system

Abstract

A system is provided.SOLUTION: A system comprising: voice input means for collecting daily conversations of a user; voice recognition means for converting the collected voice data into text data; natural language processing means for analyzing the converted text data and generating a TODO list; notification means for notifying the user of the generated TODO list; keyword extraction means for extracting keywords and contexts from the stored data; and analysis means for performing statistical analysis based on the extracted information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, recording and managing everyday forgotten items and the contents of face-to-face conversations is an important issue for many people. For the elderly and those with dementia in particular, forgetting daily to-do items is one of the factors that reduces the quality of life. There is also a growing need to efficiently manage information obtained from face-to-face conversations and save it as a life log. Furthermore, there is a demand to analyze accumulated conversation data and use it to develop new advertising markets and understand user needs. [Means for solving the problem]

[0005] The present invention provides a voice input means for collecting a user's daily conversations and converting the voice data into text data. Next, highly accurate text data is generated using a voice recognition means for converting the collected voice data into text data. Finally, a natural language processing means is provided for analyzing the converted text data and automatically generating and managing daily to-do items. Furthermore, the system includes a function for notifying the user of the generated to-do list in a timely manner using a notification means. This helps the user avoid forgetting things and allows them to live their daily lives more smoothly.

[0006] Furthermore, by providing a keyword extraction means for extracting keywords and contexts from the saved data and an analysis means for performing statistical analysis based on the extracted information, the accumulated conversation data can be used to understand user needs and develop new advertising markets. Also, by using a reminder means with the function of tracking the user's location information and time, a system is provided that provides behavioral support tailored to the user's lifestyle by reminding them at appropriate times.

[0007] The "voice input means" is a device for collecting the user's everyday conversation in real time.

[0008] A "voice recognition means" is a device or technology that converts collected voice data into text data.

[0009] The "natural language processing means" is a processing means for analyzing the converted text data and generating a TODO list.

[0010] The "notification means" is a device or technology for notifying the user of the generated TODO list.

[0011] "Keyword extraction means" refers to processing means for extracting keywords and contexts from stored data.

[0012] "Analysis tools" are devices or techniques for performing statistical analysis on extracted information.

[0013] A "reminding means" is a device or technology that tracks the user's location information and time and reminds the user at an appropriate time.

[0014] The "audio output means" refers to a device or technology for notifying the user of everyday conversations by voice.

[0015] A "system" is a collection of multiple devices or technologies that work together to achieve a specific purpose. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[0038] System configuration and program processing

[0039] Voice input means

[0040] Terminal

[0041] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and this sound data is temporarily stored in the built-in memory.

[0042] Voice recognition means

[0043] server

[0044] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[0045] Natural language processing tools

[0046] server

[0047] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0048] Notification means

[0049] Terminal

[0050] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[0051] Keyword extraction method

[0052] server

[0053] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[0054] analytical means

[0055] server

[0056] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[0057] Reminder methods

[0058] server

[0059] The system tracks the user's location and time, and sends reminders at appropriate times. This reminder information is sent to the user's device and provided to them.

[0060] Audio output means

[0061] Terminal

[0062] Reminders and to-do list items are provided to the user via audio.

[0063] Specific examples

[0064] Example 1: Creating a Shopping List

[0065] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0066] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[0067] Device: "Milk and bread have been added to your list for tomorrow evening."

[0068] Example 2: Managing tasks with deadlines

[0069] User: "I have to turn in my project report by next Monday."

[0070] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[0071] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[0072] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[0073] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[0074] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] Terminal

[0078] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[0079] Step 2:

[0080] Terminal

[0081] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[0082] Step 3:

[0083] server

[0084] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[0085] Step 4:

[0086] server

[0087] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[0088] Step 5:

[0089] server

[0090] The generated TODO list items are organized for each user and saved in a database. The saved TODO list contents are associated with the user's profile information.

[0091] Step 6:

[0092] Terminal

[0093] Notify the user when a new to-do list item is created, and read the to-do list contents aloud if the user requests it.

[0094] Step 7:

[0095] server

[0096] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate time for a reminder, and generates a reminder notification.

[0097] Step 8:

[0098] Terminal

[0099] Provide users with voice reminders, for example, a voice reminder to "buy milk and bread" when the user arrives near a supermarket.

[0100] Step 9:

[0101] server

[0102] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[0103] Step 10:

[0104] server

[0105] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[0106] This processing step allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs.

[0107] Example 1

[0108] 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."

[0109] In modern society, users need to manage numerous to-do lists in their busy daily lives, but doing so manually is cumbersome and increases the risk of forgetting. Furthermore, few systems have the functionality to provide reminders at appropriate times, taking into account the user's location and time. Furthermore, it is important to analyze collected data and understand the user's hobbies, preferences, and behavioral patterns in order to provide personalized services and advertising suggestions.

[0110] 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.

[0111] In this invention, the server includes a speech recognition means, a natural language processing means for analyzing the converted text data and generating a to-do list, and a keyword extraction means for extracting keywords and contexts from the saved data. This allows users to automatically generate to-do lists through everyday conversations, receive reminders based on location information and time, and analyze the collected data to understand the user's behavioral patterns and provide personalized services.

[0112] "Speech input means" refers to a device for collecting the user's everyday conversation, and specifically refers to a terminal with a built-in microphone.

[0113] "Speech recognition means" refers to a technology or device that converts collected voice data into text data.

[0114] "Natural language processing means" refers to the processing technology used to analyze the converted text data and generate a TODO list.

[0115] The "notification means" refers to a method or device for notifying the user of the generated TODO list.

[0116] "Keyword extraction means" refers to a technique or device that extracts important keywords and contexts from stored data.

[0117] "Analysis means" refers to a technology or device that performs statistical analysis based on extracted keywords and context.

[0118] "Reminding means" refers to a method or device for tracking the user's location information and time and reminding the user at an appropriate time.

[0119] "Audio output means" refers to technology or equipment that provides reminder notifications and TODO list contents in audio format.

[0120] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[0121] System configuration and program processing

[0122] Voice input means

[0123] Terminal

[0124] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. This sound data is temporarily stored in the built-in memory. The high-quality microphone also has a function to remove surrounding noise.

[0125] Voice recognition means

[0126] server

[0127] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Cloud Speech-to-Text). Basic recognition error checks are also performed at this stage.

[0128] Natural language processing tools

[0129] server

[0130] The converted text data is then analyzed by a natural language processing engine (e.g., OpenAI GPT-4), which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0131] Notification means

[0132] Terminal

[0133] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[0134] Keyword extraction method

[0135] server

[0136] Natural language processing techniques (e.g., spaCy) are used to extract keywords and context from the stored text data, and this information is used for subsequent analysis.

[0137] analytical means

[0138] server

[0139] The extracted keywords and contexts are used for statistical analysis (e.g., using Python's Pandas library). Based on the results of this analysis, it is possible to understand users' interests, preferences, and behavioral patterns.

[0140] Reminder methods

[0141] server

[0142] The system tracks the user's location information (e.g., GPS data) and time, and sends reminders at appropriate times. This reminder information is sent to the device and provided to the user.

[0143] Audio output means

[0144] Terminal

[0145] Reminders and to-do list items are provided to the user via voice.

[0146] Specific examples

[0147] Example 1: Creating a Shopping List

[0148] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0149] Server: Receives the voice data and converts it into text using a speech recognition system. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[0150] Device: "Milk and bread have been added to your list for tomorrow evening."

[0151] Example 2: Managing tasks with deadlines

[0152] User: "I have to turn in my project report by next Monday."

[0153] Server: Receives the voice data and converts it into text using a speech recognition system. The information "Submit the project report by next Monday" is analyzed using a natural language processing engine and added to the TODO list as a "task with a deadline."

[0154] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[0155] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[0156] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[0157] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[0158] Examples of prompt statements

[0159] If the user is creating a shopping list, the prompt might read: "I need to buy milk and bread at the supermarket tomorrow evening."

[0160] If the user manages due tasks, the prompt reads: "I need to turn in my project report by next Monday."

[0161] The above is the "Mode for carrying out the invention", and by combining these components, the present invention realizes efficient generation of a TODO list from a user's everyday conversations and appropriate management.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] System program processing flow

[0164] Step 1: Speech input from the user

[0165] Terminal

[0166] The user speaks everyday conversation into the glasses-type device. The device collects the voice using the built-in microphone (input). The collected voice data is temporarily stored in the built-in memory (output).

[0167] How it works: When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the audio is instantly captured by the glasses-type device and stored in its internal memory.

[0168] Step 2: Sending audio data to the server

[0169] Terminal

[0170] The device transmits the collected voice data to a server over the internet in real time (input), where the data is encrypted to ensure secure communication (output).

[0171] What it does: The stored audio data is split into packets in JSON format and sent to the server using the HTTPS protocol.

[0172] Step 3: Convert audio data to text

[0173] server

[0174] The voice data received by the server is passed to a voice recognition system (e.g., Google Cloud Speech-to-Text) (input). The system converts the voice data into text data and checks for misrecognition (output).

[0175] Specific operation: The server analyzes the voice data, obtains the text data "I need to buy milk and bread at the supermarket tomorrow evening," and automatically adds punctuation.

[0176] Step 4: Natural Language Processing of Text Data

[0177] server

[0178] The converted text data is sent to a natural language processing engine (e.g., OpenAI GPT-4) (input), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation (output).

[0179] Specific operation: Keywords and context such as "tomorrow evening," "supermarket," and "buy milk and bread" are extracted from the text data and formatted as TODO list entries.

[0180] Step 5: Generate a TODO list

[0181] server

[0182] Based on the extracted information, a TODO list is automatically generated and saved in a database (input and output).

[0183] Specific behavior: An entry "Buy milk and bread at the supermarket tomorrow evening" is added to the database.

[0184] Step 6: Notify users of the TODO list

[0185] Terminal

[0186] The generated TODO list is sent to the device (glasses-type device) to notify the user (input), and the notification is made via the device's built-in speaker or display (output).

[0187] What it does: The glasses announce, "Buying milk and bread tomorrow evening has been added to your list."

[0188] Step 7: Keyword extraction and analysis

[0189] server

[0190] Extract keywords and context from stored text data and create datasets for statistical analysis (input and output).

[0191] Specific operation: The server reviews the text data and extracts frequently occurring keywords, which are then used to analyze the user's behavioral patterns and preferences.

[0192] Step 8: Set reminders and notifications

[0193] Server and Device

[0194] It tracks the user's location (e.g. GPS data) and time, and sets reminders based on that (input and output). This reminder information is sent to the device and notified to the user.

[0195] Specific behavior: For example, when the user arrives near a supermarket, the glasses-type device will remind the user by voice, "There is a supermarket nearby. Let's buy some milk and bread."

[0196] The above is the flow of processing in the program for this system, and a detailed explanation of the specific operations and data processing performed at each step.

[0197] (Application example 1)

[0198] 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."

[0199] Modern consumers must manage numerous to-do lists in their busy daily lives. However, doing so manually is time-consuming and can lead to forgetting or missing out on purchases. Furthermore, locating products in many stores can be difficult and time-consuming, making the shopping experience stressful. Therefore, there is a need for a system that can automatically generate to-do lists based on the user's everyday conversations and provide product location information within the store based on those lists.

[0200] 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.

[0201] In this invention, the server includes a voice input means for collecting the user's daily conversation, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, an analysis means for performing statistical analysis based on the extracted information, a product location information acquisition means for providing product location information based on the extracted information, and a product location notification means for notifying the user of the product location information. This enables the user to shop efficiently and avoid missing out on purchases.

