This feature reduces feelings of loneliness and encourages sociability by interacting with the user. It also contributes to preventing dementia through its memory assistance function. When the user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion analysis engine. The analyzed text and emotion data are sent to a server, which uses a generative artificial intelligence model to generate an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[0398] For example, when a user asks, "What's the weather like today?", the device converts the speech into text and analyzes it. The text is then sent to the server, which generates a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this back into speech and responds to the user.

[0399] (health care)

[0400] This function aims to monitor the user's health status and provide necessary reminders and advice. The device will display or sound a reminder at the set time, and the user will follow the reminder to perform a health check. The results are then entered into the device. An emotion analysis engine analyzes the user's emotions, which are also sent as data. The device then sends this data to a server, which analyzes the data and generates necessary advice or next actions. This information is then notified to the user via the device.

[0401] For example, if the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion analysis data is sent to the server along with that information. The server analyzes this and sets an appropriate medication reminder for the next day. The server also generates advice such as, "It looks like you're taking your medicine with confidence. Keep up the good work."

[0402] (Daily life support)

[0403] This feature aims to help users manage their daily schedules and assist with household chores. Users can request the device to create schedules and lists, and the details are sent from the device to the server. An emotion analysis engine analyzes the user's emotions and sends the data to the server. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[0404] For example, if a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder, and the next day the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[0405] (Sentiment analysis engine integration)

[0406] The emotion analysis engine analyzes voice input to identify the user's emotional state. This data is fed back to the generative AI model and reminder and support features to individually optimize responses and support content, allowing users to receive close support that is adapted to their emotional state.

[0407] (Specific examples of hardware and software used)

[0408] Server: Common cloud services can be used for data processing and hosting of generative artificial intelligence models.

[0409] Device: For voice input and output, a typical smart speaker or tablet device can be used.

[0410] Sentiment analysis engine: The software used includes a general sentiment analysis API.

[0411] (Example of a prompt)

[0412] "Tell me about the weather today."

[0413] "When is my next dose?"

[0414] Please add tomorrow's schedule.

[0415] In this way, older people benefit from a holistic and comprehensive support system, enabling them to live independently and with peace of mind.

[0416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0417] Conversation partners and dementia prevention processing steps

[0418] Step 1:

[0419] The user speaks into the terminal.

[0420] Input: User's voice data.

[0421] Output: The audio data is captured to the device.

[0422] What happens: The user says, "What's the weather like today?"

[0423] Step 2:

[0424] The device converts the speech to text.

[0425] Input: Audio data captured on the device.

[0426] Output: The converted text data.

[0427] What it does: Your device uses speech recognition software to convert the voice data into text: "What's the weather like today?"

[0428] Step 3:

[0429] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[0430] Input: The converted text data.

[0431] Output: Text data and parsed sentiment data.

[0432] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "interest" and "joy."

[0433] Step 4:

[0434] The device transmits text data and emotion data to the server.

[0435] Input: Text data and parsed emotion data.

[0436] Output: The data sent to the server.

[0437] Specific operation: The device sends a data packet containing the analysis results to the server.

[0438] Step 5:

[0439] The server uses a generative AI model to generate an appropriate response.

[0440] Input: Text data and emotion data.

[0441] Output: The generated response text.

[0442] What happens: The server uses the generative AI model to generate a response like, "The weather is sunny today. It's a good day to go outside."

[0443] Step 6:

[0444] The server returns the generated response to the terminal.

[0445] Input: The generated response text.

[0446] Output: The response text is sent back to the terminal.

[0447] Specific operation: The server sends the generated text response to the device.

[0448] Step 7:

[0449] The terminal converts the response into voice and conveys it to the user.

[0450] Input: Response text data.

[0451] Output: A spoken response.

[0452] What happens: The device uses text-to-speech technology to respond to the user by saying, "The weather is sunny today. It's a good day to go outside."

[0453] Health Care Processing Steps

[0454] Step 1:

[0455] Your device will display or sound a reminder at the set time.

[0456] Input: The set reminder time.

[0457] Output: The notified reminder.

[0458] Specific operation: The device will notify you by voice, "It's time to take your medicine."

[0459] Step 2:

[0460] The user follows the reminder to perform a health check.

[0461] Input: Health check instructions.

[0462] Output: Health check completed.

[0463] Specific action: The user takes medicine.

[0464] Step 3:

[0465] The user enters the results of the health check into the terminal.

[0466] Input: Health check result (e.g. "drank").

[0467] Output: The results typed into the terminal.

[0468] Specific action: The user types "I drank it" into the terminal.

[0469] Step 4:

[0470] The terminal analyzes the user's emotional state with an emotion analysis engine so that the terminal can transmit the result data and emotion data.

[0471] Input: Health check results.

[0472] Output: Result data and analyzed sentiment data.

[0473] Specific operation: The device sends the results of the health check to an emotion analysis engine, which analyzes the emotion "relief."

[0474] Step 5:

[0475] The terminal transmits this data to the server.

[0476] Input: Health check results and parsed emotion data.

[0477] Output: The data sent to the server.

[0478] Specific operation: The device sends the results and analysis data to the server.

[0479] Step 6:

[0480] The server analyzes the data and generates the necessary advice or next action.

[0481] Input: Health check results and parsed emotion data.

[0482] Output: Any advice or next actions required.

[0483] Specific operation: The server records medication data and generates a reminder for the next day and advice such as "It looks like you're taking your medication with confidence. Keep up the good work."

[0484] Step 7:

[0485] The server returns the generated information to the terminal.

[0486] Input: Generated advice and next actions.

[0487] Output: Advice sent back to the terminal.

[0488] Specific operation: The server sends the generated information to the terminal.

[0489] Step 8:

[0490] The terminal notifies the user.

[0491] Input: Returned advice and next actions.

[0492] Output: Advice given.

[0493] Specific operation: The device will notify you with a voice message saying, "It seems you are taking your medication safely. Keep up the good work."

[0494] Daily living support processing steps

[0495] Step 1:

[0496] The user requests the device to create an event or list.

[0497] Input: User request (audio data).

[0498] Output: The request is imported into the terminal.

[0499] What happens: A user says, "Set a cleaning reminder for tomorrow at 10 AM."

[0500] Step 2:

[0501] The terminal converts the request content into text data.

[0502] Input: Audio data of the request.

[0503] Output: The converted text data.

[0504] What happens: Your device uses voice recognition software to generate the text "Set a cleaning reminder for tomorrow at 10 AM."

[0505] Step 3:

[0506] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[0507] Input: The converted text data.

[0508] Output: Text data and parsed sentiment data.

[0509] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "positivity."

[0510] Step 4:

[0511] The device transmits text data and emotion data to the server.

[0512] Input: Text data and parsed emotion data.

[0513] Output: The data sent to the server.

[0514] Specific operation: The device sends a data packet containing the analysis results to the server.

[0515] Step 5:

[0516] The server analyzes the data to generate appropriate reminders and lists.

[0517] Input: Text data and emotion data.

[0518] Output: The generated reminders and lists.

[0519] Specific behavior: The server analyzes the data for setting reminders and generates a "cleaning reminder for tomorrow at 10 AM."

[0520] Step 6:

[0521] The server returns the generated reminder information to the terminal.

[0522] Input: The generated reminder.

[0523] Output: The reminder sent back to the device.

[0524] Specific operation: The server sends the generated reminder information to the device.

[0525] Step 7:

[0526] The terminal notifies the user.

[0527] Input: The returned reminder.

[0528] Output: The notified reminder.

[0529] Specific behavior: The device will notify you at 10:00 AM the next day with a voice message saying, "It's 10:00 AM. Time to clean. You look great!"

[0530] (Application example 2)

[0531] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0532] When elderly people work in factories, it is difficult to manage the progress of their work and provide appropriate instructions. Additionally, elderly people can feel lonely while working, and emotional fluctuations can affect their work efficiency. The present invention aims to solve these problems by providing a system that allows elderly people to work in factories with peace of mind and receive emotional support.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting voice input into text using generative artificial intelligence technology and analyzing the text, means for converting the generated response into voice and notifying it, means for analyzing the user's emotions and generating a personalized response, and means for generating the next work instruction based on the past work history and the current emotional state. This allows elderly people to work safely in factories and receive appropriate instructions and emotional support.

[0534] "Generative AI technology" refers to advanced algorithms and models that analyze, predict, and generate data, and is particularly a technology that uses deep learning.

[0535] "Voice input" is a data format for acquiring and analyzing the words or voices spoken by a user as electronic data.

[0536] "Text conversion" is the process of analyzing audio data and converting it into text data.

[0537] A "generative artificial intelligence model" refers to a machine learning model that is trained to perform a specific task, such as natural language processing.

[0538] An "appropriate response" refers to a meaningful reply or action that is generated based on the input data and its context.

[0539] An "emotion engine" is a technology or algorithm for analyzing a user's emotional state based on data such as voice and text.

[0540] "Lists and Reminders" refers to the list and notification features that allow users to manage tasks and schedules.

[0541] A "terminal" refers to an electronic device that a user can directly operate or input data from, such as smart glasses or a smartphone.

[0542] A "server" refers to a computer system that centrally processes and stores data and communicates with terminals via a network.

[0543] "Work history" is a record of the work that the user has done up to now.

[0544] "Next work instruction" refers to information that instructs the user on the next work to be performed.

[0545] "Individualized responses" refer to personalized responses or actions that are generated taking into account the user's specific situation and emotional state.

[0546] "Generation" refers to the process of creating new data or responses based on data or information.

[0547] This invention is a support system for elderly people working in factories, and is composed of three main components: a server, a terminal, and a user. A specific embodiment of this system is shown below.

[0548] System Overview

[0549] This system uses smart glasses as the terminal, and a generative AI model and emotion engine are implemented on the server. The user inputs voice through the smart glasses, which converts the voice into text. The converted text is sent to the server, where it is analyzed using the generative AI model and emotion engine. Based on the analysis results, appropriate work instructions and responses are generated and returned to the user via voice via the terminal.

[0550] Hardware and software used

[0551] Hardware: Smart glasses (terminal), cloud server (server)

[0552] Software: Speech recognition libraries (speech_recognition), sentiment analysis libraries (transformers), generative AI models (e.g., GPT-3)

[0553] Data processing and calculation flow

[0554] 1. Voice input to text conversion: When a user speaks a work instruction request into the smart glasses, the smart glasses' voice recognition library converts the voice into text.

[0555] 2. Sentiment analysis: The converted text is sent from the device to the server, where the user's emotional state is analyzed using an emotion analysis library.

[0556] 3. Response generation by generative AI model: The sentiment analysis results and text are input into a generative AI model to generate appropriate work instructions and responses, such as instructions based on the next task and emotion.

[0557] 4. Speech conversion and notification: The generated response is again converted from text to speech and notified to the user via the smart glasses.

[0558] Specific examples

[0559] Example 1: Providing the next work order

[0560] When a user verbally requests the smart glasses to "tell me what to do next," the speech recognition library converts this into text and sends it to the server. The emotion analysis library on the server analyzes the text and determines the user's emotional state as "interested." Based on this information, the generative AI model generates specific work instructions, such as "Next, assemble parts A and B," and notifies the user via voice through the smart glasses.

[0561] Example prompt sentence:

[0562] User said: Tell me what to do next

[0563] Emotion: Interest

[0564] Generate the following work orders:

[0565] In this way, elderly people can receive instructions on what to do next that take their emotional state into consideration, allowing them to proceed with their work with peace of mind.

[0566] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0567] Step 1:

[0568] The user provides voice input. Specifically, they speak to the smart glasses, saying, "Tell me what to do next." This voice data is collected by the smart glasses' microphone and sent to a speech recognition library. The input is the user's voice data, and the output is data that converts that voice into text.

[0569] Step 2:

[0570] The device uses a speech recognition library to convert the voice data into text, which is an exact recording of what the user said to the smart glasses. The input is the voice data, and the output is the converted text data.

[0571] Step 3:

[0572] The terminal sends the converted text data to the server. At this time, the text data is securely sent to the server via the Internet. The input is text data, and the output is the data in the state it was sent to the server.

[0573] Step 4:

[0574] The server passes the received text data to a sentiment analysis library to analyze the user's emotional state. This process identifies the user's emotions from the text data and extracts that information. The input is the text data, and the output is the sentiment analysis result.

[0575] Step 5:

[0576] The server uses the emotion analysis results to input the prompt sentence to the generative AI model. Specifically, the prompt sentence "User said: Tell me the next task. Emotion: Interested. Please generate the next task instruction." is passed to the generative AI model. The input is text data and the emotion analysis results, and the output is the generated text data of the task instruction.

[0577] Step 6:

[0578] The server uses the generative AI model to generate the next work instruction. For example, specific instructions such as "Next, assemble parts A and B" are generated. The input is a prompt sentence, and the output is the text data of the work instruction.

[0579] Step 7:

[0580] The server sends the generated text data of the work instructions to the terminal. The input is the text data of the work instructions, and the output is the data in the state sent to the terminal.

[0581] Step 8:

[0582] The terminal converts the received text data of the work instructions into speech using a speech synthesis library. The input is the text data of the work instructions, and the output is speech data.

[0583] Step 9:

[0584] The device notifies the user of the generated voice data. Specifically, it notifies the user through the speaker of the smart glasses, saying, "Next, assemble parts A and B." The input is voice data, and the output is a voice notification to the user.

[0585] Through the above processing steps, the elderly person can continue working in the factory with peace of mind while receiving appropriate instructions on the next task to be performed.

[0586] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0587] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0589] [Second embodiment]

[0590] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0591] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0592] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0593] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0594] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0595] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0596] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0597] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0598] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0599] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0601] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0602] This invention is a system that utilizes generative artificial intelligence technology to support the health management and daily life of elderly people and to eliminate social isolation. This system consists of three main components: a server, a terminal, and a user. Below, we will explain how each component works together.

[0603] 1. Conversation partners and dementia prevention

[0604] overview

[0605] This feature aims to reduce feelings of loneliness and encourage sociability by interacting with users, and its memory support function also contributes to preventing dementia.

[0606] Specific processing

[0607] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server passes the text data to the generative AI model for analysis and generation of an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[0608] Specific examples

[0609] When a user asks, "What's the weather like today?", the device recognizes the speech, converts it into text, and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response such as "It's sunny today," and sends it to the device. The device then converts this into speech and relays it to the user.

[0610] 2. Health management

[0611] overview

[0612] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[0613] Specific processing

[0614] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The device will then send the input data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[0615] Specific examples

[0616] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[0617] 3. Daily life support

[0618] overview

[0619] This feature is intended to help users manage their daily schedules and assist with household chores.

[0620] Specific processing

[0621] Users can request the device to create schedules and lists, and the device sends the requests to the server, which then generates the appropriate reminders and lists. The generated information is returned to the device, which then notifies the user.

[0622] Specific examples

[0623] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[0624] Comprehensive System

[0625] The system provides services to users based on voice input and utilizes generative AI to generate responses and reminders. The entire process is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. The server analyzes and processes data, allowing appropriate assistance to be provided based on the user's health condition and lifestyle.

[0626] This will help seniors to improve their health management and quality of daily life without feeling socially isolated.

[0627] The processing flow will be explained below.

[0628] 1. Conversation partners and dementia prevention

[0629] Processing flow

[0630] Step 1:

[0631] The user speaks into the terminal.

[0632] Specific operation: The user speaks into the device's microphone.

[0633] Step 2:

[0634] The device converts the voice input into text.

[0635] How it works: The device's voice recognition software analyzes the voice waveform and generates a corresponding string of characters.

[0636] Step 3:

[0637] The terminal transmits the converted text data to the server.

[0638] Specific operation: Text data is transferred to the server via a communication protocol (e.g., HTTP or HTTPS).

[0639] Step 4:

[0640] The server inputs the text data into a generative AI model and analyzes it.

[0641] How it works: The generative AI model on the server receives the text data, analyzes it with an algorithm, and generates an appropriate response.

[0642] Step 5:

[0643] The server sends the generated response to the terminal.

[0644] Specific operation: The generated response text is sent back to the terminal via the communication protocol.

[0645] Step 6:

[0646] The device converts the response text into speech.

[0647] Specific operation: The speech synthesis engine in the device converts text data into speech data and outputs it from the speaker.

[0648] Step 7:

[0649] The user receives a voice response from the terminal.

[0650] Specific operation: The user hears the audio output from the device speaker.

[0651] 2. Health management

[0652] Processing flow

[0653] Step 1:

[0654] The device detects the reminder time.

[0655] What it does: The internal clock compares the set reminder time with the current time and prepares a notification if a reminder is needed.

[0656] Step 2:

[0657] The device will notify the user of the reminder.

[0658] Specific operation: The text-to-speech engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[0659] Step 3:

[0660] The user responds to the reminder.

[0661] Specific operation: The user responds by saying "confirm" or "complete" to the device using voice or touch input.

