system

Abstract

Provide a system. A method for converting voice data into text data using natural language processing; A means of analyzing text data and understanding user intent; means for generating task management information based on the analysis results; means for generating information as voice data using a voice synthesis engine; means for providing the generated voice data to a user; A system including:

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] Elderly people often experience difficulty managing tasks in their daily lives, especially managing medications and remembering schedules. Another social issue is that they have difficulty participating in local events, which can lead to isolation. This reduces the quality of life for elderly people and makes it difficult for them to manage their health and participate in society. To address these issues, there is a need for the development of a system that allows elderly people to receive information about tasks, health management, and local events while communicating naturally through voice. [Means for solving the problem]

[0005] The present invention provides a system including: means for converting voice data into text data using natural language processing; means for analyzing the text data and understanding a user's intent; means for generating task management information based on the analysis results; means for generating the information as voice data using a voice synthesis engine; and means for providing the generated voice data to the user. The present invention also provides a system including: means for analyzing user interest and behavior data and collecting recommended local event information; means for summarizing the collected local event information in a form suitable for the user; means for generating the summarized information as voice data using a voice synthesis engine; and means for providing the generated voice data to the user. The present invention also provides a system including: means for generating a notification based on the task management information and notifying the user at a specified time; means for analyzing health management information and monitoring the user's health status; means for generating advice based on the monitoring results; means for generating the generated advice as voice data using a voice synthesis engine; and means for providing the generated voice data to the user.

[0006] "Natural language processing" is a technology that analyzes voice and text data to understand and generate human language.

[0007] "Voice data" refers to data that has been converted from voice into digital format and is used for voice recognition and voice synthesis.

[0008] "Text data" refers to data that represents character information in digital form and is used as the subject of analysis for natural language processing.

[0009] "Means for converting into text data" refers to speech recognition technology for converting voice data into text data.

[0010] "User intent" refers to analyzing and understanding the requests and wishes extracted from the user's speech.

[0011] "Task management information" is information for managing tasks and schedules in the user's daily life.

[0012] A "speech synthesis engine" refers to technology for converting text data into voice data.

[0013] "Regional event information" means information about events held in a particular region.

[0014] "Notifications" are a way to notify users of reminders and information.

[0015] "Health management information" is information for monitoring and managing the user's health condition.

[0016] "Monitoring" refers to the continuous observation and recording of a user's condition.

[0017] "Advice" refers to instructions or advice provided to the user based on the analysis results.

[0018] "Speech recognition" is a technology that takes voice data as input, identifies words and phrases within it, and converts them into text data.

[0019] "Recommended" refers to information selected based on the user's needs and interests.

[0020] A "summary" is a concise summary of lengthy information.

[0021] "User interest and behavioral data" refers to information collected based on a user's preferences and behavioral history. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology and natural language processing technology. Specific embodiments for carrying out the present invention will be described below.

[0044] System Overview

[0045] This system consists of three main components: the user (elderly person), the terminal, and the server. The user can use voice commands to the terminal to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the terminal and the server.

[0046] Program processing flow

[0047] User operations

[0048] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say to the device, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[0049] Terminal handling

[0050] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[0051] Server Processing

[0052] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent.

[0053] Next, specific processing is performed based on the analysis results, such as:

[0054] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at the specified time.

[0055] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in the database.

[0056] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavior data. The summarized information is then provided in a format appropriate for the user.

[0057] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[0058] Specific examples

[0059] Medication reminder settings

[0060] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[0061] 2. Device: Records audio and sends the audio data to the server.

[0062] 3. Server: Converts the voice to text and processes the reminder. Generates a voice message saying "Reminder set" and sends it to the device.

[0063] 4. Device: Notify the user that the reminder has been set.

[0064] Obtaining local event information

[0065] 1. User: "Tell me about events near me."

[0066] 2. Device: Records audio and sends the audio data to the server.

[0067] 3. Server: Converts speech to text and collects local event information from the Internet. It summarizes the collected information, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[0068] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[0069] This series of processes enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving the quality of life for the elderly.

[0070] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice.

[0071] The processing flow will be explained below.

[0072] Specific flow of program processing

[0073] Scenario: User wants to set a medication reminder

[0074] Step 1:

[0075] On the device: The user says, "Set a reminder to take my medicine tomorrow morning at 9 AM."

[0076] Activates the microphone and records the user's voice for a certain period of time.

[0077] Step 2:

[0078] Terminal: Converts recorded audio data into digital format.

[0079] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0080] Step 3:

[0081] Device: Sends audio data to the server.

[0082] Create an HTTP POST request to the server, sending the audio data as the payload.

[0083] Step 4:

[0084] Server: Receives the audio data.

[0085] Receives the transmitted audio data and stores it in storage.

[0086] Step 5:

[0087] Server: Converts voice data into text using a speech recognition AI model.

[0088] A voice recognition engine (e.g., Google® Speech-to-Text API) is started and the voice data is converted into text data.

[0089] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[0090] Step 6:

[0091] Server: Analyzes text data using a natural language processing model to understand user intent.

[0092] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (setting a reminder).

[0093] Step 7:

[0094] Server: Generates reminder setting data based on the analysis results.

[0095] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[0096] Step 8:

[0097] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[0098] Generates the output text "Reminder set".

[0099] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[0100] Step 9:

[0101] Server: Sends audio data to the device.

[0102] The generated audio data is returned to the terminal as an HTTP response.

[0103] Step 10:

[0104] Terminal: Plays back the audio data received from the server.

[0105] The received audio data is saved in the internal storage.

[0106] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[0107] Scenario: A user wants to receive information about local events

[0108] Step 1:

[0109] User: "Tell me if there are any events nearby" says to the device.

[0110] Step 2:

[0111] Device: Records the user's voice.

[0112] Activates the microphone and records the user's voice for a certain period of time.

[0113] Step 3:

[0114] Terminal: Converts recorded audio data into digital format.

[0115] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0116] Step 4:

[0117] Device: Sends audio data to the server.

[0118] Create an HTTP POST request to the server, sending the audio data as the payload.

[0119] Step 5:

[0120] Server: Receives the audio data.

[0121] Receives the transmitted audio data and stores it in storage.

[0122] Step 6:

[0123] Server: Converts voice data into text using a speech recognition AI model.

[0124] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[0125] The output text is "Tell me if there are any events nearby."

[0126] Step 7:

[0127] Server: Analyzes text data using a natural language processing model to understand user intent.

[0128] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (provide event information).

[0129] Step 8:

[0130] Server: Collects and summarizes local event information from the Internet.

[0131] Use external APIs (e.g. Eventbrite, Meetup) to gather information about nearby events.

[0132] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[0133] Step 9:

[0134] Server: Converts the collected event information into text and sends it to the speech synthesis engine.

[0135] Produces the output text "There is a flea market in the park this weekend."

[0136] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[0137] Step 10:

[0138] Server: Sends audio data to the device.

[0139] The generated audio data is returned to the terminal as an HTTP response.

[0140] Step 11:

[0141] Terminal: Plays back the audio data received from the server.

[0142] The received audio data is saved in the internal storage.

[0143] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[0144] Example 1

[0145] 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."

[0146] In the daily lives of elderly people, task management, health management, and keeping up with local event information can be complicated, and there is a need for efficient ways to do these tasks. These tasks become even more difficult when visual and operational functions decline. An integrated system is needed to manage these functions via voice and improve the quality of life for elderly people.

[0147] 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.

[0148] In this invention, the server includes means for recording a user's voice input and converting it into a digital format, means for transmitting the converted voice data to the server, means for converting the voice data into text data at the server, means for analyzing the text data to understand the user's intentions, means for setting reminders and schedules based on the analysis results, means for providing the set information as voice data, and means for notifying the user of the generated voice data, thereby enabling task management, health management, and the collection and understanding of local event information.

[0149] "User" refers to the person who uses this system, and is particularly targeted at elderly people.

[0150] "Voice input" refers to instructions or questions given by the user to the terminal by voice.

[0151] A "terminal" is a device used by a user to perform voice input, and includes digital devices equipped with a microphone and a speaker.

[0152] "Digital format" refers to a format in which an analog audio signal is converted into digital data.

[0153] "Server" refers to a computer system set up to process data sent from a terminal.

[0154] "Voice Data" means the digital form of a recorded user's voice or generated voice information.

[0155] "Text data" refers to character string data converted from voice data by a voice recognition engine.

[0156] "Speech recognition engine" means software or technology for converting voice data into text data.

[0157] A "natural language processing engine" refers to software or technology that analyzes text data to understand user intent.

[0158] A "reminder" is a notification that prompts a user to take a specific action at a specific time or date.

[0159] "Task management" refers to managing a user's schedule and reminders.

[0160] "Health management" refers to monitoring the user's health status and providing advice.

[0161] "Local event information" refers to information about events held in the area where the user lives.

[0162] "Speech synthesis engine" means software or technology for converting text data into speech data.

[0163] The system of the present invention is designed to support the daily lives of elderly people and primarily utilizes speech recognition and natural language processing technologies. The system consists of three main components: a user, a terminal, and a server. Specific embodiments for implementing the present invention are described below.

[0164] System Overview

[0165] Users can use voice commands to manage their daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications via the device and server. This system aims to improve the quality of life for the elderly.

[0166] Hardware and software used

[0167] Hardware: Smart devices with microphones, speakers, and processors (e.g., Amazon Echo, GOOGLE HOME®)

[0168] Speech recognition engine: Google Speech-to-Text API, Amazon Transcribe

[0169] Natural language processing engine: SpaCy, Google Cloud Natural Language API

[0170] Database: MySQL(registered trademark), PostgreSQL

[0171] Web scraping tools: Beautiful Soup, Scrapy

[0172] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[0173] Overview of program processing

[0174] 1. Voice Input

[0175] The user gives the system commands by voice, for example, "Set a reminder to take my medicine tomorrow morning at 9am."

[0176] 2. Audio recording and digital conversion

[0177] The device uses a microphone to record the user's voice and converts it into digital form using an ADC (analog-to-digital conversion) processor.

[0178] 3. Data Transmission

[0179] The device then transmits the converted digital audio data to a server via Wi-Fi or a mobile network.

[0180] 4. Speech Recognition and Text Conversion

[0181] The server passes the received voice data to a voice recognition engine, which converts the voice into text data.

[0182] 5. Natural Language Processing

[0183] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[0184] 6. Reminder Settings

[0185] The server stores the reminder information in a database and notifies you at the set time.

[0186] 7. Audio data generation

[0187] The server uses a speech synthesis engine to convert the text data into speech data.

[0188] 8. User Notices

[0189] The terminal reproduces the generated voice data and notifies the user.

[0190] Specific examples

[0191] Medication reminder settings

[0192] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[0193] 2. Device: Records audio and sends the audio data to the server.

[0194] 3. Server: Converts the voice to text, sets the reminder, saves the setting in the database, generates a voice message saying "Reminder set" and sends it to the device.

[0195] 4. Device: Notify the user that the reminder has been set.

[0196] Obtaining local event information

[0197] 1. User: "Tell me about events near me."

[0198] 2. Device: Records audio and sends the audio data to the server.

[0199] 3. Server: After converting the speech into text, it collects local event information from the Internet, summarizes the collected information, and generates speech data such as "There will be a flea market in the park this weekend," and sends it to the device.

[0200] 4. Device: Notify the user that "There's a flea market in the park this weekend."

[0201] Prompt Sentence Examples

[0202] Prompt: "Design a system to support the daily lives of seniors, using speech recognition and natural language processing to include features such as medication reminders and information about local events."

[0203] In this form, the system enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, thereby improving the quality of life for the elderly.

[0204] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0205] Step 1:

[0206] The user issues commands to the system by voice.

[0207] Specifically, the user says, "Set a reminder to take my medicine at 9:00 tomorrow morning." This is the input, and is recorded in voice format into the device's microphone.

[0208] Step 2:

[0209] The device records the input audio and converts it into a digital format.

[0210] Specifically, the device's built-in microphone records audio, and the ADC processor converts the analog audio signal into digital data. The input is audio data, and the output is digital audio data.

[0211] Step 3:

[0212] The terminal transmits the converted digital audio data to the server.

[0213] Specifically, the device sends an HTTP POST request to the server over Wi-Fi or a mobile network. The input is digital audio data, and the output is transmission to the server.

[0214] Step 4:

[0215] The server passes the received voice data to a voice recognition engine and converts it into text data.

[0216] Specifically, the server calls a speech recognition engine and converts the voice data into text format. The input is digital voice data, and the output is text data.

[0217] Step 5:

[0218] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[0219] Specifically, the server uses a natural language processing engine to analyze text data and extract intent. The input is text data, and the output is the analysis result.

[0220] Step 6:

[0221] The server sets reminders based on the analysis results.

[0222] Specifically, the server accesses the database and registers the reminder information, such as "Take medicine at 9:00 AM on April 2, 2024." The input is the analysis result, and the output is registration in the database.

[0223] Step 7:

[0224] The server converts the set reminder information into audio data.

[0225] Specifically, the server invokes a speech synthesis engine and converts the text notification content into voice data. Voice data saying "Reminder set" is generated. The input is the reminder text information, and the output is voice data.

[0226] Step 8:

[0227] The server transmits the generated voice data to the terminal.

[0228] Specifically, the server sends audio data to the device using an HTTP POST request. The input is audio data, and the output is transmission to the device.

[0229] Step 9:

[0230] The terminal plays back the received audio data and notifies the user.

[0231] Specifically, the device uses the built-in speaker to play a voice message saying, "Reminder set." The input is voice data, and the output is a voice notification to the user.

[0232] The above are the specific processing steps of the system program, which allow users to set reminders and receive notifications using natural voice commands.

[0233] (Application example 1)

[0234] 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."

[0235] When seniors shop in brick-and-mortar stores, they face the problem of having to spend a lot of time and effort. Product searches and obtaining special offers are particularly burdensome for seniors. Furthermore, seniors often have limited means to effectively manage and check their shopping lists, which can lead to forgetting to buy something or making unnecessary purchases. These situations are contributing to a decline in the quality of life for seniors.

[0236] 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.

[0237] In this invention, the server includes means for converting voice data into text data using voice recognition technology, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, means for accessing a database for obtaining product location information and special offer information in a physical store, and means for analyzing the product location information and special offer information and providing the user with voice data. This enables elderly people to efficiently search for products in physical stores, obtain special offer information, and manage their shopping lists.

[0238] "Voice recognition technology" is a technology that analyzes voice data and converts it into text data.

[0239] "Text data" refers to data of character information converted using voice recognition technology.

[0240] A "voice synthesis engine" is a function for analyzing text data and generating voice data.

[0241] "Task management information" is information for managing tasks in a user's daily life, and this information becomes reminders and notifications provided to the user.

[0242] A "database" is an information management system that stores information such as product location information and special offer information.

[0243] "Product location information" is information that indicates where a particular product is displayed.

[0244] "Special offer information" is information that indicates discounts and sales on specific products in stores.

[0245] "Voice data" refers to information that is generated by a voice synthesis engine and is provided to the user audibly.

[0246] This invention is a system for supporting elderly people in efficiently shopping in brick-and-mortar stores. Its main functions are to provide product location information, special offers, and shopping list management using speech recognition and natural language processing technologies. This system is composed of multiple components, including speech recognition technology, a speech synthesis engine, and database access functionality.

[0247] System Overview

[0248] This system consists of three main components: the user (elderly person), the device (smartphone, smart glasses, head-mounted display, etc.), and the server. The user can give instructions to the system via voice and receive necessary information and notifications via the device and server.

[0249] Hardware and Software Configuration

[0250] Hardware:

[0251] Smartphone (iOS or ANDROID (registered trademark))

[0252] Smart glasses (with audio input / output functions)

[0253] Head-mounted display (with audio input / output function)

[0254] Microphone (for voice input)

[0255] Speaker (for audio output)

[0256] software:

[0257] speech_recognition: A library for speech recognition

[0258] pyttsx3: A library for audio output

[0259] requests: A library for sending API requests

[0260] Data processing and calculation

[0261] 1. Voice input:

[0262] The user provides voice input, the audio is recorded with a microphone, and the recorded audio is converted into text data using the speech_recognition library.

[0263] 2. Text data analysis and processing:

[0264] The server analyzes the text data obtained by the recorder, extracts requests for product names and special offers, requests the necessary information from the database, and outputs the obtained data in a specific format.

[0265] 3. Audio output:

[0266] Using the pyttsx3 library, the acquired information is generated as audio data and notified to the elderly.

[0267] Specific examples

[0268] Product Search:

[0269] When a user asks, "Where is the milk?", the system retrieves the product's location information from the database and responds by voice, "The milk is in the dairy section."

[0270] Get special offers:

[0271] When a user says, "Tell me what's on sale today," the system retrieves the sale information from the database and announces in voice, "Apples and bananas are on sale today."

[0272] Check your shopping list:

[0273] When the user says, "Check the shopping list," the system references the shopping list on the server and announces in voice, "The list includes milk, bread, and apples."

[0274] Prompt Sentence Examples

[0275] Below are some example prompts to apply to generative AI models:

[0276] I would like to develop a shopping support app that uses speech recognition and natural language processing technology to support the daily lives of elderly people. Shopping support includes product search, obtaining special offers, and checking the shopping list. Examples of voice commands include "Where is the milk?", "Tell me today's specials," and "Check my shopping list." Please provide specific program examples and explanations for analyzing the flow.

[0277] Through these elements, the invention allows seniors to efficiently search for products, obtain special offers, and manage their shopping lists in brick-and-mortar stores.

[0278] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0279] Step 1:

[0280] The user provides voice input. For example, the user might say, "Where is the milk?" in a physical store. As input, the user's voice data is collected through a microphone. This voice data is sent to the next processing step.

[0281] Step 2:

[0282] The device records the voice and converts the voice data into a digital format. Specifically, it uses the speech_recognition library to convert the voice to text data. The input of this step is the user's voice data, and the output is the converted text data.

[0283] Step 3:

[0284] The server receives the text data sent from the terminal. It analyzes the text data to understand the user's intent (in this case, a product search). Based on the analysis, it extracts the product name "Milk." The input for this step is the user's text data, and the output is the product name as a result of the analysis.

[0285] Step 4:

[0286] Based on the product name extracted by the server, the database is accessed to obtain the product's location information. For example, location information for the product name "Milk" is obtained from the database. The input to this step is the analysis result (product name), and the output is product location information.

[0287] Step 5:

[0288] The server generates voice data based on the location information it has acquired. Using the pyttsx3 library, it generates voice data saying, "Milk is in the dairy section." The input for this step is the product location information, and the output is the generated voice data.

[0289] Step 6:

[0290] The terminal provides the user with the voice data received from the server. Specifically, the generated voice data is played through the terminal's speaker, and the user is notified that "Milk is in the dairy section." The input of this step is the voice data from the server, and the output is the notification to the user.

[0291] Step 7:

[0292] The user issues a command, for example, "Tell me what's on sale today." The process begins again at step 1, and the required information is retrieved and provided to the user.

[0293] 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.

[0294] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology, natural language processing technology, and emotion recognition technology. Specific embodiments for carrying out the present invention will be described below.

[0295] System Overview

[0296] This system consists of three main components: the user (elderly person), the device, and the server. The user can use voice commands to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the device and server. The system also incorporates an emotion engine that recognizes the user's emotions and optimizes the information and advice it provides.