[0202] The "voice input means" is a device or system that collects the user's everyday conversation in real time.

[0203] A "voice recognition means" is a device or system that converts collected voice data into text data.

[0204] The "natural language processing means" is a device or system that analyzes the converted text data and generates a TODO list.

[0205] The "notification means" is a device or system that notifies the user of the generated TODO list.

[0206] A "keyword extractor" is a device or system that extracts keywords and contexts from stored data.

[0207] "Analysis means" refers to a device or system that performs statistical analysis based on the extracted information.

[0208] The "product location information acquisition means" is a device or system that provides product location information based on the extracted information.

[0209] The "product location notification means" is a device or system that notifies the user of product location information.

[0210] The present invention is a system that collects and analyzes a user's everyday conversations to automatically generate and manage a to-do list, and also provides product location information. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a product location information acquisition means, and a product location notification means. Specific embodiments of this system are described below.

[0211] System configuration and program processing

[0212] Voice input means

[0213] Terminal

[0214] The glasses-type device worn by the user is equipped with a built-in microphone that collects surrounding sounds in real time. This sound data is temporarily stored in the built-in memory and sent to a server as needed.

[0215] Voice recognition means

[0216] server

[0217] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Speech-to-Text API). The text data undergoes basic checks to prevent misrecognition of the voice.

[0218] Natural language processing tools

[0219] server

[0220] The converted text data is then analyzed by a natural language processing engine (e.g., SpaCy), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0221] Notification means

[0222] Terminal

[0223] The generated TODO list is notified to the user through the eyeglasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[0224] Keyword extraction method

[0225] server

[0226] The stored data is processed to extract keywords and context. Natural language processing techniques are used to accurately extract keywords and context.

[0227] analytical means

[0228] server

[0229] The extracted keywords and context are used for statistical analysis (for example, using Python's pandas and scikit-learn libraries). The results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[0230] Product location information acquisition means

[0231] server

[0232] The extracted information is used to provide product location information, which is obtained from the physical store's inventory management system or through REST API calls.

[0233] Product location notification means

[0234] Terminal

[0235] The user is notified of product location information. This notification is displayed on the HUD (head-up display) of the eyeglass-type device and is also guided to the user by voice.

[0236] Specific examples

[0237] Example 1: Creating a Shopping List

[0238] If a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the system converts the speech to text, extracts the information "milk" and "bread," and generates a shopping list based on this information and notifies the user.

[0239] Example 2: Managing tasks with deadlines

[0240] If a user says, "I have to submit my project report by next Monday," the system converts the speech to text and extracts the task "Submit project report" and the deadline "Next Monday." Based on this, it generates a task with a deadline and notifies the user at the appropriate time using the reminder function.

[0241] Prompt Sentence Examples

[0242] Create an assistant app that informs users of the location of desired products while shopping. The app must receive voice input from the microphone on the smart glasses, extract the product name using natural language processing, and display the product's location information. Specifically, you will use the speech_recognition library for speech recognition and the spacy library for natural language processing, and incorporate a REST API call to obtain product location information.

[0243] The system allows users to improve shopping efficiency, remember to purchase necessary items, and ensures important tasks are completed with reminders.

[0244] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0245] Step 1:

[0246] Audio collection

[0247] When a user is having a normal conversation, the built-in microphone of the device collects the conversation in real time. The collected voice data is temporarily stored in the built-in memory. The input here is the user's voice, and the output is voice data.

[0248] Step 2:

[0249] Sending audio to a server

[0250] The collected voice data is sent to the server periodically or under certain conditions. For example, sending can start when a certain amount of voice data has been reached or when the user manually instructs it. The input here is the voice data, and the output is the voice data sent to the server.

[0251] Step 3:

[0252] Converting audio data to text

[0253] The server converts the received voice data into text data using a speech recognition system. This conversion is performed using services such as the Google Speech-to-Text API. The input here is voice data, and the output is the converted text data.

[0254] Step 4:

[0255] Text data analysis

[0256] The server analyzes the converted text data using a natural language processing engine (e.g., SpaCy). This analysis extracts the information needed for the TODO list (action items, deadlines, locations, etc.). The input here is the text data, and the output is the analyzed information.

[0257] Step 5:

[0258] Generate a TODO list

[0259] The server automatically generates a TODO list based on the parsed information. The generated TODO list is used in the following notification step. The input here is the parsed information, and the output is the generated TODO list.

[0260] Step 6:

[0261] Generate notifications

[0262] The server sends the generated TODO list to the device. The device displays the received TODO list on the HUD (Heads-Up Display) and notifies the user. It is also possible to confirm by voice. The input here is the TODO list, and the output is a notification to the user.

[0263] Step 7:

[0264] Keyword and context extraction

[0265] The server extracts keywords and context from the stored data. This process uses natural language processing techniques. The extracted information is used in subsequent analysis steps. The input here is the stored text data, and the output is the extracted keywords and context.

[0266] Step 8:

[0267] Performing statistical analysis

[0268] The server performs statistical analysis based on the extracted keywords and context. The analysis is performed using Python's pandas and scikit-learn libraries. The results are used to understand the user's interests, preferences, and needs. The input here is the extracted keywords and context, and the output is the analysis results.

[0269] Step 9:

[0270] Obtaining product location information

[0271] The server then uses the extracted information to obtain product location information from the physical store's inventory management system or by calling a REST API. The input here is keywords and context, and the output is product location information.

[0272] Step 10:

[0273] Product location notification

[0274] The server sends the acquired product location information to the terminal. The terminal displays the received location information on the HUD and, in some cases, guides the user by voice. The input here is the product location information, and the output is a notification to the user.

[0275] These are the processing steps of the system according to the present invention, which allows users to shop efficiently and remember to purchase necessary items.

[0276] 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.

[0277] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[0278] System configuration and program processing

[0279] Voice input means

[0280] Terminal

[0281] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory.

[0282] Voice recognition means

[0283] server

[0284] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[0285] Natural language processing tools

[0286] server

[0287] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0288] emotion recognition means

[0289] server

[0290] The system analyzes user emotions from text data using technology that determines emotions based on tone of voice, words used, and context.

[0291] Notification means

[0292] Terminal

[0293] The generated to-do list is notified to the user through the glasses-type device. The notification content is customized taking into account the user's emotional state. If the user requests, the contents of the to-do list can be confirmed by voice.

[0294] Keyword extraction method

[0295] server

[0296] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[0297] analytical means

[0298] server

[0299] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[0300] Reminder methods

[0301] server

[0302] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. It determines the appropriate time for a reminder and generates a reminder notification. The content of the reminder is also adjusted taking into account the user's emotions.

[0303] Audio output means

[0304] Terminal

[0305] Reminders and to-do list items are provided to the user via audio.

[0306] Specific examples

[0307] Example 1: Shopping list generation and emotion-based notifications

[0308] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0309] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[0310] Emotion recognition: Detects anxiety from the user's tone of voice.

[0311] On the device: The voice announces, "Buying milk and bread tomorrow evening has been added to your list," with a gentle tone to calm the user.

[0312] Example 2: Emotion-conscious management of deadline-bound tasks

[0313] User: "I have to turn in my project report by next Monday."

[0314] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[0315] Emotion recognition: Detects stress from the user's voice.

[0316] On your device: You'll be notified with an encouraging message: "The project report deadline has been added to your list. Good luck!"

[0317] Server: When the deadline approaches, the server will send a reminder with the user's location. The reminder will also be provided with an inspiring message.

[0318] Device: Receive a voice notification saying, "The deadline for submitting your project report is approaching. Please stay calm and work hard."

[0319] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[0320] The processing flow will be explained below.

[0321] Step 1:

[0322] Terminal

[0323] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[0324] Step 2:

[0325] Terminal

[0326] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[0327] Step 3:

[0328] server

[0329] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[0330] Step 4:

[0331] server

[0332] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[0333] Step 5:

[0334] server

[0335] The generated TODO list items are organized by user and saved in a database.

[0336] Step 6:

[0337] server

[0338] Text data is input into an emotion recognition engine to analyze the user's emotions, using technology that determines emotions based on tone of voice, words used, and context.

[0339] Step 7:

[0340] Terminal

[0341] A new to-do list item is created and the user is notified, with the notification content customized to take into account the user's emotional state - for example, if the user is stressed, the notification will be delivered in a comforting tone.

[0342] Step 8:

[0343] server

[0344] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate reminder timing, and generates a reminder notification, which is also adjusted based on the user's emotional state.

[0345] Step 9:

[0346] Terminal

[0347] Reminders are provided to users through voice. For example, a gentle reminder to "buy milk and bread at the supermarket" will be given the next evening.

[0348] Step 10:

[0349] server

[0350] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[0351] Step 11:

[0352] server

[0353] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[0354] Specific examples

[0355] Example 1: Shopping list generation and emotion-based notifications

[0356] Step 1: The user puts on the glasses and says, "I need to buy milk and bread at the supermarket tomorrow evening."

[0357] Step 2: The device collects the voice data and sends it to the server.

[0358] Step 3: The server converts the audio data into text.

[0359] Step 4: The server parses the text data and generates a TODO list.

[0360] Step 5: Save the generated TODO list in the database.

[0361] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[0362] Step 7: Your device will announce in a gentle tone, "Buying milk and bread tomorrow evening has been added to your list."

[0363] Example 2: Emotion-conscious management of deadline-bound tasks

[0364] Step 1: A user says, "I have to submit my project report by next Monday."

[0365] Step 2: The device collects the voice data and sends it to the server.

[0366] Step 3: The server converts the audio data into text.

[0367] Step 4: The server parses the text data and generates a TODO list.

[0368] Step 5: Save the generated TODO list in the database.

[0369] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[0370] Step 7: Your device will notify you with an encouraging message: "The project report submission deadline has been added to your list. Good luck!"

[0371] Step 8: The server sends a reminder of the approaching deadline along with the location information.

[0372] Step 9: The device will announce, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[0373] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[0374] Example 2

[0375] 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."

[0376] In today's busy lives, many people need to efficiently manage numerous tasks and errands. However, manually managing these tasks and errands can be difficult, and people often forget to do them. Furthermore, conventional to-do list management systems cannot take into account the user's emotional state and therefore cannot reduce the psychological burden. The present invention aims to solve these problems by providing a system that collects and analyzes the user's daily conversations and automatically generates and manages to-do lists while recognizing their emotions.

[0377] 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.

[0378] In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, an emotion recognition means for analyzing the user's emotions from the analyzed text data, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, and an analysis means for performing statistical analysis based on the extracted information. This enables the user to efficiently manage their to-do list in a way that takes emotions into consideration, thereby reducing psychological burden.

[0379] The "voice input means" is a device that collects the user's everyday conversation and surrounding voices in real time.

[0380] A "voice recognition means" is a system that converts collected voice data into text data.

[0381] "Natural language processing means" is a technology that analyzes the converted text data and generates a to-do list.

[0382] "Emotion recognition means" is a technology that analyzes the user's emotions from the analyzed text data.

[0383] The "notification means" is a system that notifies the user of the generated TODO list.

[0384] The "keyword extraction means" is a technique for extracting important keywords and contexts from stored data.

[0385] The "analysis means" is a system that performs statistical analysis based on the extracted information.

[0386] The "reminding means" is a technology that tracks the user's location information and time and reminds them at the appropriate time.