[0662] Step 4:

[0663] The terminal sends the user's response to the server.

[0664] Specific operation: Response data is sent to the server using a communication protocol.

[0665] Step 5:

[0666] The server records and analyzes the response.

[0667] Specific operations: The responses are stored in a health database and the information is analyzed using a data analysis algorithm.

[0668] Step 6:

[0669] The server notifies the terminal of the analysis results.

[0670] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[0671] Step 7:

[0672] The device notifies the user of the analysis results and advice.

[0673] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[0674] 3. Daily life support

[0675] Processing flow

[0676] Step 1:

[0677] The user inputs a request into the terminal.

[0678] What it does: Ask your device to create a specific reminder or list through voice command or touch input.

[0679] Step 2:

[0680] The terminal sends the request to the server.

[0681] Specific operation: The user's request is converted into text data and sent to the server using a communication protocol.

[0682] Step 3:

[0683] The server generates reminders and lists based on your request.

[0684] Specific operation: Analyzes the request and creates reminders and shopping lists using an automated generation algorithm.

[0685] Step 4:

[0686] The server returns the generated results to the terminal.

[0687] Specific operation: The generated list or reminder is sent to the device using a communication protocol.

[0688] Step 5:

[0689] The terminal displays or notifies the generated result.

[0690] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[0691] This allows users to easily perform many operations through voice input, providing smooth assistance in daily life.

[0692] Example 1

[0693] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0694] In modern society, the elderly face problems such as social isolation and difficulties in managing their health. Prevention of dementia and support for daily life are particularly important issues. However, existing technologies for solving these problems are insufficient in terms of usability and effectiveness. For example, conventional health management systems and schedule management applications are difficult for the elderly to operate and lack effective dementia prevention features. Therefore, there is a need for an effective system that comprehensively supports the elderly's health management, elimination of social isolation, and dementia prevention.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0696] In this invention, the server includes: means for converting voice input into text data using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model; means for converting the generated response into voice and notifying the user; means for providing a reminder function for managing the user's health status and schedule; means for notifying the user of the reminder content and recording and analyzing the user's response; a terminal for outputting the generated response and instructions in voice; and means for communicating voice data and text data between the terminal and the server. This allows elderly people to easily and effectively receive support for health management and daily life while interacting with the terminal in natural language. Furthermore, adding a function for recognizing and communicating the faces and names of family and friends as a dementia prevention measure can promote emotional interaction, prevent social isolation, and contribute to maintaining cognitive function.

[0697] "Generative AI technology" is a technology that generates text and speech using artificial intelligence with advanced computing and data analysis capabilities.

[0698] "Voice input" means that a user gives instructions or questions to a terminal by voice.

[0699] "Character data" is data converted from voice input into text format.

[0700] A "generative artificial intelligence model" is an algorithm that uses artificial intelligence to analyze received data and generate appropriate responses or instructions.

[0701] A "response" is a reply or instruction that the generative AI model generates by analyzing the user's voice input.

[0702] "Conversion to speech" is the process of converting the generated text data back into speech.

[0703] "User notification" is the act of informing the user of a generated response or reminder in audio or visual form.

[0704] The "reminder function" is a function that memorizes the user's health condition and schedule, and notifies them at specific times or under specific conditions.

[0705] "Recording and analysis" is the process of storing and analyzing user responses and actions.

[0706] "Terminal" refers to any device that inputs and outputs audio.

[0707] A "server" is a computer system that processes data and performs various functions.

[0708] "Data communication" is a method of sending and receiving voice data and text data between a terminal and a server.

[0709] "Daily life support" refers to functions that support the user's daily life, such as schedule management and list creation.

[0710] A "memory aid" is a feature that supports a user's cognitive functions and helps them remember specific information.

[0711] "Facial recognition" is a technology that uses generative artificial intelligence techniques to identify facial features and identify people.

[0712] This invention is a system that uses generative artificial intelligence technology to manage the health of elderly people, support their daily lives, and alleviate social isolation. The system provides services to users based on voice input and utilizes a generative AI model to generate responses and reminders. The system is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. In addition, the server analyzes and processes data, so appropriate support is provided according to the user's health condition and lifestyle.

[0713] The primary hardware is a device (e.g., smartphone, smart speaker) with voice input and output, which allows users to give instructions and ask questions by voice. The server runs a generative AI model (e.g., OpenAI GPT-4) to analyze data and generate responses. Data is communicated between the device and the server via an internet connection.

[0714] Conversation partners and dementia prevention

[0715] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server uses a generative AI model to analyze the text, generate an appropriate response, and return it to the device. The device then converts the generated response into speech and relays it to the user. Here is an example:

[0716] When a user says, "What's the weather like today?", the device converts the speech into text and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response saying, "Today's weather is sunny," and sends it to the device. The device then converts this into speech and relays it to the user.

[0717] health care

[0718] The device will display or sound a reminder at the set time. The user will follow the reminder to perform a health check. When the user enters the results of the health check into the device, the device will send the data to the server. The server will analyze the data, generate any necessary advice or next actions, and notify the user via the device. Below is a specific example.

[0719] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[0720] Daily life support

[0721] The user can request the device to create a schedule or list. The device sends the request to the server, which then generates the appropriate reminder or list. The generated information is returned to the device and notified to the user. Here is an example:

[0722] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder and sends it back to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[0723] This system is designed to allow elderly people to receive support for health management and daily life while operating it naturally using voice commands, thereby improving the quality of their health management and daily life without causing them to feel socially isolated.

[0724] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0725] Conversation partners and dementia prevention

[0726] Processing Steps

[0727] Step 1:

[0728] The user speaks to the device. For example, they ask, "What's the weather like today?" This speech is the input.

[0729] Step 2:

[0730] The device collects the voice input and converts it into text using speech recognition software, such as Google Cloud Speech-to-Text, which then converts the voice data into text, providing an output for the next step.

[0731] Step 3:

[0732] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[0733] Step 4:

[0734] The server passes the received text data to a generative AI model. An example of a generative AI model used here is OpenAI GPT-4. The generative AI model analyzes the text data and generates an appropriate response. This analysis and response generation corresponds to data processing and data calculation. The input is the received text data, and the output is the generated response text.

[0735] Step 5:

[0736] The server generates a response text and sends it back to the terminal. The input is the response text and the output is what is sent to the terminal.

[0737] Step 6:

[0738] The device converts the generated response text into speech using speech synthesis software, such as Google Cloud Text-to-Speech. The input is the response text, and the output is synthesized speech.

[0739] Step 7:

[0740] The device plays the synthesized voice to the user, who receives a response such as "The weather is sunny today." At this point, all processing is complete.

[0741] health care

[0742] Processing Steps

[0743] Step 1:

[0744] The device displays or sounds a reminder at the set time. For example, it may say, "It's time to take your medicine." This notification is an input.

[0745] Step 2:

[0746] The user performs a health check or takes an action (e.g., taking medicine) according to the reminder. The user's action corresponds to the input, and the result of the action becomes the output for the next step.

[0747] Step 3:

[0748] The user inputs a result, such as "I drank it," into the terminal by voice or text. This creates input data. The input is the user's response, and the output is text data.

[0749] Step 4:

[0750] The terminal sends the input data to the server. This communication also takes place over the Internet. The input is the user's response data, and the output is the data sent to the server.

[0751] Step 5:

[0752] The data received by the server is analyzed using a data analysis tool. Python's Pandas and NumPy are used here. Reminder settings and advice for the next day are generated based on the data analysis. This analysis corresponds to data processing. The input is the user's response data, and the output is the analysis results (reminder settings and advice).

[0753] Step 6:

[0754] The server generates the analysis results and sends them back to the terminal. The input is the analysis results and the output is the data sent to the terminal.

[0755] Step 7:

[0756] The device then notifies the user of any reminders or advice for the next day, for example, "Take your next dose of medicine at 8:00 AM tomorrow." At this point, all processing is complete.

[0757] Daily life support

[0758] Processing Steps

[0759] Step 1:

[0760] The user asks the device to create an appointment or list. For example, they might say, "Set a cleaning reminder for tomorrow at 10:00 AM." This voice corresponds to input.

[0761] Step 2:

[0762] The device records the speech and uses voice recognition software to convert it into text data, which is where Google Cloud Speech-to-Text comes in. Once the audio data is converted into text data, an output is generated for the next step.

[0763] Step 3:

[0764] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[0765] Step 4:

[0766] The server parses the received request text and creates a reminder using reminder generation software. As an example, the Google Calendar API is used. Parsing and reminder creation fall under data processing and data calculation. The input is the request text, and the output is the generated reminder.

[0767] Step 5:

[0768] The server sends the generated reminder back to the device. The input is the generated reminder and the output is the transmission to the device.

[0769] Step 6:

[0770] The device will notify the user of the reminder at the specified time, for example, "It's 10:00 AM. Time to clean." At this point, all processing is complete.

[0771] (Application example 1)

[0772] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0773] In addition to existing systems aimed at managing the health of the elderly, supporting their daily lives, and eliminating social isolation, there is a lack of means to reduce the burden on workers in factories and improve their work efficiency. In particular, there is a demand for systems that integrate functions such as voice input and analysis of work instructions, schedule management, and memory assistance.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0775] In this invention, the server includes means for receiving work instructions in the factory by voice, converting them into text and sending them to the server, where the server analyzes them using a generative AI model and generates a response, means for converting the generated response into voice and notifying workers, and means for managing workers' work schedules and break times and providing reminders. This enables the reception of work instructions and response generation based on voice input, management of work schedules, and efficient work support through reminders.

[0776] "Generative AI technology" is a technology that converts voice input into text and generates appropriate responses by analyzing that text.

[0777] A "generative AI model" is an artificial intelligence model that generates appropriate responses and reminders based on input data.

[0778] "Health status" refers to information or status regarding the user's physical health.

[0779] A "reminder" is a notification function that prompts a user to take action at a specific time or in a specific situation.

[0780] "Server" means a cloud-based or on-premise computer system for analyzing data and generating responses.

[0781] A "factory robot" is a robot that assists workers in a factory by receiving and responding to work instructions, managing schedules, and providing reminders.

[0782] "Voice input" is a method by which a user verbally inputs instructions or information.

[0783] "Text conversion" is the process of converting speech input into text data.

[0784] "Analysis" is the process of generating responses and instructions based on input data.

[0785] "Notification" is the act of informing a user of generated information or reminders.

[0786] "Work instructions" refers to specific instructions and procedures required when performing work within a factory.

[0787] "Schedule management" is a function for managing a user's time and schedule.

[0788] "Memory aid" is a function that helps the user's memory.

[0789] MODE FOR CARRYING OUT THE INVENTION

[0790] The embodiment of the present invention is a system that uses a factory robot to assist workers in giving work instructions, manage schedules, and provide reminders. The system program and its processing method will be described below.

[0791] System Configuration

[0792] The system includes a terminal for voice input and text output, a server that runs the generative artificial intelligence model, a means of communicating data between the terminal and the server, and a microphone and speaker for receiving and transmitting work instructions within the factory.

[0793] Hardware

[0794] Terminal: This is a robot equipped with a microphone for voice recognition and a speaker for audio playback.

[0795] Server: A cloud-based data analysis server (e.g., Google Cloud Platform or Amazon Web Services) is used.

[0796] software

[0797] Speech recognition: Uses the speech_recognition library.

[0798] Speech synthesis: Uses the pyttsx3 library.

[0799] Generative AI model: Use the transformers library to utilize a GPT-3 based generative AI model.

[0800] Program processing and data flow

[0801] The system mainly works through the following processes:

[0802] 1. Processing voice input:

[0803] When a user speaks into the microphone, the server receives the audio and converts it into text using the speech_recognition library.

[0804] 2. Text analysis and response generation:

[0805] The converted input data is parsed by the server using the transformers library, and a generative AI model (GPT-3) generates an appropriate response based on the input.

[0806] 3. Vocalization and notification of responses:

[0807] The generated text response is sent from the server to the terminal, where it is converted into speech using the pyttsx3 library and communicated to the user (worker) through the speaker.

[0808] Specific examples

[0809] Case 1: Receiving a work order and generating a response

[0810] Worker: "Prepare the parts for the next process."

[0811] The device converts the speech into text and sends it to the server.

[0812] The server uses a generative AI model to analyze the instructions and generate a response such as, "The parts to be used in the next process have been retrieved from the warehouse."

[0813] The terminal converts the response into voice and transmits it to the worker.

[0814] Case 2: Reminder Notification

[0815] The server manages the workers' schedules and sets reminders.

[0816] Terminal: "It's lunchtime. Let's take a break."

[0817] Usage example 1: "Preparing parts for the next process"

[0818] Use case 2: "Set a reminder for your lunch break"

[0819] This embodiment enables efficient work support by receiving work instructions and generating responses based on voice input, managing work schedules, and sending reminders.

[0820] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0821] Step 1:

[0822] The user gives voice input (work instructions or requests) to the terminal. The terminal receives the voice through a microphone. The input is the user's voice, and the output is voice data.

[0823] Step 2:

[0824] The device converts voice data into text data using the speech_recognition library. The input is voice data and the output is text data. Specifically, the voice recognition engine analyzes the voice signal and generates a corresponding string of characters.

[0825] Step 3:

[0826] The terminal sends the converted text data to the server. The input is text data, and the output is text data sent to the server. Specifically, data communication is performed using a network protocol.

[0827] Step 4:

[0828] The server analyzes the received text data using the transformers library. As a result of the analysis, a generative AI model (GPT-3) generates a response text. The input is text data, and the output is response text data. Specifically, the generative AI model executes a process to generate a response text based on the text data.

[0829] Step 5:

[0830] The server sends the generated response text data to the terminal. The input is the response text data, and the output is the text data sent to the terminal. Specifically, data communication is again performed using a network protocol.

[0831] Step 6:

[0832] The terminal converts the received response text data into voice data using the pyttsx3 library. The input is the response text data, and the output is voice data. Specifically, the speech synthesis engine runs a process to generate voice signals based on the text.

[0833] Step 7:

[0834] The audio data is transmitted to the user through a speaker. The input is the audio data, and the output is the audio message that the user hears. Specifically, the audio playback device converts the audio signal into physical sound and plays it back.

[0835] Step 8:

[0836] The server manages the user's schedule and health status and generates the necessary reminders. The input is the user's schedule data and health check data, and the output is the reminder text data. Specifically, the server retrieves information from the database and runs the reminder generation algorithm.

[0837] Step 9:

[0838] The generated reminder is notified to the user via the device. The input is the text data of the reminder, and the output is a voice reminder notified to the user from the device. The specific operation involves the processes of voice synthesis and voice playback.

[0839] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0840] This invention is a system that combines generative AI and an emotion engine to provide comprehensive support for elderly people to live their lives with peace of mind, aiming to manage their health, alleviate social isolation, and support their daily lives.

[0841] This system consists of three main components: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it provides responses and support that correspond to the user's emotional state.

[0842] 1. Conversation partners and dementia prevention

[0843] overview

[0844] This feature reduces feelings of loneliness and encourages sociability through interaction with users. It also contributes to preventing dementia through its memory support function.

[0845] Specific processing

[0846] When a user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion engine. The text and emotion data are sent to the server, where a generative AI model generates an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[0847] Specific examples

[0848] When a user asks, "What's the weather like today?", the emotion engine analyzes the user's voice and identifies "interest" and "joy." The device converts the speech into text and sends it to the server. The server uses a generative AI model to generate a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this into speech and relays it to the user.

[0849] 2. Health management

[0850] overview

[0851] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[0852] Specific processing

[0853] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The emotion engine will analyze the user's emotions, which will then be sent as data. The device will then send this data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[0854] Specific examples

[0855] When the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion engine analyzes the user's sense of relief along with that information and sends the data to the server. The server records the data and sets an appropriate medication reminder for the next day. At the same time, the device provides advice such as, "It looks like you're taking your medicine with peace of mind. Keep up the good work."

[0856] 3. Daily life support

[0857] overview

[0858] This feature is intended to help users manage their daily schedules and assist with household chores.

[0859] Specific processing

[0860] Users can request the device to create schedules and lists. The device sends the request to the server, where the emotion engine analyzes the user's emotions and sends the data. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[0861] Specific examples

[0862] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the emotion engine analyzes the user's "proactivity," and the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[0863] Emotion engine integration

[0864] The emotion engine analyzes voice input and identifies the user's emotional state. This data is fed back to the generative AI model and reminder support functions to individually optimize responses and support content. This allows seniors to receive close support that is adapted to their emotional state.

[0865] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[0866] The processing flow will be explained below.

[0867] 1. Conversation partners and dementia prevention

[0868] Processing flow

[0869] Step 1:

[0870] The user speaks into the terminal.

[0871] Specific operation: The user speaks into the device's microphone.

[0872] Step 2:

[0873] The device converts the voice input into text.