[0297] Program processing flow

[0298] User operations

[0299] The user issues instructions to the system through voice. For example, when setting a medication reminder, the user might say to the device, "Set a reminder to take my medicine tomorrow morning at 9:00." Furthermore, the emotion engine recognizes the user's emotions in real time.

[0300] Terminal handling

[0301] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[0302] Server Processing

[0303] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. Furthermore, the emotion engine analyzes the user's emotions, and the results are reflected in other processes.

[0304] Next, specific processing is performed based on the analysis results, such as:

[0305] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at specified times. The content and timing of these reminders may be adjusted based on the user's emotions as recognized by an emotion engine.

[0306] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in a database. The details of the advice and the way it is presented may be changed based on the user's emotions recognized by the emotion engine.

[0307] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavioral data. The summarized information is provided in a format appropriate for the user. The user's emotions, recognized by the emotion engine, also influence the selection of event information to be provided.

[0308] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[0309] Specific examples

[0310] Medication reminder settings

[0311] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[0312] 2. Device: Records audio and sends the audio data to the server.

[0313] 3. Server: Converts voice to text and processes reminder settings. If the emotion engine analyzes the user's tone of voice and determines that the user is feeling nervous or anxious, it adjusts the notification to be softer and include encouraging words. It generates voice data saying "Reminder set" and sends it to the device.

[0314] 4. Device: Notify the user that the reminder has been set.

[0315] Obtaining local event information

[0316] 1. User: "Tell me about events near me."

[0317] 2. Device: Records audio and sends the audio data to the server.

[0318] 3. Server: Converts speech into text and collects local event information from the Internet. If the emotion engine determines that the user is bored, it prioritizes more active events, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[0319] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[0320] This series of processes allows elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving their quality of life.The introduction of an emotion engine allows the system to respond flexibly to the user's emotional state, making it possible to provide more personalized support.

[0321] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice, and the system also provides optimal advice and information according to the user's emotions.

[0322] The processing flow will be explained below.

[0323] Specific flow of program processing (including emotion engine)

[0324] Scenario: User wants to set a medication reminder

[0325] Step 1:

[0326] User: "Set a reminder to take my medicine tomorrow morning at 9am" says to the device.

[0327] Step 2:

[0328] Device: Records the user's voice input.

[0329] Activate the microphone and record audio for a set period of time.

[0330] Step 3:

[0331] Terminal: Converts recorded audio data into digital format.

[0332] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0333] Step 4:

[0334] Device: Sends audio data to the server.

[0335] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[0336] Step 5:

[0337] Server: Receives the audio data.

[0338] Receives the transmitted audio data and stores it in storage.

[0339] Step 6:

[0340] Server: Converts voice data into text using a speech recognition AI model.

[0341] A voice recognition engine is started and voice data is converted into text data.

[0342] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[0343] Step 7:

[0344] Server: Analyzes text data using a natural language processing model to understand user intent.

[0345] Use text analysis tools to extract the intent behind reminder settings.

[0346] Step 8:

[0347] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[0348] The emotion engine assesses the user's emotional state (e.g., tension, anxiety, joy) from the voice data.

[0349] Step 9:

[0350] Server: Generates reminder setting data based on the analysis results.

[0351] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[0352] Adjust the method and content of notifications based on the results of the emotion engine.

[0353] Step 10:

[0354] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[0355] Generates the output text "Reminder set".

[0356] The text is sent to a speech synthesis engine to generate speech data.

[0357] Step 11:

[0358] Server: Sends audio data to the device.

[0359] The generated audio data is returned to the terminal as an HTTP response.

[0360] Step 12:

[0361] Terminal: Plays back the audio data received from the server.

[0362] The received audio data is saved in the internal storage.

[0363] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[0364] Scenario: A user wants to receive information about local events

[0365] Step 1:

[0366] User: "Tell me if there are any events nearby" says to the device.

[0367] Step 2:

[0368] Device: Records the user's voice input.

[0369] Activate the microphone and record audio for a set period of time.

[0370] Step 3:

[0371] Terminal: Converts recorded audio data into digital format.

[0372] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0373] Step 4:

[0374] Device: Sends audio data to the server.

[0375] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[0376] Step 5:

[0377] Server: Receives the audio data and stores it in storage.

[0378] Step 6:

[0379] Server: Converts voice data into text using a speech recognition AI model.

[0380] A voice recognition engine is started and voice data is converted into text data.

[0381] The output text is "Tell me if there are any events nearby."

[0382] Step 7:

[0383] Server: Analyzes text data using a natural language processing model to understand user intent.

[0384] Use text analysis tools to extract the intent behind providing event information.

[0385] Step 8:

[0386] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[0387] The emotion engine assesses the user's emotional state (e.g., boredom, interest) from the voice data.

[0388] Step 9:

[0389] Server: Collects and summarizes local event information from the Internet.

[0390] Use external APIs to gather nearby event information.

[0391] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[0392] The selection and presentation of event information is adjusted based on the results of the emotion engine.

[0393] Step 10:

[0394] Server: Converts the summarized event information into text and sends it to the speech synthesis engine.

[0395] Produces the output text "There is a flea market in the park this weekend."

[0396] The text is sent to a speech synthesis engine to generate speech data.

[0397] Step 11:

[0398] Server: Sends audio data to the device.

[0399] The generated audio data is returned to the terminal as an HTTP response.

[0400] Step 12:

[0401] Terminal: Plays back the audio data received from the server.

[0402] The received audio data is saved in the internal storage.

[0403] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[0404] This specific processing flow makes it easy for elderly people to manage tasks in their daily lives, manage their health, and participate in local events, and by using an emotion engine, it is possible to provide services that take into consideration the user's emotions.

[0405] Example 2

[0406] 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."

[0407] There is a need for a system that allows elderly people to easily manage their daily tasks, health management, and obtain information about local events using voice commands. However, existing systems lack the ability to recognize and process voice commands, and lack the ability to recognize the user's emotions and optimize responses. Therefore, a user-friendly system that can be used naturally by elderly people is needed.

[0408] 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.

[0409] In this invention, the server includes means for recording voice data and converting it into a digital format, means for transmitting the converted digital voice data to the server, means for converting the voice data into text data using natural language processing, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analyzed text data and the results of the user's emotion analysis, means for saving the generated task management information in a database, means for generating and transmitting a notification at a specified time, means for analyzing the user's emotion using an emotion recognition engine, means for generating information as voice data using a voice synthesis engine, means for transmitting the generated voice data to a terminal, and means for playing the voice data on the terminal and providing it to the user. This enables elderly people to easily manage their daily tasks, health management, and local event information by voice, and provides optimal support according to the user's emotions.

[0410] "Voice data" refers to information input by voice by a user that has been recorded and stored in digital form.

[0411] "Digital format" refers to the format in which analog audio is converted into a digital signal that allows a computer to process the audio data.

[0412] A "server" is a central system that processes and stores data over a network.

[0413] "Natural language processing" is a technology that uses computers to process and understand human language.

[0414] "Text data" refers to text information converted from voice data using natural language processing technology.

[0415] "User intent" refers to the purpose or desire that the user is trying to convey through voice instructions.

[0416] "Task management information" is information about tasks to be managed, such as the user's daily schedule and reminders.

[0417] A "database" is a system for efficiently storing and accessing data.

[0418] A "notification" is a message or sound generated for the purpose of informing the user of pre-defined information.

[0419] An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice and facial expressions.

[0420] A "speech synthesis engine" is a technology that converts text data into voice data.

[0421] A "terminal" is a device for transmitting and receiving data between a user and a system.

[0422] "Recommended local event information" is information about local events selected based on the user's interests and behavioral data.

[0423] "Health management information" is information that indicates the user's health condition and health-related data.

[0424] "Advice" refers to advice or guidance provided to the user based on the analysis results.

[0425] "Interests and search history" refers to the user's past interests and search history.

[0426] This invention is a system that uses speech recognition, natural language processing, and emotion recognition technologies to support the daily lives of elderly people. The system consists of three main components: a user, a terminal, and a server.

[0427] System Overview

[0428] Users can issue voice commands to the system to manage daily tasks, manage their health, and obtain information about local events. The device records the user's voice and sends it to the server. The server analyzes the voice data, understands the user's intentions and emotions, and generates appropriate information and notifications. The generated information is converted into voice data and notified to the user via the device.

[0429] Hardware and software used

[0430] Hardware:

[0431] Devices: smart speakers, smartphones, etc.

[0432] Server: A server with high-performance processing power

[0433] software:

[0434] Speech recognition engine: converts voice data into text data (e.g., Google Speech-to-Text API)

[0435] Natural language processing engine: Analyzes text data and understands user intent (e.g., Google Cloud Natural Language)

[0436] Emotion recognition engine: Analyzes the user's emotional state (e.g., Affectiva SDK)

[0437] Speech synthesis engine: converts text data into speech data (e.g., Amazon Polly)

[0438] Program processing flow

[0439] The program in this system follows these steps:

[0440] 1. User Instructions

[0441] The user gives instructions to the system through voice. For example, to set a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[0442] 2. Device Operation

[0443] The device records the user's voice, converts the voice data into a digital format, and then transmits it to the server.

[0444] 3. Server Processing

[0445] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. At the same time, the emotion recognition engine analyzes the user's emotions, and the results are reflected in other processes.

[0446] Specific examples:

[0447] Setting a medication reminder: When a user says, "Set a reminder to take my medicine tomorrow morning at 9:00," the server converts this instruction into text data and sets the reminder. If the emotion recognition engine detects that the user is nervous or anxious, it softens the notification and generates a response such as, "The reminder has been set."

[0448] Obtaining local event information: When a user says, "Tell me about any events happening nearby," the server converts this instruction into text data and analyzes it using a natural language processing engine. If the emotion recognition engine detects boredom, it prioritizes active event information and generates a response such as, "There's a flea market in the park this weekend."

[0449] 4. Audio data generation and notification

[0450] The generated text and information are converted into voice data by a speech synthesis engine, and this voice data is then sent back to the device, which then plays the received voice data and provides the user with appropriate information and notifications.

[0451] Specific examples

[0452] The following example prompts show how the system works:

[0453] Example prompt sentence:

[0454] "Set a reminder to take my medicine tomorrow morning at 9am"

[0455] "Tell me if there's any events happening nearby."

[0456] As described above, this invention enables elderly people to easily manage and obtain information about daily tasks, health management, and local events using their voice. In particular, by using an emotion recognition engine, it is possible to provide optimal support according to the user's emotional state.

[0457] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0458] Program processing step details

[0459] Step 1: User voice command

[0460] Subject: User

[0461] Input: Voice command

[0462] Output: Recorded audio data

[0463] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning." This gives the system a starting point for understanding the user's needs.

[0464] Step 2: Record and transmit audio data using your device

[0465] Subject: Device

[0466] Input: User's voice command

[0467] Output: Digital audio data

[0468] The device records the user's voice commands and converts the voice data into digital format. This digital voice data is sent to the server, where the voice is recorded through a microphone, converted into a digital format such as PCM, and sent to the server via IP communication.

[0469] Step 3: The server receives and converts the audio data

[0470] Subject: Server

[0471] Input: Digital audio data

[0472] Output: Text data

[0473] The server receives the voice data sent from the device and converts it into text data using a voice recognition engine. For example, it converts the voice data into text data using the Google Speech-to-Text API. This allows the server to handle voice instructions as text information.

[0474] Step 4: Parsing the text data by the server

[0475] Subject: Server

[0476] Input: Text data

[0477] Output: Analysis results indicating the user's intent

[0478] The server uses a natural language processing engine to analyze the text data converted from the speech and understand the user's intent. Specifically, it uses Google Cloud Natural Language to analyze the text and understand the user's instructions. During this process, it performs morphological and contextual analysis of the text data to identify the intent and required actions.

[0479] Step 5: Emotion recognition and response generation by the server

[0480] Subject: Server

[0481] Input: Text data and user voice tone

[0482] Output: Response text and response voice data to the user

[0483] The server uses an emotion engine to analyze the user's emotions and generate responses based on the results. For example, it uses the Affectiva SDK to detect emotions from voice tones and analyzes them together with text data. It then generates specific response text, such as setting medication reminders or providing information about local events, and converts it into voice data using Amazon Polly if necessary.

[0484] Step 6: Sending a response from the server to the device

[0485] Subject: Server

[0486] Input: Voice data and text response

[0487] Output: Data sent to the terminal

[0488] The server sends the generated voice and text data to the terminal using IP communications, in a format that the terminal can receive (e.g., WAV format voice data).

[0489] Step 7: Device notifies the user

[0490] Subject: Device

[0491] Input: Audio and text data sent from the server

[0492] Output: A spoken audio notification

[0493] The device receives the audio data sent from the server and plays it through the speaker, providing appropriate feedback and notifications to the user. The device decodes the audio data, converts it into an analog signal that can be heard by the user, and plays it through the speaker.

[0494] Through these specific processing steps, the present invention enables elderly people to efficiently and user-friendly perform daily tasks, manage their health, and obtain information about local events using their voice. In addition, by using an emotion recognition engine, the system can provide optimal responses tailored to the user's emotional state.

[0495] (Application example 2)

[0496] 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."

[0497] There is a need for methods to help seniors maintain independence in their daily lives and make their shopping experience in brick-and-mortar stores smooth and stress-free. However, seniors are often unfamiliar with voice input and digital interfaces, and are sensitive to emotional changes, which can make shopping in brick-and-mortar stores difficult. Therefore, a system that is easy for seniors to use and allows them to receive information and take action with confidence is needed.

[0498] The identification process by the identification 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 data into text data using natural language processing, means for analyzing the text data and understanding the user's intention, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, emotion recognition means for recognizing the user's emotions in real time, and means for adjusting the information to be provided based on the emotion recognition results. This enables elderly people to easily manage their shopping experience in a physical store by voice and receive optimal information and advice tailored to their emotions in real time.

[0499] "Natural language processing" is a technology that converts voice data into text data and analyzes that text to understand its meaning and intent.

[0500] "Speech recognition" is a technology that converts a user's voice input into digital text data.

[0501] "Text data analysis" is the process of understanding the user's intent and extracting necessary information and tasks based on the converted text data.

[0502] "Generating task management information" refers to generating task management information such as reminders and schedules based on the analyzed user's intentions.

[0503] A "voice synthesis engine" is a technology that converts generated text data into voice data and provides it to the user.

[0504] "Emotion recognition" is a technology that detects and analyzes emotions from a user's voice, facial expressions, etc.

[0505] "Adjusting the information provided based on the emotion recognition results" refers to optimizing the content and presentation of the information and advice provided according to the detected emotion.

[0506] "Health management information" is data for monitoring the user's health condition and providing appropriate health advice.

[0507] "Local event information" is data for collecting and providing information on local events and activities based on user interests and behavioral data.

[0508] The system of this invention is designed to support the daily lives of elderly people, specifically supporting their shopping experience in brick-and-mortar stores. The main components of the system are a user (elderly person), a terminal (smartphone), and a server. The system utilizes technologies such as natural language processing, speech recognition, and emotion recognition to enable elderly people to comfortably shop in brick-and-mortar stores.

[0509] Hardware and software used

[0510] Hardware:

[0511] Smartphone

[0512] software:

[0513] Speech recognition engine (e.g. Google Speech-to-Text)

[0514] Natural language processing engine (e.g. Google Natural Language)

[0515] Emotion recognition engine (Microsoft® Azure® Emotion API, etc.)

[0516] Speech synthesis engine (such as Google Text-to-Speech)

[0517] System Operation Overview

[0518] 1. User operations

[0519] The user says to their smartphone, "Tell me where the milk is."

[0520] 2. Terminal Processing

[0521] The smartphone's microphone records the user's voice and converts the voice data into a digital format.

[0522] The audio data converted into a digital format is transmitted to a server.

[0523] 3. Server Processing

[0524] Speech recognition: The server uses a speech recognition engine such as Google Speech-to-Text to convert the voice data into text data.

[0525] Natural Language Processing: Use natural language processing engines such as Google Natural Language to analyze text data and understand user intent.

[0526] Emotion Recognition: Recognize user emotions in real time using emotion recognition engines such as the Microsoft Azure Emotion API.

[0527] Information acquisition and adjustment: Based on the text data and emotion recognition results, the location information of the relevant product is acquired from the database, and the information provided is adjusted according to the user's emotional state.

[0528] Speech synthesis: Using a speech synthesis engine such as Google Text-to-Speech, the tailored information is generated as audio data.

[0529] 4. Device notifications

[0530] The smartphone plays the generated voice data and notifies the user, "The milk is in the refrigerated section." If the user shows signs of anxiety, the notification message includes a reassuring phrase such as "You'll find it soon."

[0531] Specific examples

[0532] Prompt Sentence Examples

[0533] User: "Tell me where the milk is."

[0534] Application: "Milk is in the refrigerated section. You'll find it easily."

[0535] This system is designed to allow users to easily manage their shopping in physical stores using voice commands and receive optimal information and advice tailored to their emotions in real time, ultimately aiming to improve the quality of life for the elderly.

[0536] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0537] Step 1:

[0538] A user speaks to a smartphone saying, "Tell me where the milk is." The smartphone's microphone picks up the voice input. The input is raw voice data, and the output is also voice data.

[0539] Step 2:

[0540] The smartphone converts the captured audio data into a digital format. This conversion is performed by the smartphone's internal processing. The input is the captured raw audio data, and the output is digital audio data.

[0541] Step 3:

[0542] The smartphone sends the converted digital audio data to the server. The input is digital audio data, and the output is the audio data sent to the server.

[0543] Step 4:

[0544] The voice data received by the server is converted into text data using a voice recognition engine (such as Google Speech-to-Text). The input is the digital voice data received by the server, and the output is text data.

[0545] Step 5:

[0546] The server uses a natural language processing engine (such as Google Natural Language) to analyze the text data and understand the user's intent. The input is the text data generated by the speech recognition engine, and the output is the analysis result that includes the user's intent.

[0547] Step 6:

[0548] The server uses an emotion recognition engine (such as Microsoft Azure Emotion API) to recognize the user's emotions in real time during speech recognition. The input is voice or text data stored on the server, and the output is the analysis result regarding the user's emotional state.

[0549] Step 7:

[0550] The server retrieves the location information of the relevant product from the database based on the analysis results and adjusts the provided information based on the emotion recognition results. The input is the analysis results regarding the user's intention and emotional state, and the output is the adjusted notification information.

[0551] Step 8:

[0552] The server generates the adjusted notification information as voice data using a speech synthesis engine (such as Google Text-to-Speech). The input is the text data of the adjusted notification information, and the output is the voice data generated by the speech synthesis engine.

[0553] Step 9:

[0554] The server sends the generated voice data to the smartphone. The input is the voice data generated by the server, and the output is the voice data sent to the smartphone.

[0555] Step 10:

[0556] The smartphone plays the received voice data to notify the user. For example, digital voice data can be played to tell the user, "The milk is in the refrigerated section. You'll find it soon." The input is the voice data received by the smartphone, and the output is the voice notification to the user.

[0557] 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.

[0558] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0559] 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.

[0560] [Second embodiment]

[0561] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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. 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] In the smart glasses 214, 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.

[0572] 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."

[0573] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology and natural language processing technology. Specific embodiments for carrying out the present invention will be described below.

[0574] System Overview

[0575] This system consists of three main components: the user (elderly person), the terminal, and the server. The user can use voice commands to the terminal to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the terminal and the server.