[0387] The "audio output means" is a device that notifies the user of daily conversations and to-do lists by voice.

[0388] MODE FOR CARRYING OUT THE INVENTION

[0389] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. Furthermore, by combining this system with an emotion engine, it can provide notifications and reminders while taking the user's emotions into consideration. This system includes a voice input means, a voice recognition means, a natural language processing means, an emotion recognition means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[0390] Voice input means

[0391] Terminal

[0392] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. For example, if the user says, "I need to buy milk and bread at the supermarket tomorrow evening," the sound is recorded by the glasses-type device. The collected sound data is temporarily stored in the built-in memory.

[0393] Voice recognition means

[0394] server

[0395] The collected voice data is transferred to a server via Bluetooth or Wi-Fi. The server converts the received voice data into text data using a voice recognition system (e.g., a voice recognition API). This converted text data is then checked for initial misrecognition.

[0396] Natural language processing tools

[0397] server

[0398] The converted text data is analyzed using a natural language processing engine (e.g., a natural language processing API). During this analysis, the information required for the TODO list (action items, deadlines, and locations) is automatically extracted from the conversation. For example, keywords such as "tomorrow evening," "supermarket," "milk," and "bread" are analyzed.

[0399] emotion recognition means

[0400] server

[0401] The system analyzes user emotions from text data. This analysis uses an emotion recognition API. It determines the user's emotional state based on the tone of voice, the words used, and the context. For example, if the user is feeling stressed, that information can also be detected.

[0402] Notification means

[0403] Terminal

[0404] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[0405] Keyword extraction method

[0406] server

[0407] The process involves extracting keywords and context from the stored audio and text data. This process uses natural language processing techniques such as TF-IDF and BERT. The most important keywords are extracted from the user's everyday conversations and used for subsequent analysis.

[0408] analytical means

[0409] server

[0410] Statistical analysis is performed on the extracted keywords and phrases. This analysis uses statistical analysis tools and in-house developed models to understand user needs, preferences, and help propose new advertisements. For example, if a user repeatedly uses the words "supermarket" or "milk," data is generated to suggest related advertisements.

[0411] Reminder methods

[0412] server

[0413] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. For example, if there is a to-do list set for "tomorrow evening," a reminder notification is generated based on this information. Reminder notifications are also customized with emotions in mind.

[0414] Terminal

[0415] The generated reminder notification is sent to the user via audio through the glasses-type device. For example, the notification may say, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[0416] Audio output means

[0417] Terminal

[0418] Reminders and to-do list items are provided to the user via voice using a speech generation API, for example, "Buying milk and bread tomorrow evening has been added to your list."

[0419] Specific examples

[0420] Example 1: Shopping list generation and emotion-based notifications

[0421] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0422] Server: Receives the voice data and converts it into text using a speech recognition API. The text "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing API and added to the TODO list.

[0423] Emotion recognition: Recognizes anxiety from the user's tone of voice.

[0424] Device: The speech generation API notifies the user by saying, "Buying milk and bread tomorrow evening has been added to the list," in a gentle tone to calm the user.

[0425] Example 2: Managing deadline-bound tasks with consideration for emotions

[0426] User: "I have to turn in my project report by next Monday."

[0427] Server: Receives the voice data and converts it into text using a speech recognition API. The text "Submit the project report by next Monday" is analyzed using a natural language processing API and added to the TODO list.

[0428] Emotion recognition: Detects stress from the user's voice.

[0429] Device: The voice generation API sends encouraging words such as, "The project report submission deadline has been added to the list. Let's do our best!"

[0430] Server: When the deadline for submission approaches, the server sends a reminder to the user along with their location information obtained via GPS. The reminder also provides an encouraging message to the user.

[0431] Device: The voice generation API notifies the user, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[0432] In this way, the present invention can efficiently manage the user's daily to-do items, provide appropriate reminders at the necessary times, and provide a more comfortable user experience by taking the user's emotions into consideration.

[0433] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0434] A detailed explanation of the program processing flow for this system

[0435] Step 1: Collecting audio data

[0436] Terminal

[0437] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time.

[0438] Input: User's everyday conversations and surrounding sounds

[0439] Output: Collected audio data (temporarily stored in internal memory)

[0440] Step 2: Transferring audio data

[0441] Terminal

[0442] The collected audio data is transferred to a server via Bluetooth or Wi-Fi.

[0443] Input: Collected voice data (voice data stored in the internal memory)

[0444] Output: Audio data sent to the server

[0445] Step 3: Voice Recognition

[0446] server

[0447] The received voice data is converted to text data using a speech recognition API, and this converted text data is checked for initial misrecognition.

[0448] Input: Audio data transferred to the server

[0449] Output: Text data (converted by speech recognition API)

[0450] Step 4: Natural Language Processing Analysis

[0451] server

[0452] The text data is analyzed using a natural language processing engine to extract the information needed for the TODO list (action items, deadlines, locations).

[0453] Input: Text data from step 3

[0454] Output: Extracted TODO list related information (analysis results using natural language processing API)

[0455] Step 5: Emotion Recognition

[0456] server

[0457] The emotion recognition API analyzes user emotions from text data, determining their emotional state based on the tone of voice, words used, and context.

[0458] Input: Text data from step 4

[0459] Output: User's emotional state (analysis results from emotion recognition API)

[0460] Step 6: Generate a TODO list

[0461] server

[0462] A TODO list is automatically generated based on the analyzed information and managed within the server.

[0463] Input: TODO list related information in step 4, user's emotional state in step 5

[0464] Output: Generated TODO list

[0465] Step 7: Notify users

[0466] Terminal

[0467] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[0468] Input: Generated TODO list, user's emotional state

[0469] Output: Audio notification to the user

[0470] Step 8: Keyword extraction

[0471] server

[0472] The process involves extracting keywords and context from the stored audio and text data using natural language processing techniques such as TF-IDF and BERT.

[0473] Input: Text data and its analysis results

[0474] Output: Extracted keywords and context information

[0475] Step 9: Statistical analysis

[0476] server

[0477] Statistical analysis is performed based on the extracted keywords and phrases, which can be used to understand user needs, interests, and preferences, and to suggest new advertisements.

[0478] Input: Extracted keywords and context information

[0479] Output: Statistical analysis results and proposed data

[0480] Step 10: Reminders

[0481] server

[0482] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, customizes the reminder content at the appropriate time, and sends the generated reminder notification to the user.

[0483] Input: User location, time, TODO list, user emotional state

[0484] Output: Generate reminder notification

[0485] Step 11: Audio output for reminder notifications

[0486] Terminal

[0487] The generated reminder notification is then provided to the user via audio through the glasses-type device. For example, the notification might say, "The deadline for submitting a project report is approaching. Please stay calm and work on it."

[0488] Input: Reminder notification

[0489] Output: Audio notification to the user

[0490] In this way, by performing specific processing at each step, users can efficiently manage their daily to-do items and receive reminders at the appropriate time.In addition, by linking the components, the system provides a more comfortable user experience by taking into account the user's emotions.

[0491] (Application example 2)

[0492] 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."

[0493] Modern brick-and-mortar shopping experiences lack sufficient consideration for users' individual needs and emotional states, making it difficult to provide an efficient and pleasant shopping experience. This problem is particularly pronounced when users experience emotional stress while shopping. This can lead to users overlooking necessary items or losing motivation to purchase. Therefore, it is important to develop a system that can automatically generate a to-do list based on the user's everyday conversations, provide reminders while taking their emotions into account, and provide an efficient and pleasant shopping experience.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for customizing the generated to-do list based on the user's emotional state and notifying the user, a keyword extraction means for extracting keywords and context from the stored data, and an analysis means for performing statistical analysis on the extracted information and providing the data analysis results taking the user's emotional state into consideration. This enables automatic generation of a to-do list based on the user's daily conversations and emotional reminders, thereby making the user's shopping experience efficient and comfortable.

[0495] The "voice input means" is a means for collecting the user's everyday conversations and surrounding voices.

[0496] "Speech recognition means" is a means for converting voice data into text data.

[0497] The "natural language processing means" is a means for analyzing the converted text data, extracting necessary information, and processing it.

[0498] The "notification means" is a means for notifying the user of the generated data or information.

[0499] The "keyword extraction means" is a means for extracting important keywords and contexts from the stored data.

[0500] "Analysis means" refers to means for conducting statistical analysis or other data analysis based on the extracted information.

[0501] The "reminding means" is a means for notifying or reminding the user at an appropriate time by using the user's location information and time.

[0502] The "audio output means" is a means for providing information to the user by voice.

[0503] The "emotional state" is a psychological state determined from the user's tone of voice and speech content.

[0504] A "TODO list" is a list of tasks or action items that a user needs to accomplish.

[0505] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[0506] System configuration

[0507] Voice input means

[0508] Terminal

[0509] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory and later transmitted to a server.

[0510] Voice recognition means

[0511] server

[0512] The voice data sent from the terminal is converted into text data by a voice recognition system on the server, which can use, for example, the Google Speech Recognition API.

[0513] Natural language processing tools

[0514] server

[0515] The converted text data is analyzed by a natural language processing engine, which automatically extracts the information necessary for the TODO list from the user's everyday conversations.

[0516] emotion recognition means

[0517] server

[0518] Analyze user emotions from text data. This analysis uses machine learning techniques such as transformer models. For example, we use the Hugging Face transformers library to load an emotion model and recognize emotions from text.

[0519] Notification means

[0520] Terminal

[0521] The generated TODO list is then sent to the user via a glasses-type device, where the notification content is customized based on emotion recognition and provided as a voice notification.

[0522] Keyword extraction method

[0523] server

[0524] Important keywords and context are extracted from the stored data using natural language processing technology. The extracted keywords are used to understand user needs and interests.

[0525] analytical means

[0526] server

[0527] Statistical analysis is performed based on the extracted keywords and context, which allows us to analyze user preferences and behavioral patterns and provide data analysis results that take into account their emotional state.

[0528] Reminder methods

[0529] server

[0530] The system uses the user's location and time to send reminders at appropriate times, and the content of the reminders is also adjusted according to the user's emotional state.

[0531] Audio output means

[0532] Terminal

[0533] Reminders and to-do list items are provided to the user via voice, for example, using the pyttsx3 library for speech synthesis.

[0534] Specific examples

[0535] Example 1: Shopping list generation and emotion-based notifications

[0536] When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the server receives this as voice data and converts it into text. The natural language processing engine then registers this information as a "shopping list" in the TODO list. Furthermore, the emotion recognition function detects anxiousness in the user's voice tone, and the device issues a gentle voice notification saying, "Buying milk and bread tomorrow evening has been added to the list."

[0537] Example 2: Emotion-conscious management of deadline-bound tasks

[0538] When a user says, "I have to submit my project report by next Monday," the server receives this as voice data and converts it into text. A natural language processing engine registers this information as a "task with a deadline" in the TODO list. Furthermore, emotion recognition detects the user's stress and notifies them with encouraging words, such as, "The deadline for submitting the project report has been added to the list. Let's do our best!" Furthermore, when the deadline approaches, the server sends a reminder, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[0539] Example prompts to input to the generative AI model

[0540] User input:

[0541] "I need to buy some milk and bread."

[0542] Expected output:

[0543] Emotion: Stress

[0544] Notification message: "Milk and bread added to the list. Good luck!"

[0545] User input:

[0546] "I want a new book."