[0874] How it works: Speech recognition software analyzes the audio waveform and converts it into a string of characters.

[0875] Step 3:

[0876] The terminal transmits the text data to the server.

[0877] Specific operation: Sends text data to the server using a communication protocol (HTTP / HTTPS).

[0878] Step 4:

[0879] The server inputs the text data into a generative AI model and analyzes it.

[0880] How it works: The generative AI model analyzes the text data and generates an appropriate response.

[0881] Step 5:

[0882] The server sends the generated response to the terminal.

[0883] Specific operation: The generated response text is sent to the terminal using a communication protocol.

[0884] Step 6:

[0885] The device converts the response text into speech.

[0886] Specific operation: The speech synthesis engine converts text data into speech and outputs it from the speaker.

[0887] Step 7:

[0888] The user receives a voice response from the terminal.

[0889] Specific operation: Listen to the audio output from the device's speaker.

[0890] 2. Health management

[0891] Processing flow

[0892] Step 1:

[0893] The device detects the reminder time.

[0894] What it does: The internal clock compares the set reminder time with the current time and prepares a notification when the time comes.

[0895] Step 2:

[0896] The device will notify the user of the reminder.

[0897] Specific operation: The speech synthesis engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[0898] Step 3:

[0899] The user responds to the reminder.

[0900] Specific action: Respond by voice or touch input, for example, "Confirm," "Done," etc.

[0901] Step 4:

[0902] The terminal inputs the user's response into the emotion engine.

[0903] Specific operation: The emotion engine analyzes the user's response and generates emotion data.

[0904] Step 5:

[0905] The terminal transmits the response and emotion data to the server.

[0906] Specific operation: Response data and emotion data are sent to the server using a communication protocol.

[0907] Step 6:

[0908] The server records and analyzes the responses and emotional data.

[0909] Specific operation: Response and emotion data is stored in a health database, and the information is analyzed using data analysis algorithms.

[0910] Step 7:

[0911] The server notifies the terminal of the analysis results.

[0912] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[0913] Step 8:

[0914] The device notifies the user of the analysis results and advice.

[0915] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[0916] 3. Daily life support

[0917] Processing flow

[0918] Step 1:

[0919] The user inputs a request into the terminal.

[0920] What it does: Use voice commands or touch input to ask your device to create a specific reminder or list.

[0921] Step 2:

[0922] The device inputs the request into an emotion engine and analyzes the emotion.

[0923] Specific operation: Analyze the user's emotions along with the request content and generate text and emotion data.

[0924] Step 3:

[0925] The terminal transmits the request content and emotion data to the server.

[0926] Specific operation: Text and emotion data are sent to the server using a communication protocol.

[0927] Step 4:

[0928] The server generates reminders and lists based on the request.

[0929] What it does: Analyzes requests and creates reminders and lists using an automated generation algorithm.

[0930] Step 5:

[0931] The server returns the generated results to the terminal.

[0932] Specific operation: The generated lists and reminders are sent to the device using a communication protocol.

[0933] Step 6:

[0934] The terminal displays or notifies the generated result.

[0935] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[0936] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[0937] Example 2

[0938] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0939] Elderly people often need health management, support for daily life, and communication tailored to their emotional state, but there is a lack of an integrated system to effectively do this. In particular, systems with health reminders, daily schedule management, and emotional response functions are becoming increasingly important in modern society, but are difficult to realize.

[0940] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0941] In this invention, the server includes: means for converting voice input into text using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model; means for converting the generated response into voice and notifying the user; means for providing a reminder function for managing the user's health status and schedule; means for notifying the user of the reminder content and recording and analyzing the user's response; means for generating lists and reminders to support daily activities using generative artificial intelligence technology; means for notifying the user of the generated lists and reminders; and means for analyzing the user's emotional state using an emotion analysis engine and individually optimizing the response and reminder content. This enables health management, daily life support, and emotionally appropriate communication for the elderly.

[0942] "Generative AI technology" refers to AI that converts a user's voice input into text data and generates an appropriate response based on that text data.

[0943] A "generative artificial intelligence model" is an algorithm or system that analyzes voice or text data and generates appropriate responses or actions based on that data.

[0944] An "emotion analysis engine" is software that analyzes a user's voice data to identify their emotional state.

[0945] The "reminder function" is a function that manages the user's health status and schedule and notifies them at specified times.

[0946] A "list" is a list that summarizes the user's requests and schedules.

[0947] "Server" means a computing system for running generative artificial intelligence models and analyzing and processing data sent from terminals.

[0948] A "terminal" is a device that inputs and outputs audio.

[0949] "Users" refer to the elderly who use the system and those receiving their care.

[0950] A "response" is a reply or instruction generated by a generative artificial intelligence model in response to input from a user.

[0951] "Support for daily activities" refers to the function of managing the user's schedule and assisting with housework.

[0952] This invention is a system that aims to provide comprehensive support for elderly people to live their lives with peace of mind. This system combines generative artificial intelligence technology and an emotion analysis engine to manage the health of elderly people, eliminate social isolation, and support their daily lives. This system consists of three main components: a server, a terminal, and a user.

[0953] (Conversation partners and dementia prevention)

[0954] This feature reduces feelings of loneliness and encourages sociability by interacting with the user. It also contributes to preventing dementia through its memory assistance function. When the user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion analysis engine. The analyzed text and emotion data are sent to a server, which uses a generative artificial intelligence model to generate an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[0955] For example, when a user asks, "What's the weather like today?", the device converts the speech into text and analyzes it. The text is then sent to the server, which generates a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this back into speech and responds to the user.

[0956] (health care)

[0957] This function aims to monitor the user's health status and provide necessary reminders and advice. The device will display or sound a reminder at the set time, and the user will follow the reminder to perform a health check. The results are then entered into the device. An emotion analysis engine analyzes the user's emotions, which are also sent as data. The device then sends this data to a server, which analyzes the data and generates necessary advice or next actions. This information is then notified to the user via the device.

[0958] For example, if the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion analysis data is sent to the server along with that information. The server analyzes this and sets an appropriate medication reminder for the next day. The server also generates advice such as, "It looks like you're taking your medicine with confidence. Keep up the good work."

[0959] (Daily life support)

[0960] This feature aims to help users manage their daily schedules and assist with household chores. Users can request the device to create schedules and lists, and the details are sent from the device to the server. An emotion analysis engine analyzes the user's emotions and sends the data to the server. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[0961] For example, if a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder, and the next day the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[0962] (Sentiment analysis engine integration)

[0963] The emotion analysis engine analyzes voice input to identify the user's emotional state. This data is fed back to the generative AI model and reminder and support features to individually optimize responses and support content, allowing users to receive close support that is adapted to their emotional state.

[0964] (Specific examples of hardware and software used)

[0965] Server: Common cloud services can be used for data processing and hosting of generative artificial intelligence models.

[0966] Device: For voice input and output, a typical smart speaker or tablet device can be used.

[0967] Sentiment analysis engine: The software used includes a general sentiment analysis API.

[0968] (Example of a prompt)

[0969] "Tell me about the weather today."

[0970] "When is my next dose?"

[0971] Please add tomorrow's schedule.

[0972] In this way, older people benefit from a holistic and comprehensive support system, enabling them to live independently and with peace of mind.

[0973] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0974] Conversation partners and dementia prevention processing steps

[0975] Step 1:

[0976] The user speaks into the terminal.

[0977] Input: User's voice data.

[0978] Output: The audio data is captured to the device.

[0979] What happens: The user says, "What's the weather like today?"

[0980] Step 2:

[0981] The device converts the speech to text.

[0982] Input: Audio data captured on the device.

[0983] Output: The converted text data.

[0984] What it does: Your device uses speech recognition software to convert the voice data into text: "What's the weather like today?"

[0985] Step 3:

[0986] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[0987] Input: The converted text data.

[0988] Output: Text data and parsed sentiment data.

[0989] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "interest" and "joy."

[0990] Step 4:

[0991] The device transmits text data and emotion data to the server.

[0992] Input: Text data and parsed emotion data.

[0993] Output: The data sent to the server.

[0994] Specific operation: The device sends a data packet containing the analysis results to the server.

[0995] Step 5:

[0996] The server uses a generative AI model to generate an appropriate response.

[0997] Input: Text data and emotion data.

[0998] Output: The generated response text.

[0999] What happens: The server uses the generative AI model to generate a response like, "The weather is sunny today. It's a good day to go outside."

[1000] Step 6:

[1001] The server returns the generated response to the terminal.

[1002] Input: The generated response text.

[1003] Output: The response text is sent back to the terminal.

[1004] Specific operation: The server sends the generated text response to the device.

[1005] Step 7:

[1006] The terminal converts the response into voice and conveys it to the user.

[1007] Input: Response text data.

[1008] Output: A spoken response.

[1009] What happens: The device uses text-to-speech technology to respond to the user by saying, "The weather is sunny today. It's a good day to go outside."

[1010] Health Care Processing Steps

[1011] Step 1:

[1012] Your device will display or sound a reminder at the set time.

[1013] Input: The set reminder time.

[1014] Output: The notified reminder.

[1015] Specific operation: The device will notify you by voice, "It's time to take your medicine."

[1016] Step 2:

[1017] The user follows the reminder to perform a health check.

[1018] Input: Health check instructions.

[1019] Output: Health check completed.

[1020] Specific action: The user takes medicine.

[1021] Step 3:

[1022] The user enters the results of the health check into the terminal.

[1023] Input: Health check result (e.g. "drank").

[1024] Output: The results typed into the terminal.

[1025] Specific action: The user types "I drank it" into the terminal.

[1026] Step 4:

[1027] The terminal analyzes the user's emotional state with an emotion analysis engine so that the terminal can transmit the result data and emotion data.

[1028] Input: Health check results.

[1029] Output: Result data and analyzed sentiment data.

[1030] Specific operation: The device sends the results of the health check to an emotion analysis engine, which analyzes the emotion "relief."

[1031] Step 5:

[1032] The terminal transmits this data to the server.

[1033] Input: Health check results and parsed emotion data.

[1034] Output: The data sent to the server.

[1035] Specific operation: The device sends the results and analysis data to the server.

[1036] Step 6:

[1037] The server analyzes the data and generates the necessary advice or next action.

[1038] Input: Health check results and parsed emotion data.

[1039] Output: Any advice or next actions required.

[1040] Specific operation: The server records medication data and generates a reminder for the next day and advice such as "It looks like you're taking your medication with confidence. Keep up the good work."

[1041] Step 7:

[1042] The server returns the generated information to the terminal.

[1043] Input: Generated advice and next actions.

[1044] Output: Advice sent back to the terminal.

[1045] Specific operation: The server sends the generated information to the terminal.

[1046] Step 8:

[1047] The terminal notifies the user.

[1048] Input: Returned advice and next actions.

[1049] Output: Advice given.

[1050] Specific operation: The device will notify you with a voice message saying, "It seems you are taking your medication safely. Keep up the good work."

[1051] Daily living support processing steps

[1052] Step 1:

[1053] The user requests the device to create an event or list.

[1054] Input: User request (audio data).

[1055] Output: The request is imported into the terminal.

[1056] What happens: A user says, "Set a cleaning reminder for tomorrow at 10 AM."

[1057] Step 2:

[1058] The terminal converts the request content into text data.

[1059] Input: Audio data of the request.

[1060] Output: The converted text data.

[1061] What happens: Your device uses voice recognition software to generate the text "Set a cleaning reminder for tomorrow at 10 AM."

[1062] Step 3:

[1063] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[1064] Input: The converted text data.

[1065] Output: Text data and parsed sentiment data.

[1066] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "positivity."

[1067] Step 4:

[1068] The device transmits text data and emotion data to the server.

[1069] Input: Text data and parsed emotion data.

[1070] Output: The data sent to the server.

[1071] Specific operation: The device sends a data packet containing the analysis results to the server.

[1072] Step 5:

[1073] The server analyzes the data to generate appropriate reminders and lists.

[1074] Input: Text data and emotion data.

[1075] Output: The generated reminders and lists.

[1076] Specific behavior: The server analyzes the data for setting reminders and generates a "cleaning reminder for tomorrow at 10 AM."

[1077] Step 6:

[1078] The server returns the generated reminder information to the terminal.

[1079] Input: The generated reminder.

[1080] Output: The reminder sent back to the device.

[1081] Specific operation: The server sends the generated reminder information to the device.

[1082] Step 7:

[1083] The terminal notifies the user.

[1084] Input: The returned reminder.

[1085] Output: The notified reminder.

[1086] Specific behavior: The device will notify you at 10:00 AM the next day with a voice message saying, "It's 10:00 AM. Time to clean. You look great!"

[1087] (Application example 2)

[1088] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1089] When elderly people work in factories, it is difficult to manage the progress of their work and provide appropriate instructions. Additionally, elderly people can feel lonely while working, and emotional fluctuations can affect their work efficiency. The present invention aims to solve these problems by providing a system that allows elderly people to work in factories with peace of mind and receive emotional support.

[1090] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting voice input into text using generative artificial intelligence technology and analyzing the text, means for converting the generated response into voice and notifying it, means for analyzing the user's emotions and generating a personalized response, and means for generating the next work instruction based on the past work history and the current emotional state. This allows elderly people to work safely in factories and receive appropriate instructions and emotional support.

[1091] "Generative AI technology" refers to advanced algorithms and models that analyze, predict, and generate data, and is particularly a technology that uses deep learning.

[1092] "Voice input" is a data format for acquiring and analyzing the words or voices spoken by a user as electronic data.

[1093] "Text conversion" is the process of analyzing audio data and converting it into text data.

[1094] A "generative artificial intelligence model" refers to a machine learning model that is trained to perform a specific task, such as natural language processing.

[1095] An "appropriate response" refers to a meaningful reply or action that is generated based on the input data and its context.

[1096] An "emotion engine" is a technology or algorithm for analyzing a user's emotional state based on data such as voice and text.

[1097] "Lists and Reminders" refers to the list and notification features that allow users to manage tasks and schedules.

[1098] A "terminal" refers to an electronic device that a user can directly operate or input data from, such as smart glasses or a smartphone.

[1099] A "server" refers to a computer system that centrally processes and stores data and communicates with terminals via a network.

[1100] "Work history" is a record of the work that the user has done up to now.

[1101] "Next work instruction" refers to information that instructs the user on the next work to be performed.

[1102] "Individualized responses" refer to personalized responses or actions that are generated taking into account the user's specific situation and emotional state.

[1103] "Generation" refers to the process of creating new data or responses based on data or information.

[1104] This invention is a support system for elderly people working in factories, and is composed of three main components: a server, a terminal, and a user. A specific embodiment of this system is shown below.

[1105] System Overview

[1106] This system uses smart glasses as the terminal, and a generative AI model and emotion engine are implemented on the server. The user inputs voice through the smart glasses, which converts the voice into text. The converted text is sent to the server, where it is analyzed using the generative AI model and emotion engine. Based on the analysis results, appropriate work instructions and responses are generated and returned to the user via voice via the terminal.

[1107] Hardware and software used

[1108] Hardware: Smart glasses (terminal), cloud server (server)

[1109] Software: Speech recognition libraries (speech_recognition), sentiment analysis libraries (transformers), generative AI models (e.g., GPT-3)

[1110] Data processing and calculation flow

[1111] 1. Voice input to text conversion: When a user speaks a work instruction request into the smart glasses, the smart glasses' voice recognition library converts the voice into text.

[1112] 2. Sentiment analysis: The converted text is sent from the device to the server, where the user's emotional state is analyzed using an emotion analysis library.

[1113] 3. Response generation by generative AI model: The sentiment analysis results and text are input into a generative AI model to generate appropriate work instructions and responses, such as instructions based on the next task and emotion.

[1114] 4. Speech conversion and notification: The generated response is again converted from text to speech and notified to the user via the smart glasses.

[1115] Specific examples

[1116] Example 1: Providing the next work order

[1117] When a user verbally requests the smart glasses to "tell me what to do next," the speech recognition library converts this into text and sends it to the server. The emotion analysis library on the server analyzes the text and determines the user's emotional state as "interested." Based on this information, the generative AI model generates specific work instructions, such as "Next, assemble parts A and B," and notifies the user via voice through the smart glasses.

[1118] Example prompt sentence:

[1119] User said: Tell me what to do next

[1120] Emotion: Interest

[1121] Generate the following work orders:

[1122] In this way, elderly people can receive instructions on what to do next that take their emotional state into consideration, allowing them to proceed with their work with peace of mind.

[1123] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1124] Step 1:

[1125] The user provides voice input. Specifically, they speak to the smart glasses, saying, "Tell me what to do next." This voice data is collected by the smart glasses' microphone and sent to a speech recognition library. The input is the user's voice data, and the output is data that converts that voice into text.

[1126] Step 2:

[1127] The device uses a speech recognition library to convert the voice data into text, which is an exact recording of what the user said to the smart glasses. The input is the voice data, and the output is the converted text data.