[0576] Program processing flow

[0577] User operations

[0578] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say to the device, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[0579] Terminal handling

[0580] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[0581] Server Processing

[0582] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent.

[0583] Next, specific processing is performed based on the analysis results, such as:

[0584] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at the specified time.

[0585] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in the database.

[0586] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavior data. The summarized information is then provided in a format appropriate for the user.

[0587] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[0588] Specific examples

[0589] Medication reminder settings

[0590] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[0591] 2. Device: Records audio and sends the audio data to the server.

[0592] 3. Server: Converts the voice to text and processes the reminder. Generates a voice message saying "Reminder set" and sends it to the device.

[0593] 4. Device: Notify the user that the reminder has been set.

[0594] Obtaining local event information

[0595] 1. User: "Tell me about events near me."

[0596] 2. Device: Records audio and sends the audio data to the server.

[0597] 3. Server: Converts speech to text and collects local event information from the Internet. It summarizes the collected information, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[0598] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[0599] This series of processes enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving the quality of life for the elderly.

[0600] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice.

[0601] The processing flow will be explained below.

[0602] Specific flow of program processing

[0603] Scenario: User wants to set a medication reminder

[0604] Step 1:

[0605] On the device: The user says, "Set a reminder to take my medicine tomorrow morning at 9 AM."

[0606] Activates the microphone and records the user's voice for a certain period of time.

[0607] Step 2:

[0608] Terminal: Converts recorded audio data into digital format.

[0609] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0610] Step 3:

[0611] Device: Sends audio data to the server.

[0612] Create an HTTP POST request to the server, sending the audio data as the payload.

[0613] Step 4:

[0614] Server: Receives the audio data.

[0615] Receives the transmitted audio data and stores it in storage.

[0616] Step 5:

[0617] Server: Converts voice data into text using a speech recognition AI model.

[0618] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[0619] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[0620] Step 6:

[0621] Server: Analyzes text data using a natural language processing model to understand user intent.

[0622] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (setting a reminder).

[0623] Step 7:

[0624] Server: Generates reminder setting data based on the analysis results.

[0625] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[0626] Step 8:

[0627] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[0628] Generates the output text "Reminder set".

[0629] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[0630] Step 9:

[0631] Server: Sends audio data to the device.

[0632] The generated audio data is returned to the terminal as an HTTP response.

[0633] Step 10:

[0634] Terminal: Plays back the audio data received from the server.

[0635] The received audio data is saved in the internal storage.

[0636] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[0637] Scenario: A user wants to receive information about local events

[0638] Step 1:

[0639] User: "Tell me if there are any events nearby" says to the device.

[0640] Step 2:

[0641] Device: Records the user's voice.

[0642] Activates the microphone and records the user's voice for a certain period of time.

[0643] Step 3:

[0644] Terminal: Converts recorded audio data into digital format.

[0645] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0646] Step 4:

[0647] Device: Sends audio data to the server.

[0648] Create an HTTP POST request to the server, sending the audio data as the payload.

[0649] Step 5:

[0650] Server: Receives the audio data.

[0651] Receives the transmitted audio data and stores it in storage.

[0652] Step 6:

[0653] Server: Converts voice data into text using a speech recognition AI model.

[0654] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[0655] The output text is "Tell me if there are any events nearby."

[0656] Step 7:

[0657] Server: Analyzes text data using a natural language processing model to understand user intent.

[0658] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (provide event information).

[0659] Step 8:

[0660] Server: Collects and summarizes local event information from the Internet.

[0661] Use external APIs (e.g. Eventbrite, Meetup) to gather information about nearby events.

[0662] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[0663] Step 9:

[0664] Server: Converts the collected event information into text and sends it to the speech synthesis engine.

[0665] Produces the output text "There is a flea market in the park this weekend."

[0666] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[0667] Step 10:

[0668] Server: Sends audio data to the device.

[0669] The generated audio data is returned to the terminal as an HTTP response.

[0670] Step 11:

[0671] Terminal: Plays back the audio data received from the server.

[0672] The received audio data is saved in the internal storage.

[0673] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[0674] Example 1

[0675] 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."

[0676] In the daily lives of elderly people, task management, health management, and keeping up with local event information can be complicated, and there is a need for efficient ways to do these tasks. These tasks become even more difficult when visual and operational functions decline. An integrated system is needed to manage these functions via voice and improve the quality of life for elderly people.

[0677] 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.

[0678] In this invention, the server includes means for recording a user's voice input and converting it into a digital format, means for transmitting the converted voice data to the server, means for converting the voice data into text data at the server, means for analyzing the text data to understand the user's intentions, means for setting reminders and schedules based on the analysis results, means for providing the set information as voice data, and means for notifying the user of the generated voice data, thereby enabling task management, health management, and the collection and understanding of local event information.

[0679] "User" refers to the person who uses this system, and is particularly targeted at elderly people.

[0680] "Voice input" refers to instructions or questions given by the user to the terminal by voice.

[0681] A "terminal" is a device used by a user to perform voice input, and includes digital devices equipped with a microphone and a speaker.

[0682] "Digital format" refers to a format in which an analog audio signal is converted into digital data.

[0683] "Server" refers to a computer system set up to process data sent from a terminal.

[0684] "Voice Data" means the digital form of a recorded user's voice or generated voice information.

[0685] "Text data" refers to character string data converted from voice data by a voice recognition engine.

[0686] "Speech recognition engine" means software or technology for converting voice data into text data.

[0687] A "natural language processing engine" refers to software or technology that analyzes text data to understand user intent.

[0688] A "reminder" is a notification that prompts a user to take a specific action at a specific time or date.

[0689] "Task management" refers to managing a user's schedule and reminders.

[0690] "Health management" refers to monitoring the user's health status and providing advice.

[0691] "Local event information" refers to information about events held in the area where the user lives.

[0692] "Speech synthesis engine" means software or technology for converting text data into speech data.

[0693] The system of the present invention is designed to support the daily lives of elderly people and primarily utilizes speech recognition and natural language processing technologies. The system consists of three main components: a user, a terminal, and a server. Specific embodiments for implementing the present invention are described below.

[0694] System Overview

[0695] Users can use voice commands to manage their daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications via the device and server. This system aims to improve the quality of life for the elderly.

[0696] Hardware and software used

[0697] Hardware: Smart devices with microphones, speakers, and processors (e.g., Amazon Echo, Google Home)

[0698] Speech recognition engine: Google Speech-to-Text API, Amazon Transcribe

[0699] Natural language processing engine: SpaCy, Google Cloud Natural Language API

[0700] Database: MySQL, PostgreSQL

[0701] Web scraping tools: Beautiful Soup, Scrapy

[0702] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[0703] Overview of program processing

[0704] 1. Voice Input

[0705] The user gives the system commands by voice, for example, "Set a reminder to take my medicine tomorrow morning at 9am."

[0706] 2. Audio recording and digital conversion

[0707] The device uses a microphone to record the user's voice and converts it into digital form using an ADC (analog-to-digital conversion) processor.

[0708] 3. Data Transmission

[0709] The device then transmits the converted digital audio data to a server via Wi-Fi or a mobile network.

[0710] 4. Speech Recognition and Text Conversion

[0711] The server passes the received voice data to a voice recognition engine, which converts the voice into text data.

[0712] 5. Natural Language Processing

[0713] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[0714] 6. Reminder Settings

[0715] The server stores the reminder information in a database and notifies you at the set time.

[0716] 7. Audio data generation

[0717] The server uses a speech synthesis engine to convert the text data into speech data.

[0718] 8. User Notices

[0719] The terminal reproduces the generated voice data and notifies the user.

[0720] Specific examples

[0721] Medication reminder settings

[0722] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[0723] 2. Device: Records audio and sends the audio data to the server.

[0724] 3. Server: Converts the voice to text, sets the reminder, saves the setting in the database, generates a voice message saying "Reminder set" and sends it to the device.

[0725] 4. Device: Notify the user that the reminder has been set.

[0726] Obtaining local event information

[0727] 1. User: "Tell me about events near me."

[0728] 2. Device: Records audio and sends the audio data to the server.

[0729] 3. Server: After converting the speech into text, it collects local event information from the Internet, summarizes the collected information, and generates speech data such as "There will be a flea market in the park this weekend," and sends it to the device.

[0730] 4. Device: Notify the user that "There's a flea market in the park this weekend."

[0731] Prompt Sentence Examples

[0732] Prompt: "Design a system to support the daily lives of seniors, using speech recognition and natural language processing to include features such as medication reminders and information about local events."

[0733] In this form, the system enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, thereby improving the quality of life for the elderly.

[0734] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0735] Step 1:

[0736] The user issues commands to the system by voice.

[0737] Specifically, the user says, "Set a reminder to take my medicine at 9:00 tomorrow morning." This is the input, and is recorded in voice format into the device's microphone.

[0738] Step 2:

[0739] The device records the input audio and converts it into a digital format.

[0740] Specifically, the device's built-in microphone records audio, and the ADC processor converts the analog audio signal into digital data. The input is audio data, and the output is digital audio data.

[0741] Step 3:

[0742] The terminal transmits the converted digital audio data to the server.

[0743] Specifically, the device sends an HTTP POST request to the server over Wi-Fi or a mobile network. The input is digital audio data, and the output is transmission to the server.

[0744] Step 4:

[0745] The server passes the received voice data to a voice recognition engine and converts it into text data.

[0746] Specifically, the server calls a speech recognition engine and converts the voice data into text format. The input is digital voice data, and the output is text data.

[0747] Step 5:

[0748] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[0749] Specifically, the server uses a natural language processing engine to analyze text data and extract intent. The input is text data, and the output is the analysis result.

[0750] Step 6:

[0751] The server sets reminders based on the analysis results.

[0752] Specifically, the server accesses the database and registers the reminder information, such as "Take medicine at 9:00 AM on April 2, 2024." The input is the analysis result, and the output is registration in the database.

[0753] Step 7:

[0754] The server converts the set reminder information into audio data.

[0755] Specifically, the server invokes a speech synthesis engine and converts the text notification content into voice data. Voice data saying "Reminder set" is generated. The input is the reminder text information, and the output is voice data.

[0756] Step 8:

[0757] The server transmits the generated voice data to the terminal.

[0758] Specifically, the server sends audio data to the device using an HTTP POST request. The input is audio data, and the output is transmission to the device.

[0759] Step 9:

[0760] The terminal plays back the received audio data and notifies the user.

[0761] Specifically, the device uses the built-in speaker to play a voice message saying, "Reminder set." The input is voice data, and the output is a voice notification to the user.

[0762] The above are the specific processing steps of the system program, which allow users to set reminders and receive notifications using natural voice commands.

[0763] (Application example 1)

[0764] 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."

[0765] When seniors shop in brick-and-mortar stores, they face the problem of having to spend a lot of time and effort. Product searches and obtaining special offers are particularly burdensome for seniors. Furthermore, seniors often have limited means to effectively manage and check their shopping lists, which can lead to forgetting to buy something or making unnecessary purchases. These situations are contributing to a decline in the quality of life for seniors.

[0766] 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.

[0767] In this invention, the server includes means for converting voice data into text data using voice recognition technology, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, means for accessing a database for obtaining product location information and special offer information in a physical store, and means for analyzing the product location information and special offer information and providing the user with voice data. This enables elderly people to efficiently search for products in physical stores, obtain special offer information, and manage their shopping lists.

[0768] "Voice recognition technology" is a technology that analyzes voice data and converts it into text data.

[0769] "Text data" refers to data of character information converted using voice recognition technology.

[0770] A "voice synthesis engine" is a function for analyzing text data and generating voice data.

[0771] "Task management information" is information for managing tasks in a user's daily life, and this information becomes reminders and notifications provided to the user.

[0772] A "database" is an information management system that stores information such as product location information and special offer information.

[0773] "Product location information" is information that indicates where a particular product is displayed.

[0774] "Special offer information" is information that indicates discounts and sales on specific products in stores.

[0775] "Voice data" refers to information that is generated by a voice synthesis engine and is provided to the user audibly.

[0776] This invention is a system for supporting elderly people in efficiently shopping in brick-and-mortar stores. Its main functions are to provide product location information, special offers, and shopping list management using speech recognition and natural language processing technologies. This system is composed of multiple components, including speech recognition technology, a speech synthesis engine, and database access functionality.

[0777] System Overview

[0778] This system consists of three main components: the user (elderly person), the device (smartphone, smart glasses, head-mounted display, etc.), and the server. The user can give instructions to the system through voice and receive necessary information and notifications via the device and server.

[0779] Hardware and Software Configuration

[0780] Hardware:

[0781] Smartphone (iOS or Android)

[0782] Smart glasses (with audio input / output functions)

[0783] Head-mounted display (with audio input / output function)

[0784] Microphone (for voice input)

[0785] Speaker (for audio output)

[0786] software:

[0787] speech_recognition: A library for speech recognition

[0788] pyttsx3: A library for audio output

[0789] requests: A library for sending API requests

[0790] Data processing and calculation

[0791] 1. Voice input:

[0792] The user provides voice input, the audio is recorded with a microphone, and the recorded audio is converted into text data using the speech_recognition library.

[0793] 2. Text data analysis and processing:

[0794] The server analyzes the text data obtained by the recorder, extracts requests for product names and special offers, requests the necessary information from the database, and outputs the obtained data in a specific format.

[0795] 3. Audio output:

[0796] Using the pyttsx3 library, the acquired information is generated as audio data and notified to the elderly.

[0797] Specific examples

[0798] Product Search:

[0799] When a user asks, "Where is the milk?", the system retrieves the product's location information from the database and responds by voice, "The milk is in the dairy section."

[0800] Get special offers:

[0801] When a user says, "Tell me what's on sale today," the system retrieves the sale information from the database and announces in voice, "Apples and bananas are on sale today."

[0802] Check your shopping list:

[0803] When the user says, "Check the shopping list," the system references the shopping list on the server and announces in voice, "The list includes milk, bread, and apples."

[0804] Prompt Sentence Examples

[0805] Below are some example prompts to apply to generative AI models:

[0806] I would like to develop a shopping support app that uses speech recognition and natural language processing technology to support the daily lives of elderly people. Shopping support includes product search, obtaining special offers, and checking the shopping list. Examples of voice commands include "Where is the milk?", "Tell me today's specials," and "Check my shopping list." Please provide specific program examples and explanations for analyzing the flow.

[0807] Through these elements, the invention allows seniors to efficiently search for products, obtain special offers, and manage their shopping lists in brick-and-mortar stores.

[0808] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0809] Step 1:

[0810] The user provides voice input. For example, the user might say, "Where is the milk?" in a physical store. As input, the user's voice data is collected through a microphone. This voice data is sent to the next processing step.

[0811] Step 2:

[0812] The device records the voice and converts the voice data into a digital format. Specifically, it uses the speech_recognition library to convert the voice to text data. The input of this step is the user's voice data, and the output is the converted text data.

[0813] Step 3:

[0814] The server receives the text data sent from the terminal. It analyzes the text data to understand the user's intent (in this case, a product search). Based on the analysis, it extracts the product name "Milk." The input for this step is the user's text data, and the output is the product name as a result of the analysis.

[0815] Step 4:

[0816] Based on the product name extracted by the server, the database is accessed to obtain the product's location information. For example, location information for the product name "Milk" is obtained from the database. The input to this step is the analysis result (product name), and the output is product location information.

[0817] Step 5:

[0818] The server generates voice data based on the location information it has acquired. Using the pyttsx3 library, it generates voice data saying, "Milk is in the dairy section." The input for this step is the product location information, and the output is the generated voice data.

[0819] Step 6:

[0820] The terminal provides the user with the voice data received from the server. Specifically, the generated voice data is played through the terminal's speaker, and the user is notified that "Milk is in the dairy section." The input of this step is the voice data from the server, and the output is the notification to the user.

[0821] Step 7:

[0822] The user issues a command, for example, "Tell me what's on sale today." The process begins again at step 1, and the required information is retrieved and provided to the user.

[0823] 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.

[0824] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology, natural language processing technology, and emotion recognition technology. Specific embodiments for carrying out the present invention will be described below.

[0825] System Overview

[0826] This system consists of three main components: the user (elderly person), the device, and the server. The user can use voice commands to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the device and server. The system also incorporates an emotion engine that recognizes the user's emotions and optimizes the information and advice it provides.

[0827] Program processing flow

[0828] User operations

[0829] The user issues instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine tomorrow morning at 9:00." Furthermore, the emotion engine recognizes the user's emotions in real time.

[0830] Terminal handling

[0831] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[0832] Server Processing

[0833] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. Furthermore, the emotion engine analyzes the user's emotions, and the results are reflected in other processes.

[0834] Next, specific processing is performed based on the analysis results, such as:

[0835] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at specified times. The content and timing of these reminders may be adjusted based on the user's emotions as recognized by an emotion engine.

[0836] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in a database. The details of the advice and the way it is presented may be changed based on the user's emotions recognized by the emotion engine.

[0837] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavioral data. The summarized information is provided in a format appropriate for the user. The user's emotions, recognized by the emotion engine, also influence the selection of event information to be provided.

[0838] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[0839] Specific examples

[0840] Medication reminder settings

[0841] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[0842] 2. Device: Records audio and sends the audio data to the server.

[0843] 3. Server: Converts voice to text and processes reminder settings. If the emotion engine analyzes the user's tone of voice and determines that the user is feeling nervous or anxious, it adjusts the notification to be softer and include encouraging words. It generates voice data saying "Reminder set" and sends it to the device.

[0844] 4. Device: Notify the user that the reminder has been set.

[0845] Obtaining local event information

[0846] 1. User: "Tell me about events near me."

[0847] 2. Device: Records audio and sends the audio data to the server.

[0848] 3. Server: Converts speech into text and collects local event information from the Internet. If the emotion engine determines that the user is bored, it prioritizes more active events, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[0849] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[0850] This series of processes allows elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving their quality of life.The introduction of an emotion engine allows the system to respond flexibly to the user's emotional state, making it possible to provide more personalized support.

[0851] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice, and the system also provides optimal advice and information according to the user's emotions.

[0852] The processing flow will be explained below.

[0853] Specific flow of program processing (including emotion engine)

[0854] Scenario: User wants to set a medication reminder

[0855] Step 1:

[0856] User: "Set a reminder to take my medicine tomorrow morning at 9am" says to the device.

[0857] Step 2:

[0858] Device: Records the user's voice input.

[0859] Activate the microphone and record audio for a set period of time.

[0860] Step 3:

[0861] Terminal: Converts recorded audio data into digital format.

[0862] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0863] Step 4:

[0864] Device: Sends audio data to the server.

[0865] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[0866] Step 5:

[0867] Server: Receives the audio data.

[0868] Receives the transmitted audio data and stores it in storage.

[0869] Step 6:

[0870] Server: Converts voice data into text using a speech recognition AI model.

[0871] A voice recognition engine is started and voice data is converted into text data.

[0872] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[0873] Step 7:

[0874] Server: Analyzes text data using a natural language processing model to understand user intent.

[0875] Use text analysis tools to extract the intent behind reminder settings.

[0876] Step 8:

[0877] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[0878] The emotion engine assesses the user's emotional state (e.g., tension, anxiety, joy) from the voice data.

[0879] Step 9:

[0880] Server: Generates reminder setting data based on the analysis results.

[0881] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[0882] Adjust the method and content of notifications based on the results of the emotion engine.

[0883] Step 10:

[0884] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[0885] Generates the output text "Reminder set".