[0547] Expected output:

[0548] Emotion: Enjoyment

[0549] Notification message: "You've added a new book to your list. Keep it up!"

[0550] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0551] Step 1:

[0552] The user wears the glasses-type device and performs voice input.

[0553] Input: User's everyday conversation (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[0554] Specific operation: The built-in microphone of the glasses-type device collects surrounding sounds in real time and temporarily stores the sound data in the device's built-in memory.

[0555] Step 2:

[0556] The terminal transmits the collected voice data to the server.

[0557] Input: Audio data stored in the device's internal memory

[0558] Specific operation: The device sends voice data to the server via the Internet.

[0559] Step 3:

[0560] The server converts the voice data into text data.

[0561] Input: Audio data sent to the server

[0562] Output: Text data corresponding to the speech data (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[0563] Specific operation: The server uses the Google Speech Recognition API to convert the voice data into text data.

[0564] Step 4:

[0565] The server analyzes the text data using a natural language processing engine and extracts the information necessary for the TODO list.

[0566] Input: Text data generated by a speech recognition tool

[0567] Output: TODO list items (e.g. "Buy milk and bread at the supermarket tomorrow evening")

[0568] Specific operation: The server's natural language processing engine analyzes the text data and extracts the information necessary for the TODO list (date, time, location, action items, etc.).

[0569] Step 5:

[0570] The server uses an emotion recognition engine to analyze the user's emotions from the text data.

[0571] Input: Text data processed by natural language processing means

[0572] Output: User's emotional state (e.g., anxious)

[0573] Specific operation: The server loads the emotion model using the Hugging Face transformers library and recognizes emotions from text data.

[0574] Step 6:

[0575] The device will notify the user of the TODO list based on their emotions.

[0576] Input: TODO list items and emotional state sent from the server

[0577] Output: A notification message to the user (e.g. "Buying milk and bread tomorrow evening has been added to your list")

[0578] Specific behavior: The device generates voice notifications using a speech synthesis engine (e.g., the pyttsx3 library) and adjusts the tone and content depending on the user's emotional state.

[0579] Step 7:

[0580] The server extracts keywords and context from the stored data.

[0581] Input: Text and audio data

[0582] Output: Extracted keywords and context

[0583] Specific operation: The server uses natural language processing techniques to extract important keywords and context from the stored data.

[0584] Step 8:

[0585] The server performs statistical analysis based on the extracted keywords and context.

[0586] Input: Extracted keywords and context

[0587] Output: Analysis results (user preferences and behavioral patterns)

[0588] Specific operation: The server uses statistical analysis techniques to analyze the user's preferences and behavioral patterns.

[0589] Step 9:

[0590] The server uses the user's location and time to generate appropriate reminders.

[0591] Input: User's location, time, and TODO list information

[0592] Output: Reminder notification

[0593] Specific operation: The server generates a reminder notification at the appropriate time based on GPS data and timer information, comparing it with the contents of the TODO list.

[0594] Step 10:

[0595] The device provides the user with a voice reminder notification.

[0596] Input: Reminder notification information sent from the server

[0597] Output: Reminder audio notification to the user

[0598] Specific operation: The device uses a speech synthesis engine to generate a reminder notification and provides it to the user aloud.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Second embodiment]

[0603] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0614] 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."

[0615] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[0616] System configuration and program processing

[0617] Voice input means

[0618] Terminal

[0619] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and this sound data is temporarily stored in the built-in memory.

[0620] Voice recognition means

[0621] server

[0622] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[0623] Natural language processing tools

[0624] server

[0625] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0626] Notification means

[0627] Terminal

[0628] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[0629] Keyword extraction method

[0630] server

[0631] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[0632] analytical means

[0633] server

[0634] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[0635] Reminder methods

[0636] server

[0637] The system tracks the user's location and time, and sends reminders at appropriate times. This reminder information is sent to the user's device and provided to them.

[0638] Audio output means

[0639] Terminal

[0640] Reminders and to-do list items are provided to the user via audio.

[0641] Specific examples

[0642] Example 1: Creating a Shopping List

[0643] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0644] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[0645] Device: "Milk and bread have been added to your list for tomorrow evening."

[0646] Example 2: Managing tasks with deadlines

[0647] User: "I have to turn in my project report by next Monday."

[0648] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[0649] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[0650] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[0651] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[0652] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[0653] The processing flow will be explained below.

[0654] Step 1:

[0655] Terminal

[0656] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[0657] Step 2:

[0658] Terminal

[0659] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[0660] Step 3:

[0661] server

[0662] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[0663] Step 4:

[0664] server

[0665] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[0666] Step 5:

[0667] server

[0668] The generated TODO list items are organized for each user and saved in a database. The saved TODO list contents are associated with the user's profile information.

[0669] Step 6:

[0670] Terminal

[0671] Notify the user when a new to-do list item is created, and read the to-do list contents aloud if the user requests it.

[0672] Step 7:

[0673] server

[0674] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate time for a reminder, and generates a reminder notification.

[0675] Step 8:

[0676] Terminal

[0677] Provide users with voice reminders, for example, a voice reminder to "buy milk and bread" when the user arrives near a supermarket.

[0678] Step 9:

[0679] server

[0680] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[0681] Step 10:

[0682] server

[0683] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[0684] This processing step allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs.

[0685] Example 1

[0686] 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."

[0687] In modern society, users need to manage numerous to-do lists in their busy daily lives, but doing so manually is cumbersome and increases the risk of forgetting. Furthermore, few systems have the functionality to provide reminders at appropriate times, taking into account the user's location and time. Furthermore, it is important to analyze collected data and understand the user's hobbies, preferences, and behavioral patterns in order to provide personalized services and advertising suggestions.

[0688] 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.

[0689] In this invention, the server includes a speech recognition means, a natural language processing means for analyzing the converted text data and generating a to-do list, and a keyword extraction means for extracting keywords and contexts from the saved data. This allows users to automatically generate to-do lists through everyday conversations, receive reminders based on location information and time, and analyze the collected data to understand the user's behavioral patterns and provide personalized services.

[0690] "Speech input means" refers to a device for collecting the user's everyday conversation, and specifically refers to a terminal with a built-in microphone.

[0691] "Speech recognition means" refers to a technology or device that converts collected voice data into text data.

[0692] "Natural language processing means" refers to the processing technology used to analyze the converted text data and generate a TODO list.

[0693] The "notification means" refers to a method or device for notifying the user of the generated TODO list.

[0694] "Keyword extraction means" refers to a technique or device that extracts important keywords and contexts from stored data.

[0695] "Analysis means" refers to a technology or device that performs statistical analysis based on extracted keywords and context.

[0696] "Reminding means" refers to a method or device for tracking the user's location information and time and reminding the user at an appropriate time.

[0697] "Audio output means" refers to technology or equipment that provides reminder notifications and TODO list contents in audio format.

[0698] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[0699] System configuration and program processing

[0700] Voice input means

[0701] Terminal

[0702] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. This sound data is temporarily stored in the built-in memory. The high-quality microphone also has a function to remove surrounding noise.

[0703] Voice recognition means

[0704] server

[0705] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Cloud Speech-to-Text). Basic recognition error checks are also performed at this stage.

[0706] Natural language processing tools

[0707] server

[0708] The converted text data is then analyzed by a natural language processing engine (e.g., OpenAI GPT-4), which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0709] Notification means

[0710] Terminal

[0711] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[0712] Keyword extraction method

[0713] server

[0714] Natural language processing techniques (e.g., spaCy) are used to extract keywords and context from the stored text data, and this information is used for subsequent analysis.

[0715] analytical means

[0716] server

[0717] The extracted keywords and contexts are used for statistical analysis (e.g., using Python's Pandas library). Based on the results of this analysis, it is possible to understand users' interests, preferences, and behavioral patterns.

[0718] Reminder methods

[0719] server

[0720] The system tracks the user's location information (e.g., GPS data) and time, and sends reminders at appropriate times. This reminder information is sent to the device and provided to the user.

[0721] Audio output means

[0722] Terminal

[0723] Reminders and to-do list items are provided to the user via voice.

[0724] Specific examples

[0725] Example 1: Creating a Shopping List

[0726] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0727] Server: Receives the voice data and converts it into text using a speech recognition system. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[0728] Device: "Milk and bread have been added to your list for tomorrow evening."

[0729] Example 2: Managing tasks with deadlines

[0730] User: "I have to turn in my project report by next Monday."

[0731] Server: Receives the voice data and converts it into text using a speech recognition system. The information "Submit the project report by next Monday" is analyzed using a natural language processing engine and added to the TODO list as a "task with a deadline."

[0732] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[0733] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[0734] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[0735] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[0736] Examples of prompt statements

[0737] If the user is creating a shopping list, the prompt might read: "I need to buy milk and bread at the supermarket tomorrow evening."

[0738] If the user manages due tasks, the prompt reads: "I need to turn in my project report by next Monday."

[0739] The above is the "Mode for carrying out the invention", and by combining these components, the present invention realizes efficient generation of a TODO list from a user's everyday conversations and appropriate management.

[0740] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0741] System program processing flow

[0742] Step 1: Speech input from the user

[0743] Terminal

[0744] The user speaks everyday conversation into the glasses-type device. The device collects the voice using the built-in microphone (input). The collected voice data is temporarily stored in the built-in memory (output).

[0745] How it works: When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the audio is instantly captured by the glasses-type device and stored in its internal memory.

[0746] Step 2: Sending audio data to the server

[0747] Terminal

[0748] The device transmits the collected voice data to a server over the internet in real time (input), where the data is encrypted to ensure secure communication (output).

[0749] What it does: The stored audio data is split into packets in JSON format and sent to the server using the HTTPS protocol.

[0750] Step 3: Convert audio data to text

[0751] server

[0752] The voice data received by the server is passed to a voice recognition system (e.g., Google Cloud Speech-to-Text) (input). The system converts the voice data into text data and checks for misrecognition (output).

[0753] Specific operation: The server analyzes the voice data, obtains the text data "I need to buy milk and bread at the supermarket tomorrow evening," and automatically adds punctuation.

[0754] Step 4: Natural Language Processing of Text Data

[0755] server

[0756] The converted text data is sent to a natural language processing engine (e.g., OpenAI GPT-4) (input), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation (output).

[0757] Specific operation: Keywords and context such as "tomorrow evening," "supermarket," and "buy milk and bread" are extracted from the text data and formatted as TODO list entries.

[0758] Step 5: Generate a TODO list

[0759] server

[0760] Based on the extracted information, a TODO list is automatically generated and saved in a database (input and output).

[0761] Specific behavior: An entry "Buy milk and bread at the supermarket tomorrow evening" is added to the database.

[0762] Step 6: Notify users of the TODO list

[0763] Terminal

[0764] The generated TODO list is sent to the device (glasses-type device) to notify the user (input), and the notification is made via the device's built-in speaker or display (output).

[0765] What it does: The glasses announce, "Buying milk and bread tomorrow evening has been added to your list."

[0766] Step 7: Keyword extraction and analysis

[0767] server

[0768] Extract keywords and context from stored text data and create datasets for statistical analysis (input and output).

[0769] Specific operation: The server reviews the text data and extracts frequently occurring keywords, which are then used to analyze the user's behavioral patterns and preferences.

[0770] Step 8: Set reminders and notifications

[0771] Server and Device

[0772] It tracks the user's location (e.g. GPS data) and time, and sets reminders based on that (input and output). This reminder information is sent to the device and notified to the user.