[1128] Step 3:

[1129] The terminal sends the converted text data to the server. At this time, the text data is securely sent to the server via the Internet. The input is text data, and the output is the data in the state it was sent to the server.

[1130] Step 4:

[1131] The server passes the received text data to a sentiment analysis library to analyze the user's emotional state. This process identifies the user's emotions from the text data and extracts that information. The input is the text data, and the output is the sentiment analysis result.

[1132] Step 5:

[1133] The server uses the emotion analysis results to input the prompt sentence to the generative AI model. Specifically, the prompt sentence "User said: Tell me the next task. Emotion: Interested. Please generate the next task instruction." is passed to the generative AI model. The input is text data and the emotion analysis results, and the output is the generated text data of the task instruction.

[1134] Step 6:

[1135] The server uses the generative AI model to generate the next work instruction. For example, specific instructions such as "Next, assemble parts A and B" are generated. The input is a prompt sentence, and the output is the text data of the work instruction.

[1136] Step 7:

[1137] The server sends the generated text data of the work instructions to the terminal. The input is the text data of the work instructions, and the output is the data in the state sent to the terminal.

[1138] Step 8:

[1139] The terminal converts the received text data of the work instructions into speech using a speech synthesis library. The input is the text data of the work instructions, and the output is speech data.

[1140] Step 9:

[1141] The device notifies the user of the generated voice data. Specifically, it notifies the user through the speaker of the smart glasses, saying, "Next, assemble parts A and B." The input is voice data, and the output is a voice notification to the user.

[1142] Through the above processing steps, the elderly person can continue working in the factory with peace of mind while receiving appropriate instructions on the next task to be performed.

[1143] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1145] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1146] [Third embodiment]

[1147] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1148] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1151] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1155] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1157] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1158] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1159] This invention is a system that utilizes generative artificial intelligence technology to support the health management and daily life of elderly people and to eliminate social isolation. This system consists of three main components: a server, a terminal, and a user. Below, we will explain how each component works together.

[1160] 1. Conversation partners and dementia prevention

[1161] overview

[1162] This feature aims to reduce feelings of loneliness and encourage sociability by interacting with users, and its memory support function also contributes to preventing dementia.

[1163] Specific processing

[1164] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server passes the text data to the generative AI model for analysis and generation of an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[1165] Specific examples

[1166] When a user asks, "What's the weather like today?", the device recognizes the speech, converts it into text, and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response such as "It's sunny today," and sends it to the device. The device then converts this into speech and relays it to the user.

[1167] 2. Health management

[1168] overview

[1169] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[1170] Specific processing

[1171] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The device will then send the input data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[1172] Specific examples

[1173] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[1174] 3. Daily life support

[1175] overview

[1176] This feature is intended to help users manage their daily schedules and assist with household chores.

[1177] Specific processing

[1178] Users can request the device to create schedules and lists, and the device sends the requests to the server, which then generates the appropriate reminders and lists. The generated information is returned to the device, which then notifies the user.

[1179] Specific examples

[1180] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[1181] Comprehensive System

[1182] The system provides services to users based on voice input and utilizes generative AI to generate responses and reminders. The entire process is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. The server analyzes and processes data, allowing appropriate assistance to be provided based on the user's health condition and lifestyle.

[1183] This will help seniors to improve their health management and quality of daily life without feeling socially isolated.

[1184] The processing flow will be explained below.

[1185] 1. Conversation partners and dementia prevention

[1186] Processing flow

[1187] Step 1:

[1188] The user speaks into the terminal.

[1189] Specific operation: The user speaks into the device's microphone.

[1190] Step 2:

[1191] The device converts the voice input into text.

[1192] How it works: The device's voice recognition software analyzes the voice waveform and generates a corresponding string of characters.

[1193] Step 3:

[1194] The terminal transmits the converted text data to the server.

[1195] Specific operation: Text data is transferred to the server via a communication protocol (e.g., HTTP or HTTPS).

[1196] Step 4:

[1197] The server inputs the text data into a generative AI model and analyzes it.

[1198] How it works: The generative AI model on the server receives the text data, analyzes it with an algorithm, and generates an appropriate response.

[1199] Step 5:

[1200] The server sends the generated response to the terminal.

[1201] Specific operation: The generated response text is sent back to the terminal via the communication protocol.

[1202] Step 6:

[1203] The device converts the response text into speech.

[1204] Specific operation: The speech synthesis engine in the device converts text data into speech data and outputs it from the speaker.

[1205] Step 7:

[1206] The user receives a voice response from the terminal.

[1207] Specific operation: The user hears the audio output from the device speaker.

[1208] 2. Health management

[1209] Processing flow

[1210] Step 1:

[1211] The device detects the reminder time.

[1212] What it does: The internal clock compares the set reminder time with the current time and prepares a notification if a reminder is needed.

[1213] Step 2:

[1214] The device will notify the user of the reminder.

[1215] Specific operation: The text-to-speech engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[1216] Step 3:

[1217] The user responds to the reminder.

[1218] Specific operation: The user responds by saying "confirm" or "complete" to the device using voice or touch input.

[1219] Step 4:

[1220] The terminal sends the user's response to the server.

[1221] Specific operation: Response data is sent to the server using a communication protocol.

[1222] Step 5:

[1223] The server records and analyzes the response.

[1224] Specific operations: The responses are stored in a health database and the information is analyzed using a data analysis algorithm.

[1225] Step 6:

[1226] The server notifies the terminal of the analysis results.

[1227] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[1228] Step 7:

[1229] The device notifies the user of the analysis results and advice.

[1230] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[1231] 3. Daily life support

[1232] Processing flow

[1233] Step 1:

[1234] The user inputs a request into the terminal.

[1235] What it does: Ask your device to create a specific reminder or list through voice command or touch input.

[1236] Step 2:

[1237] The terminal sends the request to the server.

[1238] Specific operation: The user's request is converted into text data and sent to the server using a communication protocol.

[1239] Step 3:

[1240] The server generates reminders and lists based on your request.

[1241] Specific operation: Analyzes the request and creates reminders and shopping lists using an automated generation algorithm.

[1242] Step 4:

[1243] The server returns the generated results to the terminal.

[1244] Specific operation: The generated list or reminder is sent to the device using a communication protocol.

[1245] Step 5:

[1246] The terminal displays or notifies the generated result.

[1247] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[1248] This allows users to easily perform many operations through voice input, providing smooth assistance in daily life.

[1249] Example 1

[1250] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1251] In modern society, the elderly face problems such as social isolation and difficulties in managing their health. Prevention of dementia and support for daily life are particularly important issues. However, existing technologies for solving these problems are insufficient in terms of usability and effectiveness. For example, conventional health management systems and schedule management applications are difficult for the elderly to operate and lack effective dementia prevention features. Therefore, there is a need for an effective system that comprehensively supports the elderly's health management, elimination of social isolation, and dementia prevention.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1253] In this invention, the server includes: means for converting voice input into text data using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model; means for converting the generated response into voice and notifying the user; means for providing a reminder function for managing the user's health status and schedule; means for notifying the user of the reminder content and recording and analyzing the user's response; a terminal for outputting the generated response and instructions in voice; and means for communicating voice data and text data between the terminal and the server. This allows elderly people to easily and effectively receive support for health management and daily life while interacting with the terminal in natural language. Furthermore, adding a function for recognizing and communicating the faces and names of family and friends as a dementia prevention measure can promote emotional interaction, prevent social isolation, and contribute to maintaining cognitive function.

[1254] "Generative AI technology" is a technology that generates text and speech using artificial intelligence with advanced computing and data analysis capabilities.

[1255] "Voice input" means that a user gives instructions or questions to a terminal by voice.

[1256] "Character data" is data converted from voice input into text format.

[1257] A "generative artificial intelligence model" is an algorithm that uses artificial intelligence to analyze received data and generate appropriate responses or instructions.

[1258] A "response" is a reply or instruction that the generative AI model generates by analyzing the user's voice input.

[1259] "Conversion to speech" is the process of converting the generated text data back into speech.

[1260] "User notification" is the act of informing the user of a generated response or reminder in audio or visual form.

[1261] The "reminder function" is a function that memorizes the user's health condition and schedule, and notifies them at specific times or under specific conditions.

[1262] "Recording and analysis" is the process of storing and analyzing user responses and actions.

[1263] "Terminal" refers to any device that inputs and outputs audio.

[1264] A "server" is a computer system that processes data and performs various functions.

[1265] "Data communication" is a method of sending and receiving voice data and text data between a terminal and a server.

[1266] "Daily life support" refers to functions that support the user's daily life, such as schedule management and list creation.

[1267] A "memory aid" is a feature that supports a user's cognitive functions and helps them remember specific information.

[1268] "Facial recognition" is a technology that uses generative artificial intelligence techniques to identify facial features and identify people.

[1269] This invention is a system that uses generative artificial intelligence technology to manage the health of elderly people, support their daily lives, and alleviate social isolation. The system provides services to users based on voice input and utilizes a generative AI model to generate responses and reminders. The system is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. In addition, the server analyzes and processes data, so appropriate support is provided according to the user's health condition and lifestyle.

[1270] The primary hardware is a device (e.g., smartphone, smart speaker) with voice input and output, which allows users to give instructions and ask questions by voice. The server runs a generative AI model (e.g., OpenAI GPT-4) to analyze data and generate responses. Data is communicated between the device and the server via an internet connection.

[1271] Conversation partners and dementia prevention

[1272] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server uses a generative AI model to analyze the text, generate an appropriate response, and return it to the device. The device then converts the generated response into speech and relays it to the user. Here is an example:

[1273] When a user says, "What's the weather like today?", the device converts the speech into text and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response saying, "Today's weather is sunny," and sends it to the device. The device then converts this into speech and relays it to the user.

[1274] health care

[1275] The device will display or sound a reminder at the set time. The user will follow the reminder to perform a health check. When the user enters the results of the health check into the device, the device will send the data to the server. The server will analyze the data, generate any necessary advice or next actions, and notify the user via the device. Below is a specific example.

[1276] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[1277] Daily life support

[1278] The user can request the device to create a schedule or list. The device sends the request to the server, which then generates the appropriate reminder or list. The generated information is returned to the device and notified to the user. Here is an example:

[1279] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder and sends it back to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[1280] This system is designed to allow elderly people to receive support for health management and daily life while operating it naturally using voice commands, thereby improving the quality of their health management and daily life without causing them to feel socially isolated.

[1281] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1282] Conversation partners and dementia prevention

[1283] Processing Steps

[1284] Step 1:

[1285] The user speaks to the device. For example, they ask, "What's the weather like today?" This speech is the input.

[1286] Step 2:

[1287] The device collects the voice input and converts it into text using speech recognition software, such as Google Cloud Speech-to-Text, which then converts the voice data into text, providing an output for the next step.

[1288] Step 3:

[1289] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[1290] Step 4:

[1291] The server passes the received text data to a generative AI model. An example of a generative AI model used here is OpenAI GPT-4. The generative AI model analyzes the text data and generates an appropriate response. This analysis and response generation corresponds to data processing and data calculation. The input is the received text data, and the output is the generated response text.

[1292] Step 5:

[1293] The server generates a response text and sends it back to the terminal. The input is the response text and the output is what is sent to the terminal.

[1294] Step 6:

[1295] The device converts the generated response text into speech using speech synthesis software, such as Google Cloud Text-to-Speech. The input is the response text, and the output is synthesized speech.

[1296] Step 7:

[1297] The device plays the synthesized voice to the user, who receives a response such as "The weather is sunny today." At this point, all processing is complete.

[1298] health care

[1299] Processing Steps

[1300] Step 1:

[1301] The device displays or sounds a reminder at the set time. For example, it may say, "It's time to take your medicine." This notification is an input.

[1302] Step 2:

[1303] The user performs a health check or takes an action (e.g., taking medicine) according to the reminder. The user's action corresponds to the input, and the result of the action becomes the output for the next step.

[1304] Step 3:

[1305] The user inputs a result, such as "I drank it," into the terminal by voice or text. This creates input data. The input is the user's response, and the output is text data.

[1306] Step 4:

[1307] The terminal sends the input data to the server. This communication also takes place over the Internet. The input is the user's response data, and the output is the data sent to the server.

[1308] Step 5:

[1309] The data received by the server is analyzed using a data analysis tool. Python's Pandas and NumPy are used here. Reminder settings and advice for the next day are generated based on the data analysis. This analysis corresponds to data processing. The input is the user's response data, and the output is the analysis results (reminder settings and advice).

[1310] Step 6:

[1311] The server generates the analysis results and sends them back to the terminal. The input is the analysis results and the output is the data sent to the terminal.

[1312] Step 7:

[1313] The device then notifies the user of any reminders or advice for the next day, for example, "Take your next dose of medicine at 8:00 AM tomorrow." At this point, all processing is complete.

[1314] Daily life support

[1315] Processing Steps

[1316] Step 1:

[1317] The user asks the device to create an appointment or list. For example, they might say, "Set a cleaning reminder for tomorrow at 10:00 AM." This voice corresponds to input.

[1318] Step 2:

[1319] The device records what you say and uses voice recognition software to convert it into text data, which is where Google Cloud Speech-to-Text comes in. Once the audio data is converted into text data, it provides an output for the next step.

[1320] Step 3:

[1321] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[1322] Step 4:

[1323] The server parses the received request text and creates a reminder using reminder generation software. As an example, the Google Calendar API is used. Parsing and reminder creation fall under data processing and data calculation. The input is the request text, and the output is the generated reminder.

[1324] Step 5:

[1325] The server sends the generated reminder back to the device. The input is the generated reminder and the output is the transmission to the device.

[1326] Step 6:

[1327] The device will notify the user of the reminder at the specified time, for example, "It's 10:00 AM. Time to clean." At this point, all processing is complete.

[1328] (Application example 1)

[1329] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1330] In addition to existing systems aimed at managing the health of the elderly, supporting their daily lives, and eliminating social isolation, there is a lack of means to reduce the burden on workers in factories and improve their work efficiency. In particular, there is a demand for systems that integrate functions such as voice input and analysis of work instructions, schedule management, and memory assistance.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1332] In this invention, the server includes means for receiving work instructions in the factory by voice, converting them into text and sending them to the server, where the server analyzes them using a generative AI model and generates a response, means for converting the generated response into voice and notifying workers, and means for managing workers' work schedules and break times and providing reminders. This enables the reception of work instructions and response generation based on voice input, management of work schedules, and efficient work support through reminders.

[1333] "Generative AI technology" is a technology that converts voice input into text and generates appropriate responses by analyzing that text.

[1334] A "generative AI model" is an artificial intelligence model that generates appropriate responses and reminders based on input data.

[1335] "Health status" refers to information or status regarding the user's physical health.

[1336] A "reminder" is a notification function that prompts a user to take action at a specific time or in a specific situation.

[1337] "Server" means a cloud-based or on-premise computer system for analyzing data and generating responses.

[1338] A "factory robot" is a robot that assists workers in a factory by receiving and responding to work instructions, managing schedules, and providing reminders.

[1339] "Voice input" is a method by which a user verbally inputs instructions or information.

[1340] "Text conversion" is the process of converting speech input into text data.

[1341] "Analysis" is the process of generating responses and instructions based on input data.

[1342] "Notification" is the act of informing a user of generated information or reminders.

[1343] "Work instructions" refers to specific instructions and procedures required when performing work within a factory.

[1344] "Schedule management" is a function for managing a user's time and schedule.

[1345] "Memory aid" is a function that helps the user's memory.

[1346] MODE FOR CARRYING OUT THE INVENTION

[1347] The embodiment of the present invention is a system that uses a factory robot to assist workers in giving work instructions, manage schedules, and provide reminders. The system program and its processing method will be described below.

[1348] System Configuration

[1349] The system includes a terminal for voice input and text output, a server that runs the generative artificial intelligence model, a means of communicating data between the terminal and the server, and a microphone and speaker for receiving and transmitting work instructions within the factory.

[1350] Hardware

[1351] Terminal: This is a robot equipped with a microphone for voice recognition and a speaker for audio playback.

[1352] Server: A cloud-based data analysis server (e.g., Google Cloud Platform or Amazon Web Services) is used.

[1353] software

[1354] Speech recognition: Uses the speech_recognition library.

[1355] Speech synthesis: Uses the pyttsx3 library.

[1356] Generative AI model: Use the transformers library to utilize a GPT-3 based generative AI model.

[1357] Program processing and data flow

[1358] The system mainly works through the following processes:

[1359] 1. Handling voice input:

[1360] When a user speaks into the microphone, the server receives the audio and converts it into text using the speech_recognition library.

[1361] 2. Text analysis and response generation:

[1362] The converted input data is parsed by the server using the transformers library, and a generative AI model (GPT-3) generates an appropriate response based on the input.