[0886] The text is sent to a speech synthesis engine to generate speech data.

[0887] Step 11:

[0888] Server: Sends audio data to the device.

[0889] The generated audio data is returned to the terminal as an HTTP response.

[0890] Step 12:

[0891] Terminal: Plays back the audio data received from the server.

[0892] The received audio data is saved in the internal storage.

[0893] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[0894] Scenario: A user wants to receive information about local events

[0895] Step 1:

[0896] User: "Tell me if there are any events nearby" says to the device.

[0897] Step 2:

[0898] Device: Records the user's voice input.

[0899] Activate the microphone and record audio for a set period of time.

[0900] Step 3:

[0901] Terminal: Converts recorded audio data into digital format.

[0902] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[0903] Step 4:

[0904] Device: Sends audio data to the server.

[0905] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[0906] Step 5:

[0907] Server: Receives the audio data and stores it in storage.

[0908] Step 6:

[0909] Server: Converts voice data into text using a speech recognition AI model.

[0910] A voice recognition engine is started and voice data is converted into text data.

[0911] The output text is "Tell me if there are any events nearby."

[0912] Step 7:

[0913] Server: Analyzes text data using a natural language processing model to understand user intent.

[0914] Use text analysis tools to extract the intent behind providing event information.

[0915] Step 8:

[0916] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[0917] The emotion engine assesses the user's emotional state (e.g., boredom, interest) from the voice data.

[0918] Step 9:

[0919] Server: Collects and summarizes local event information from the Internet.

[0920] Use external APIs to gather nearby event information.

[0921] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[0922] The selection and presentation of event information is adjusted based on the results of the emotion engine.

[0923] Step 10:

[0924] Server: Converts the summarized event information into text and sends it to the speech synthesis engine.

[0925] Produces the output text "There is a flea market in the park this weekend."

[0926] The text is sent to a speech synthesis engine to generate speech data.

[0927] Step 11:

[0928] Server: Sends audio data to the device.

[0929] The generated audio data is returned to the terminal as an HTTP response.

[0930] Step 12:

[0931] Terminal: Plays back the audio data received from the server.

[0932] The received audio data is saved in the internal storage.

[0933] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[0934] This specific processing flow makes it easy for elderly people to manage tasks in their daily lives, manage their health, and participate in local events, and by using an emotion engine, it is possible to provide services that take into consideration the user's emotions.

[0935] Example 2

[0936] 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."

[0937] There is a need for a system that allows elderly people to easily manage their daily tasks, health management, and obtain information about local events using voice commands. However, existing systems lack the ability to recognize and process voice commands, and lack the ability to recognize the user's emotions and optimize responses. Therefore, a user-friendly system that can be used naturally by elderly people is needed.

[0938] 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.

[0939] In this invention, the server includes means for recording voice data and converting it into a digital format, means for transmitting the converted digital voice data to the server, means for converting the voice data into text data using natural language processing, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analyzed text data and the results of the user's emotion analysis, means for saving the generated task management information in a database, means for generating and transmitting a notification at a specified time, means for analyzing the user's emotion using an emotion recognition engine, means for generating information as voice data using a voice synthesis engine, means for transmitting the generated voice data to a terminal, and means for playing the voice data on the terminal and providing it to the user. This enables elderly people to easily manage their daily tasks, health management, and local event information by voice, and provides optimal support according to the user's emotions.

[0940] "Voice data" refers to information input by voice by a user that has been recorded and stored in digital form.

[0941] "Digital format" refers to the conversion of analog audio into a digital signal that allows a computer to process the audio data.

[0942] A "server" is a central system that processes and stores data over a network.

[0943] "Natural language processing" is a technology that uses computers to process and understand human language.

[0944] "Text data" refers to text information converted from voice data using natural language processing technology.

[0945] "User intent" refers to the purpose or desire that the user is trying to convey through voice instructions.

[0946] "Task management information" is information about tasks to be managed, such as the user's daily schedule and reminders.

[0947] A "database" is a system for efficiently storing and accessing data.

[0948] A "notification" is a message or sound generated for the purpose of informing the user of pre-defined information.

[0949] An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice and facial expressions.

[0950] A "speech synthesis engine" is a technology that converts text data into voice data.

[0951] A "terminal" is a device for transmitting and receiving data between a user and a system.

[0952] "Recommended local event information" is information about local events selected based on the user's interests and behavioral data.

[0953] "Health management information" is information that indicates the user's health condition and health-related data.

[0954] "Advice" refers to advice or guidance provided to the user based on the analysis results.

[0955] "Interests and search history" refers to the user's past interests and search history.

[0956] This invention is a system that uses speech recognition, natural language processing, and emotion recognition technologies to support the daily lives of elderly people. The system consists of three main components: a user, a terminal, and a server.

[0957] System Overview

[0958] Users can issue voice commands to the system to manage daily tasks, manage their health, and obtain information about local events. The device records the user's voice and sends it to the server. The server analyzes the voice data, understands the user's intentions and emotions, and generates appropriate information and notifications. The generated information is converted into voice data and notified to the user via the device.

[0959] Hardware and software used

[0960] Hardware:

[0961] Devices: smart speakers, smartphones, etc.

[0962] Server: A server with high-performance processing power

[0963] software:

[0964] Speech recognition engine: converts voice data into text data (e.g., Google Speech-to-Text API)

[0965] Natural language processing engine: Analyzes text data and understands user intent (e.g., Google Cloud Natural Language)

[0966] Emotion recognition engine: Analyzes the user's emotional state (e.g., Affectiva SDK)

[0967] Speech synthesis engine: converts text data into speech data (e.g., Amazon Polly)

[0968] Program processing flow

[0969] The program in this system follows these steps:

[0970] 1. User Instructions

[0971] The user gives instructions to the system through voice. For example, to set a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[0972] 2. Device Operation

[0973] The device records the user's voice, converts the voice data into a digital format, and then transmits it to the server.

[0974] 3. Server Processing

[0975] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. At the same time, the emotion recognition engine analyzes the user's emotions, and the results are reflected in other processes.

[0976] Specific examples:

[0977] Setting a medication reminder: When a user says, "Set a reminder to take my medicine tomorrow morning at 9:00," the server converts this instruction into text data and sets the reminder. If the emotion recognition engine detects that the user is nervous or anxious, it softens the notification and generates a response such as, "The reminder has been set."

[0978] Obtaining local event information: When a user says, "Tell me about any events happening nearby," the server converts this instruction into text data and analyzes it using a natural language processing engine. If the emotion recognition engine detects boredom, it prioritizes active event information and generates a response such as, "There's a flea market in the park this weekend."

[0979] 4. Audio data generation and notification

[0980] The generated text and information are converted into voice data by a speech synthesis engine, and this voice data is then sent back to the device, which then plays the received voice data and provides the user with appropriate information and notifications.

[0981] Specific examples

[0982] The following example prompts show how the system works:

[0983] Example prompt sentence:

[0984] "Set a reminder to take my medicine tomorrow morning at 9am"

[0985] "Tell me if there's any events happening nearby."

[0986] As described above, this invention enables elderly people to easily manage and obtain information about daily tasks, health management, and local events using their voice. In particular, by using an emotion recognition engine, it is possible to provide optimal support according to the user's emotional state.

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Program processing step details

[0989] Step 1: User voice command

[0990] Subject: User

[0991] Input: Voice command

[0992] Output: Recorded audio data

[0993] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning." This gives the system a starting point for understanding the user's needs.

[0994] Step 2: Record and transmit audio data using your device

[0995] Subject: Device

[0996] Input: User's voice command

[0997] Output: Digital audio data

[0998] The device records the user's voice commands and converts the voice data into digital format. This digital voice data is sent to the server, where the voice is recorded through a microphone, converted into a digital format such as PCM, and sent to the server via IP communication.

[0999] Step 3: The server receives and converts the audio data

[1000] Subject: Server

[1001] Input: Digital audio data

[1002] Output: Text data

[1003] The server receives the voice data sent from the device and converts it into text data using a voice recognition engine. For example, it converts the voice data into text data using the Google Speech-to-Text API. This allows the server to handle voice instructions as text information.

[1004] Step 4: Parsing the text data by the server

[1005] Subject: Server

[1006] Input: Text data

[1007] Output: Analysis results indicating the user's intent

[1008] The server uses a natural language processing engine to analyze the text data converted from the speech and understand the user's intent. Specifically, it uses Google Cloud Natural Language to analyze the text and understand the user's instructions. During this process, it performs morphological and contextual analysis of the text data to identify the intent and required actions.

[1009] Step 5: Emotion recognition and response generation by the server

[1010] Subject: Server

[1011] Input: Text data and user voice tone

[1012] Output: Response text and response voice data to the user

[1013] The server uses an emotion engine to analyze the user's emotions and generate responses based on the results. For example, it uses the Affectiva SDK to detect emotions from voice tones and analyzes them together with text data. It then generates specific response text, such as setting medication reminders or providing information about local events, and converts it into voice data using Amazon Polly if necessary.

[1014] Step 6: Sending a response from the server to the device

[1015] Subject: Server

[1016] Input: Voice data and text response

[1017] Output: Data sent to the terminal

[1018] The server sends the generated voice and text data to the terminal using IP communications, in a format that the terminal can receive (e.g., WAV format voice data).

[1019] Step 7: Device notifies the user

[1020] Subject: Device

[1021] Input: Audio and text data sent from the server

[1022] Output: A spoken audio notification

[1023] The device receives the audio data sent from the server and plays it through the speaker, providing appropriate feedback and notifications to the user. The device decodes the audio data, converts it into an analog signal that can be heard by the user, and plays it through the speaker.

[1024] Through these specific processing steps, the present invention enables elderly people to efficiently and user-friendly perform daily tasks, manage their health, and obtain information about local events using their voice. In addition, by using an emotion recognition engine, the system can provide optimal responses tailored to the user's emotional state.

[1025] (Application example 2)

[1026] 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."

[1027] There is a need for methods to help seniors maintain independence in their daily lives and make their shopping experience in brick-and-mortar stores smooth and stress-free. However, seniors are often unfamiliar with voice input and digital interfaces, and are sensitive to emotional changes, which can make shopping in brick-and-mortar stores difficult. Therefore, a system that is easy for seniors to use and allows them to receive information and take action with confidence is needed.

[1028] The identification process by the identification 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 data into text data using natural language processing, means for analyzing the text data and understanding the user's intention, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, emotion recognition means for recognizing the user's emotions in real time, and means for adjusting the information to be provided based on the emotion recognition results. This enables elderly people to easily manage their shopping experience in a physical store by voice and receive optimal information and advice tailored to their emotions in real time.

[1029] "Natural language processing" is a technology that converts voice data into text data and analyzes that text to understand its meaning and intent.

[1030] "Speech recognition" is a technology that converts a user's voice input into digital text data.

[1031] "Text data analysis" is the process of understanding the user's intent and extracting necessary information and tasks based on the converted text data.

[1032] "Generating task management information" refers to generating task management information such as reminders and schedules based on the analyzed user's intentions.

[1033] A "voice synthesis engine" is a technology that converts generated text data into voice data and provides it to the user.

[1034] "Emotion recognition" is a technology that detects and analyzes emotions from a user's voice, facial expressions, etc.

[1035] "Adjusting the information provided based on the emotion recognition results" refers to optimizing the content and presentation of the information and advice provided according to the detected emotion.

[1036] "Health management information" is data for monitoring the user's health condition and providing appropriate health advice.

[1037] "Local event information" is data for collecting and providing information on local events and activities based on user interests and behavioral data.

[1038] The system of this invention is designed to support the daily lives of elderly people, specifically supporting their shopping experience in brick-and-mortar stores. The main components of the system are a user (elderly person), a terminal (smartphone), and a server. The system utilizes technologies such as natural language processing, speech recognition, and emotion recognition to enable elderly people to comfortably shop in brick-and-mortar stores.

[1039] Hardware and software used

[1040] Hardware:

[1041] Smartphone

[1042] software:

[1043] Speech recognition engine (e.g. Google Speech-to-Text)

[1044] Natural language processing engine (e.g. Google Natural Language)

[1045] Emotion recognition engine (such as Microsoft Azure Emotion API)

[1046] Speech synthesis engine (such as Google Text-to-Speech)

[1047] System Operation Overview

[1048] 1. User operations

[1049] The user says to their smartphone, "Tell me where the milk is."

[1050] 2. Terminal Processing

[1051] The smartphone's microphone records the user's voice and converts the voice data into a digital format.

[1052] The audio data converted into a digital format is transmitted to a server.

[1053] 3. Server Processing

[1054] Speech recognition: The server uses a speech recognition engine such as Google Speech-to-Text to convert the voice data into text data.

[1055] Natural Language Processing: Use natural language processing engines such as Google Natural Language to analyze text data and understand user intent.

[1056] Emotion Recognition: Recognize user emotions in real time using emotion recognition engines such as the Microsoft Azure Emotion API.

[1057] Information acquisition and adjustment: Based on the text data and emotion recognition results, the location information of the relevant product is acquired from the database, and the information provided is adjusted according to the user's emotional state.

[1058] Speech synthesis: Using a speech synthesis engine such as Google Text-to-Speech, the tailored information is generated as audio data.

[1059] 4. Device notifications

[1060] The smartphone plays the generated voice data and notifies the user, "The milk is in the refrigerated section." If the user shows signs of anxiety, the notification message includes a reassuring phrase such as "You'll find it soon."

[1061] Specific examples

[1062] Prompt Sentence Examples

[1063] User: "Tell me where the milk is."

[1064] Application: "Milk is in the refrigerated section. You'll find it easily."

[1065] This system is designed to allow users to easily manage their shopping in physical stores using voice commands and receive optimal information and advice tailored to their emotions in real time, ultimately aiming to improve the quality of life for the elderly.

[1066] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1067] Step 1:

[1068] A user speaks to a smartphone saying, "Tell me where the milk is." The smartphone's microphone picks up the voice input. The input is raw voice data, and the output is also voice data.

[1069] Step 2:

[1070] The smartphone converts the captured audio data into a digital format. This conversion is performed by the smartphone's internal processing. The input is the captured raw audio data, and the output is digital audio data.

[1071] Step 3:

[1072] The smartphone sends the converted digital audio data to the server. The input is digital audio data, and the output is the audio data sent to the server.

[1073] Step 4:

[1074] The voice data received by the server is converted into text data using a voice recognition engine (such as Google Speech-to-Text). The input is the digital voice data received by the server, and the output is text data.

[1075] Step 5:

[1076] The server uses a natural language processing engine (such as Google Natural Language) to analyze the text data and understand the user's intent. The input is the text data generated by the speech recognition engine, and the output is the analysis result that includes the user's intent.

[1077] Step 6:

[1078] The server uses an emotion recognition engine (such as Microsoft Azure Emotion API) to recognize the user's emotions in real time during speech recognition. The input is voice or text data stored on the server, and the output is the analysis result regarding the user's emotional state.

[1079] Step 7:

[1080] The server retrieves the location information of the relevant product from the database based on the analysis results and adjusts the provided information based on the emotion recognition results. The input is the analysis results regarding the user's intention and emotional state, and the output is the adjusted notification information.

[1081] Step 8:

[1082] The server generates the adjusted notification information as audio data using a speech synthesis engine (such as Google Text-to-Speech). The input is the text data of the adjusted notification information, and the output is the audio data generated by the speech synthesis engine.

[1083] Step 9:

[1084] The server sends the generated voice data to the smartphone. The input is the voice data generated by the server, and the output is the voice data sent to the smartphone.

[1085] Step 10:

[1086] The smartphone plays the received voice data and notifies the user. For example, digital voice data is played to tell the user, "The milk is in the refrigerated section. You'll find it soon." The input is the voice data received by the smartphone, and the output is the voice notification to the user.

[1087] 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.

[1088] 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.

[1089] 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.

[1090] [Third embodiment]

[1091] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1092] 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.

[1093] 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).

[1094] 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.

[1095] 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.

[1096] 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).

[1097] 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. 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.

[1098] 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.

[1099] 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.

[1100] 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.

[1101] 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.

[1102] 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."

[1103] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology and natural language processing technology. Specific embodiments for carrying out the present invention will be described below.

[1104] System Overview

[1105] This system consists of three main components: the user (elderly person), the terminal, and the server. The user can use voice commands to the terminal to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the terminal and the server.

[1106] Program processing flow

[1107] User operations

[1108] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say to the device, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[1109] Terminal handling

[1110] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[1111] Server Processing

[1112] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent.

[1113] Next, specific processing is performed based on the analysis results, such as:

[1114] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at the specified time.

[1115] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in the database.

[1116] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavior data. The summarized information is then provided in a format appropriate for the user.

[1117] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[1118] Specific examples

[1119] Medication reminder settings

[1120] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[1121] 2. Device: Records audio and sends the audio data to the server.

[1122] 3. Server: Converts the voice to text and processes the reminder. Generates a voice message saying "Reminder set" and sends it to the device.

[1123] 4. Device: Notify the user that the reminder has been set.

[1124] Obtaining local event information

[1125] 1. User: "Tell me about events near me."

[1126] 2. Device: Records audio and sends the audio data to the server.

[1127] 3. Server: Converts speech to text and collects local event information from the Internet. It summarizes the collected information, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[1128] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[1129] This series of processes enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving the quality of life for the elderly.

[1130] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice.

[1131] The processing flow will be explained below.

[1132] Specific flow of program processing

[1133] Scenario: User wants to set a medication reminder

[1134] Step 1:

[1135] On the device: The user says, "Set a reminder to take my medicine tomorrow morning at 9 AM."

[1136] Activates the microphone and records the user's voice for a certain period of time.

[1137] Step 2:

[1138] Terminal: Converts recorded audio data into digital format.

[1139] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1140] Step 3:

[1141] Device: Sends audio data to the server.

[1142] Create an HTTP POST request to the server, sending the audio data as the payload.

[1143] Step 4:

[1144] Server: Receives the audio data.

[1145] Receives the transmitted audio data and stores it in storage.

[1146] Step 5:

[1147] Server: Converts voice data into text using a speech recognition AI model.

[1148] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[1149] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[1150] Step 6:

[1151] Server: Analyzes text data using a natural language processing model to understand user intent.

[1152] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (setting a reminder).

[1153] Step 7:

[1154] Server: Generates reminder setting data based on the analysis results.

[1155] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[1156] Step 8:

[1157] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[1158] Generates the output text "Reminder set".

[1159] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[1160] Step 9:

[1161] Server: Sends audio data to the device.

[1162] The generated audio data is returned to the terminal as an HTTP response.

[1163] Step 10:

[1164] Terminal: Plays back the audio data received from the server.

[1165] The received audio data is saved in the internal storage.

[1166] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[1167] Scenario: A user wants to receive information about local events

[1168] Step 1:

[1169] User: "Tell me if there are any events nearby" says to the device.

[1170] Step 2:

[1171] Device: Records the user's voice.

[1172] Activates the microphone and records the user's voice for a certain period of time.

[1173] Step 3:

[1174] Terminal: Converts recorded audio data into digital format.

[1175] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1176] Step 4:

[1177] Device: Sends audio data to the server.

[1178] Create an HTTP POST request to the server, sending the audio data as the payload.

[1179] Step 5:

[1180] Server: Receives the audio data.

[1181] Receives the transmitted audio data and stores it in storage.

[1182] Step 6:

[1183] Server: Converts voice data into text using a speech recognition AI model.