[0773] Specific behavior: For example, when the user arrives near a supermarket, the glasses-type device will remind the user by voice, "There is a supermarket nearby. Let's buy some milk and bread."

[0774] The above is the flow of processing in the program for this system, and a detailed explanation of the specific operations and data processing performed at each step.

[0775] (Application example 1)

[0776] 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."

[0777] Modern consumers must manage numerous to-do lists in their busy daily lives. However, doing so manually is time-consuming and can lead to forgetting or missing out on purchases. Furthermore, locating products in many stores can be difficult and time-consuming, making the shopping experience stressful. Therefore, there is a need for a system that can automatically generate to-do lists based on the user's everyday conversations and provide product location information within the store based on those lists.

[0778] 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.

[0779] In this invention, the server includes a voice input means for collecting the user's daily conversation, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, an analysis means for performing statistical analysis based on the extracted information, a product location information acquisition means for providing product location information based on the extracted information, and a product location notification means for notifying the user of the product location information. This enables the user to shop efficiently and avoid missing out on purchases.

[0780] The "voice input means" is a device or system that collects the user's everyday conversation in real time.

[0781] A "voice recognition means" is a device or system that converts collected voice data into text data.

[0782] The "natural language processing means" is a device or system that analyzes the converted text data and generates a TODO list.

[0783] The "notification means" is a device or system that notifies the user of the generated TODO list.

[0784] A "keyword extractor" is a device or system that extracts keywords and contexts from stored data.

[0785] "Analysis means" refers to a device or system that performs statistical analysis based on the extracted information.

[0786] The "product location information acquisition means" is a device or system that provides product location information based on the extracted information.

[0787] The "product location notification means" is a device or system that notifies the user of product location information.

[0788] The present invention is a system that collects and analyzes a user's everyday conversations to automatically generate and manage a to-do list, and also provides product location information. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a product location information acquisition means, and a product location notification means. Specific embodiments of this system are described below.

[0789] System configuration and program processing

[0790] Voice input means

[0791] Terminal

[0792] The glasses-type device worn by the user is equipped with a built-in microphone that collects surrounding sounds in real time. This sound data is temporarily stored in the built-in memory and sent to a server as needed.

[0793] Voice recognition means

[0794] server

[0795] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Speech-to-Text API). The text data undergoes basic checks to prevent misrecognition of the voice.

[0796] Natural language processing tools

[0797] server

[0798] The converted text data is then analyzed by a natural language processing engine (e.g., SpaCy), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0799] Notification means

[0800] Terminal

[0801] The generated TODO list is notified to the user through the eyeglasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[0802] Keyword extraction method

[0803] server

[0804] The stored data is processed to extract keywords and context. Natural language processing techniques are used to accurately extract keywords and context.

[0805] analytical means

[0806] server

[0807] The extracted keywords and context are used for statistical analysis (for example, using Python's pandas and scikit-learn libraries). The results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[0808] Product location information acquisition means

[0809] server

[0810] The extracted information is used to provide product location information, which is obtained from the physical store's inventory management system or through REST API calls.

[0811] Product location notification means

[0812] Terminal

[0813] The user is notified of product location information. This notification is displayed on the HUD (head-up display) of the eyeglass-type device and is also guided to the user by voice.

[0814] Specific examples

[0815] Example 1: Creating a Shopping List

[0816] If a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the system converts the speech to text, extracts the information "milk" and "bread," and generates a shopping list based on this information and notifies the user.

[0817] Example 2: Managing tasks with deadlines

[0818] If a user says, "I have to submit my project report by next Monday," the system converts the speech to text and extracts the task "Submit project report" and the deadline "Next Monday." Based on this, it generates a task with a deadline and notifies the user at the appropriate time using the reminder function.

[0819] Prompt Sentence Examples

[0820] Create an assistant app that informs users of the location of desired products while shopping. The app must receive voice input from the microphone on the smart glasses, extract the product name using natural language processing, and display the product's location information. Specifically, you will use the speech_recognition library for speech recognition and the spacy library for natural language processing, and incorporate a REST API call to obtain product location information.

[0821] The system allows users to improve shopping efficiency, remember to purchase necessary items, and ensures important tasks are completed with reminders.

[0822] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0823] Step 1:

[0824] Audio collection

[0825] When a user is having a normal conversation, the built-in microphone of the device collects the conversation in real time. The collected voice data is temporarily stored in the built-in memory. The input here is the user's voice, and the output is voice data.

[0826] Step 2:

[0827] Sending audio to a server

[0828] The collected voice data is sent to the server periodically or under certain conditions. For example, sending can start when a certain amount of voice data has been reached or when the user manually instructs it. The input here is the voice data, and the output is the voice data sent to the server.

[0829] Step 3:

[0830] Converting audio data to text

[0831] The server converts the received voice data into text data using a speech recognition system. This conversion is performed using services such as the Google Speech-to-Text API. The input here is voice data, and the output is the converted text data.

[0832] Step 4:

[0833] Text data analysis

[0834] The server analyzes the converted text data using a natural language processing engine (e.g., SpaCy). This analysis extracts the information needed for the TODO list (action items, deadlines, locations, etc.). The input here is the text data, and the output is the analyzed information.

[0835] Step 5:

[0836] Generate a TODO list

[0837] The server automatically generates a TODO list based on the parsed information. The generated TODO list is used in the following notification step. The input here is the parsed information, and the output is the generated TODO list.

[0838] Step 6:

[0839] Generate notifications

[0840] The server sends the generated TODO list to the device. The device displays the received TODO list on the HUD (Heads-Up Display) and notifies the user. It is also possible to confirm by voice. The input here is the TODO list, and the output is a notification to the user.

[0841] Step 7:

[0842] Keyword and context extraction

[0843] The server extracts keywords and context from the stored data. This process uses natural language processing techniques. The extracted information is used in subsequent analysis steps. The input here is the stored text data, and the output is the extracted keywords and context.

[0844] Step 8:

[0845] Performing statistical analysis

[0846] The server performs statistical analysis based on the extracted keywords and context. The analysis is performed using Python's pandas and scikit-learn libraries. The results are used to understand the user's interests, preferences, and needs. The input here is the extracted keywords and context, and the output is the analysis results.

[0847] Step 9:

[0848] Obtaining product location information

[0849] The server then uses the extracted information to obtain product location information from the physical store's inventory management system or by calling a REST API. The input here is keywords and context, and the output is product location information.

[0850] Step 10:

[0851] Product location notification

[0852] The server sends the acquired product location information to the terminal. The terminal displays the received location information on the HUD and, in some cases, guides the user by voice. The input here is the product location information, and the output is a notification to the user.

[0853] These are the processing steps of the system according to the present invention, which allows users to shop efficiently and remember to purchase necessary items.

[0854] 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.

[0855] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[0856] System configuration and program processing

[0857] Voice input means

[0858] Terminal

[0859] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory.

[0860] Voice recognition means

[0861] server

[0862] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[0863] Natural language processing tools

[0864] server

[0865] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[0866] emotion recognition means

[0867] server

[0868] The system analyzes user emotions from text data using technology that determines emotions based on tone of voice, words used, and context.

[0869] Notification means

[0870] Terminal

[0871] The generated to-do list is notified to the user through the glasses-type device. The notification content is customized taking into account the user's emotional state. If the user requests, the contents of the to-do list can be confirmed by voice.

[0872] Keyword extraction method

[0873] server

[0874] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[0875] analytical means

[0876] server

[0877] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[0878] Reminder methods

[0879] server

[0880] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. It determines the appropriate time for a reminder and generates a reminder notification. The content of the reminder is also adjusted taking into account the user's emotions.

[0881] Audio output means

[0882] Terminal

[0883] Reminders and to-do list items are provided to the user via audio.

[0884] Specific examples

[0885] Example 1: Shopping list generation and emotion-based notifications

[0886] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[0887] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[0888] Emotion recognition: Detects anxiety from the user's tone of voice.

[0889] On the device: The voice announces, "Buying milk and bread tomorrow evening has been added to your list," with a gentle tone to calm the user.

[0890] Example 2: Emotion-conscious management of deadline-bound tasks

[0891] User: "I have to turn in my project report by next Monday."

[0892] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[0893] Emotion recognition: Detects stress from the user's voice.

[0894] On your device: You'll be notified with an encouraging message: "The project report deadline has been added to your list. Good luck!"

[0895] Server: When the deadline approaches, the server will send a reminder with the user's location. The reminder will also be provided with an inspiring message.

[0896] Device: Receive a voice notification saying, "The deadline for submitting your project report is approaching. Please stay calm and work hard."

[0897] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[0898] The processing flow will be explained below.

[0899] Step 1:

[0900] Terminal

[0901] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[0902] Step 2:

[0903] Terminal

[0904] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[0905] Step 3:

[0906] server

[0907] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[0908] Step 4:

[0909] server

[0910] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[0911] Step 5:

[0912] server

[0913] The generated TODO list items are organized by user and saved in a database.

[0914] Step 6:

[0915] server

[0916] Text data is input into an emotion recognition engine to analyze the user's emotions, using technology that determines emotions based on tone of voice, words used, and context.

[0917] Step 7:

[0918] Terminal

[0919] A new to-do list item is created and the user is notified, with the notification content customized to take into account the user's emotional state - for example, if the user is stressed, the notification will be delivered in a comforting tone.

[0920] Step 8:

[0921] server

[0922] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate reminder timing, and generates a reminder notification, which is also adjusted based on the user's emotional state.

[0923] Step 9:

[0924] Terminal

[0925] Reminders are provided to users through voice. For example, a gentle reminder to "buy milk and bread at the supermarket" will be given the next evening.

[0926] Step 10:

[0927] server

[0928] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[0929] Step 11:

[0930] server

[0931] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[0932] Specific examples

[0933] Example 1: Shopping list generation and emotion-based notifications

[0934] Step 1: The user puts on the glasses and says, "I need to buy milk and bread at the supermarket tomorrow evening."

[0935] Step 2: The device collects the voice data and sends it to the server.

[0936] Step 3: The server converts the audio data into text.

[0937] Step 4: The server parses the text data and generates a TODO list.

[0938] Step 5: Save the generated TODO list in the database.

[0939] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[0940] Step 7: Your device will announce in a gentle tone, "Buying milk and bread tomorrow evening has been added to your list."

[0941] Example 2: Emotion-conscious management of deadline-bound tasks

[0942] Step 1: A user says, "I have to submit my project report by next Monday."

[0943] Step 2: The device collects the voice data and sends it to the server.

[0944] Step 3: The server converts the audio data into text.

[0945] Step 4: The server parses the text data and generates a TODO list.

[0946] Step 5: Save the generated TODO list in the database.

[0947] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[0948] Step 7: Your device will notify you with an encouraging message: "The project report submission deadline has been added to your list. Good luck!"

[0949] Step 8: The server sends a reminder of the approaching deadline along with the location information.

[0950] Step 9: The device will announce, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[0951] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[0952] Example 2

[0953] 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."

[0954] In today's busy lives, many people need to efficiently manage numerous tasks and errands. However, manually managing these tasks and errands can be difficult, and people often forget to do them. Furthermore, conventional to-do list management systems cannot take into account the user's emotional state and therefore cannot reduce the psychological burden. The present invention aims to solve these problems by providing a system that collects and analyzes the user's daily conversations and automatically generates and manages to-do lists while recognizing their emotions.

[0955] 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.