[1363] 3. Vocalization and notification of responses:

[1364] The generated text response is sent from the server to the terminal, where it is converted into speech using the pyttsx3 library and communicated to the user (worker) through the speaker.

[1365] Specific examples

[1366] Case 1: Receiving a work order and generating a response

[1367] Worker: "Prepare the parts for the next process."

[1368] The device converts the speech into text and sends it to the server.

[1369] The server uses a generative AI model to analyze the instructions and generate a response such as, "The parts to be used in the next process have been retrieved from the warehouse."

[1370] The terminal converts the response into voice and transmits it to the worker.

[1371] Case 2: Reminder Notification

[1372] The server manages the workers' schedules and sets reminders.

[1373] Terminal: "It's lunchtime. Let's take a break."

[1374] Usage example 1: "Preparing parts for the next process"

[1375] Use case 2: "Set a reminder for your lunch break"

[1376] This embodiment enables efficient work support by receiving work instructions and generating responses based on voice input, managing work schedules, and sending reminders.

[1377] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1378] Step 1:

[1379] The user gives voice input (work instructions or requests) to the terminal. The terminal receives the voice through a microphone. The input is the user's voice, and the output is voice data.

[1380] Step 2:

[1381] The device converts voice data into text data using the speech_recognition library. The input is voice data and the output is text data. Specifically, the voice recognition engine analyzes the voice signal and generates a corresponding string of characters.

[1382] Step 3:

[1383] The terminal sends the converted text data to the server. The input is text data, and the output is text data sent to the server. Specifically, data communication is performed using a network protocol.

[1384] Step 4:

[1385] The server analyzes the received text data using the transformers library. As a result of the analysis, a generative AI model (GPT-3) generates a response text. The input is text data, and the output is response text data. Specifically, the generative AI model executes a process to generate a response text based on the text data.

[1386] Step 5:

[1387] The server sends the generated response text data to the terminal. The input is the response text data, and the output is the text data sent to the terminal. Specifically, data communication is again performed using a network protocol.

[1388] Step 6:

[1389] The terminal converts the received response text data into voice data using the pyttsx3 library. The input is the response text data, and the output is voice data. Specifically, the speech synthesis engine runs a process to generate voice signals based on the text.

[1390] Step 7:

[1391] The audio data is transmitted to the user through a speaker. The input is the audio data, and the output is the audio message that the user hears. Specifically, the audio playback device converts the audio signal into physical sound and plays it back.

[1392] Step 8:

[1393] The server manages the user's schedule and health status and generates the necessary reminders. The input is the user's schedule data and health check data, and the output is the reminder text data. Specifically, the server retrieves information from the database and runs the reminder generation algorithm.

[1394] Step 9:

[1395] The generated reminder is notified to the user via the device. The input is the text data of the reminder, and the output is a voice reminder notified to the user from the device. The specific operation involves the processes of voice synthesis and voice playback.

[1396] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1397] This invention is a system that combines generative AI and an emotion engine to provide comprehensive support for elderly people to live their lives with peace of mind, aiming to manage their health, alleviate social isolation, and support their daily lives.

[1398] This system consists of three main components: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it provides responses and support that correspond to the user's emotional state.

[1399] 1. Conversation partners and dementia prevention

[1400] overview

[1401] This feature reduces feelings of loneliness and encourages sociability through interaction with users. It also contributes to preventing dementia through its memory support function.

[1402] Specific processing

[1403] When a user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion engine. The text and emotion data are sent to the server, where a generative AI model generates an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[1404] Specific examples

[1405] When a user asks, "What's the weather like today?", the emotion engine analyzes the user's voice and identifies "interest" and "joy." The device converts the speech into text and sends it to the server. The server uses a generative AI model to generate a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this into speech and relays it to the user.

[1406] 2. Health management

[1407] overview

[1408] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[1409] Specific processing

[1410] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The emotion engine will analyze the user's emotions, which will then be sent as data. The device will then send this data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[1411] Specific examples

[1412] When the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion engine analyzes the user's sense of relief along with that information and sends the data to the server. The server records the data and sets an appropriate medication reminder for the next day. At the same time, the device provides advice such as, "It looks like you're taking your medicine with peace of mind. Keep up the good work."

[1413] 3. Daily life support

[1414] overview

[1415] This feature is intended to help users manage their daily schedules and assist with household chores.

[1416] Specific processing

[1417] Users can request the device to create schedules and lists. The device sends the request to the server, where the emotion engine analyzes the user's emotions and sends the data. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[1418] Specific examples

[1419] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the emotion engine analyzes the user's "proactivity," and the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[1420] Emotion engine integration

[1421] The emotion engine analyzes voice input and identifies the user's emotional state. This data is fed back to the generative AI model and reminder support functions to individually optimize responses and support content. This allows seniors to receive close support that is adapted to their emotional state.

[1422] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[1423] The processing flow will be explained below.

[1424] 1. Conversation partners and dementia prevention

[1425] Processing flow

[1426] Step 1:

[1427] The user speaks into the terminal.

[1428] Specific operation: The user speaks into the device's microphone.

[1429] Step 2:

[1430] The device converts the voice input into text.

[1431] How it works: Speech recognition software analyzes the audio waveform and converts it into a string of characters.

[1432] Step 3:

[1433] The terminal transmits the text data to the server.

[1434] Specific operation: Sends text data to the server using a communication protocol (HTTP / HTTPS).

[1435] Step 4:

[1436] The server inputs the text data into a generative AI model and analyzes it.

[1437] How it works: The generative AI model analyzes the text data and generates an appropriate response.

[1438] Step 5:

[1439] The server sends the generated response to the terminal.

[1440] Specific operation: The generated response text is sent to the terminal using a communication protocol.

[1441] Step 6:

[1442] The device converts the response text into speech.

[1443] Specific operation: The speech synthesis engine converts text data into speech and outputs it from the speaker.

[1444] Step 7:

[1445] The user receives a voice response from the terminal.

[1446] Specific operation: Listen to the audio output from the device's speaker.

[1447] 2. Health management

[1448] Processing flow

[1449] Step 1:

[1450] The device detects the reminder time.

[1451] What it does: The internal clock compares the set reminder time with the current time and prepares a notification when the time comes.

[1452] Step 2:

[1453] The device will notify the user of the reminder.

[1454] Specific operation: The speech synthesis engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[1455] Step 3:

[1456] The user responds to the reminder.

[1457] Specific action: Respond by voice or touch input, for example, "Confirm," "Done," etc.

[1458] Step 4:

[1459] The terminal inputs the user's response into the emotion engine.

[1460] Specific operation: The emotion engine analyzes the user's response and generates emotion data.

[1461] Step 5:

[1462] The terminal transmits the response and emotion data to the server.

[1463] Specific operation: Response data and emotion data are sent to the server using a communication protocol.

[1464] Step 6:

[1465] The server records and analyzes the responses and emotional data.

[1466] Specific operation: Response and emotion data is stored in a health database, and the information is analyzed using data analysis algorithms.

[1467] Step 7:

[1468] The server notifies the terminal of the analysis results.

[1469] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[1470] Step 8:

[1471] The device notifies the user of the analysis results and advice.

[1472] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[1473] 3. Daily life support

[1474] Processing flow

[1475] Step 1:

[1476] The user inputs a request into the terminal.

[1477] What it does: Use voice commands or touch input to ask your device to create a specific reminder or list.

[1478] Step 2:

[1479] The device inputs the request into an emotion engine and analyzes the emotion.

[1480] Specific operation: Analyze the user's emotions along with the request content and generate text and emotion data.

[1481] Step 3:

[1482] The terminal transmits the request content and emotion data to the server.

[1483] Specific operation: Text and emotion data are sent to the server using a communication protocol.

[1484] Step 4:

[1485] The server generates reminders and lists based on the request.

[1486] What it does: Analyzes requests and creates reminders and lists using an automated generation algorithm.

[1487] Step 5:

[1488] The server returns the generated results to the terminal.

[1489] Specific operation: The generated lists and reminders are sent to the device using a communication protocol.

[1490] Step 6:

[1491] The terminal displays or notifies the generated result.

[1492] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[1493] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[1494] Example 2

[1495] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1496] Elderly people often need health management, support for daily life, and communication tailored to their emotional state, but there is a lack of an integrated system to effectively do this. In particular, systems with health reminders, daily schedule management, and emotional response functions are becoming increasingly important in modern society, but are difficult to realize.

[1497] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1498] In this invention, the server includes means for converting voice input into text using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model, means for converting the generated response into voice and notifying the user, means for providing a reminder function for managing the user's health status and schedule, means for notifying the user of the reminder content and recording and analyzing the user's response, means for generating lists and reminders to support daily activities using generative artificial intelligence technology, means for notifying the user of the generated lists and reminders, and means for analyzing the user's emotional state using an emotion analysis engine and individually optimizing the response and reminder content, thereby enabling health management, daily life support, and emotionally appropriate communication for the elderly.

[1499] "Generative AI technology" refers to AI that converts a user's voice input into text data and generates an appropriate response based on that text data.

[1500] A "generative artificial intelligence model" is an algorithm or system that analyzes voice or text data and generates appropriate responses or actions based on that data.

[1501] An "emotion analysis engine" is software that analyzes a user's voice data to identify their emotional state.

[1502] The "reminder function" is a function that manages the user's health status and schedule and notifies them at specified times.

[1503] A "list" is a list that summarizes the user's requests and schedules.

[1504] "Server" means a computing system for running generative artificial intelligence models and analyzing and processing data sent from terminals.

[1505] A "terminal" is a device that inputs and outputs audio.

[1506] "Users" refer to the elderly who use the system and those receiving their care.

[1507] A "response" is a reply or instruction generated by a generative artificial intelligence model in response to input from a user.

[1508] "Support for daily activities" refers to the function of managing the user's schedule and assisting with housework.

[1509] This invention is a system that aims to provide comprehensive support for elderly people to live their lives with peace of mind. This system combines generative artificial intelligence technology and an emotion analysis engine to manage the health of elderly people, eliminate social isolation, and support their daily lives. This system consists of three main components: a server, a terminal, and a user.

[1510] (Conversation partners and dementia prevention)

[1511] This feature reduces feelings of loneliness and encourages sociability by interacting with the user. It also contributes to preventing dementia through its memory assistance function. When the user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion analysis engine. The analyzed text and emotion data are sent to a server, which uses a generative artificial intelligence model to generate an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[1512] For example, when a user asks, "What's the weather like today?", the device converts the speech into text and analyzes it. The text is then sent to the server, which generates a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this back into speech and responds to the user.

[1513] (health care)

[1514] This function aims to monitor the user's health status and provide necessary reminders and advice. The device will display or sound a reminder at the set time, and the user will follow the reminder to perform a health check. The results are then entered into the device. An emotion analysis engine analyzes the user's emotions, which are also sent as data. The device then sends this data to a server, which analyzes the data and generates necessary advice or next actions. This information is then notified to the user via the device.

[1515] For example, if the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion analysis data is sent to the server along with that information. The server analyzes this and sets an appropriate medication reminder for the next day. The server also generates advice such as, "It looks like you're taking your medicine with confidence. Keep up the good work."

[1516] (Daily life support)

[1517] This feature aims to help users manage their daily schedules and assist with household chores. Users can request the device to create schedules and lists, and the details are sent from the device to the server. An emotion analysis engine analyzes the user's emotions and sends the data to the server. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[1518] For example, if a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder, and the next day the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[1519] (Sentiment analysis engine integration)

[1520] The emotion analysis engine analyzes voice input to identify the user's emotional state. This data is fed back to the generative AI model and reminder and support features to individually optimize responses and support content, allowing users to receive close support that is adapted to their emotional state.

[1521] (Specific examples of hardware and software used)

[1522] Server: Common cloud services can be used for data processing and hosting of generative artificial intelligence models.

[1523] Device: For voice input and output, a typical smart speaker or tablet device can be used.

[1524] Sentiment analysis engine: The software used includes a general sentiment analysis API.

[1525] (Example of a prompt)

[1526] "Tell me about the weather today."

[1527] "When is my next dose?"

[1528] Please add tomorrow's schedule.

[1529] In this way, older people benefit from a holistic and comprehensive support system, enabling them to live independently and with peace of mind.

[1530] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1531] Conversation partners and dementia prevention processing steps

[1532] Step 1:

[1533] The user speaks into the terminal.

[1534] Input: User's voice data.

[1535] Output: The audio data is captured to the device.

[1536] What happens: The user says, "What's the weather like today?"

[1537] Step 2:

[1538] The device converts the speech to text.

[1539] Input: Audio data captured on the device.

[1540] Output: The converted text data.

[1541] What it does: Your device uses speech recognition software to convert the voice data into text: "What's the weather like today?"

[1542] Step 3:

[1543] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[1544] Input: The converted text data.

[1545] Output: Text data and parsed sentiment data.

[1546] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "interest" and "joy."

[1547] Step 4:

[1548] The device transmits text data and emotion data to the server.

[1549] Input: Text data and parsed emotion data.

[1550] Output: The data sent to the server.

[1551] Specific operation: The device sends a data packet containing the analysis results to the server.

[1552] Step 5:

[1553] The server uses a generative AI model to generate an appropriate response.

[1554] Input: Text data and emotion data.

[1555] Output: The generated response text.

[1556] What happens: The server uses the generative AI model to generate a response like, "The weather is sunny today. It's a good day to go outside."

[1557] Step 6:

[1558] The server returns the generated response to the terminal.

[1559] Input: The generated response text.

[1560] Output: The response text is sent back to the terminal.

[1561] Specific operation: The server sends the generated text response to the device.

[1562] Step 7:

[1563] The terminal converts the response into voice and conveys it to the user.

[1564] Input: Response text data.

[1565] Output: A spoken response.

[1566] What happens: The device uses text-to-speech technology to respond to the user by saying, "The weather is sunny today. It's a good day to go outside."

[1567] Health Care Processing Steps

[1568] Step 1:

[1569] Your device will display or sound a reminder at the set time.

[1570] Input: The set reminder time.

[1571] Output: The notified reminder.

[1572] Specific operation: The device will notify you by voice, "It's time to take your medicine."

[1573] Step 2:

[1574] The user follows the reminder to perform a health check.

[1575] Input: Health check instructions.

[1576] Output: Health check completed.

[1577] Specific action: The user takes medicine.

[1578] Step 3:

[1579] The user enters the results of the health check into the terminal.

[1580] Input: Health check result (e.g. "drank").

[1581] Output: The results typed into the terminal.

[1582] Specific action: The user types "I drank it" into the terminal.

[1583] Step 4:

[1584] The terminal analyzes the user's emotional state with an emotion analysis engine so that the terminal can transmit the result data and emotion data.

[1585] Input: Health check results.

[1586] Output: Result data and analyzed sentiment data.

[1587] Specific operation: The device sends the results of the health check to an emotion analysis engine, which analyzes the emotion "relief."

[1588] Step 5:

[1589] The terminal transmits this data to the server.

[1590] Input: Health check results and parsed emotion data.

[1591] Output: The data sent to the server.

[1592] Specific operation: The device sends the results and analysis data to the server.

[1593] Step 6:

[1594] The server analyzes the data and generates the necessary advice or next action.

[1595] Input: Health check results and parsed emotion data.

[1596] Output: Any advice or next actions required.

[1597] Specific operation: The server records medication data and generates a reminder for the next day and advice such as "It looks like you're taking your medication with confidence. Keep up the good work."

[1598] Step 7:

[1599] The server returns the generated information to the terminal.

[1600] Input: Generated advice and next actions.

[1601] Output: Advice sent back to the terminal.

[1602] Specific operation: The server sends the generated information to the terminal.

[1603] Step 8:

[1604] The terminal notifies the user.

[1605] Input: Returned advice and next actions.

[1606] Output: Advice given.

[1607] Specific operation: The device will notify you with a voice message saying, "It seems you are taking your medication safely. Keep up the good work."

[1608] Daily living support processing steps

[1609] Step 1:

[1610] The user requests the device to create an event or list.

[1611] Input: User request (audio data).

[1612] Output: The request is imported into the terminal.

[1613] What happens: A user says, "Set a cleaning reminder for tomorrow at 10 AM."

[1614] Step 2:

[1615] The terminal converts the request content into text data.

[1616] Input: Audio data of the request.

[1617] Output: The converted text data.

[1618] What happens: Your device uses voice recognition software to generate the text "Set a cleaning reminder for tomorrow at 10 AM."

[1619] Step 3:

[1620] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[1621] Input: The converted text data.

[1622] Output: Text data and parsed sentiment data.

[1623] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "positivity."

[1624] Step 4:

[1625] The device transmits text data and emotion data to the server.

[1626] Input: Text data and parsed emotion data.

[1627] Output: The data sent to the server.

[1628] Specific operation: The device sends a data packet containing the analysis results to the server.

[1629] Step 5:

[1630] The server analyzes the data to generate appropriate reminders and lists.