[1184] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[1185] The output text is "Tell me if there are any events nearby."

[1186] Step 7:

[1187] Server: Analyzes text data using a natural language processing model to understand user intent.

[1188] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (provide event information).

[1189] Step 8:

[1190] Server: Collects and summarizes local event information from the Internet.

[1191] Use external APIs (e.g. Eventbrite, Meetup) to gather information about nearby events.

[1192] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[1193] Step 9:

[1194] Server: Converts the collected event information into text and sends it to the speech synthesis engine.

[1195] Produces the output text "There is a flea market in the park this weekend."

[1196] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[1197] Step 10:

[1198] Server: Sends audio data to the device.

[1199] The generated audio data is returned to the terminal as an HTTP response.

[1200] Step 11:

[1201] Terminal: Plays back the audio data received from the server.

[1202] The received audio data is saved in the internal storage.

[1203] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[1204] Example 1

[1205] 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."

[1206] In the daily lives of elderly people, task management, health management, and keeping up with local event information can be complicated, and there is a need for efficient ways to do these tasks. These tasks become even more difficult when visual and operational functions decline. An integrated system is needed to manage these functions via voice and improve the quality of life for elderly people.

[1207] 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.

[1208] In this invention, the server includes means for recording a user's voice input and converting it into a digital format, means for transmitting the converted voice data to the server, means for converting the voice data into text data at the server, means for analyzing the text data to understand the user's intentions, means for setting reminders and schedules based on the analysis results, means for providing the set information as voice data, and means for notifying the user of the generated voice data, thereby enabling task management, health management, and the collection and understanding of local event information.

[1209] "User" refers to the person who uses this system, and is particularly targeted at elderly people.

[1210] "Voice input" refers to instructions or questions given by the user to the terminal by voice.

[1211] A "terminal" is a device used by a user to perform voice input, and includes digital devices equipped with a microphone and a speaker.

[1212] "Digital format" refers to a format in which an analog audio signal is converted into digital data.

[1213] "Server" refers to a computer system set up to process data sent from a terminal.

[1214] "Voice Data" means the digital form of a recorded user's voice or generated voice information.

[1215] "Text data" refers to character string data converted from voice data by a voice recognition engine.

[1216] "Speech recognition engine" means software or technology for converting voice data into text data.

[1217] A "natural language processing engine" refers to software or technology that analyzes text data to understand user intent.

[1218] A "reminder" is a notification that prompts a user to take a specific action at a specific time or date.

[1219] "Task management" refers to managing a user's schedule and reminders.

[1220] "Health management" refers to monitoring the user's health status and providing advice.

[1221] "Local event information" refers to information about events held in the area where the user lives.

[1222] "Speech synthesis engine" means software or technology for converting text data into speech data.

[1223] The system of the present invention is designed to support the daily lives of elderly people and primarily utilizes speech recognition and natural language processing technologies. The system consists of three main components: a user, a terminal, and a server. Specific embodiments for implementing the present invention are described below.

[1224] System Overview

[1225] Users can use voice commands to manage their daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications via the device and server. This system aims to improve the quality of life for the elderly.

[1226] Hardware and software used

[1227] Hardware: Smart devices with microphones, speakers, and processors (e.g., Amazon Echo, Google Home)

[1228] Speech recognition engine: Google Speech-to-Text API, Amazon Transcribe

[1229] Natural language processing engine: SpaCy, Google Cloud Natural Language API

[1230] Database: MySQL, PostgreSQL

[1231] Web scraping tools: Beautiful Soup, Scrapy

[1232] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[1233] Overview of program processing

[1234] 1. Voice Input

[1235] The user gives the system commands by voice, for example, "Set a reminder to take my medicine tomorrow morning at 9am."

[1236] 2. Audio recording and digital conversion

[1237] The device uses a microphone to record the user's voice and converts it into digital form using an ADC (analog-to-digital conversion) processor.

[1238] 3. Data Transmission

[1239] The device then transmits the converted digital audio data to a server via Wi-Fi or a mobile network.

[1240] 4. Speech Recognition and Text Conversion

[1241] The server passes the received voice data to a voice recognition engine, which converts the voice into text data.

[1242] 5. Natural Language Processing

[1243] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[1244] 6. Reminder Settings

[1245] The server stores the reminder information in a database and notifies you at the set time.

[1246] 7. Audio data generation

[1247] The server uses a speech synthesis engine to convert the text data into speech data.

[1248] 8. User Notices

[1249] The terminal reproduces the generated voice data and notifies the user.

[1250] Specific examples

[1251] Medication reminder settings

[1252] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[1253] 2. Device: Records audio and sends the audio data to the server.

[1254] 3. Server: Converts the voice to text, sets the reminder, saves the setting in the database, generates a voice message saying "Reminder set" and sends it to the device.

[1255] 4. Device: Notify the user that the reminder has been set.

[1256] Obtaining local event information

[1257] 1. User: "Tell me about events near me."

[1258] 2. Device: Records audio and sends the audio data to the server.

[1259] 3. Server: After converting the speech into text, it collects local event information from the Internet, summarizes the collected information, and generates speech data such as "There will be a flea market in the park this weekend," and sends it to the device.

[1260] 4. Device: Notify the user that "There's a flea market in the park this weekend."

[1261] Prompt Sentence Examples

[1262] Prompt: "Design a system to support the daily lives of seniors, using speech recognition and natural language processing to include features such as medication reminders and information about local events."

[1263] In this form, the system enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, thereby improving the quality of life for the elderly.

[1264] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1265] Step 1:

[1266] The user issues commands to the system by voice.

[1267] Specifically, the user says, "Set a reminder to take my medicine at 9:00 tomorrow morning." This is the input, and is recorded in voice format into the device's microphone.

[1268] Step 2:

[1269] The device records the input audio and converts it into a digital format.

[1270] Specifically, the device's built-in microphone records audio, and the ADC processor converts the analog audio signal into digital data. The input is audio data, and the output is digital audio data.

[1271] Step 3:

[1272] The terminal transmits the converted digital audio data to the server.

[1273] Specifically, the device sends an HTTP POST request to the server over Wi-Fi or a mobile network. The input is digital audio data, and the output is transmission to the server.

[1274] Step 4:

[1275] The server passes the received voice data to a voice recognition engine and converts it into text data.

[1276] Specifically, the server calls a speech recognition engine and converts the voice data into text format. The input is digital voice data, and the output is text data.

[1277] Step 5:

[1278] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[1279] Specifically, the server uses a natural language processing engine to analyze text data and extract intent. The input is text data, and the output is the analysis result.

[1280] Step 6:

[1281] The server sets reminders based on the analysis results.

[1282] Specifically, the server accesses the database and registers the reminder information, such as "Take medicine at 9:00 AM on April 2, 2024." The input is the analysis result, and the output is registration in the database.

[1283] Step 7:

[1284] The server converts the set reminder information into audio data.

[1285] Specifically, the server invokes a speech synthesis engine and converts the text notification content into voice data. Voice data saying "Reminder set" is generated. The input is the reminder text information, and the output is voice data.

[1286] Step 8:

[1287] The server transmits the generated voice data to the terminal.

[1288] Specifically, the server sends audio data to the device using an HTTP POST request. The input is audio data, and the output is transmission to the device.

[1289] Step 9:

[1290] The terminal plays back the received audio data and notifies the user.

[1291] Specifically, the device uses the built-in speaker to play a voice message saying, "Reminder set." The input is voice data, and the output is a voice notification to the user.

[1292] The above are the specific processing steps of the system program, which allow users to set reminders and receive notifications using natural voice commands.

[1293] (Application example 1)

[1294] 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."

[1295] When seniors shop in brick-and-mortar stores, they face the problem of having to spend a lot of time and effort. Product searches and obtaining special offers are particularly burdensome for seniors. Furthermore, seniors often have limited means to effectively manage and check their shopping lists, which can lead to forgetting to buy something or making unnecessary purchases. These situations are contributing to a decline in the quality of life for seniors.

[1296] 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.

[1297] In this invention, the server includes means for converting voice data into text data using voice recognition technology, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, means for accessing a database for obtaining product location information and special offer information in a physical store, and means for analyzing the product location information and special offer information and providing the user with voice data. This enables elderly people to efficiently search for products in physical stores, obtain special offer information, and manage their shopping lists.

[1298] "Voice recognition technology" is a technology that analyzes voice data and converts it into text data.

[1299] "Text data" refers to data of character information converted using voice recognition technology.

[1300] A "voice synthesis engine" is a function for analyzing text data and generating voice data.

[1301] "Task management information" is information for managing tasks in a user's daily life, and this information becomes reminders and notifications provided to the user.

[1302] A "database" is an information management system that stores information such as product location information and special offer information.

[1303] "Product location information" is information that indicates where a particular product is displayed.

[1304] "Special offer information" is information that indicates discounts and sales on specific products in stores.

[1305] "Voice data" refers to information that is generated by a voice synthesis engine and is provided to the user audibly.

[1306] This invention is a system for supporting elderly people in efficiently shopping in brick-and-mortar stores. Its main functions are to provide product location information, special offers, and shopping list management using speech recognition and natural language processing technologies. This system is composed of multiple components, including speech recognition technology, a speech synthesis engine, and database access functionality.

[1307] System Overview

[1308] This system consists of three main components: the user (elderly person), the device (smartphone, smart glasses, head-mounted display, etc.), and the server. The user can give instructions to the system through voice and receive necessary information and notifications via the device and server.

[1309] Hardware and Software Configuration

[1310] Hardware:

[1311] Smartphone (iOS or Android)

[1312] Smart glasses (with audio input / output functions)

[1313] Head-mounted display (with audio input / output function)

[1314] Microphone (for voice input)

[1315] Speaker (for audio output)

[1316] software:

[1317] speech_recognition: A library for speech recognition

[1318] pyttsx3: A library for audio output

[1319] requests: A library for sending API requests

[1320] Data processing and calculation

[1321] 1. Voice input:

[1322] The user provides voice input, the audio is recorded with a microphone, and the recorded audio is converted into text data using the speech_recognition library.

[1323] 2. Text data analysis and processing:

[1324] The server analyzes the text data obtained by the recorder, extracts requests for product names and special offers, requests the necessary information from the database, and outputs the obtained data in a specific format.

[1325] 3. Audio output:

[1326] Using the pyttsx3 library, the acquired information is generated as audio data and notified to the elderly.

[1327] Specific examples

[1328] Product Search:

[1329] When a user asks, "Where is the milk?", the system retrieves the product's location information from the database and responds by voice, "The milk is in the dairy section."

[1330] Get special offers:

[1331] When a user says, "Tell me what's on sale today," the system retrieves the sale information from the database and announces in voice, "Apples and bananas are on sale today."

[1332] Check your shopping list:

[1333] When the user says, "Check the shopping list," the system references the shopping list on the server and announces in voice, "The list includes milk, bread, and apples."

[1334] Prompt Sentence Examples

[1335] Below are some example prompts to apply to generative AI models:

[1336] I would like to develop a shopping support app that uses speech recognition and natural language processing technology to support the daily lives of elderly people. Shopping support includes product search, obtaining special offers, and checking the shopping list. Examples of voice commands include "Where is the milk?", "Tell me today's specials," and "Check my shopping list." Please provide specific program examples and explanations for analyzing the flow.

[1337] Through these elements, the invention allows seniors to efficiently search for products, obtain special offers, and manage their shopping lists in brick-and-mortar stores.

[1338] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1339] Step 1:

[1340] The user provides voice input. For example, the user might say, "Where is the milk?" in a physical store. As input, the user's voice data is collected through a microphone. This voice data is sent to the next processing step.

[1341] Step 2:

[1342] The device records the voice and converts the voice data into a digital format. Specifically, it uses the speech_recognition library to convert the voice to text data. The input of this step is the user's voice data, and the output is the converted text data.

[1343] Step 3:

[1344] The server receives the text data sent from the terminal. It analyzes the text data to understand the user's intent (in this case, a product search). Based on the analysis, it extracts the product name "Milk." The input for this step is the user's text data, and the output is the product name as a result of the analysis.

[1345] Step 4:

[1346] Based on the product name extracted by the server, the database is accessed to obtain the product's location information. For example, location information for the product name "Milk" is obtained from the database. The input to this step is the analysis result (product name), and the output is product location information.

[1347] Step 5:

[1348] The server generates voice data based on the location information it has acquired. Using the pyttsx3 library, it generates voice data saying, "Milk is in the dairy section." The input for this step is the product location information, and the output is the generated voice data.

[1349] Step 6:

[1350] The terminal provides the user with the voice data received from the server. Specifically, the generated voice data is played through the terminal's speaker, and the user is notified that "Milk is in the dairy section." The input of this step is the voice data from the server, and the output is the notification to the user.

[1351] Step 7:

[1352] The user issues a command, for example, "Tell me what's on sale today." The process begins again at step 1, and the required information is retrieved and provided to the user.

[1353] 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.

[1354] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology, natural language processing technology, and emotion recognition technology. Specific embodiments for carrying out the present invention will be described below.

[1355] System Overview

[1356] This system consists of three main components: the user (elderly person), the device, and the server. The user can use voice commands to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the device and server. The system also incorporates an emotion engine that recognizes the user's emotions and optimizes the information and advice it provides.

[1357] Program processing flow

[1358] User operations

[1359] The user issues instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine tomorrow morning at 9:00." Furthermore, the emotion engine recognizes the user's emotions in real time.

[1360] Terminal handling

[1361] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[1362] Server Processing

[1363] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. Furthermore, the emotion engine analyzes the user's emotions, and the results are reflected in other processes.

[1364] Next, specific processing is performed based on the analysis results, such as:

[1365] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at specified times. The content and timing of these reminders may be adjusted based on the user's emotions as recognized by an emotion engine.

[1366] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in a database. The details of the advice and the way it is presented may be changed based on the user's emotions recognized by the emotion engine.

[1367] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavioral data. The summarized information is provided in a format appropriate for the user. The user's emotions, recognized by the emotion engine, also influence the selection of event information to be provided.

[1368] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[1369] Specific examples

[1370] Medication reminder settings

[1371] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[1372] 2. Device: Records audio and sends the audio data to the server.

[1373] 3. Server: Converts voice to text and processes reminder settings. If the emotion engine analyzes the user's tone of voice and determines that the user is feeling nervous or anxious, it adjusts the notification to be softer and include encouraging words. It generates voice data saying "Reminder set" and sends it to the device.

[1374] 4. Device: Notify the user that the reminder has been set.

[1375] Obtaining local event information

[1376] 1. User: "Tell me about events near me."

[1377] 2. Device: Records audio and sends the audio data to the server.

[1378] 3. Server: Converts speech into text and collects local event information from the Internet. If the emotion engine determines that the user is bored, it prioritizes more active events, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[1379] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[1380] This series of processes allows elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving their quality of life.The introduction of an emotion engine allows the system to respond flexibly to the user's emotional state, making it possible to provide more personalized support.

[1381] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice, and the system also provides optimal advice and information according to the user's emotions.

[1382] The processing flow will be explained below.

[1383] Specific flow of program processing (including emotion engine)

[1384] Scenario: User wants to set a medication reminder

[1385] Step 1:

[1386] User: "Set a reminder to take my medicine tomorrow morning at 9am" says to the device.

[1387] Step 2:

[1388] Device: Records the user's voice input.

[1389] Activate the microphone and record audio for a set period of time.

[1390] Step 3:

[1391] Terminal: Converts recorded audio data into digital format.

[1392] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1393] Step 4:

[1394] Device: Sends audio data to the server.

[1395] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[1396] Step 5:

[1397] Server: Receives the audio data.

[1398] Receives the transmitted audio data and stores it in storage.

[1399] Step 6:

[1400] Server: Converts voice data into text using a speech recognition AI model.

[1401] A voice recognition engine is started and voice data is converted into text data.

[1402] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[1403] Step 7:

[1404] Server: Analyzes text data using a natural language processing model to understand user intent.

[1405] Use text analysis tools to extract the intent behind reminder settings.

[1406] Step 8:

[1407] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[1408] The emotion engine assesses the user's emotional state (e.g., tension, anxiety, joy) from the voice data.

[1409] Step 9:

[1410] Server: Generates reminder setting data based on the analysis results.

[1411] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[1412] Adjust the method and content of notifications based on the results of the emotion engine.

[1413] Step 10:

[1414] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[1415] Generates the output text "Reminder set".

[1416] The text is sent to a speech synthesis engine to generate speech data.

[1417] Step 11:

[1418] Server: Sends audio data to the device.

[1419] The generated audio data is returned to the terminal as an HTTP response.

[1420] Step 12:

[1421] Terminal: Plays back the audio data received from the server.

[1422] The received audio data is saved in the internal storage.

[1423] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[1424] Scenario: A user wants to receive information about local events

[1425] Step 1:

[1426] User: "Tell me if there are any events nearby" says to the device.

[1427] Step 2:

[1428] Device: Records the user's voice input.

[1429] Activate the microphone and record audio for a set period of time.

[1430] Step 3:

[1431] Terminal: Converts recorded audio data into digital format.

[1432] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1433] Step 4:

[1434] Device: Sends audio data to the server.

[1435] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[1436] Step 5:

[1437] Server: Receives the audio data and stores it in storage.

[1438] Step 6:

[1439] Server: Converts voice data into text using a speech recognition AI model.

[1440] A voice recognition engine is started and voice data is converted into text data.

[1441] The output text is "Tell me if there are any events nearby."

[1442] Step 7:

[1443] Server: Analyzes text data using a natural language processing model to understand user intent.

[1444] Use text analysis tools to extract the intent behind providing event information.

[1445] Step 8:

[1446] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[1447] The emotion engine assesses the user's emotional state (e.g., boredom, interest) from the voice data.

[1448] Step 9:

[1449] Server: Collects and summarizes local event information from the Internet.

[1450] Use external APIs to gather nearby event information.

[1451] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[1452] The selection and presentation of event information is adjusted based on the results of the emotion engine.

[1453] Step 10:

[1454] Server: Converts the summarized event information into text and sends it to the speech synthesis engine.

[1455] Produces the output text "There is a flea market in the park this weekend."

[1456] The text is sent to a speech synthesis engine to generate speech data.

[1457] Step 11:

[1458] Server: Sends audio data to the device.

[1459] The generated audio data is returned to the terminal as an HTTP response.

[1460] Step 12:

[1461] Terminal: Plays back the audio data received from the server.

[1462] The received audio data is saved in the internal storage.

[1463] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[1464] This specific processing flow makes it easy for elderly people to manage tasks in their daily lives, manage their health, and participate in local events, and by using an emotion engine, it is possible to provide services that take into consideration the user's emotions.

[1465] Example 2

[1466] 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."

[1467] There is a need for a system that allows elderly people to easily manage their daily tasks, health management, and obtain information about local events using voice commands. However, existing systems lack the ability to recognize and process voice commands, and lack the ability to recognize the user's emotions and optimize responses. Therefore, a user-friendly system that can be used naturally by elderly people is needed.

[1468] 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.

[1469] In this invention, the server includes means for recording voice data and converting it into a digital format, means for transmitting the converted digital voice data to the server, means for converting the voice data into text data using natural language processing, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analyzed text data and the results of the user's emotion analysis, means for saving the generated task management information in a database, means for generating and transmitting a notification at a specified time, means for analyzing the user's emotion using an emotion recognition engine, means for generating information as voice data using a voice synthesis engine, means for transmitting the generated voice data to a terminal, and means for playing the voice data on the terminal and providing it to the user. This enables elderly people to easily manage their daily tasks, health management, and local event information by voice, and provides optimal support according to the user's emotions.