[0956] In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, an emotion recognition means for analyzing the user's emotions from the analyzed text data, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, and an analysis means for performing statistical analysis based on the extracted information. This enables the user to efficiently manage their to-do list in a way that takes emotions into consideration, thereby reducing psychological burden.

[0957] The "voice input means" is a device that collects the user's everyday conversation and surrounding voices in real time.

[0958] A "voice recognition means" is a system that converts collected voice data into text data.

[0959] "Natural language processing means" is a technology that analyzes the converted text data and generates a to-do list.

[0960] "Emotion recognition means" is a technology that analyzes the user's emotions from the analyzed text data.

[0961] The "notification means" is a system that notifies the user of the generated TODO list.

[0962] The "keyword extraction means" is a technique for extracting important keywords and contexts from stored data.

[0963] The "analysis means" is a system that performs statistical analysis based on the extracted information.

[0964] The "reminding means" is a technology that tracks the user's location information and time and reminds them at the appropriate time.

[0965] The "audio output means" is a device that notifies the user of daily conversations and to-do lists by voice.

[0966] MODE FOR CARRYING OUT THE INVENTION

[0967] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. Furthermore, by combining this system with an emotion engine, it can provide notifications and reminders while taking the user's emotions into consideration. This system includes a voice input means, a voice recognition means, a natural language processing means, an emotion recognition means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[0968] Voice input means

[0969] Terminal

[0970] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. For example, if the user says, "I need to buy milk and bread at the supermarket tomorrow evening," the sound is recorded by the glasses-type device. The collected sound data is temporarily stored in the built-in memory.

[0971] Voice recognition means

[0972] server

[0973] The collected voice data is transferred to a server via Bluetooth or Wi-Fi. The server converts the received voice data into text data using a voice recognition system (e.g., a voice recognition API). This converted text data is then checked for initial misrecognition.

[0974] Natural language processing tools

[0975] server

[0976] The converted text data is analyzed using a natural language processing engine (e.g., a natural language processing API). During this analysis, the information required for the TODO list (action items, deadlines, and locations) is automatically extracted from the conversation. For example, keywords such as "tomorrow evening," "supermarket," "milk," and "bread" are analyzed.

[0977] emotion recognition means

[0978] server

[0979] The system analyzes user emotions from text data. This analysis uses an emotion recognition API. It determines the user's emotional state based on the tone of voice, the words used, and the context. For example, if the user is feeling stressed, that information can also be detected.

[0980] Notification means

[0981] Terminal

[0982] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[0983] Keyword extraction method

[0984] server

[0985] The process involves extracting keywords and context from the stored audio and text data. This process uses natural language processing techniques such as TF-IDF and BERT. The most important keywords are extracted from the user's everyday conversations and used for subsequent analysis.

[0986] analytical means

[0987] server

[0988] Statistical analysis is performed on the extracted keywords and phrases. This analysis uses statistical analysis tools and in-house developed models to understand user needs, preferences, and help propose new advertisements. For example, if a user repeatedly uses the words "supermarket" or "milk," data is generated to suggest related advertisements.

[0989] Reminder methods

[0990] server

[0991] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. For example, if there is a to-do list set for "tomorrow evening," a reminder notification is generated based on this information. Reminder notifications are also customized with emotions in mind.

[0992] Terminal

[0993] The generated reminder notification is sent to the user via audio through the glasses-type device. For example, the notification may say, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[0994] Audio output means

[0995] Terminal

[0996] Reminders and to-do list items are provided to the user via voice using a speech generation API, for example, "Buying milk and bread tomorrow evening has been added to your list."

[0997] Specific examples

[0998] Example 1: Shopping list generation and emotion-based notifications

[0999] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1000] Server: Receives the voice data and converts it into text using a speech recognition API. The text "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing API and added to the TODO list.

[1001] Emotion recognition: Recognizes anxiety from the user's tone of voice.

[1002] Device: The speech generation API notifies the user by saying, "Buying milk and bread tomorrow evening has been added to the list," in a gentle tone to calm the user.

[1003] Example 2: Managing deadline-bound tasks with consideration for emotions

[1004] User: "I have to turn in my project report by next Monday."

[1005] Server: Receives the voice data and converts it into text using a speech recognition API. The text "Submit the project report by next Monday" is analyzed using a natural language processing API and added to the TODO list.

[1006] Emotion recognition: Detects stress from the user's voice.

[1007] Device: The voice generation API sends encouraging words such as, "The project report submission deadline has been added to the list. Let's do our best!"

[1008] Server: When the deadline for submission approaches, the server sends a reminder to the user along with their location information obtained via GPS. The reminder also provides an encouraging message to the user.

[1009] Device: The voice generation API notifies the user, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[1010] In this way, the present invention can efficiently manage the user's daily to-do items, provide appropriate reminders at the necessary times, and provide a more comfortable user experience by taking the user's emotions into consideration.

[1011] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1012] A detailed explanation of the program processing flow for this system

[1013] Step 1: Collecting audio data

[1014] Terminal

[1015] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time.

[1016] Input: User's everyday conversations and surrounding sounds

[1017] Output: Collected audio data (temporarily stored in internal memory)

[1018] Step 2: Transferring audio data

[1019] Terminal

[1020] The collected audio data is transferred to a server via Bluetooth or Wi-Fi.

[1021] Input: Collected voice data (voice data stored in the internal memory)

[1022] Output: Audio data sent to the server

[1023] Step 3: Voice Recognition

[1024] server

[1025] The received voice data is converted to text data using a speech recognition API, and this converted text data is checked for initial misrecognition.

[1026] Input: Audio data transferred to the server

[1027] Output: Text data (converted by speech recognition API)

[1028] Step 4: Natural Language Processing Analysis

[1029] server

[1030] The text data is analyzed using a natural language processing engine to extract the information needed for the TODO list (action items, deadlines, locations).

[1031] Input: Text data from step 3

[1032] Output: Extracted TODO list related information (analysis results using natural language processing API)

[1033] Step 5: Emotion Recognition

[1034] server

[1035] The emotion recognition API analyzes user emotions from text data, determining their emotional state based on the tone of voice, words used, and context.

[1036] Input: Text data from step 4

[1037] Output: User's emotional state (analysis results from emotion recognition API)

[1038] Step 6: Generate a TODO list

[1039] server

[1040] A TODO list is automatically generated based on the analyzed information and managed within the server.

[1041] Input: TODO list related information in step 4, user's emotional state in step 5

[1042] Output: Generated TODO list

[1043] Step 7: Notify users

[1044] Terminal

[1045] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[1046] Input: Generated TODO list, user's emotional state

[1047] Output: Audio notification to the user

[1048] Step 8: Keyword extraction

[1049] server

[1050] The process involves extracting keywords and context from the stored audio and text data using natural language processing techniques such as TF-IDF and BERT.

[1051] Input: Text data and its analysis results

[1052] Output: Extracted keywords and context information

[1053] Step 9: Statistical analysis

[1054] server

[1055] Statistical analysis is performed based on the extracted keywords and phrases, which can be used to understand user needs, interests, and preferences, and to suggest new advertisements.

[1056] Input: Extracted keywords and context information

[1057] Output: Statistical analysis results and proposed data

[1058] Step 10: Reminders

[1059] server

[1060] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, customizes the reminder content at the appropriate time, and sends the generated reminder notification to the user.

[1061] Input: User location, time, TODO list, user emotional state

[1062] Output: Generate reminder notification

[1063] Step 11: Audio output for reminder notifications

[1064] Terminal

[1065] The generated reminder notification is then provided to the user via audio through the glasses-type device. For example, the notification might say, "The deadline for submitting a project report is approaching. Please stay calm and work on it."

[1066] Input: Reminder notification

[1067] Output: Audio notification to the user

[1068] In this way, by performing specific processing at each step, users can efficiently manage their daily to-do items and receive reminders at the appropriate time.In addition, by linking the components, the system provides a more comfortable user experience by taking into account the user's emotions.

[1069] (Application example 2)

[1070] 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."

[1071] Modern brick-and-mortar shopping experiences lack sufficient consideration for users' individual needs and emotional states, making it difficult to provide an efficient and pleasant shopping experience. This problem is particularly pronounced when users experience emotional stress while shopping. This can lead to users overlooking necessary items or losing motivation to purchase. Therefore, it is important to develop a system that can automatically generate a to-do list based on the user's everyday conversations, provide reminders while taking their emotions into account, and provide an efficient and pleasant shopping experience.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for customizing the generated to-do list based on the user's emotional state and notifying the user, a keyword extraction means for extracting keywords and context from the stored data, and an analysis means for performing statistical analysis on the extracted information and providing the data analysis results taking the user's emotional state into consideration. This enables automatic generation of a to-do list based on the user's daily conversations and emotional reminders, thereby making the user's shopping experience efficient and comfortable.

[1073] The "voice input means" is a means for collecting the user's everyday conversations and surrounding voices.

[1074] "Speech recognition means" is a means for converting voice data into text data.

[1075] The "natural language processing means" is a means for analyzing the converted text data, extracting necessary information, and processing it.

[1076] The "notification means" is a means for notifying the user of the generated data or information.

[1077] The "keyword extraction means" is a means for extracting important keywords and contexts from the stored data.

[1078] "Analysis means" refers to means for conducting statistical analysis or other data analysis based on the extracted information.

[1079] The "reminding means" is a means for notifying or reminding the user at an appropriate time by using the user's location information and time.

[1080] The "audio output means" is a means for providing information to the user by voice.

[1081] The "emotional state" is a psychological state determined from the user's tone of voice and speech content.

[1082] A "TODO list" is a list of tasks or action items that a user needs to accomplish.

[1083] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[1084] System configuration

[1085] Voice input means

[1086] Terminal

[1087] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory and later transmitted to a server.

[1088] Voice recognition means

[1089] server

[1090] The voice data sent from the terminal is converted into text data by a voice recognition system on the server, which can use, for example, the Google Speech Recognition API.

[1091] Natural language processing tools

[1092] server

[1093] The converted text data is analyzed by a natural language processing engine, which automatically extracts the information necessary for the TODO list from the user's everyday conversations.

[1094] emotion recognition means

[1095] server

[1096] Analyze user emotions from text data. This analysis uses machine learning techniques such as transformer models. For example, we use the Hugging Face transformers library to load an emotion model and recognize emotions from text.

[1097] Notification means

[1098] Terminal

[1099] The generated TODO list is then sent to the user via a glasses-type device, where the notification content is customized based on emotion recognition and provided as a voice notification.

[1100] Keyword extraction method

[1101] server

[1102] Important keywords and context are extracted from the stored data using natural language processing technology. The extracted keywords are used to understand user needs and interests.

[1103] analytical means

[1104] server

[1105] Statistical analysis is performed based on the extracted keywords and context, which allows us to analyze user preferences and behavioral patterns and provide data analysis results that take into account their emotional state.

[1106] Reminder methods

[1107] server

[1108] The system uses the user's location and time to send reminders at appropriate times, and the content of the reminders is also adjusted according to the user's emotional state.

[1109] Audio output means

[1110] Terminal

[1111] Reminders and to-do list items are provided to the user via voice, for example, using the pyttsx3 library for speech synthesis.

[1112] Specific examples

[1113] Example 1: Shopping list generation and emotion-based notifications

[1114] When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the server receives this as voice data and converts it into text. The natural language processing engine then registers this information as a "shopping list" in the TODO list. Furthermore, the emotion recognition function detects anxiousness in the user's voice tone, and the device issues a gentle voice notification saying, "Buying milk and bread tomorrow evening has been added to the list."