[1631] Input: Text data and emotion data.

[1632] Output: The generated reminders and lists.

[1633] Specific behavior: The server analyzes the data for setting reminders and generates a "cleaning reminder for tomorrow at 10 AM."

[1634] Step 6:

[1635] The server returns the generated reminder information to the terminal.

[1636] Input: The generated reminder.

[1637] Output: The reminder sent back to the device.

[1638] Specific operation: The server sends the generated reminder information to the device.

[1639] Step 7:

[1640] The terminal notifies the user.

[1641] Input: The returned reminder.

[1642] Output: The notified reminder.

[1643] Specific behavior: The device will notify you at 10:00 AM the next day with a voice message saying, "It's 10:00 AM. Time to clean. You look great!"

[1644] (Application example 2)

[1645] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1646] When elderly people work in factories, it is difficult to manage the progress of their work and provide appropriate instructions. Additionally, elderly people can feel lonely while working, and emotional fluctuations can affect their work efficiency. The present invention aims to solve these problems by providing a system that allows elderly people to work in factories with peace of mind and receive emotional support.

[1647] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting voice input into text using generative artificial intelligence technology and analyzing the text, means for converting the generated response into voice and notifying it, means for analyzing the user's emotions and generating a personalized response, and means for generating the next work instruction based on the past work history and the current emotional state. This allows elderly people to work safely in factories and receive appropriate instructions and emotional support.

[1648] "Generative AI technology" refers to advanced algorithms and models that analyze, predict, and generate data, and is particularly a technology that uses deep learning.

[1649] "Voice input" is a data format for acquiring and analyzing the words or voices spoken by a user as electronic data.

[1650] "Text conversion" is the process of analyzing audio data and converting it into text data.

[1651] A "generative artificial intelligence model" refers to a machine learning model that is trained to perform a specific task, such as natural language processing.

[1652] An "appropriate response" refers to a meaningful reply or action that is generated based on the input data and its context.

[1653] An "emotion engine" is a technology or algorithm for analyzing a user's emotional state based on data such as voice and text.

[1654] "Lists and Reminders" refers to the list and notification features that allow users to manage tasks and schedules.

[1655] A "terminal" refers to an electronic device that a user can directly operate or input data from, such as smart glasses or a smartphone.

[1656] A "server" refers to a computer system that centrally processes and stores data and communicates with terminals via a network.

[1657] "Work history" is a record of the work that the user has done up to now.

[1658] "Next work instruction" refers to information that instructs the user on the next work to be performed.

[1659] "Individualized responses" refer to personalized responses or actions that are generated taking into account the user's specific situation and emotional state.

[1660] "Generation" refers to the process of creating new data or responses based on data or information.

[1661] This invention is a support system for elderly people working in factories, and is composed of three main components: a server, a terminal, and a user. A specific embodiment of this system is shown below.

[1662] System Overview

[1663] This system uses smart glasses as the terminal, and a generative AI model and emotion engine are implemented on the server. The user inputs voice through the smart glasses, which converts the voice into text. The converted text is sent to the server, where it is analyzed using the generative AI model and emotion engine. Based on the analysis results, appropriate work instructions and responses are generated and returned to the user via voice via the terminal.

[1664] Hardware and software used

[1665] Hardware: Smart glasses (terminal), cloud server (server)

[1666] Software: Speech recognition libraries (speech_recognition), sentiment analysis libraries (transformers), generative AI models (e.g., GPT-3)

[1667] Data processing and calculation flow

[1668] 1. Voice input to text conversion: When a user speaks a work instruction request into the smart glasses, the smart glasses' voice recognition library converts the voice into text.

[1669] 2. Sentiment analysis: The converted text is sent from the device to the server, where the user's emotional state is analyzed using an emotion analysis library.

[1670] 3. Response generation by generative AI model: The sentiment analysis results and text are input into a generative AI model to generate appropriate work instructions and responses, such as instructions based on the next task and emotion.

[1671] 4. Speech conversion and notification: The generated response is again converted from text to speech and audibly notified to the user through the smart glasses.

[1672] Specific examples

[1673] Example 1: Providing the next work order

[1674] When a user verbally requests the smart glasses to "tell me what to do next," the speech recognition library converts this into text and sends it to the server. The emotion analysis library on the server analyzes the text and determines the user's emotional state as "interested." Based on this information, the generative AI model generates specific work instructions, such as "Next, assemble parts A and B," and notifies the user via voice through the smart glasses.

[1675] Example prompt sentence:

[1676] User said: Tell me what to do next

[1677] Emotion: Interest

[1678] Generate the following work orders:

[1679] In this way, elderly people can proceed with their work with peace of mind because they will be instructed on what to do next in a way that takes their emotional state into consideration.

[1680] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1681] Step 1:

[1682] The user provides voice input. Specifically, they say to the smart glasses, "Tell me what to do next." This voice data is collected by the smart glasses' microphone and sent to a speech recognition library. The input is the user's voice data, and the output is data that converts that voice into text.

[1683] Step 2:

[1684] The device uses a speech recognition library to convert the voice data into text, which is an exact recording of what the user said to the smart glasses. The input is the voice data, and the output is the converted text data.

[1685] Step 3:

[1686] The terminal sends the converted text data to the server. At this time, the text data is securely sent to the server via the Internet. The input is text data, and the output is the data in the state it was sent to the server.

[1687] Step 4:

[1688] The server passes the received text data to a sentiment analysis library to analyze the user's emotional state. This process identifies the user's emotions from the text data and extracts that information. The input is the text data, and the output is the sentiment analysis result.

[1689] Step 5:

[1690] The server uses the emotion analysis results to input the prompt sentence to the generative AI model. Specifically, the prompt sentence "User said: Tell me the next task. Emotion: Interested. Please generate the next task instruction." is passed to the generative AI model. The input is text data and the emotion analysis results, and the output is the generated text data of the task instruction.

[1691] Step 6:

[1692] The server uses the generative AI model to generate the next work instruction. For example, specific instructions such as "Next, assemble parts A and B" are generated. The input is a prompt sentence, and the output is the text data of the work instruction.

[1693] Step 7:

[1694] The server sends the generated text data of the work instructions to the terminal. The input is the text data of the work instructions, and the output is the data in the state sent to the terminal.

[1695] Step 8:

[1696] The terminal converts the received text data of the work instructions into speech using a speech synthesis library. The input is the text data of the work instructions, and the output is speech data.

[1697] Step 9:

[1698] The device notifies the user of the generated voice data. Specifically, it notifies the user through the speaker of the smart glasses, saying, "Next, assemble parts A and B." The input is voice data, and the output is a voice notification to the user.

[1699] Through the above processing steps, the elderly person can continue working in the factory with peace of mind while receiving appropriate instructions on the next task to be performed.

[1700] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1701] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1702] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1703] [Fourth embodiment]

[1704] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1705] 7, a 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.

[1706] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1707] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1708] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1709] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1710] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1711] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1712] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1713] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1714] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1715] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1716] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1717] This invention is a system that utilizes generative artificial intelligence technology to support the health management and daily life of elderly people and to eliminate social isolation. This system consists of three main components: a server, a terminal, and a user. Below, we will explain how each component works together.

[1718] 1. Conversation partners and dementia prevention

[1719] overview

[1720] This feature aims to reduce feelings of loneliness and encourage sociability by interacting with users, and its memory support function also contributes to preventing dementia.

[1721] Specific processing

[1722] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server passes the text data to the generative AI model for analysis and generation of an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[1723] Specific examples

[1724] When a user asks, "What's the weather like today?", the device recognizes the speech, converts it into text, and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response such as "It's sunny today," and sends it to the device. The device then converts this into speech and relays it to the user.

[1725] 2. Health management

[1726] overview

[1727] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[1728] Specific processing

[1729] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The device will then send the input data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[1730] Specific examples

[1731] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[1732] 3. Daily life support

[1733] overview

[1734] This feature is intended to help users manage their daily schedules and assist with household chores.

[1735] Specific processing

[1736] Users can request the device to create schedules and lists, and the device sends the requests to the server, which then generates the appropriate reminders and lists. The generated information is returned to the device, which then notifies the user.

[1737] Specific examples

[1738] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[1739] Comprehensive System

[1740] The system provides services to users based on voice input and utilizes generative AI to generate responses and reminders. The entire process is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. The server analyzes and processes data, allowing appropriate assistance to be provided based on the user's health condition and lifestyle.

[1741] This will help seniors to improve their health management and quality of daily life without feeling socially isolated.

[1742] The processing flow will be explained below.

[1743] 1. Conversation partners and dementia prevention

[1744] Processing flow

[1745] Step 1:

[1746] The user speaks into the terminal.

[1747] Specific operation: The user speaks into the device's microphone.

[1748] Step 2:

[1749] The device converts the voice input into text.

[1750] How it works: The device's voice recognition software analyzes the voice waveform and generates a corresponding string of characters.

[1751] Step 3:

[1752] The terminal transmits the converted text data to the server.

[1753] Specific operation: Text data is transferred to the server via a communication protocol (e.g., HTTP or HTTPS).

[1754] Step 4:

[1755] The server inputs the text data into a generative AI model and analyzes it.

[1756] How it works: The generative AI model on the server receives the text data, analyzes it with an algorithm, and generates an appropriate response.

[1757] Step 5:

[1758] The server sends the generated response to the terminal.

[1759] Specific operation: The generated response text is sent back to the terminal via the communication protocol.

[1760] Step 6:

[1761] The device converts the response text into speech.

[1762] Specific operation: The speech synthesis engine in the device converts text data into speech data and outputs it from the speaker.

[1763] Step 7:

[1764] The user receives a voice response from the terminal.

[1765] Specific operation: The user hears the audio output from the device speaker.

[1766] 2. Health management

[1767] Processing flow

[1768] Step 1:

[1769] The device detects the reminder time.

[1770] What it does: The internal clock compares the set reminder time with the current time and prepares a notification if a reminder is needed.

[1771] Step 2:

[1772] The device will notify the user of the reminder.

[1773] Specific operation: The text-to-speech engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[1774] Step 3:

[1775] The user responds to the reminder.

[1776] Specific operation: The user responds by saying "confirm" or "complete" to the device using voice or touch input.

[1777] Step 4:

[1778] The terminal sends the user's response to the server.

[1779] Specific operation: Response data is sent to the server using a communication protocol.

[1780] Step 5:

[1781] The server records and analyzes the response.

[1782] Specific operations: The responses are stored in a health database and the information is analyzed using a data analysis algorithm.

[1783] Step 6:

[1784] The server notifies the terminal of the analysis results.

[1785] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[1786] Step 7:

[1787] The device notifies the user of the analysis results and advice.

[1788] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[1789] 3. Daily life support

[1790] Processing flow

[1791] Step 1:

[1792] The user inputs a request into the terminal.

[1793] What it does: Ask your device to create a specific reminder or list through voice command or touch input.

[1794] Step 2:

[1795] The terminal sends the request to the server.

[1796] Specific operation: The user's request is converted into text data and sent to the server using a communication protocol.

[1797] Step 3:

[1798] The server generates reminders and lists based on your request.

[1799] Specific operation: Analyzes the request and creates reminders and shopping lists using an automated generation algorithm.

[1800] Step 4:

[1801] The server returns the generated results to the terminal.

[1802] Specific operation: The generated list or reminder is sent to the device using a communication protocol.

[1803] Step 5:

[1804] The terminal displays or notifies the generated result.

[1805] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[1806] This allows users to easily perform many operations through voice input, providing smooth assistance in daily life.

[1807] Example 1

[1808] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1809] In modern society, the elderly face problems such as social isolation and difficulties in managing their health. Prevention of dementia and support for daily life are particularly important issues. However, existing technologies for solving these problems are insufficient in terms of usability and effectiveness. For example, conventional health management systems and schedule management applications are difficult for the elderly to operate and lack effective dementia prevention features. Therefore, there is a need for an effective system that comprehensively supports the elderly's health management, elimination of social isolation, and dementia prevention.

[1810] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1811] In this invention, the server includes: means for converting voice input into text data using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model; means for converting the generated response into voice and notifying the user; means for providing a reminder function for managing the user's health status and schedule; means for notifying the user of the reminder content and recording and analyzing the user's response; a terminal for outputting the generated response and instructions in voice; and means for communicating voice data and text data between the terminal and the server. This allows elderly people to easily and effectively receive support for health management and daily life while interacting with the terminal in natural language. Furthermore, adding a function for recognizing and communicating the faces and names of family and friends as a dementia prevention measure can promote emotional interaction, prevent social isolation, and contribute to maintaining cognitive function.

[1812] "Generative AI technology" is a technology that generates text and speech using artificial intelligence with advanced computing and data analysis capabilities.

[1813] "Voice input" means that a user gives instructions or questions to a terminal by voice.

[1814] "Character data" is data converted from voice input into text format.

[1815] A "generative artificial intelligence model" is an algorithm that uses artificial intelligence to analyze received data and generate appropriate responses or instructions.

[1816] A "response" is a reply or instruction that the generative AI model generates by analyzing the user's voice input.

[1817] "Conversion to speech" is the process of converting the generated text data back into speech.

[1818] "User notification" is the act of informing the user of a generated response or reminder in audio or visual form.

[1819] The "reminder function" is a function that memorizes the user's health condition and schedule, and notifies them at specific times or under specific conditions.

[1820] "Recording and analysis" is the process of storing and analyzing user responses and actions.

[1821] "Terminal" refers to any device that inputs and outputs audio.

[1822] A "server" is a computer system that processes data and performs various functions.

[1823] "Data communication" is a method of sending and receiving voice data and text data between a terminal and a server.

[1824] "Daily life support" refers to functions that support the user's daily life, such as schedule management and list creation.

[1825] A "memory aid" is a feature that supports a user's cognitive functions and helps them remember specific information.

[1826] "Facial recognition" is a technology that uses generative artificial intelligence techniques to identify facial features and identify people.

[1827] This invention is a system that uses generative artificial intelligence technology to manage the health of elderly people, support their daily lives, and alleviate social isolation. The system provides services to users based on voice input and utilizes a generative AI model to generate responses and reminders. The system is designed to facilitate direct user interaction and ensure uninterrupted support for daily life. In addition, the server analyzes and processes data, so appropriate support is provided according to the user's health condition and lifestyle.

[1828] The primary hardware is a device (e.g., smartphone, smart speaker) with voice input and output, which allows users to give instructions and ask questions by voice. The server runs a generative AI model (e.g., OpenAI GPT-4) to analyze data and generate responses. Data is communicated between the device and the server via an internet connection.

[1829] Conversation partners and dementia prevention

[1830] When a user speaks to the device, the device converts the speech into text and sends it to the server. The server uses a generative AI model to analyze the text, generate an appropriate response, and return it to the device. The device then converts the generated response into speech and relays it to the user. Here is an example:

[1831] When a user says, "What's the weather like today?", the device converts the speech into text and sends it to the server. The server uses a generative AI model to obtain weather information, generates a response saying, "Today's weather is sunny," and sends it to the device. The device then converts this into speech and relays it to the user.

[1832] health care

[1833] The device will display or sound a reminder at the set time. The user will follow the reminder to perform a health check. When the user enters the results of the health check into the device, the device will send the data to the server. The server will analyze the data, generate any necessary advice or next actions, and notify the user via the device. Below is a specific example.

[1834] When the device notifies the user that it is time to take their medicine and the user responds by saying that they have taken it, the information is sent to the server, which records the data and sets the appropriate medication reminder for the next day.

[1835] Daily life support

[1836] The user can request the device to create a schedule or list. The device sends the request to the server, which then generates the appropriate reminder or list. The generated information is returned to the device and notified to the user. Here is an example:

[1837] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder and sends it back to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean."

[1838] This system is designed to allow elderly people to receive support for health management and daily life while operating it naturally using voice commands, thereby improving the quality of their health management and daily life without causing them to feel socially isolated.

[1839] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1840] Conversation partners and dementia prevention

[1841] Processing Steps

[1842] Step 1:

[1843] The user speaks to the device. For example, they ask, "What's the weather like today?" This speech is the input.

[1844] Step 2:

[1845] The device collects the voice input and converts it into text using speech recognition software, such as Google Cloud Speech-to-Text, which then converts the voice data into text, providing an output for the next step.

[1846] Step 3:

[1847] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[1848] Step 4:

[1849] The server passes the received text data to a generative AI model. An example of a generative AI model used here is OpenAI GPT-4. The generative AI model analyzes the text data and generates an appropriate response. This analysis and response generation corresponds to data processing and data calculation. The input is the received text data, and the output is the generated response text.

[1850] Step 5:

[1851] The server generates a response text and sends it back to the terminal. The input is the response text and the output is what is sent to the terminal.

[1852] Step 6:

[1853] The device converts the generated response text into speech using speech synthesis software, such as Google Cloud Text-to-Speech. The input is the response text, and the output is synthesized speech.