[1470] "Voice data" refers to information input by voice by a user that has been recorded and stored in digital form.

[1471] "Digital format" refers to the conversion of analog audio into a digital signal that allows a computer to process the audio data.

[1472] A "server" is a central system that processes and stores data over a network.

[1473] "Natural language processing" is a technology that uses computers to process and understand human language.

[1474] "Text data" refers to text information converted from voice data using natural language processing technology.

[1475] "User intent" refers to the purpose or desire that the user is trying to convey through voice instructions.

[1476] "Task management information" is information about tasks to be managed, such as the user's daily schedule and reminders.

[1477] A "database" is a system for efficiently storing and accessing data.

[1478] A "notification" is a message or sound generated for the purpose of informing the user of pre-defined information.

[1479] An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice and facial expressions.

[1480] A "speech synthesis engine" is a technology that converts text data into voice data.

[1481] A "terminal" is a device for transmitting and receiving data between a user and a system.

[1482] "Recommended local event information" is information about local events selected based on the user's interests and behavioral data.

[1483] "Health management information" is information that indicates the user's health condition and health-related data.

[1484] "Advice" refers to advice or guidance provided to the user based on the analysis results.

[1485] "Interests and search history" refers to the user's past interests and search history.

[1486] This invention is a system that uses speech recognition, natural language processing, and emotion recognition technologies to support the daily lives of elderly people. The system consists of three main components: a user, a terminal, and a server.

[1487] System Overview

[1488] Users can issue voice commands to the system to manage daily tasks, manage their health, and obtain information about local events. The device records the user's voice and sends it to the server. The server analyzes the voice data, understands the user's intentions and emotions, and generates appropriate information and notifications. The generated information is converted into voice data and notified to the user via the device.

[1489] Hardware and software used

[1490] Hardware:

[1491] Devices: smart speakers, smartphones, etc.

[1492] Server: A server with high-performance processing power

[1493] software:

[1494] Speech recognition engine: converts voice data into text data (e.g., Google Speech-to-Text API)

[1495] Natural language processing engine: Analyzes text data and understands user intent (e.g., Google Cloud Natural Language)

[1496] Emotion recognition engine: Analyzes the user's emotional state (e.g., Affectiva SDK)

[1497] Speech synthesis engine: converts text data into speech data (e.g., Amazon Polly)

[1498] Program processing flow

[1499] The program in this system follows these steps:

[1500] 1. User Instructions

[1501] The user gives instructions to the system through voice. For example, to set a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[1502] 2. Device Operation

[1503] The device records the user's voice, converts the voice data into a digital format, and then transmits it to the server.

[1504] 3. Server Processing

[1505] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. At the same time, the emotion recognition engine analyzes the user's emotions, and the results are reflected in other processes.

[1506] Specific examples:

[1507] Setting a medication reminder: When a user says, "Set a reminder to take my medicine tomorrow morning at 9:00," the server converts this instruction into text data and sets the reminder. If the emotion recognition engine detects that the user is nervous or anxious, it softens the notification and generates a response such as, "The reminder has been set."

[1508] Obtaining local event information: When a user says, "Tell me about any events happening nearby," the server converts this instruction into text data and analyzes it using a natural language processing engine. If the emotion recognition engine detects boredom, it prioritizes active event information and generates a response such as, "There's a flea market in the park this weekend."

[1509] 4. Audio data generation and notification

[1510] The generated text and information are converted into voice data by a speech synthesis engine, and this voice data is then sent back to the device, which then plays the received voice data and provides the user with appropriate information and notifications.

[1511] Specific examples

[1512] The following example prompts show how the system works:

[1513] Example prompt sentence:

[1514] "Set a reminder to take my medicine tomorrow morning at 9am"

[1515] "Tell me if there's any events happening nearby."

[1516] As described above, this invention enables elderly people to easily manage and obtain information about daily tasks, health management, and local events using their voice. In particular, by using an emotion recognition engine, it is possible to provide optimal support according to the user's emotional state.

[1517] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1518] Program processing step details

[1519] Step 1: User voice command

[1520] Subject: User

[1521] Input: Voice command

[1522] Output: Recorded audio data

[1523] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning." This gives the system a starting point for understanding the user's needs.

[1524] Step 2: Record and transmit audio data using your device

[1525] Subject: Device

[1526] Input: User's voice command

[1527] Output: Digital audio data

[1528] The device records the user's voice commands and converts the voice data into digital format. This digital voice data is sent to the server, where the voice is recorded through a microphone, converted into a digital format such as PCM, and sent to the server via IP communication.

[1529] Step 3: The server receives and converts the audio data

[1530] Subject: Server

[1531] Input: Digital audio data

[1532] Output: Text data

[1533] The server receives the voice data sent from the device and converts it into text data using a voice recognition engine. For example, it converts the voice data into text data using the Google Speech-to-Text API. This allows the server to handle voice instructions as text information.

[1534] Step 4: Parsing the text data by the server

[1535] Subject: Server

[1536] Input: Text data

[1537] Output: Analysis results indicating the user's intent

[1538] The server uses a natural language processing engine to analyze the text data converted from the speech and understand the user's intent. Specifically, it uses Google Cloud Natural Language to analyze the text and understand the user's instructions. During this process, it performs morphological and contextual analysis of the text data to identify the intent and required actions.

[1539] Step 5: Emotion recognition and response generation by the server

[1540] Subject: Server

[1541] Input: Text data and user voice tone

[1542] Output: Response text and response voice data to the user

[1543] The server uses an emotion engine to analyze the user's emotions and generate responses based on the results. For example, it uses the Affectiva SDK to detect emotions from voice tones and analyzes them together with text data. It then generates specific response text, such as setting medication reminders or providing information about local events, and converts it into voice data using Amazon Polly if necessary.

[1544] Step 6: Sending a response from the server to the device

[1545] Subject: Server

[1546] Input: Voice data and text response

[1547] Output: Data sent to the terminal

[1548] The server sends the generated voice and text data to the terminal using IP communications, in a format that the terminal can receive (e.g., WAV format voice data).

[1549] Step 7: Device notifies the user

[1550] Subject: Device

[1551] Input: Audio and text data sent from the server

[1552] Output: A spoken audio notification

[1553] The device receives the audio data sent from the server and plays it through the speaker, providing appropriate feedback and notifications to the user. The device decodes the audio data, converts it into an analog signal that can be heard by the user, and plays it through the speaker.

[1554] Through these specific processing steps, the present invention enables elderly people to efficiently and user-friendly perform daily tasks, manage their health, and obtain information about local events using their voice. In addition, by using an emotion recognition engine, the system can provide optimal responses tailored to the user's emotional state.

[1555] (Application example 2)

[1556] 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."

[1557] There is a need for methods to help seniors maintain independence in their daily lives and make their shopping experience in brick-and-mortar stores smooth and stress-free. However, seniors are often unfamiliar with voice input and digital interfaces, and are sensitive to emotional changes, which can make shopping in brick-and-mortar stores difficult. Therefore, a system that is easy for seniors to use and allows them to receive information and take action with confidence is needed.

[1558] The identification process by the identification 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 data into text data using natural language processing, means for analyzing the text data and understanding the user's intention, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, emotion recognition means for recognizing the user's emotions in real time, and means for adjusting the information to be provided based on the emotion recognition results. This enables elderly people to easily manage their shopping experience in a physical store by voice and receive optimal information and advice tailored to their emotions in real time.

[1559] "Natural language processing" is a technology that converts voice data into text data and analyzes that text to understand its meaning and intent.

[1560] "Speech recognition" is a technology that converts a user's voice input into digital text data.

[1561] "Text data analysis" is the process of understanding the user's intent and extracting necessary information and tasks based on the converted text data.

[1562] "Generating task management information" refers to generating task management information such as reminders and schedules based on the analyzed user's intentions.

[1563] A "voice synthesis engine" is a technology that converts generated text data into voice data and provides it to the user.

[1564] "Emotion recognition" is a technology that detects and analyzes emotions from a user's voice, facial expressions, etc.

[1565] "Adjusting the information provided based on the emotion recognition results" refers to optimizing the content and presentation of the information and advice provided according to the detected emotion.

[1566] "Health management information" is data for monitoring the user's health condition and providing appropriate health advice.

[1567] "Local event information" is data for collecting and providing information on local events and activities based on user interests and behavioral data.

[1568] The system of this invention is designed to support the daily lives of elderly people, specifically supporting their shopping experience in brick-and-mortar stores. The main components of the system are a user (elderly person), a terminal (smartphone), and a server. The system utilizes technologies such as natural language processing, speech recognition, and emotion recognition to enable elderly people to comfortably shop in brick-and-mortar stores.

[1569] Hardware and software used

[1570] Hardware:

[1571] Smartphone

[1572] software:

[1573] Speech recognition engine (e.g. Google Speech-to-Text)

[1574] Natural language processing engine (e.g. Google Natural Language)

[1575] Emotion recognition engine (such as Microsoft Azure Emotion API)

[1576] Speech synthesis engine (such as Google Text-to-Speech)

[1577] System Operation Overview

[1578] 1. User operations

[1579] The user says to their smartphone, "Tell me where the milk is."

[1580] 2. Terminal Processing

[1581] The smartphone's microphone records the user's voice and converts the voice data into a digital format.

[1582] The audio data converted into a digital format is transmitted to a server.

[1583] 3. Server Processing

[1584] Speech recognition: The server uses a speech recognition engine such as Google Speech-to-Text to convert the voice data into text data.

[1585] Natural Language Processing: Use natural language processing engines such as Google Natural Language to analyze text data and understand user intent.

[1586] Emotion Recognition: Recognize user emotions in real time using emotion recognition engines such as the Microsoft Azure Emotion API.

[1587] Information acquisition and adjustment: Based on the text data and emotion recognition results, the location information of the relevant product is acquired from the database, and the information provided is adjusted according to the user's emotional state.

[1588] Speech synthesis: Using a speech synthesis engine such as Google Text-to-Speech, the tailored information is generated as audio data.

[1589] 4. Device notifications

[1590] The smartphone plays the generated voice data and notifies the user, "The milk is in the refrigerated section." If the user shows signs of anxiety, the notification message includes a reassuring phrase such as "You'll find it soon."

[1591] Specific examples

[1592] Prompt Sentence Examples

[1593] User: "Tell me where the milk is."

[1594] Application: "Milk is in the refrigerated section. You'll find it easily."

[1595] This system is designed to allow users to easily manage their shopping in physical stores using voice commands and receive optimal information and advice tailored to their emotions in real time, ultimately aiming to improve the quality of life for the elderly.

[1596] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1597] Step 1:

[1598] A user speaks to a smartphone saying, "Tell me where the milk is." The smartphone's microphone picks up the voice input. The input is raw voice data, and the output is also voice data.

[1599] Step 2:

[1600] The smartphone converts the captured audio data into a digital format. This conversion is performed by the smartphone's internal processing. The input is the captured raw audio data, and the output is digital audio data.

[1601] Step 3:

[1602] The smartphone sends the converted digital audio data to the server. The input is digital audio data, and the output is the audio data sent to the server.

[1603] Step 4:

[1604] The voice data received by the server is converted into text data using a voice recognition engine (such as Google Speech-to-Text). The input is the digital voice data received by the server, and the output is text data.

[1605] Step 5:

[1606] The server uses a natural language processing engine (such as Google Natural Language) to analyze the text data and understand the user's intent. The input is the text data generated by the speech recognition engine, and the output is the analysis result that includes the user's intent.

[1607] Step 6:

[1608] The server uses an emotion recognition engine (such as Microsoft Azure Emotion API) to recognize the user's emotions in real time during speech recognition. The input is voice or text data stored on the server, and the output is the analysis result regarding the user's emotional state.

[1609] Step 7:

[1610] The server retrieves the location information of the relevant product from the database based on the analysis results and adjusts the provided information based on the emotion recognition results. The input is the analysis results regarding the user's intention and emotional state, and the output is the adjusted notification information.

[1611] Step 8:

[1612] The server generates the adjusted notification information as audio data using a speech synthesis engine (such as Google Text-to-Speech). The input is the text data of the adjusted notification information, and the output is the audio data generated by the speech synthesis engine.

[1613] Step 9:

[1614] The server sends the generated voice data to the smartphone. The input is the voice data generated by the server, and the output is the voice data sent to the smartphone.

[1615] Step 10:

[1616] The smartphone plays the received voice data and notifies the user. For example, digital voice data is played to tell the user, "The milk is in the refrigerated section. You'll find it soon." The input is the voice data received by the smartphone, and the output is the voice notification to the user.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] [Fourth embodiment]

[1621] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1622] 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.

[1623] 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).

[1624] 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.

[1625] 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.

[1626] 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).

[1627] 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. 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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."

[1634] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology and natural language processing technology. Specific embodiments for carrying out the present invention will be described below.

[1635] System Overview

[1636] This system consists of three main components: the user (elderly person), the terminal, and the server. The user can use voice commands to the terminal to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the terminal and the server.

[1637] Program processing flow

[1638] User operations

[1639] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say to the device, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[1640] Terminal handling

[1641] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[1642] Server Processing

[1643] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent.

[1644] Next, specific processing is performed based on the analysis results, such as:

[1645] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at the specified time.

[1646] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in the database.

[1647] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavior data. The summarized information is then provided in a format appropriate for the user.

[1648] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[1649] Specific examples

[1650] Medication reminder settings

[1651] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[1652] 2. Device: Records audio and sends the audio data to the server.

[1653] 3. Server: Converts the voice to text and processes the reminder. Generates a voice message saying "Reminder set" and sends it to the device.

[1654] 4. Device: Notify the user that the reminder has been set.

[1655] Obtaining local event information

[1656] 1. User: "Tell me about events near me."

[1657] 2. Device: Records audio and sends the audio data to the server.

[1658] 3. Server: Converts speech to text and collects local event information from the Internet. It summarizes the collected information, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[1659] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[1660] This series of processes enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving the quality of life for the elderly.

[1661] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice.

[1662] The processing flow will be explained below.

[1663] Specific flow of program processing

[1664] Scenario: User wants to set a medication reminder

[1665] Step 1:

[1666] On the device: The user says, "Set a reminder to take my medicine tomorrow morning at 9 AM."

[1667] Activates the microphone and records the user's voice for a certain period of time.

[1668] Step 2:

[1669] Terminal: Converts recorded audio data into digital format.

[1670] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1671] Step 3:

[1672] Device: Sends audio data to the server.

[1673] Create an HTTP POST request to the server, sending the audio data as the payload.

[1674] Step 4:

[1675] Server: Receives the audio data.

[1676] Receives the transmitted audio data and stores it in storage.

[1677] Step 5:

[1678] Server: Converts voice data into text using a speech recognition AI model.

[1679] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[1680] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[1681] Step 6:

[1682] Server: Analyzes text data using a natural language processing model to understand user intent.

[1683] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (setting a reminder).

[1684] Step 7:

[1685] Server: Generates reminder setting data based on the analysis results.

[1686] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[1687] Step 8:

[1688] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[1689] Generates the output text "Reminder set".

[1690] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[1691] Step 9:

[1692] Server: Sends audio data to the device.

[1693] The generated audio data is returned to the terminal as an HTTP response.

[1694] Step 10:

[1695] Terminal: Plays back the audio data received from the server.

[1696] The received audio data is saved in the internal storage.

[1697] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[1698] Scenario: A user wants to receive information about local events

[1699] Step 1:

[1700] User: "Tell me if there are any events nearby" says to the device.

[1701] Step 2:

[1702] Device: Records the user's voice.

[1703] Activates the microphone and records the user's voice for a certain period of time.

[1704] Step 3:

[1705] Terminal: Converts recorded audio data into digital format.

[1706] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1707] Step 4:

[1708] Device: Sends audio data to the server.

[1709] Create an HTTP POST request to the server, sending the audio data as the payload.

[1710] Step 5:

[1711] Server: Receives the audio data.

[1712] Receives the transmitted audio data and stores it in storage.

[1713] Step 6:

[1714] Server: Converts voice data into text using a speech recognition AI model.

[1715] A speech recognition engine (e.g., Google Speech-to-Text API) is started and the voice data is converted into text data.

[1716] The output text is "Tell me if there are any events nearby."

[1717] Step 7:

[1718] Server: Analyzes text data using a natural language processing model to understand user intent.

[1719] Use text analysis tools (e.g., spaCy, NLTK) to extract user intent (provide event information).

[1720] Step 8:

[1721] Server: Collects and summarizes local event information from the Internet.

[1722] Use external APIs (e.g. Eventbrite, Meetup) to gather information about nearby events.

[1723] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[1724] Step 9:

[1725] Server: Converts the collected event information into text and sends it to the speech synthesis engine.

[1726] Produces the output text "There is a flea market in the park this weekend."

[1727] Send the text to a speech synthesis engine (e.g., Google Text-to-Speech API) to generate audio data.

[1728] Step 10:

[1729] Server: Sends audio data to the device.

[1730] The generated audio data is returned to the terminal as an HTTP response.

[1731] Step 11:

[1732] Terminal: Plays back the audio data received from the server.

[1733] The received audio data is saved in the internal storage.

[1734] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[1735] Example 1

[1736] 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."

[1737] In the daily lives of elderly people, task management, health management, and keeping up with local event information can be complicated, and there is a need for efficient ways to do these tasks. These tasks become even more difficult when visual and operational functions decline. An integrated system is needed to manage these functions via voice and improve the quality of life for elderly people.

[1738] 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.

[1739] In this invention, the server includes means for recording a user's voice input and converting it into a digital format, means for transmitting the converted voice data to the server, means for converting the voice data into text data at the server, means for analyzing the text data to understand the user's intentions, means for setting reminders and schedules based on the analysis results, means for providing the set information as voice data, and means for notifying the user of the generated voice data, thereby enabling task management, health management, and the collection and understanding of local event information.

[1740] "User" refers to the person who uses this system, and is particularly targeted at elderly people.

[1741] "Voice input" refers to instructions or questions given by the user to the terminal by voice.

[1742] A "terminal" is a device used by a user to perform voice input, and includes digital devices equipped with a microphone and a speaker.

[1743] "Digital format" refers to a format in which an analog audio signal is converted into digital data.

[1744] "Server" refers to a computer system set up to process data sent from a terminal.

[1745] "Voice Data" means the digital form of a recorded user's voice or generated voice information.

[1746] "Text data" refers to character string data converted from voice data by a voice recognition engine.

[1747] "Speech recognition engine" means software or technology for converting voice data into text data.

[1748] A "natural language processing engine" refers to software or technology that analyzes text data to understand user intent.

[1749] A "reminder" is a notification that prompts a user to take a specific action at a specific time or date.

[1750] "Task management" refers to managing a user's schedule and reminders.

[1751] "Health management" refers to monitoring the user's health status and providing advice.

[1752] "Local event information" refers to information about events held in the area where the user lives.

[1753] "Speech synthesis engine" means software or technology for converting text data into speech data.

[1754] The system of the present invention is designed to support the daily lives of elderly people and primarily utilizes speech recognition and natural language processing technologies. The system consists of three main components: a user, a terminal, and a server. Specific embodiments for implementing the present invention are described below.