[1115] Example 2: Emotion-conscious management of deadline-bound tasks

[1116] When a user says, "I have to submit my project report by next Monday," the server receives this as voice data and converts it into text. A natural language processing engine registers this information as a "task with a deadline" in the TODO list. Furthermore, emotion recognition detects the user's stress and notifies them with encouraging words, such as, "The deadline for submitting the project report has been added to the list. Let's do our best!" Furthermore, when the deadline approaches, the server sends a reminder, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[1117] Example prompts to input to the generative AI model

[1118] User input:

[1119] "I need to buy some milk and bread."

[1120] Expected output:

[1121] Emotion: Stress

[1122] Notification message: "Milk and bread added to the list. Good luck!"

[1123] User input:

[1124] "I want a new book."

[1125] Expected output:

[1126] Emotion: Enjoyment

[1127] Notification message: "You've added a new book to your list. Keep it up!"

[1128] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1129] Step 1:

[1130] The user wears the glasses-type device and performs voice input.

[1131] Input: User's everyday conversation (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[1132] Specific operation: The built-in microphone of the glasses-type device collects surrounding sounds in real time and temporarily stores the sound data in the device's built-in memory.

[1133] Step 2:

[1134] The terminal transmits the collected voice data to the server.

[1135] Input: Audio data stored in the device's internal memory

[1136] Specific operation: The device sends voice data to the server via the Internet.

[1137] Step 3:

[1138] The server converts the voice data into text data.

[1139] Input: Audio data sent to the server

[1140] Output: Text data corresponding to the speech data (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[1141] Specific operation: The server uses the Google Speech Recognition API to convert the voice data into text data.

[1142] Step 4:

[1143] The server analyzes the text data using a natural language processing engine and extracts the information necessary for the TODO list.

[1144] Input: Text data generated by a speech recognition tool

[1145] Output: TODO list items (e.g. "Buy milk and bread at the supermarket tomorrow evening")

[1146] Specific operation: The server's natural language processing engine analyzes the text data and extracts the information necessary for the TODO list (date, time, location, action items, etc.).

[1147] Step 5:

[1148] The server uses an emotion recognition engine to analyze the user's emotions from the text data.

[1149] Input: Text data processed by natural language processing means

[1150] Output: User's emotional state (e.g., anxious)

[1151] Specific operation: The server loads the emotion model using the Hugging Face transformers library and recognizes emotions from text data.

[1152] Step 6:

[1153] The device will notify the user of the TODO list based on their emotions.

[1154] Input: TODO list items and emotional state sent from the server

[1155] Output: A notification message to the user (e.g. "Buying milk and bread tomorrow evening has been added to your list")

[1156] Specific behavior: The device generates voice notifications using a speech synthesis engine (e.g., the pyttsx3 library) and adjusts the tone and content depending on the user's emotional state.

[1157] Step 7:

[1158] The server extracts keywords and context from the stored data.

[1159] Input: Text and audio data

[1160] Output: Extracted keywords and context

[1161] Specific operation: The server uses natural language processing techniques to extract important keywords and context from the stored data.

[1162] Step 8:

[1163] The server performs statistical analysis based on the extracted keywords and context.

[1164] Input: Extracted keywords and context

[1165] Output: Analysis results (user preferences and behavioral patterns)

[1166] Specific operation: The server uses statistical analysis techniques to analyze the user's preferences and behavioral patterns.

[1167] Step 9:

[1168] The server uses the user's location and time to generate appropriate reminders.

[1169] Input: User's location, time, and TODO list information

[1170] Output: Reminder notification

[1171] Specific operation: The server generates a reminder notification at the appropriate time based on GPS data and timer information, comparing it with the contents of the TODO list.

[1172] Step 10:

[1173] The device provides the user with a voice reminder notification.

[1174] Input: Reminder notification information sent from the server

[1175] Output: Reminder audio notification to the user

[1176] Specific operation: The device uses a speech synthesis engine to generate a reminder notification and provides it to the user aloud.

[1177] 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.

[1178] 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.

[1179] 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.

[1180] [Third embodiment]

[1181] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1182] 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.

[1183] 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).

[1184] 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.

[1185] 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.

[1186] 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).

[1187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1188] 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.

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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."

[1193] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[1194] System configuration and program processing

[1195] Voice input means

[1196] Terminal

[1197] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and this sound data is temporarily stored in the built-in memory.

[1198] Voice recognition means

[1199] server

[1200] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[1201] Natural language processing tools

[1202] server

[1203] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1204] Notification means

[1205] Terminal

[1206] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[1207] Keyword extraction method

[1208] server

[1209] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[1210] analytical means

[1211] server

[1212] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[1213] Reminder methods

[1214] server

[1215] The system tracks the user's location and time, and sends reminders at appropriate times. This reminder information is sent to the user's device and provided to them.

[1216] Audio output means

[1217] Terminal

[1218] Reminders and to-do list items are provided to the user via audio.

[1219] Specific examples

[1220] Example 1: Creating a Shopping List

[1221] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1222] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[1223] Device: "Milk and bread have been added to your list for tomorrow evening."

[1224] Example 2: Managing tasks with deadlines

[1225] User: "I have to turn in my project report by next Monday."

[1226] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[1227] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[1228] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[1229] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[1230] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[1231] The processing flow will be explained below.

[1232] Step 1:

[1233] Terminal

[1234] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[1235] Step 2:

[1236] Terminal

[1237] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[1238] Step 3:

[1239] server

[1240] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[1241] Step 4:

[1242] server

[1243] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[1244] Step 5:

[1245] server

[1246] The generated TODO list items are organized for each user and saved in a database. The saved TODO list contents are associated with the user's profile information.

[1247] Step 6:

[1248] Terminal

[1249] Notify the user when a new to-do list item is created, and read the to-do list contents aloud if the user requests it.

[1250] Step 7:

[1251] server

[1252] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate time for a reminder, and generates a reminder notification.

[1253] Step 8:

[1254] Terminal

[1255] Provide users with voice reminders, for example, a voice reminder to "buy milk and bread" when the user arrives near a supermarket.

[1256] Step 9:

[1257] server

[1258] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[1259] Step 10:

[1260] server

[1261] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[1262] This processing step allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs.

[1263] Example 1

[1264] 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."

[1265] In modern society, users need to manage numerous to-do lists in their busy daily lives, but doing so manually is cumbersome and increases the risk of forgetting. Furthermore, few systems have the functionality to provide reminders at appropriate times, taking into account the user's location and time. Furthermore, it is important to analyze collected data and understand the user's hobbies, preferences, and behavioral patterns in order to provide personalized services and advertising suggestions.

[1266] 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.

[1267] In this invention, the server includes a speech recognition means, a natural language processing means for analyzing the converted text data and generating a to-do list, and a keyword extraction means for extracting keywords and contexts from the saved data. This allows users to automatically generate to-do lists through everyday conversations, receive reminders based on location information and time, and analyze the collected data to understand the user's behavioral patterns and provide personalized services.

[1268] "Speech input means" refers to a device for collecting the user's everyday conversation, and specifically refers to a terminal with a built-in microphone.

[1269] "Speech recognition means" refers to a technology or device that converts collected voice data into text data.

[1270] "Natural language processing means" refers to the processing technology used to analyze the converted text data and generate a TODO list.

[1271] The "notification means" refers to a method or device for notifying the user of the generated TODO list.

[1272] "Keyword extraction means" refers to a technique or device that extracts important keywords and contexts from stored data.

[1273] "Analysis means" refers to a technology or device that performs statistical analysis based on extracted keywords and context.

[1274] "Reminding means" refers to a method or device for tracking the user's location information and time and reminding the user at an appropriate time.

[1275] "Audio output means" refers to technology or equipment that provides reminder notifications and TODO list contents in audio format.

[1276] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[1277] System configuration and program processing

[1278] Voice input means

[1279] Terminal

[1280] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. This sound data is temporarily stored in the built-in memory. The high-quality microphone also has a function to remove surrounding noise.

[1281] Voice recognition means

[1282] server

[1283] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Cloud Speech-to-Text). Basic recognition error checks are also performed at this stage.

[1284] Natural language processing tools

[1285] server

[1286] The converted text data is then analyzed by a natural language processing engine (e.g., OpenAI GPT-4), which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1287] Notification means

[1288] Terminal

[1289] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[1290] Keyword extraction method

[1291] server

[1292] Natural language processing techniques (e.g., spaCy) are used to extract keywords and context from the stored text data, and this information is used for subsequent analysis.

[1293] analytical means

[1294] server

[1295] The extracted keywords and contexts are used for statistical analysis (e.g., using Python's Pandas library). Based on the results of this analysis, it is possible to understand users' interests, preferences, and behavioral patterns.

[1296] Reminder methods

[1297] server

[1298] The system tracks the user's location information (e.g., GPS data) and time, and sends reminders at appropriate times. This reminder information is sent to the device and provided to the user.

[1299] Audio output means

[1300] Terminal

[1301] Reminders and to-do list items are provided to the user via voice.

[1302] Specific examples

[1303] Example 1: Creating a Shopping List

[1304] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1305] Server: Receives the voice data and converts it into text using a speech recognition system. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[1306] Device: "Milk and bread have been added to your list for tomorrow evening."

[1307] Example 2: Managing tasks with deadlines

[1308] User: "I have to turn in my project report by next Monday."

[1309] Server: Receives the voice data and converts it into text using a speech recognition system. The information "Submit the project report by next Monday" is analyzed using a natural language processing engine and added to the TODO list as a "task with a deadline."

[1310] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[1311] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[1312] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[1313] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[1314] Examples of prompt statements

[1315] If the user is creating a shopping list, the prompt might read: "I need to buy milk and bread at the supermarket tomorrow evening."

[1316] If the user manages due tasks, the prompt reads: "I need to turn in my project report by next Monday."

[1317] The above is the "Mode for carrying out the invention", and by combining these components, the present invention realizes efficient generation of a TODO list from a user's everyday conversations and appropriate management.

[1318] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1319] System program processing flow

[1320] Step 1: Speech input from the user

[1321] Terminal

[1322] The user speaks everyday conversation into the glasses-type device. The device collects the voice using the built-in microphone (input). The collected voice data is temporarily stored in the built-in memory (output).

[1323] How it works: When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the audio is instantly captured by the glasses-type device and stored in its internal memory.

[1324] Step 2: Sending audio data to the server

[1325] Terminal

[1326] The device transmits the collected voice data to a server over the internet in real time (input), where the data is encrypted to ensure secure communication (output).

[1327] What it does: The stored audio data is split into packets in JSON format and sent to the server using the HTTPS protocol.

[1328] Step 3: Convert audio data to text

[1329] server

[1330] The voice data received by the server is passed to a voice recognition system (e.g., Google Cloud Speech-to-Text) (input). The system converts the voice data into text data and checks for misrecognition (output).

[1331] Specific operation: The server analyzes the voice data, obtains the text data "I need to buy milk and bread at the supermarket tomorrow evening," and automatically adds punctuation.

[1332] Step 4: Natural Language Processing of Text Data

[1333] server

[1334] The converted text data is sent to a natural language processing engine (e.g., OpenAI GPT-4) (input), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation (output).

[1335] Specific operation: Keywords and context such as "tomorrow evening," "supermarket," and "buy milk and bread" are extracted from the text data and formatted as TODO list entries.