[1854] Step 7:

[1855] The device plays the synthesized voice to the user, who receives a response such as "The weather is sunny today." At this point, all processing is complete.

[1856] health care

[1857] Processing Steps

[1858] Step 1:

[1859] The device displays or sounds a reminder at the set time. For example, it may say, "It's time to take your medicine." This notification is an input.

[1860] Step 2:

[1861] The user performs a health check or takes an action (e.g., taking medicine) according to the reminder. The user's action corresponds to the input, and the result of the action becomes the output for the next step.

[1862] Step 3:

[1863] The user inputs a result, such as "I drank it," into the terminal by voice or text. This creates input data. The input is the user's response, and the output is text data.

[1864] Step 4:

[1865] The terminal sends the input data to the server. This communication also takes place over the Internet. The input is the user's response data, and the output is the data sent to the server.

[1866] Step 5:

[1867] The data received by the server is analyzed using a data analysis tool. Python's Pandas and NumPy are used here. Reminder settings and advice for the next day are generated based on the data analysis. This analysis corresponds to data processing. The input is the user's response data, and the output is the analysis results (reminder settings and advice).

[1868] Step 6:

[1869] The server generates the analysis results and sends them back to the terminal. The input is the analysis results and the output is the data sent to the terminal.

[1870] Step 7:

[1871] The device then notifies the user of any reminders or advice for the next day, for example, "Take your next dose of medicine at 8:00 AM tomorrow." At this point, all processing is complete.

[1872] Daily life support

[1873] Processing Steps

[1874] Step 1:

[1875] The user asks the device to create an appointment or list. For example, they might say, "Set a cleaning reminder for tomorrow at 10:00 AM." This voice corresponds to input.

[1876] Step 2:

[1877] The device records the speech and uses voice recognition software to convert it into text data, which is where Google Cloud Speech-to-Text comes in. Once the audio data is converted into text data, an output is generated for the next step.

[1878] Step 3:

[1879] The device sends the converted text data to the server. This communication is done over an internet connection. The input is the converted text data, and the output is the data sent to the server.

[1880] Step 4:

[1881] The server parses the received request text and creates a reminder using reminder generation software. As an example, the Google Calendar API is used. Parsing and reminder creation fall under data processing and data calculation. The input is the request text, and the output is the generated reminder.

[1882] Step 5:

[1883] The server sends the generated reminder back to the device. The input is the generated reminder and the output is the transmission to the device.

[1884] Step 6:

[1885] The device will notify the user of the reminder at the specified time, for example, "It's 10:00 AM. Time to clean." At this point, all processing is complete.

[1886] (Application example 1)

[1887] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1888] In addition to existing systems aimed at managing the health of the elderly, supporting their daily lives, and eliminating social isolation, there is a lack of means to reduce the burden on workers in factories and improve their work efficiency. In particular, there is a demand for systems that integrate functions such as voice input and analysis of work instructions, schedule management, and memory assistance.

[1889] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1890] In this invention, the server includes means for receiving work instructions in the factory by voice, converting them into text and sending them to the server, where the server analyzes them using a generative AI model and generates a response, means for converting the generated response into voice and notifying workers, and means for managing workers' work schedules and break times and providing reminders. This enables the reception of work instructions and response generation based on voice input, management of work schedules, and efficient work support through reminders.

[1891] "Generative AI technology" is a technology that converts voice input into text and generates appropriate responses by analyzing that text.

[1892] A "generative AI model" is an artificial intelligence model that generates appropriate responses and reminders based on input data.

[1893] "Health status" refers to information or status regarding the user's physical health.

[1894] A "reminder" is a notification function that prompts a user to take action at a specific time or in a specific situation.

[1895] "Server" means a cloud-based or on-premise computer system for analyzing data and generating responses.

[1896] A "factory robot" is a robot that assists workers in a factory by receiving and responding to work instructions, managing schedules, and providing reminders.

[1897] "Voice input" is a method by which a user verbally inputs instructions or information.

[1898] "Text conversion" is the process of converting speech input into text data.

[1899] "Analysis" is the process of generating responses and instructions based on input data.

[1900] "Notification" is the act of informing a user of generated information or reminders.

[1901] "Work instructions" refers to specific instructions and procedures required when performing work within a factory.

[1902] "Schedule management" is a function for managing a user's time and schedule.

[1903] "Memory aid" is a function that helps the user's memory.

[1904] MODE FOR CARRYING OUT THE INVENTION

[1905] The embodiment of the present invention is a system that uses a factory robot to assist workers in giving work instructions, manage schedules, and provide reminders. The system program and its processing method will be described below.

[1906] System Configuration

[1907] The system includes a terminal for voice input and text output, a server that runs the generative artificial intelligence model, a means of communicating data between the terminal and the server, and a microphone and speaker for receiving and transmitting work instructions within the factory.

[1908] Hardware

[1909] Terminal: This is a robot equipped with a microphone for voice recognition and a speaker for audio playback.

[1910] Server: A cloud-based data analysis server (e.g., Google Cloud Platform or Amazon Web Services) is used.

[1911] software

[1912] Speech recognition: Uses the speech_recognition library.

[1913] Speech synthesis: Uses the pyttsx3 library.

[1914] Generative AI model: Use the transformers library to utilize a GPT-3 based generative AI model.

[1915] Program processing and data flow

[1916] The system mainly works through the following processes:

[1917] 1. Handling voice input:

[1918] When a user speaks into the microphone, the server receives the audio and converts it into text using the speech_recognition library.

[1919] 2. Text analysis and response generation:

[1920] The converted input data is parsed by the server using the transformers library, and a generative AI model (GPT-3) generates an appropriate response based on the input.

[1921] 3. Vocalization and notification of responses:

[1922] The generated text response is sent from the server to the terminal, where it is converted into speech using the pyttsx3 library and communicated to the user (worker) through the speaker.

[1923] Specific examples

[1924] Case 1: Receiving a work order and generating a response

[1925] Worker: "Prepare the parts for the next process."

[1926] The device converts the speech into text and sends it to the server.

[1927] The server uses a generative AI model to analyze the instructions and generate a response such as, "The parts to be used in the next process have been retrieved from the warehouse."

[1928] The terminal converts the response into voice and transmits it to the worker.

[1929] Case 2: Reminder Notification

[1930] The server manages the workers' schedules and sets reminders.

[1931] Terminal: "It's lunchtime. Let's take a break."

[1932] Usage example 1: "Preparing parts for the next process"

[1933] Use case 2: "Set a reminder for your lunch break"

[1934] This embodiment enables efficient work support by receiving work instructions and generating responses based on voice input, managing work schedules, and sending reminders.

[1935] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1936] Step 1:

[1937] The user gives voice input (work instructions or requests) to the terminal. The terminal receives the voice through a microphone. The input is the user's voice, and the output is voice data.

[1938] Step 2:

[1939] The device converts voice data into text data using the speech_recognition library. The input is voice data and the output is text data. Specifically, the voice recognition engine analyzes the voice signal and generates a corresponding string of characters.

[1940] Step 3:

[1941] The terminal sends the converted text data to the server. The input is text data, and the output is text data sent to the server. Specifically, data communication is performed using a network protocol.

[1942] Step 4:

[1943] The server analyzes the received text data using the transformers library. As a result of the analysis, a generative AI model (GPT-3) generates a response text. The input is text data, and the output is response text data. Specifically, the generative AI model executes a process to generate a response text based on the text data.

[1944] Step 5:

[1945] The server sends the generated response text data to the terminal. The input is the response text data, and the output is the text data sent to the terminal. Specifically, data communication is again performed using a network protocol.

[1946] Step 6:

[1947] The terminal converts the received response text data into voice data using the pyttsx3 library. The input is the response text data, and the output is voice data. Specifically, the speech synthesis engine runs a process to generate voice signals based on the text.

[1948] Step 7:

[1949] The audio data is transmitted to the user through a speaker. The input is the audio data, and the output is the audio message that the user hears. Specifically, the audio playback device converts the audio signal into physical sound and plays it back.

[1950] Step 8:

[1951] The server manages the user's schedule and health status and generates the necessary reminders. The input is the user's schedule data and health check data, and the output is the reminder text data. Specifically, the server retrieves information from the database and runs the reminder generation algorithm.

[1952] Step 9:

[1953] The generated reminder is notified to the user via the device. The input is the text data of the reminder, and the output is a voice reminder notified to the user from the device. The specific operation involves the processes of voice synthesis and voice playback.

[1954] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1955] This invention is a system that combines generative AI and an emotion engine to provide comprehensive support for elderly people to live their lives with peace of mind, aiming to manage their health, alleviate social isolation, and support their daily lives.

[1956] This system consists of three main components: a server, a terminal, and a user. In addition, by combining it with an emotion engine, it provides responses and support that correspond to the user's emotional state.

[1957] 1. Conversation partners and dementia prevention

[1958] overview

[1959] This feature reduces feelings of loneliness and encourages sociability through interaction with users. It also contributes to preventing dementia through its memory support function.

[1960] Specific processing

[1961] When a user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion engine. The text and emotion data are sent to the server, where a generative AI model generates an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[1962] Specific examples

[1963] When a user asks, "What's the weather like today?", the emotion engine analyzes the user's voice and identifies "interest" and "joy." The device converts the speech into text and sends it to the server. The server uses a generative AI model to generate a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this into speech and relays it to the user.

[1964] 2. Health management

[1965] overview

[1966] The feature aims to monitor the user's health status and provide necessary reminders and advice.

[1967] Specific processing

[1968] The device will display or sound a reminder at the set time. The user will then follow the reminder to perform a health check and enter the results into the device. The emotion engine will analyze the user's emotions, which will then be sent as data. The device will then send this data to the server, which will analyze the data and generate the necessary advice or next action. This information will then be sent to the user via the device.

[1969] Specific examples

[1970] When the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion engine analyzes the user's sense of relief along with that information and sends the data to the server. The server records the data and sets an appropriate medication reminder for the next day. At the same time, the device provides advice such as, "It looks like you're taking your medicine with peace of mind. Keep up the good work."

[1971] 3. Daily life support

[1972] overview

[1973] This feature is intended to help users manage their daily schedules and assist with household chores.

[1974] Specific processing

[1975] Users can request the device to create schedules and lists. The device sends the request to the server, where the emotion engine analyzes the user's emotions and sends the data. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[1976] Specific examples

[1977] When a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the emotion engine analyzes the user's "proactivity," and the device sends the request to the server. The server generates a reminder and returns it to the device. The next day, the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[1978] Emotion engine integration

[1979] The emotion engine analyzes voice input and identifies the user's emotional state. This data is fed back to the generative AI model and reminder support functions to individually optimize responses and support content. This allows seniors to receive close support that is adapted to their emotional state.

[1980] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[1981] The processing flow will be explained below.

[1982] 1. Conversation partners and dementia prevention

[1983] Processing flow

[1984] Step 1:

[1985] The user speaks into the terminal.

[1986] Specific operation: The user speaks into the device's microphone.

[1987] Step 2:

[1988] The device converts the voice input into text.

[1989] How it works: Speech recognition software analyzes the audio waveform and converts it into a string of characters.

[1990] Step 3:

[1991] The terminal transmits the text data to the server.

[1992] Specific operation: Sends text data to the server using a communication protocol (HTTP / HTTPS).

[1993] Step 4:

[1994] The server inputs the text data into a generative AI model and analyzes it.

[1995] How it works: The generative AI model analyzes the text data and generates an appropriate response.

[1996] Step 5:

[1997] The server sends the generated response to the terminal.

[1998] Specific operation: The generated response text is sent to the terminal using a communication protocol.

[1999] Step 6:

[2000] The device converts the response text into speech.

[2001] Specific operation: The speech synthesis engine converts text data into speech and outputs it from the speaker.

[2002] Step 7:

[2003] The user receives a voice response from the terminal.

[2004] Specific operation: Listen to the audio output from the device's speaker.

[2005] 2. Health management

[2006] Processing flow

[2007] Step 1:

[2008] The device detects the reminder time.

[2009] What it does: The internal clock compares the set reminder time with the current time and prepares a notification when the time comes.

[2010] Step 2:

[2011] The device will notify the user of the reminder.

[2012] Specific operation: The speech synthesis engine converts the reminder content into voice and outputs it through the speaker, or displays the reminder content on the screen.

[2013] Step 3:

[2014] The user responds to the reminder.

[2015] Specific action: Respond by voice or touch input, for example, "Confirm," "Done," etc.

[2016] Step 4:

[2017] The terminal inputs the user's response into the emotion engine.

[2018] Specific operation: The emotion engine analyzes the user's response and generates emotion data.

[2019] Step 5:

[2020] The terminal transmits the response and emotion data to the server.

[2021] Specific operation: Response data and emotion data are sent to the server using a communication protocol.

[2022] Step 6:

[2023] The server records and analyzes the responses and emotional data.

[2024] Specific operation: Response and emotion data is stored in a health database, and the information is analyzed using data analysis algorithms.

[2025] Step 7:

[2026] The server notifies the terminal of the analysis results.

[2027] Specific operation: Summarize the analysis results, generate appropriate advice and next reminders, and send them to the device.

[2028] Step 8:

[2029] The device notifies the user of the analysis results and advice.

[2030] Specific operation: When notifying by voice, a speech synthesis engine is used, and when notifying by text, the text is displayed on the screen.

[2031] 3. Daily life support

[2032] Processing flow

[2033] Step 1:

[2034] The user inputs a request into the terminal.

[2035] What it does: Use voice commands or touch input to ask your device to create a specific reminder or list.

[2036] Step 2:

[2037] The device inputs the request into an emotion engine and analyzes the emotion.

[2038] Specific operation: Analyze the user's emotions along with the request content and generate text and emotion data.

[2039] Step 3:

[2040] The terminal transmits the request content and emotion data to the server.

[2041] Specific operation: Text and emotion data are sent to the server using a communication protocol.

[2042] Step 4:

[2043] The server generates reminders and lists based on the request.

[2044] What it does: Analyzes requests and creates reminders and lists using an automated generation algorithm.

[2045] Step 5:

[2046] The server returns the generated results to the terminal.

[2047] Specific operation: The generated lists and reminders are sent to the device using a communication protocol.

[2048] Step 6:

[2049] The terminal displays or notifies the generated result.

[2050] Specific operation: The generated information is converted into voice using a speech synthesis engine and output from the speaker or displayed on the screen.

[2051] This allows users to easily perform many operations through voice input and receive emotion-based feedback, further improving the quality of their daily lives.

[2052] Example 2

[2053] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2054] Elderly people often need health management, support for daily life, and communication tailored to their emotional state, but there is a lack of an integrated system to effectively do this. In particular, systems with health reminders, daily schedule management, and emotional response functions are becoming increasingly important in modern society, but are difficult to realize.

[2055] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2056] In this invention, the server includes: means for converting voice input into text using generative artificial intelligence technology and generating an appropriate response by analyzing it with a generative artificial intelligence model; means for converting the generated response into voice and notifying the user; means for providing a reminder function for managing the user's health status and schedule; means for notifying the user of the reminder content and recording and analyzing the user's response; means for generating lists and reminders to support daily activities using generative artificial intelligence technology; means for notifying the user of the generated lists and reminders; and means for analyzing the user's emotional state using an emotion analysis engine and individually optimizing the response and reminder content. This enables health management, daily life support, and emotionally appropriate communication for the elderly.

[2057] "Generative AI technology" refers to AI that converts a user's voice input into text data and generates an appropriate response based on that text data.

[2058] A "generative artificial intelligence model" is an algorithm or system that analyzes voice or text data and generates appropriate responses or actions based on that data.

[2059] An "emotion analysis engine" is software that analyzes a user's voice data to identify their emotional state.

[2060] The "reminder function" is a function that manages the user's health status and schedule and notifies them at specified times.

[2061] A "list" is a list that summarizes the user's requests and schedules.

[2062] "Server" means a computing system for running generative artificial intelligence models and analyzing and processing data sent from terminals.

[2063] A "terminal" is a device that inputs and outputs audio.

[2064] "Users" refer to the elderly who use the system and those receiving their care.

[2065] A "response" is a reply or instruction generated by a generative artificial intelligence model in response to input from a user.

[2066] "Support for daily activities" refers to the function of managing the user's schedule and assisting with housework.

[2067] This invention is a system that aims to provide comprehensive support for elderly people to live their lives with peace of mind. This system combines generative artificial intelligence technology and an emotion analysis engine to manage the health of elderly people, eliminate social isolation, and support their daily lives. This system consists of three main components: a server, a terminal, and a user.

[2068] (Conversation partners and dementia prevention)

[2069] This feature reduces feelings of loneliness and encourages sociability by interacting with the user. It also contributes to preventing dementia through its memory assistance function. When the user speaks to the device, the device converts the speech into text and analyzes the user's emotions using an emotion analysis engine. The analyzed text and emotion data are sent to a server, which uses a generative artificial intelligence model to generate an appropriate response. The generated response is then returned to the device, which converts it into speech and responds to the user.