[1755] System Overview

[1756] Users can use voice commands to manage their daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications via the device and server. This system aims to improve the quality of life for the elderly.

[1757] Hardware and software used

[1758] Hardware: Smart devices with microphones, speakers, and processors (e.g., Amazon Echo, Google Home)

[1759] Speech recognition engine: Google Speech-to-Text API, Amazon Transcribe

[1760] Natural language processing engine: SpaCy, Google Cloud Natural Language API

[1761] Database: MySQL, PostgreSQL

[1762] Web scraping tools: Beautiful Soup, Scrapy

[1763] Speech synthesis engine: Amazon Polly, Google Text-to-Speech

[1764] Overview of program processing

[1765] 1. Voice Input

[1766] The user gives the system commands by voice, for example, "Set a reminder to take my medicine tomorrow morning at 9am."

[1767] 2. Audio recording and digital conversion

[1768] The device uses a microphone to record the user's voice and converts it into digital form using an ADC (analog-to-digital conversion) processor.

[1769] 3. Data Transmission

[1770] The device then transmits the converted digital audio data to a server via Wi-Fi or a mobile network.

[1771] 4. Speech Recognition and Text Conversion

[1772] The server passes the received voice data to a voice recognition engine, which converts the voice into text data.

[1773] 5. Natural Language Processing

[1774] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[1775] 6. Reminder Settings

[1776] The server stores the reminder information in a database and notifies you at the set time.

[1777] 7. Audio data generation

[1778] The server uses a speech synthesis engine to convert the text data into speech data.

[1779] 8. User Notices

[1780] The terminal reproduces the generated voice data and notifies the user.

[1781] Specific examples

[1782] Medication reminder settings

[1783] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[1784] 2. Device: Records audio and sends the audio data to the server.

[1785] 3. Server: Converts the voice to text, sets the reminder, saves the setting in the database, generates a voice message saying "Reminder set" and sends it to the device.

[1786] 4. Device: Notify the user that the reminder has been set.

[1787] Obtaining local event information

[1788] 1. User: "Tell me about events near me."

[1789] 2. Device: Records audio and sends the audio data to the server.

[1790] 3. Server: After converting the speech into text, it collects local event information from the Internet, summarizes the collected information, and generates speech data such as "There will be a flea market in the park this weekend," and sends it to the device.

[1791] 4. Device: Notify the user that "There's a flea market in the park this weekend."

[1792] Prompt Sentence Examples

[1793] Prompt: "Design a system to support the daily lives of seniors, using speech recognition and natural language processing to include features such as medication reminders and information about local events."

[1794] In this form, the system enables elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, thereby improving the quality of life for the elderly.

[1795] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1796] Step 1:

[1797] The user issues commands to the system by voice.

[1798] Specifically, the user says, "Set a reminder to take my medicine at 9:00 tomorrow morning." This is the input, and is recorded in voice format into the device's microphone.

[1799] Step 2:

[1800] The device records the input audio and converts it into a digital format.

[1801] Specifically, the device's built-in microphone records audio, and the ADC processor converts the analog audio signal into digital data. The input is audio data, and the output is digital audio data.

[1802] Step 3:

[1803] The terminal transmits the converted digital audio data to the server.

[1804] Specifically, the device sends an HTTP POST request to the server over Wi-Fi or a mobile network. The input is digital audio data, and the output is transmission to the server.

[1805] Step 4:

[1806] The server passes the received voice data to a voice recognition engine and converts it into text data.

[1807] Specifically, the server calls a speech recognition engine and converts the voice data into text format. The input is digital voice data, and the output is text data.

[1808] Step 5:

[1809] The server analyzes the text data using a natural language processing engine to understand the user's intent.

[1810] Specifically, the server uses a natural language processing engine to analyze text data and extract intent. The input is text data, and the output is the analysis result.

[1811] Step 6:

[1812] The server sets reminders based on the analysis results.

[1813] Specifically, the server accesses the database and registers the reminder information, such as "Take medicine at 9:00 AM on April 2, 2024." The input is the analysis result, and the output is registration in the database.

[1814] Step 7:

[1815] The server converts the set reminder information into audio data.

[1816] Specifically, the server invokes a speech synthesis engine and converts the text notification content into voice data. Voice data saying "Reminder set" is generated. The input is the reminder text information, and the output is voice data.

[1817] Step 8:

[1818] The server transmits the generated voice data to the terminal.

[1819] Specifically, the server sends audio data to the device using an HTTP POST request. The input is audio data, and the output is transmission to the device.

[1820] Step 9:

[1821] The terminal plays back the received audio data and notifies the user.

[1822] Specifically, the device uses the built-in speaker to play a voice message saying, "Reminder set." The input is voice data, and the output is a voice notification to the user.

[1823] The above are the specific processing steps of the system program, which allow users to set reminders and receive notifications using natural voice commands.

[1824] (Application example 1)

[1825] 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."

[1826] When seniors shop in brick-and-mortar stores, they face the problem of having to spend a lot of time and effort. Product searches and obtaining special offers are particularly burdensome for seniors. Furthermore, seniors often have limited means to effectively manage and check their shopping lists, which can lead to forgetting to buy something or making unnecessary purchases. These situations are contributing to a decline in the quality of life for seniors.

[1827] 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.

[1828] In this invention, the server includes means for converting voice data into text data using voice recognition technology, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, means for accessing a database for obtaining product location information and special offer information in a physical store, and means for analyzing the product location information and special offer information and providing the user with voice data. This enables elderly people to efficiently search for products in physical stores, obtain special offer information, and manage their shopping lists.

[1829] "Voice recognition technology" is a technology that analyzes voice data and converts it into text data.

[1830] "Text data" refers to data of character information converted using voice recognition technology.

[1831] A "voice synthesis engine" is a function for analyzing text data and generating voice data.

[1832] "Task management information" is information for managing tasks in a user's daily life, and this information becomes reminders and notifications provided to the user.

[1833] A "database" is an information management system that stores information such as product location information and special offer information.

[1834] "Product location information" is information that indicates where a particular product is displayed.

[1835] "Special offer information" is information that indicates discounts and sales on specific products in stores.

[1836] "Voice data" refers to information that is generated by a voice synthesis engine and is provided to the user audibly.

[1837] This invention is a system for supporting elderly people in efficiently shopping in brick-and-mortar stores. Its main functions are to provide product location information, special offers, and shopping list management using speech recognition and natural language processing technologies. This system is composed of multiple components, including speech recognition technology, a speech synthesis engine, and database access functionality.

[1838] System Overview

[1839] This system consists of three main components: the user (elderly person), the device (smartphone, smart glasses, head-mounted display, etc.), and the server. The user can give instructions to the system through voice and receive necessary information and notifications via the device and server.

[1840] Hardware and Software Configuration

[1841] Hardware:

[1842] Smartphone (iOS or Android)

[1843] Smart glasses (with audio input / output functions)

[1844] Head-mounted display (with audio input / output function)

[1845] Microphone (for voice input)

[1846] Speaker (for audio output)

[1847] software:

[1848] speech_recognition: A library for speech recognition

[1849] pyttsx3: A library for audio output

[1850] requests: A library for sending API requests

[1851] Data processing and calculation

[1852] 1. Voice input:

[1853] The user provides voice input, the audio is recorded with a microphone, and the recorded audio is converted into text data using the speech_recognition library.

[1854] 2. Text data analysis and processing:

[1855] The server analyzes the text data obtained by the recorder, extracts requests for product names and special offers, requests the necessary information from the database, and outputs the obtained data in a specific format.

[1856] 3. Audio output:

[1857] Using the pyttsx3 library, the acquired information is generated as audio data and notified to the elderly.

[1858] Specific examples

[1859] Product Search:

[1860] When a user asks, "Where is the milk?", the system retrieves the product's location information from the database and responds by voice, "The milk is in the dairy section."

[1861] Get special offers:

[1862] When a user says, "Tell me what's on sale today," the system retrieves the sale information from the database and announces in voice, "Apples and bananas are on sale today."

[1863] Check your shopping list:

[1864] When the user says, "Check the shopping list," the system references the shopping list on the server and announces in voice, "The list includes milk, bread, and apples."

[1865] Prompt Sentence Examples

[1866] Below are some example prompts to apply to generative AI models:

[1867] I would like to develop a shopping support app that uses speech recognition and natural language processing technology to support the daily lives of elderly people. Shopping support includes product search, obtaining special offers, and checking the shopping list. Examples of voice commands include "Where is the milk?", "Tell me today's specials," and "Check my shopping list." Please provide specific program examples and explanations for analyzing the flow.

[1868] Through these elements, the invention allows seniors to efficiently search for products, obtain special offers, and manage their shopping lists in brick-and-mortar stores.

[1869] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1870] Step 1:

[1871] The user provides voice input. For example, the user might say, "Where is the milk?" in a physical store. As input, the user's voice data is collected through a microphone. This voice data is sent to the next processing step.

[1872] Step 2:

[1873] The device records the voice and converts the voice data into a digital format. Specifically, it uses the speech_recognition library to convert the voice to text data. The input of this step is the user's voice data, and the output is the converted text data.

[1874] Step 3:

[1875] The server receives the text data sent from the terminal. It analyzes the text data to understand the user's intent (in this case, a product search). Based on the analysis, it extracts the product name "Milk." The input for this step is the user's text data, and the output is the product name as a result of the analysis.

[1876] Step 4:

[1877] Based on the product name extracted by the server, the database is accessed to obtain the product's location information. For example, location information for the product name "Milk" is obtained from the database. The input to this step is the analysis result (product name), and the output is product location information.

[1878] Step 5:

[1879] The server generates voice data based on the location information it has acquired. Using the pyttsx3 library, it generates voice data saying, "Milk is in the dairy section." The input for this step is the product location information, and the output is the generated voice data.

[1880] Step 6:

[1881] The terminal provides the user with the voice data received from the server. Specifically, the generated voice data is played through the terminal's speaker, and the user is notified that "Milk is in the dairy section." The input of this step is the voice data from the server, and the output is the notification to the user.

[1882] Step 7:

[1883] The user issues a command, for example, "Tell me what's on sale today." The process begins again at step 1, and the required information is retrieved and provided to the user.

[1884] 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.

[1885] The system of the present invention is designed to support the daily lives of elderly people and mainly utilizes speech recognition technology, natural language processing technology, and emotion recognition technology. Specific embodiments for carrying out the present invention will be described below.

[1886] System Overview

[1887] This system consists of three main components: the user (elderly person), the device, and the server. The user can use voice commands to manage daily tasks, manage their health, and obtain information about local events, and receive necessary information and notifications through the device and server. The system also incorporates an emotion engine that recognizes the user's emotions and optimizes the information and advice it provides.

[1888] Program processing flow

[1889] User operations

[1890] The user issues instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine tomorrow morning at 9:00." Furthermore, the emotion engine recognizes the user's emotions in real time.

[1891] Terminal handling

[1892] The device records the user's voice input and converts the voice data into a digital format. This voice data is sent to the server. When the server responds, the device plays the voice data and notifies the user.

[1893] Server Processing

[1894] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. Furthermore, the emotion engine analyzes the user's emotions, and the results are reflected in other processes.

[1895] Next, specific processing is performed based on the analysis results, such as:

[1896] Task management: Set reminders and schedules for users based on information extracted from text data. The settings are saved in a database and notifications are sent at specified times. The content and timing of these reminders may be adjusted based on the user's emotions as recognized by an emotion engine.

[1897] Health management: Manages the user's health information (such as the type and time of medication) and generates appropriate advice. This information is also stored in a database. The details of the advice and the way it is presented may be changed based on the user's emotions recognized by the emotion engine.

[1898] Providing local event information: Local event information is collected from the Internet and summarized based on the user's interests and past behavioral data. The summarized information is provided in a format appropriate for the user. The user's emotions, recognized by the emotion engine, also influence the selection of event information to be provided.

[1899] The generated information is again converted into voice data by the voice synthesis engine, and this voice data is sent to the terminal, which then plays the received voice data back to the user to provide feedback on the instruction.

[1900] Specific examples

[1901] Medication reminder settings

[1902] 1. User: "Set a reminder to take my medicine tomorrow morning at 9am."

[1903] 2. Device: Records audio and sends the audio data to the server.

[1904] 3. Server: Converts voice to text and processes reminder settings. If the emotion engine analyzes the user's tone of voice and determines that the user is feeling nervous or anxious, it adjusts the notification to be softer and include encouraging words. It generates voice data saying "Reminder set" and sends it to the device.

[1905] 4. Device: Notify the user that the reminder has been set.

[1906] Obtaining local event information

[1907] 1. User: "Tell me about events near me."

[1908] 2. Device: Records audio and sends the audio data to the server.

[1909] 3. Server: Converts speech into text and collects local event information from the Internet. If the emotion engine determines that the user is bored, it prioritizes more active events, generates speech data such as "There's a flea market in the park this weekend," and sends it to the device.

[1910] 4. Terminal: Notify the user that "There's a flea market in the park this weekend."

[1911] This series of processes allows elderly people to easily and naturally manage their daily tasks, health management, and participation in local events using voice, improving their quality of life.The introduction of an emotion engine allows the system to respond flexibly to the user's emotional state, making it possible to provide more personalized support.

[1912] The above is an embodiment of the present invention. By following this embodiment, elderly people can easily manage and receive information about daily tasks, health management, and local events through voice, and the system also provides optimal advice and information according to the user's emotions.

[1913] The processing flow will be explained below.

[1914] Specific flow of program processing (including emotion engine)

[1915] Scenario: User wants to set a medication reminder

[1916] Step 1:

[1917] User: "Set a reminder to take my medicine tomorrow morning at 9am" says to the device.

[1918] Step 2:

[1919] Device: Records the user's voice input.

[1920] Activate the microphone and record audio for a set period of time.

[1921] Step 3:

[1922] Terminal: Converts recorded audio data into digital format.

[1923] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1924] Step 4:

[1925] Device: Sends audio data to the server.

[1926] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[1927] Step 5:

[1928] Server: Receives the audio data.

[1929] Receives the transmitted audio data and stores it in storage.

[1930] Step 6:

[1931] Server: Converts voice data into text using a speech recognition AI model.

[1932] A voice recognition engine is started and voice data is converted into text data.

[1933] The output text is "Set a reminder to take my medicine tomorrow morning at 9am."

[1934] Step 7:

[1935] Server: Analyzes text data using a natural language processing model to understand user intent.

[1936] Use text analysis tools to extract the intent behind reminder settings.

[1937] Step 8:

[1938] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[1939] The emotion engine assesses the user's emotional state (e.g., tension, anxiety, joy) from the voice data.

[1940] Step 9:

[1941] Server: Generates reminder setting data based on the analysis results.

[1942] The reminder date and time (tomorrow morning 9:00) and task content (take medicine) are extracted and saved in the database.

[1943] Adjust the method and content of notifications based on the results of the emotion engine.

[1944] Step 10:

[1945] Server: Convert the reminder setting results into text and send it to the speech synthesis engine.

[1946] Generates the output text "Reminder set".

[1947] The text is sent to a speech synthesis engine to generate speech data.

[1948] Step 11:

[1949] Server: Sends audio data to the device.

[1950] The generated audio data is returned to the terminal as an HTTP response.

[1951] Step 12:

[1952] Terminal: Plays back the audio data received from the server.

[1953] The received audio data is saved in the internal storage.

[1954] Use the audio playback engine to play the audio data and notify the user, "Reminder set."

[1955] Scenario: A user wants to receive information about local events

[1956] Step 1:

[1957] User: "Tell me if there are any events nearby" says to the device.

[1958] Step 2:

[1959] Device: Records the user's voice input.

[1960] Activate the microphone and record audio for a set period of time.

[1961] Step 3:

[1962] Terminal: Converts recorded audio data into digital format.

[1963] Convert the audio data to an appropriate format (e.g. WAV, MP3).

[1964] Step 4:

[1965] Device: Sends audio data to the server.

[1966] Make an HTTP POST request to the server, sending the converted audio data as the payload.

[1967] Step 5:

[1968] Server: Receives the audio data and stores it in storage.

[1969] Step 6:

[1970] Server: Converts voice data into text using a speech recognition AI model.

[1971] A voice recognition engine is started and voice data is converted into text data.

[1972] The output text is "Tell me if there are any events nearby."

[1973] Step 7:

[1974] Server: Analyzes text data using a natural language processing model to understand user intent.

[1975] Use text analysis tools to extract the intent behind providing event information.

[1976] Step 8:

[1977] Server: Analyzes the user's voice data using the emotion engine and evaluates their emotions.

[1978] The emotion engine assesses the user's emotional state (e.g., boredom, interest) from the voice data.

[1979] Step 9:

[1980] Server: Collects and summarizes local event information from the Internet.

[1981] Use external APIs to gather nearby event information.

[1982] The collected event information is summarized using a natural language processing model and narrowed down to information appropriate for the user.

[1983] The selection and presentation of event information is adjusted based on the results of the emotion engine.

[1984] Step 10:

[1985] Server: Converts the summarized event information into text and sends it to the speech synthesis engine.

[1986] Produces the output text "There is a flea market in the park this weekend."

[1987] The text is sent to a speech synthesis engine to generate speech data.

[1988] Step 11:

[1989] Server: Sends audio data to the device.

[1990] The generated audio data is returned to the terminal as an HTTP response.

[1991] Step 12:

[1992] Terminal: Plays back the audio data received from the server.

[1993] The received audio data is saved in the internal storage.

[1994] The audio data is played back using the audio playback engine to notify the user that "There's a flea market in the park this weekend."

[1995] This specific processing flow makes it easy for elderly people to manage tasks in their daily lives, manage their health, and participate in local events, and by using an emotion engine, it is possible to provide services that take into consideration the user's emotions.

[1996] Example 2

[1997] 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."

[1998] There is a need for a system that allows elderly people to easily manage their daily tasks, health management, and obtain information about local events using voice commands. However, existing systems lack the ability to recognize and process voice commands, and lack the ability to recognize the user's emotions and optimize responses. Therefore, a user-friendly system that can be used naturally by elderly people is needed.

[1999] 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.

[2000] In this invention, the server includes means for recording voice data and converting it into a digital format, means for transmitting the converted digital voice data to the server, means for converting the voice data into text data using natural language processing, means for analyzing the text data and understanding the user's intent, means for generating task management information based on the analyzed text data and the results of the user's emotion analysis, means for saving the generated task management information in a database, means for generating and transmitting a notification at a specified time, means for analyzing the user's emotion using an emotion recognition engine, means for generating information as voice data using a voice synthesis engine, means for transmitting the generated voice data to a terminal, and means for playing the voice data on the terminal and providing it to the user. This enables elderly people to easily manage their daily tasks, health management, and local event information by voice, and provides optimal support according to the user's emotions.

[2001] "Voice data" refers to information input by voice by a user that has been recorded and stored in digital form.

[2002] "Digital format" refers to the conversion of analog audio into a digital signal that allows a computer to process the audio data.

[2003] A "server" is a central system that processes and stores data over a network.

[2004] "Natural language processing" is a technology that uses computers to process and understand human language.

[2005] "Text data" refers to text information converted from voice data using natural language processing technology.

[2006] "User intent" refers to the purpose or desire that the user is trying to convey through voice instructions.

[2007] "Task management information" is information about tasks to be managed, such as the user's daily schedule and reminders.

[2008] A "database" is a system for efficiently storing and accessing data.

[2009] A "notification" is a message or sound generated for the purpose of informing the user of pre-defined information.

[2010] An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice and facial expressions.

[2011] A "speech synthesis engine" is a technology that converts text data into voice data.

[2012] A "terminal" is a device for transmitting and receiving data between a user and a system.

[2013] "Recommended local event information" is information about local events selected based on the user's interests and behavioral data.

[2014] "Health management information" is information that indicates the user's health condition and health-related data.

[2015] "Advice" refers to advice or guidance provided to the user based on the analysis results.

[2016] "Interests and search history" refers to the user's past interests and search history.

[2017] This invention is a system that uses speech recognition, natural language processing, and emotion recognition technologies to support the daily lives of elderly people. The system consists of three main components: a user, a terminal, and a server.

[2018] System Overview

[2019] Users can issue voice commands to the system to manage daily tasks, manage their health, and obtain information about local events. The device records the user's voice and sends it to the server. The server analyzes the voice data, understands the user's intentions and emotions, and generates appropriate information and notifications. The generated information is converted into voice data and notified to the user via the device.

[2020] Hardware and software used

[2021] Hardware:

[2022] Devices: smart speakers, smartphones, etc.

[2023] Server: A server with high-performance processing power

[2024] software:

[2025] Speech recognition engine: converts voice data into text data (e.g., Google Speech-to-Text API)

[2026] Natural language processing engine: Analyzes text data and understands user intent (e.g., Google Cloud Natural Language)

[2027] Emotion recognition engine: Analyzes the user's emotional state (e.g., Affectiva SDK)

[2028] Speech synthesis engine: converts text data into speech data (e.g., Amazon Polly)

[2029] Program processing flow

[2030] The program in this system follows these steps:

[2031] 1. User Instructions

[2032] The user gives instructions to the system through voice. For example, to set a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning."

[2033] 2. Device Operation

[2034] The device records the user's voice, converts the voice data into a digital format, and then transmits it to the server.

[2035] 3. Server Processing

[2036] The server receives the voice data sent from the device and converts it into text data using a speech recognition engine. The converted text data is then analyzed by a natural language processing engine to understand the user's intent. At the same time, the emotion recognition engine analyzes the user's emotions, and the results are reflected in other processes.

[2037] Specific examples:

[2038] Setting a medication reminder: When a user says, "Set a reminder to take my medicine tomorrow morning at 9:00," the server converts this instruction into text data and sets the reminder. If the emotion recognition engine detects that the user is nervous or anxious, it softens the notification and generates a response such as, "The reminder has been set."

[2039] Obtaining local event information: When a user says, "Tell me about any events happening nearby," the server converts this instruction into text data and analyzes it using a natural language processing engine. If the emotion recognition engine detects boredom, it prioritizes active event information and generates a response such as, "There's a flea market in the park this weekend."

[2040] 4. Audio data generation and notification

[2041] The generated text and information are converted into voice data by a speech synthesis engine, and this voice data is then sent back to the device, which then plays the received voice data and provides the user with appropriate information and notifications.

[2042] Specific examples

[2043] The following example prompts show how the system works:

[2044] Example prompt sentence:

[2045] "Set a reminder to take my medicine tomorrow morning at 9am"

[2046] "Tell me if there's any events happening nearby."

[2047] As described above, this invention enables elderly people to easily manage and obtain information about daily tasks, health management, and local events using their voice. In particular, by using an emotion recognition engine, it is possible to provide optimal support according to the user's emotional state.

[2048] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2049] Program processing step details

[2050] Step 1: User voice command

[2051] Subject: User

[2052] Input: Voice command

[2053] Output: Recorded audio data

[2054] The user gives instructions to the system through voice. For example, when setting a medication reminder, the user might say, "Set a reminder to take my medicine at 9:00 tomorrow morning." This gives the system a starting point for understanding the user's needs.

[2055] Step 2: Record and transmit audio data using your device

[2056] Subject: Device

[2057] Input: User's voice command

[2058] Output: Digital audio data

[2059] The device records the user's voice commands and converts the voice data into digital format. This digital voice data is sent to the server, where the voice is recorded through a microphone, converted into a digital format such as PCM, and sent to the server via IP communication.

[2060] Step 3: The server receives and converts the audio data

[2061] Subject: Server

[2062] Input: Digital audio data

[2063] Output: Text data

[2064] The server receives the voice data sent from the device and converts it into text data using a voice recognition engine. For example, it converts the voice data into text data using the Google Speech-to-Text API. This allows the server to handle voice instructions as text information.

[2065] Step 4: Parsing the text data by the server

[2066] Subject: Server

[2067] Input: Text data

[2068] Output: Analysis results indicating the user's intent

[2069] The server uses a natural language processing engine to analyze the text data converted from the speech and understand the user's intent. Specifically, it uses Google Cloud Natural Language to analyze the text and understand the user's instructions. During this process, it performs morphological and contextual analysis of the text data to identify the intent and required actions.

[2070] Step 5: Emotion recognition and response generation by the server

[2071] Subject: Server

[2072] Input: Text data and user voice tone

[2073] Output: Response text and response voice data to the user

[2074] The server uses an emotion engine to analyze the user's emotions and generate responses based on the results. For example, it uses the Affectiva SDK to detect emotions from voice tones and analyzes them together with text data. It then generates specific response text, such as setting medication reminders or providing information about local events, and converts it into voice data using Amazon Polly if necessary.

[2075] Step 6: Sending a response from the server to the device

[2076] Subject: Server

[2077] Input: Voice data and text response

[2078] Output: Data sent to the terminal

[2079] The server sends the generated voice and text data to the terminal using IP communications, in a format that the terminal can receive (e.g., WAV format voice data).

[2080] Step 7: Device notifies the user

[2081] Subject: Device

[2082] Input: Audio and text data sent from the server

[2083] Output: A spoken audio notification

[2084] The device receives the audio data sent from the server and plays it through the speaker, providing appropriate feedback and notifications to the user. The device decodes the audio data, converts it into an analog signal that can be heard by the user, and plays it through the speaker.

[2085] Through these specific processing steps, the present invention enables elderly people to efficiently and user-friendly perform daily tasks, manage their health, and obtain information about local events using their voice. In addition, by using an emotion recognition engine, the system can provide optimal responses tailored to the user's emotional state.

[2086] (Application example 2)

[2087] 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."

[2088] There is a need for methods to help seniors maintain independence in their daily lives and make their shopping experience in brick-and-mortar stores smooth and stress-free. However, seniors are often unfamiliar with voice input and digital interfaces, and are sensitive to emotional changes, which can make shopping in brick-and-mortar stores difficult. Therefore, a system that is easy for seniors to use and allows them to receive information and take action with confidence is needed.

[2089] The identification process by the identification 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 data into text data using natural language processing, means for analyzing the text data and understanding the user's intention, means for generating task management information based on the analysis results, means for generating information as voice data using a voice synthesis engine, means for providing the generated voice data to the user, emotion recognition means for recognizing the user's emotions in real time, and means for adjusting the information to be provided based on the emotion recognition results. This enables elderly people to easily manage their shopping experience in a physical store by voice and receive optimal information and advice tailored to their emotions in real time.

[2090] "Natural language processing" is a technology that converts voice data into text data and analyzes that text to understand its meaning and intent.

[2091] "Speech recognition" is a technology that converts a user's voice input into digital text data.

[2092] "Text data analysis" is the process of understanding the user's intent and extracting necessary information and tasks based on the converted text data.

[2093] "Generating task management information" refers to generating task management information such as reminders and schedules based on the analyzed user's intentions.

[2094] A "voice synthesis engine" is a technology that converts generated text data into voice data and provides it to the user.

[2095] "Emotion recognition" is a technology that detects and analyzes emotions from a user's voice, facial expressions, etc.

[2096] "Adjusting the information provided based on the emotion recognition results" refers to optimizing the content and presentation of the information and advice provided according to the detected emotion.

[2097] "Health management information" is data for monitoring the user's health condition and providing appropriate health advice.

[2098] "Local event information" is data for collecting and providing information on local events and activities based on user interests and behavioral data.

[2099] The system of this invention is designed to support the daily lives of elderly people, specifically supporting their shopping experience in brick-and-mortar stores. The main components of the system are a user (elderly person), a terminal (smartphone), and a server. The system utilizes technologies such as natural language processing, speech recognition, and emotion recognition to enable elderly people to comfortably shop in brick-and-mortar stores.

[2100] Hardware and software used

[2101] Hardware:

[2102] Smartphone

[2103] software:

[2104] Speech recognition engine (e.g. Google Speech-to-Text)

[2105] Natural language processing engine (e.g. Google Natural Language)

[2106] Emotion recognition engine (such as Microsoft Azure Emotion API)

[2107] Speech synthesis engine (such as Google Text-to-Speech)

[2108] System Operation Overview

[2109] 1. User operations

[2110] The user says to their smartphone, "Tell me where the milk is."

[2111] 2. Terminal Processing

[2112] The smartphone's microphone records the user's voice and converts the voice data into a digital format.

[2113] The audio data converted into a digital format is transmitted to a server.

[2114] 3. Server Processing

[2115] Speech recognition: The server uses a speech recognition engine such as Google Speech-to-Text to convert the voice data into text data.

[2116] Natural Language Processing: Use natural language processing engines such as Google Natural Language to analyze text data and understand user intent.

[2117] Emotion Recognition: Recognize user emotions in real time using emotion recognition engines such as the Microsoft Azure Emotion API.

[2118] Information acquisition and adjustment: Based on the text data and emotion recognition results, the location information of the relevant product is acquired from the database, and the information provided is adjusted according to the user's emotional state.

[2119] Speech synthesis: Using a speech synthesis engine such as Google Text-to-Speech, the tailored information is generated as audio data.

[2120] 4. Device notifications

[2121] The smartphone plays the generated voice data and notifies the user, "The milk is in the refrigerated section." If the user shows signs of anxiety, the notification message includes a reassuring phrase such as "You'll find it soon."

[2122] Specific examples

[2123] Prompt Sentence Examples

[2124] User: "Tell me where the milk is."

[2125] Application: "Milk is in the refrigerated section. You'll find it easily."

[2126] This system is designed to allow users to easily manage their shopping in physical stores using voice commands and receive optimal information and advice tailored to their emotions in real time, ultimately aiming to improve the quality of life for the elderly.

[2127] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2128] Step 1:

[2129] A user speaks to a smartphone saying, "Tell me where the milk is." The smartphone's microphone picks up the voice input. The input is raw voice data, and the output is also voice data.

[2130] Step 2:

[2131] The smartphone converts the captured audio data into a digital format. This conversion is performed by the smartphone's internal processing. The input is the captured raw audio data, and the output is digital audio data.

[2132] Step 3:

[2133] The smartphone sends the converted digital audio data to the server. The input is digital audio data, and the output is the audio data sent to the server.

[2134] Step 4:

[2135] The voice data received by the server is converted into text data using a voice recognition engine (such as Google Speech-to-Text). The input is the digital voice data received by the server, and the output is text data.

[2136] Step 5:

[2137] The server uses a natural language processing engine (such as Google Natural Language) to analyze the text data and understand the user's intent. The input is the text data generated by the speech recognition engine, and the output is the analysis result that includes the user's intent.

[2138] Step 6:

[2139] The server uses an emotion recognition engine (such as Microsoft Azure Emotion API) to recognize the user's emotions in real time during speech recognition. The input is voice or text data stored on the server, and the output is the analysis result regarding the user's emotional state.

[2140] Step 7:

[2141] The server retrieves the location information of the relevant product from the database based on the analysis results and adjusts the provided information based on the emotion recognition results. The input is the analysis results regarding the user's intention and emotional state, and the output is the adjusted notification information.

[2142] Step 8:

[2143] The server generates the adjusted notification information as audio data using a speech synthesis engine (such as Google Text-to-Speech). The input is the text data of the adjusted notification information, and the output is the audio data generated by the speech synthesis engine.

[2144] Step 9:

[2145] The server sends the generated voice data to the smartphone. The input is the voice data generated by the server, and the output is the voice data sent to the smartphone.

[2146] Step 10:

[2147] The smartphone plays the received voice data and notifies the user. For example, digital voice data is played to tell the user, "The milk is in the refrigerated section. You'll find it soon." The input is the voice data received by the smartphone, and the output is the voice notification to the user.

[2148] 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.

[2149] 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.

[2150] 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.

[2151] 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.

[2152] 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.

[2153] 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.

[2154] 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).

[2155] 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.

[2156] 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."

[2157] 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.

[2158] 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).

[2159] 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.

[2160] 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.

[2161] 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.

[2162] 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.

[2163] 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.

[2164] 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.

[2165] 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.

[2166] 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.

[2167] 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.

[2168] 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.

[2169] The following is further disclosed regarding the above embodiment.

[2170] (Claim 1)

[2171] A means for converting voice data into text data using natural language processing;

[2172] A means of analyzing text data and understanding user intent;

[2173] means for generating task management information based on the analysis results;

[2174] means for generating information as voice data using a voice synthesis engine;

[2175] means for providing the generated voice data to a user;

[2176] A system including:

[2177] (Claim 2)

[2178] A means of analyzing user interest and behavior data to collect recommended local event information;

[2179] A means for summarizing the collected local event information in a form suitable for the user;

[2180] means for generating the summarized information as voice data using a voice synthesis engine;

[2181] means for providing the generated voice data to a user;

[2182] The system of claim 1 further comprising:

[2183] (Claim 3)

[2184] means for generating a notification based on the task management information and notifying the user at a specified time;

[2185] means for analyzing health management information and monitoring the user's health condition;

[2186] a means for generating advice based on the monitoring results;

[2187] means for generating the generated advice as voice data using a voice synthesis engine;

[2188] means for providing the generated voice data to a user;

[2189] The system of claim 1 further comprising:

[2190] "Example 1"

[2191] (Claim 1)

[2192] means for recording and converting a user's voice input into a digital form;

[2193] means for transmitting the converted voice data to a server;

[2194] A means for converting voice data into text data on a server;

[2195] A means of analyzing text data to understand user intent;

[2196] A way to set reminders and schedules based on the analysis results,

[2197] means for providing the set information as voice data;

[2198] means for notifying a user of the generated voice data;

[2199] A system including:

[2200] (Claim 2)

[2201] A means of analyzing user interest and behavior data to collect recommended local event information;

[2202] a means for summarizing the collected local event information;

[2203] means for providing the summarized information in the form of audio data;

[2204] 10. The system of claim 1.

[2205] (Claim 3)

[2206] a means for generating a notification based on the task management information and notifying the user;

[2207] A means for analyzing health information and monitoring health status;

[2208] a means for generating advice based on the monitoring results;

[2209] a means for providing the generated advice as audio data;

[2210] 10. The system of claim 1.

[2211] "Application Example 1"

[2212] (Claim 1)

[2213] A means for converting voice data into text data using voice recognition technology;

[2214] A means of analyzing text data and understanding user intent;

[2215] means for generating task management information based on the analysis results;

[2216] means for generating information as voice data using a voice synthesis engine;

[2217] means for providing the generated voice data to a user;

[2218] A means of accessing a database to obtain product location information and special offers within a physical store;

[2219] A means for analyzing product location information and special offer information and providing it to users as voice data;

[2220] A system including:

[2221] (Claim 2)

[2222] A means of analyzing user interest and behavior data to collect recommended local event information;

[2223] A means for summarizing the collected local event information in a form suitable for the user;

[2224] means for generating the summarized information as voice data using a voice synthesis engine;

[2225] means for providing the generated voice data to a user;

[2226] The system of claim 1 further comprising:

[2227] (Claim 3)

[2228] means for generating a notification based on the task management information and notifying the user at a specified time;

[2229] means for analyzing health management information and monitoring the user's health condition;

[2230] a means for generating advice based on the monitoring results;

[2231] means for generating the generated advice as voice data using a voice synthesis engine;

[2232] means for providing the generated voice data to a user;

[2233] The system of claim 1 further comprising:

[2234] "Example 2: Combining Emotion Engines"

[2235] (Claim 1)

[2236] a means for recording and converting the audio data into a digital format;

[2237] means for transmitting the converted digital audio data to a server;

[2238] A means for converting voice data into text data using natural language processing;

[2239] A means of analyzing text data and understanding user intent;

[2240] means for generating task management information based on the analyzed text data and the user's emotion analysis results;

[2241] A means for storing the generated task management information in a database;

[2242] means for generating and sending notifications at specified times;

[2243] means for analyzing a user's emotions using an emotion recognition engine;

[2244] means for generating information as voice data using a voice synthesis engine;

[2245] means for transmitting the generated voice data to a terminal;

[2246] means for playing back audio data at a terminal and providing the audio data to a user;

[2247] A system including:

[2248] (Claim 2)

[2249] A means of analyzing users' interests and search history to collect recommended local event information;

[2250] A means for summarizing the collected local event information in a form suitable for the user;

[2251] means for generating the summarized information as voice data using a voice synthesis engine;

[2252] means for transmitting the generated voice data to a terminal and playing it back;

[2253] The system of claim 1 further comprising:

[2254] (Claim 3)

[2255] means for analyzing health management information and monitoring the user's health condition;

[2256] a means for generating advice based on the monitoring results;

[2257] means for generating the generated advice as voice data using a voice synthesis engine;

[2258] means for transmitting the generated voice data to a terminal and playing it back;

[2259] The system of claim 1 further comprising:

[2260] "Application example 2 when combining emotion engines"

[2261] (Claim 1)

[2262] A means for converting voice data into text data using natural language processing;

[2263] A means of analyzing text data and understanding user intent;

[2264] means for generating task management information based on the analysis results;

[2265] means for generating information as voice data using a voice synthesis engine;

[2266] means for providing the generated voice data to a user;

[2267] emotion recognition means for recognizing the user's emotions in real time;

[2268] means for adjusting the provided information based on the emotion recognition result;

[2269] A system including:

[2270] (Claim 2)

[2271] A means of analyzing user interest and behavior data to collect recommended local event information;

[2272] A means for summarizing the collected local event information in a form suitable for the user;

[2273] means for generating the summarized information as voice data using a voice synthesis engine;

[2274] means for providing the generated voice data to a user;

[2275] A means for recognizing a user's emotions and reflecting them in information;

[2276] The system of claim 1 further comprising:

[2277] (Claim 3)

[2278] means for generating a notification based on the task management information and notifying the user at a specified time;

[2279] means for analyzing health management information and monitoring the user's health condition;

[2280] a means for generating advice based on the monitoring results;

[2281] means for generating the generated advice as voice data using a voice synthesis engine;

[2282] means for providing the generated voice data to a user;

[2283] A means of analyzing user emotions and reflecting them in notifications and advice,

[2284] The system of claim 1 further comprising: [Explanation of symbols]

[2285] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for converting voice data into text data using natural language processing; A means of analyzing text data and understanding user intent; means for generating task management information based on the analysis results; means for generating information as voice data using a voice synthesis engine; means for providing the generated voice data to a user; A system including:

2. A means of analyzing user interest and behavior data to collect recommended local event information; A means for summarizing the collected local event information in a form suitable for the user; means for generating the summarized information as voice data using a voice synthesis engine; means for providing the generated voice data to a user; The system of claim 1 further comprising:

3. means for generating a notification based on the task management information and notifying the user at a specified time; means for analyzing health management information and monitoring the user's health condition; a means for generating advice based on the monitoring results; means for generating the generated advice as voice data using a voice synthesis engine; means for providing the generated voice data to a user; The system of claim 1 further comprising:

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