[2070] For example, when a user asks, "What's the weather like today?", the device converts the speech into text and analyzes it. The text is then sent to the server, which generates a response such as, "The weather is sunny today. It's a good day to go outside." The device then converts this back into speech and responds to the user.

[2071] (health care)

[2072] This function aims to monitor the user's health status and provide necessary reminders and advice. The device will display or sound a reminder at the set time, and the user will follow the reminder to perform a health check. The results are then entered into the device. An emotion analysis engine analyzes the user's emotions, which are also sent as data. The device then sends this data to a server, which analyzes the data and generates necessary advice or next actions. This information is then notified to the user via the device.

[2073] For example, if the device notifies the user that it's time to take their medicine and the user responds with "I've taken it," the emotion analysis data is sent to the server along with that information. The server analyzes this and sets an appropriate medication reminder for the next day. The server also generates advice such as, "It looks like you're taking your medicine with confidence. Keep up the good work."

[2074] (Daily life support)

[2075] This feature aims to help users manage their daily schedules and assist with household chores. Users can request the device to create schedules and lists, and the details are sent from the device to the server. An emotion analysis engine analyzes the user's emotions and sends the data to the server. The server generates appropriate reminders and lists, and the generated information is returned to the device, which then notifies the user.

[2076] For example, if a user requests, "Set a cleaning reminder for tomorrow at 10 AM," the device sends the request to the server. The server generates the reminder, and the next day the device notifies the user, "It's 10 AM. Time to clean. You look great!"

[2077] (Sentiment analysis engine integration)

[2078] The emotion analysis engine analyzes voice input to identify the user's emotional state. This data is fed back to the generative AI model and reminder and support features to individually optimize responses and support content, allowing users to receive close support that is adapted to their emotional state.

[2079] (Specific examples of hardware and software used)

[2080] Server: Common cloud services can be used for data processing and hosting of generative artificial intelligence models.

[2081] Device: For voice input and output, a typical smart speaker or tablet device can be used.

[2082] Sentiment analysis engine: The software used includes a general sentiment analysis API.

[2083] (Example of a prompt)

[2084] "Tell me about the weather today."

[2085] "When is my next dose?"

[2086] Please add tomorrow's schedule.

[2087] In this way, older people benefit from a holistic and comprehensive support system, enabling them to live independently and with peace of mind.

[2088] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2089] Conversation partners and dementia prevention processing steps

[2090] Step 1:

[2091] The user speaks into the terminal.

[2092] Input: User's voice data.

[2093] Output: The audio data is captured to the device.

[2094] What happens: The user says, "What's the weather like today?"

[2095] Step 2:

[2096] The device converts the speech to text.

[2097] Input: Audio data captured on the device.

[2098] Output: The converted text data.

[2099] What it does: Your device uses speech recognition software to convert the voice data into text: "What's the weather like today?"

[2100] Step 3:

[2101] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[2102] Input: The converted text data.

[2103] Output: Text data and parsed sentiment data.

[2104] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "interest" and "joy."

[2105] Step 4:

[2106] The device transmits text data and emotion data to the server.

[2107] Input: Text data and parsed emotion data.

[2108] Output: The data sent to the server.

[2109] Specific operation: The device sends a data packet containing the analysis results to the server.

[2110] Step 5:

[2111] The server uses a generative AI model to generate an appropriate response.

[2112] Input: Text data and emotion data.

[2113] Output: The generated response text.

[2114] What happens: The server uses the generative AI model to generate a response like, "The weather is sunny today. It's a good day to go outside."

[2115] Step 6:

[2116] The server returns the generated response to the terminal.

[2117] Input: The generated response text.

[2118] Output: The response text is sent back to the terminal.

[2119] Specific operation: The server sends the generated text response to the device.

[2120] Step 7:

[2121] The terminal converts the response into voice and conveys it to the user.

[2122] Input: Response text data.

[2123] Output: A spoken response.

[2124] What happens: The device uses text-to-speech technology to respond to the user by saying, "The weather is sunny today. It's a good day to go outside."

[2125] Health Care Processing Steps

[2126] Step 1:

[2127] Your device will display or sound a reminder at the set time.

[2128] Input: The set reminder time.

[2129] Output: The notified reminder.

[2130] Specific operation: The device will notify you by voice, "It's time to take your medicine."

[2131] Step 2:

[2132] The user follows the reminder to perform a health check.

[2133] Input: Health check instructions.

[2134] Output: Health check completed.

[2135] Specific action: The user takes medicine.

[2136] Step 3:

[2137] The user enters the results of the health check into the terminal.

[2138] Input: Health check result (e.g. "drank").

[2139] Output: The results typed into the terminal.

[2140] Specific action: The user types "I drank it" into the terminal.

[2141] Step 4:

[2142] The terminal analyzes the user's emotional state with an emotion analysis engine so that the terminal can transmit the result data and emotion data.

[2143] Input: Health check results.

[2144] Output: Result data and analyzed sentiment data.

[2145] Specific operation: The device sends the results of the health check to an emotion analysis engine, which analyzes the emotion "relief."

[2146] Step 5:

[2147] The terminal transmits this data to the server.

[2148] Input: Health check results and parsed emotion data.

[2149] Output: The data sent to the server.

[2150] Specific operation: The device sends the results and analysis data to the server.

[2151] Step 6:

[2152] The server analyzes the data and generates the necessary advice or next action.

[2153] Input: Health check results and parsed emotion data.

[2154] Output: Any advice or next actions required.

[2155] Specific operation: The server records medication data and generates a reminder for the next day and advice such as "It looks like you're taking your medication with confidence. Keep up the good work."

[2156] Step 7:

[2157] The server returns the generated information to the terminal.

[2158] Input: Generated advice and next actions.

[2159] Output: Advice sent back to the terminal.

[2160] Specific operation: The server sends the generated information to the terminal.

[2161] Step 8:

[2162] The terminal notifies the user.

[2163] Input: Returned advice and next actions.

[2164] Output: Advice given.

[2165] Specific operation: The device will notify you with a voice message saying, "It seems you are taking your medication safely. Keep up the good work."

[2166] Daily living support processing steps

[2167] Step 1:

[2168] The user requests the device to create an event or list.

[2169] Input: User request (audio data).

[2170] Output: The request is imported into the terminal.

[2171] What happens: A user says, "Set a cleaning reminder for tomorrow at 10 AM."

[2172] Step 2:

[2173] The terminal converts the request content into text data.

[2174] Input: Audio data of the request.

[2175] Output: The converted text data.

[2176] What happens: Your device uses voice recognition software to generate the text "Set a cleaning reminder for tomorrow at 10 AM."

[2177] Step 3:

[2178] The device sends the text data to an emotion analysis engine to analyze the user's emotional state.

[2179] Input: The converted text data.

[2180] Output: Text data and parsed sentiment data.

[2181] What it does: The device sends the text to an emotion analysis engine to analyze emotions such as "positivity."

[2182] Step 4:

[2183] The device transmits text data and emotion data to the server.

[2184] Input: Text data and parsed emotion data.

[2185] Output: The data sent to the server.

[2186] Specific operation: The device sends a data packet containing the analysis results to the server.

[2187] Step 5:

[2188] The server analyzes the data to generate appropriate reminders and lists.

[2189] Input: Text data and emotion data.

[2190] Output: The generated reminders and lists.

[2191] Specific behavior: The server analyzes the data for setting reminders and generates a "cleaning reminder for tomorrow at 10 AM."

[2192] Step 6:

[2193] The server returns the generated reminder information to the terminal.

[2194] Input: The generated reminder.

[2195] Output: The reminder sent back to the device.

[2196] Specific operation: The server sends the generated reminder information to the device.

[2197] Step 7:

[2198] The terminal notifies the user.

[2199] Input: The returned reminder.

[2200] Output: The notified reminder.

[2201] Specific behavior: The device will notify you at 10:00 AM the next day with a voice message saying, "It's 10:00 AM. Time to clean. You look great!"

[2202] (Application example 2)

[2203] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2204] When elderly people work in factories, it is difficult to manage the progress of their work and provide appropriate instructions. Additionally, elderly people can feel lonely while working, and emotional fluctuations can affect their work efficiency. The present invention aims to solve these problems by providing a system that allows elderly people to work in factories with peace of mind and receive emotional support.

[2205] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting voice input into text using generative artificial intelligence technology and analyzing the text, means for converting the generated response into voice and notifying it, means for analyzing the user's emotions and generating a personalized response, and means for generating the next work instruction based on the past work history and the current emotional state. This allows elderly people to work safely in factories and receive appropriate instructions and emotional support.

[2206] "Generative AI technology" refers to advanced algorithms and models that analyze, predict, and generate data, and is particularly a technology that uses deep learning.

[2207] "Voice input" is a data format for acquiring and analyzing the words or voices spoken by a user as electronic data.

[2208] "Text conversion" is the process of analyzing audio data and converting it into text data.

[2209] A "generative artificial intelligence model" refers to a machine learning model that is trained to perform a specific task, such as natural language processing.

[2210] An "appropriate response" refers to a meaningful reply or action that is generated based on the input data and its context.

[2211] An "emotion engine" is a technology or algorithm for analyzing a user's emotional state based on data such as voice and text.

[2212] "Lists and Reminders" refers to the list and notification features that allow users to manage tasks and schedules.

[2213] A "terminal" refers to an electronic device that a user can directly operate or input data from, such as smart glasses or a smartphone.

[2214] A "server" refers to a computer system that centrally processes and stores data and communicates with terminals via a network.

[2215] "Work history" is a record of the work that the user has done up to now.

[2216] "Next work instruction" refers to information that instructs the user on the next work to be performed.

[2217] "Individualized responses" refer to personalized responses or actions that are generated taking into account the user's specific situation and emotional state.

[2218] "Generation" refers to the process of creating new data or responses based on data or information.

[2219] This invention is a support system for elderly people working in factories, and is composed of three main components: a server, a terminal, and a user. A specific embodiment of this system is shown below.

[2220] System Overview

[2221] This system uses smart glasses as the terminal, and a generative AI model and emotion engine are implemented on the server. The user inputs voice through the smart glasses, which converts the voice into text. The converted text is sent to the server, where it is analyzed using the generative AI model and emotion engine. Based on the analysis results, appropriate work instructions and responses are generated and returned to the user via voice via the terminal.

[2222] Hardware and software used

[2223] Hardware: Smart glasses (terminal), cloud server (server)

[2224] Software: Speech recognition libraries (speech_recognition), sentiment analysis libraries (transformers), generative AI models (e.g., GPT-3)

[2225] Data processing and calculation flow

[2226] 1. Voice input to text conversion: When a user speaks a work instruction request into the smart glasses, the smart glasses' voice recognition library converts the voice into text.

[2227] 2. Sentiment analysis: The converted text is sent from the device to the server, where the user's emotional state is analyzed using an emotion analysis library.

[2228] 3. Response generation by generative AI model: The sentiment analysis results and text are input into a generative AI model to generate appropriate work instructions and responses, such as instructions based on the next task and emotion.

[2229] 4. Speech conversion and notification: The generated response is again converted from text to speech and audibly notified to the user through the smart glasses.

[2230] Specific examples

[2231] Example 1: Providing the next work order

[2232] When a user verbally requests the smart glasses to "tell me what to do next," the speech recognition library converts this into text and sends it to the server. The emotion analysis library on the server analyzes the text and determines the user's emotional state as "interested." Based on this information, the generative AI model generates specific work instructions, such as "Next, assemble parts A and B," and notifies the user via voice through the smart glasses.

[2233] Example prompt sentence:

[2234] User said: Tell me what to do next

[2235] Emotion: Interest

[2236] Generate the following work orders:

[2237] In this way, elderly people can receive instructions on what to do next that take their emotional state into consideration, allowing them to proceed with their work with peace of mind.

[2238] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2239] Step 1:

[2240] The user provides voice input. Specifically, they speak to the smart glasses, saying, "Tell me what to do next." This voice data is collected by the smart glasses' microphone and sent to a speech recognition library. The input is the user's voice data, and the output is data that converts that voice into text.

[2241] Step 2:

[2242] The device uses a speech recognition library to convert the voice data into text, which is an exact recording of what the user said to the smart glasses. The input is the voice data, and the output is the converted text data.

[2243] Step 3:

[2244] The terminal sends the converted text data to the server. At this time, the text data is securely sent to the server via the Internet. The input is text data, and the output is the data in the state it was sent to the server.

[2245] Step 4:

[2246] The server passes the received text data to a sentiment analysis library to analyze the user's emotional state. This process identifies the user's emotions from the text data and extracts that information. The input is the text data, and the output is the sentiment analysis result.

[2247] Step 5:

[2248] The server uses the emotion analysis results to input the prompt sentence to the generative AI model. Specifically, the prompt sentence "User said: Tell me the next task. Emotion: Interested. Please generate the next task instruction." is passed to the generative AI model. The input is text data and the emotion analysis results, and the output is the generated text data of the task instruction.

[2249] Step 6:

[2250] The server uses the generative AI model to generate the next work instruction. For example, specific instructions such as "Next, assemble parts A and B" are generated. The input is a prompt sentence, and the output is the text data of the work instruction.

[2251] Step 7:

[2252] The server sends the generated text data of the work instructions to the terminal. The input is the text data of the work instructions, and the output is the data in the state sent to the terminal.

[2253] Step 8:

[2254] The terminal converts the received text data of the work instructions into speech using a speech synthesis library. The input is the text data of the work instructions, and the output is speech data.

[2255] Step 9:

[2256] The device notifies the user of the generated voice data. Specifically, it notifies the user through the speaker of the smart glasses, saying, "Next, assemble parts A and B." The input is voice data, and the output is a voice notification to the user.

[2257] Through the above processing steps, the elderly person can continue working in the factory with peace of mind while receiving appropriate instructions on the next task to be performed.

[2258] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2259] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2260] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2261] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2262] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2263] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2264] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2265] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2266] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2267] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2268] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2269] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2270] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2271] 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.

[2272] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2273] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2274] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2275] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2276] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2277] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2278] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2279] The following is further disclosed regarding the above embodiment.

[2280] (Claim 1)

[2281] means for converting speech input into text using generative artificial intelligence technology and analyzing the text with a generative artificial intelligence model to generate an appropriate response;

[2282] means for converting the generated response into speech and notifying the user;

[2283] A means for providing a reminder function for managing a user's health condition and schedule;

[2284] A means for notifying the user of the reminder content and recording and analyzing the user's response;

[2285] A means for generating lists and reminders to support daily life using generative artificial intelligence technology;

[2286] a means for notifying the user of the generated list or reminder;

[2287] A system including:

[2288] (Claim 2)

[2289] The system of claim 1, further comprising means for using generative artificial intelligence technology to recognize the faces and names of family and friends and convey them to the elderly for memory assistance, thereby contributing to dementia prevention.

[2290] (Claim 3)

[2291] 10. The system of claim 1, comprising a terminal for speech input and text output, a server for executing the generative artificial intelligence model, and means for communicating data between the terminal and the server.

[2292] "Example 1"

[2293] (Claim 1)

[2294] A means for converting voice input into text data using generative artificial intelligence technology and generating an appropriate response by analyzing the text data with a generative artificial intelligence model;

[2295] means for converting the generated response into speech and notifying the user;

[2296] A means for providing a reminder function for managing the user's health condition and schedule;

[2297] A means for notifying the user of the reminder content and recording and analyzing the user's response;

[2298] A means for generating lists and reminders to assist with daily life using generative artificial intelligence technology;

[2299] a means for notifying the user of the generated list or reminder;

[2300] a terminal that outputs the generated response or instruction by voice;

[2301] A means for communicating voice data and text data between the terminal and the server;

[2302] A system including:

[2303] (Claim 2)

[2304] The system of claim 1 further comprising means for using generative artificial intelligence technology to recognize faces and names of family and friends for memory assistance and conveying the names and faces to elderly people, thereby contributing to the prevention of dementia. ...

Claims

1. means for converting speech input into text using generative artificial intelligence technology and analyzing the text with a generative artificial intelligence model to generate an appropriate response; means for converting the generated response into speech and notifying the user; A means for providing a reminder function for managing a user's health condition and schedule; a means for notifying the user of the reminder content and recording and analyzing the user's response; A means for generating lists and reminders to support daily life using generative artificial intelligence technology; a means for notifying the user of the generated list or reminder; A system including:

2. The system of claim 1 further comprising means for using generative artificial intelligence technology to recognize the faces and names of family and friends for memory assistance and conveying the names and faces to the elderly, thereby contributing to the prevention of dementia.

3. 10. The system of claim 1, further comprising a terminal for speech input and text output, a server for executing the generative artificial intelligence model, and means for communicating data between the terminal and the server.

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