[1336] Step 5: Generate a TODO list

[1337] server

[1338] Based on the extracted information, a TODO list is automatically generated and saved in a database (input and output).

[1339] Specific behavior: An entry "Buy milk and bread at the supermarket tomorrow evening" is added to the database.

[1340] Step 6: Notify users of the TODO list

[1341] Terminal

[1342] The generated TODO list is sent to the device (glasses-type device) to notify the user (input), and the notification is made via the device's built-in speaker or display (output).

[1343] What it does: The glasses announce, "Buying milk and bread tomorrow evening has been added to your list."

[1344] Step 7: Keyword extraction and analysis

[1345] server

[1346] Extract keywords and context from stored text data and create datasets for statistical analysis (input and output).

[1347] Specific operation: The server reviews the text data and extracts frequently occurring keywords, which are then used to analyze the user's behavioral patterns and preferences.

[1348] Step 8: Set reminders and notifications

[1349] Server and Device

[1350] It tracks the user's location (e.g. GPS data) and time, and sets reminders based on that (input and output). This reminder information is sent to the device and notified to the user.

[1351] Specific behavior: For example, when the user arrives near a supermarket, the glasses-type device will remind the user by voice, "There is a supermarket nearby. Let's buy some milk and bread."

[1352] The above is the flow of processing in the program for this system, and a detailed explanation of the specific operations and data processing performed at each step.

[1353] (Application example 1)

[1354] 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."

[1355] Modern consumers must manage numerous to-do lists in their busy daily lives. However, doing so manually is time-consuming and can lead to forgetting or missing out on purchases. Furthermore, locating products in many stores can be difficult and time-consuming, making the shopping experience stressful. Therefore, there is a need for a system that can automatically generate to-do lists based on the user's everyday conversations and provide product location information within the store based on those lists.

[1356] 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.

[1357] In this invention, the server includes a voice input means for collecting the user's daily conversation, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, an analysis means for performing statistical analysis based on the extracted information, a product location information acquisition means for providing product location information based on the extracted information, and a product location notification means for notifying the user of the product location information. This enables the user to shop efficiently and avoid missing out on purchases.

[1358] The "voice input means" is a device or system that collects the user's everyday conversation in real time.

[1359] A "voice recognition means" is a device or system that converts collected voice data into text data.

[1360] The "natural language processing means" is a device or system that analyzes the converted text data and generates a TODO list.

[1361] The "notification means" is a device or system that notifies the user of the generated TODO list.

[1362] A "keyword extractor" is a device or system that extracts keywords and contexts from stored data.

[1363] "Analysis means" refers to a device or system that performs statistical analysis based on the extracted information.

[1364] The "product location information acquisition means" is a device or system that provides product location information based on the extracted information.

[1365] The "product location notification means" is a device or system that notifies the user of product location information.

[1366] The present invention is a system that collects and analyzes a user's everyday conversations to automatically generate and manage a to-do list, and also provides product location information. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a product location information acquisition means, and a product location notification means. Specific embodiments of this system are described below.

[1367] System configuration and program processing

[1368] Voice input means

[1369] Terminal

[1370] The glasses-type device worn by the user is equipped with a built-in microphone that collects surrounding sounds in real time. This sound data is temporarily stored in the built-in memory and sent to a server as needed.

[1371] Voice recognition means

[1372] server

[1373] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Speech-to-Text API). The text data undergoes basic checks to prevent misrecognition of the voice.

[1374] Natural language processing tools

[1375] server

[1376] The converted text data is then analyzed by a natural language processing engine (e.g., SpaCy), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1377] Notification means

[1378] Terminal

[1379] The generated TODO list is notified to the user through the eyeglasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[1380] Keyword extraction method

[1381] server

[1382] The stored data is processed to extract keywords and context. Natural language processing techniques are used to accurately extract keywords and context.

[1383] analytical means

[1384] server

[1385] The extracted keywords and context are used for statistical analysis (for example, using Python's pandas and scikit-learn libraries). The results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[1386] Product location information acquisition means

[1387] server

[1388] The extracted information is used to provide product location information, which is obtained from the physical store's inventory management system or through REST API calls.

[1389] Product location notification means

[1390] Terminal

[1391] The user is notified of product location information. This notification is displayed on the HUD (head-up display) of the eyeglass-type device and is also guided to the user by voice.

[1392] Specific examples

[1393] Example 1: Creating a Shopping List

[1394] If a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the system converts the speech to text, extracts the information "milk" and "bread," and generates a shopping list based on this information and notifies the user.

[1395] Example 2: Managing tasks with deadlines

[1396] If a user says, "I have to submit my project report by next Monday," the system converts the speech to text and extracts the task "Submit project report" and the deadline "Next Monday." Based on this, it generates a task with a deadline and notifies the user at the appropriate time using the reminder function.

[1397] Prompt Sentence Examples

[1398] Create an assistant app that informs users of the location of desired products while shopping. The app must receive voice input from the microphone on the smart glasses, extract the product name using natural language processing, and display the product's location information. Specifically, you will use the speech_recognition library for speech recognition and the spacy library for natural language processing, and incorporate a REST API call to obtain product location information.

[1399] The system allows users to improve shopping efficiency, remember to purchase necessary items, and ensures important tasks are completed with reminders.

[1400] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1401] Step 1:

[1402] Audio collection

[1403] When a user is having a normal conversation, the built-in microphone of the device collects the conversation in real time. The collected voice data is temporarily stored in the built-in memory. The input here is the user's voice, and the output is voice data.

[1404] Step 2:

[1405] Sending audio to a server

[1406] The collected voice data is sent to the server periodically or under certain conditions. For example, sending can start when a certain amount of voice data has been reached or when the user manually instructs it. The input here is the voice data, and the output is the voice data sent to the server.

[1407] Step 3:

[1408] Converting audio data to text

[1409] The server converts the received voice data into text data using a speech recognition system. This conversion is performed using services such as the Google Speech-to-Text API. The input here is voice data, and the output is the converted text data.

[1410] Step 4:

[1411] Text data analysis

[1412] The server analyzes the converted text data using a natural language processing engine (e.g., SpaCy). This analysis extracts the information needed for the TODO list (action items, deadlines, locations, etc.). The input here is the text data, and the output is the analyzed information.

[1413] Step 5:

[1414] Generate a TODO list

[1415] The server automatically generates a TODO list based on the parsed information. The generated TODO list is used in the following notification step. The input here is the parsed information, and the output is the generated TODO list.

[1416] Step 6:

[1417] Generate notifications

[1418] The server sends the generated TODO list to the device. The device displays the received TODO list on the HUD (Heads-Up Display) and notifies the user. It is also possible to confirm by voice. The input here is the TODO list, and the output is a notification to the user.

[1419] Step 7:

[1420] Keyword and context extraction

[1421] The server extracts keywords and context from the stored data. This process uses natural language processing techniques. The extracted information is used in subsequent analysis steps. The input here is the stored text data, and the output is the extracted keywords and context.

[1422] Step 8:

[1423] Performing statistical analysis

[1424] The server performs statistical analysis based on the extracted keywords and context. The analysis is performed using Python's pandas and scikit-learn libraries. The results are used to understand the user's interests, preferences, and needs. The input here is the extracted keywords and context, and the output is the analysis results.

[1425] Step 9:

[1426] Obtaining product location information

[1427] The server then uses the extracted information to obtain product location information from the physical store's inventory management system or by calling a REST API. The input here is keywords and context, and the output is product location information.

[1428] Step 10:

[1429] Product location notification

[1430] The server sends the acquired product location information to the terminal. The terminal displays the received location information on the HUD and, in some cases, guides the user by voice. The input here is the product location information, and the output is a notification to the user.

[1431] These are the processing steps of the system according to the present invention, which allows users to shop efficiently and remember to purchase necessary items.

[1432] 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.

[1433] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[1434] System configuration and program processing

[1435] Voice input means

[1436] Terminal

[1437] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory.

[1438] Voice recognition means

[1439] server

[1440] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[1441] Natural language processing tools

[1442] server

[1443] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1444] emotion recognition means

[1445] server

[1446] The system analyzes user emotions from text data using technology that determines emotions based on tone of voice, words used, and context.

[1447] Notification means

[1448] Terminal

[1449] The generated to-do list is notified to the user through the glasses-type device. The notification content is customized taking into account the user's emotional state. If the user requests, the contents of the to-do list can be confirmed by voice.

[1450] Keyword extraction method

[1451] server

[1452] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[1453] analytical means

[1454] server

[1455] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[1456] Reminder methods

[1457] server

[1458] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. It determines the appropriate time for a reminder and generates a reminder notification. The content of the reminder is also adjusted taking into account the user's emotions.

[1459] Audio output means

[1460] Terminal

[1461] Reminders and to-do list items are provided to the user via audio.

[1462] Specific examples

[1463] Example 1: Shopping list generation and emotion-based notifications

[1464] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1465] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[1466] Emotion recognition: Detects anxiety from the user's tone of voice.

[1467] On the device: The voice announces, "Buying milk and bread tomorrow evening has been added to your list," with a gentle tone to calm the user.

[1468] Example 2: Emotion-conscious management of deadline-bound tasks

[1469] User: "I have to turn in my project report by next Monday."

[1470] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[1471] Emotion recognition: Detects stress from the user's voice.

[1472] On your device: You'll be notified with an encouraging message: "The project report deadline has been added to your list. Good luck!"

[1473] Server: When the deadline approaches, the server will send a reminder with the user's location. The reminder will also be provided with an inspiring message.

[1474] Device: Receive a voice notification saying, "The deadline for submitting your project report is approaching. Please stay calm and work hard."

[1475] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[1476] The processing flow will be explained below.

[1477] Step 1:

[1478] Terminal

[1479] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[1480] Step 2:

[1481] Terminal

[1482] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[1483] Step 3:

[1484] server

[1485] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[1486] Step 4:

[1487] server

[1488] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[1489] Step 5:

[1490] server

[1491] The generated TODO list items are organized by user and saved in a database.

[1492] Step 6:

[1493] server

[1494] Text data is input into an emotion recognition engine to analyze the user's emotions, using technology that determines emotions based on tone of voice, words used, and context.

[1495] Step 7:

[1496] Terminal

[1497] A new to-do list item is created and the user is notified, with the notification content customized to take into account the user's emotional state - for example, if the user is stressed, the notification will be delivered in a comforting tone.

[1498] Step 8:

[1499] server

[1500] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate reminder timing, and generates a reminder notification, which is also adjusted based on the user's emotional state.

[1501] Step 9:

[1502] Terminal

[1503] Reminders are provided to users through voice. For example, a gentle reminder to "buy milk and bread at the supermarket" will be given the next evening.

[1504] Step 10:

[1505] server

[1506] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[1507] Step 11:

[1508] server

[1509] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[1510] Specific examples

[1511] Example 1: Shopping list generation and emotion-based notifications

[1512] Step 1: The user puts on the glasses and says, "I need to buy milk and bread at the supermarket tomorrow evening."

[1513] Step 2: The device collects the voice data and sends it to the server.

[1514] Step 3: The server converts the audio data into text.

[1515] Step 4: The server parses the text data and generates a TODO list.

[1516] Step 5: Save the generated TODO list in the database.

[1517] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[1518] Step 7: Your device will announce in a gentle tone, "Buying milk and bread tomorrow evening has been added to your list."

[1519] Example 2: Emotion-conscious management of deadline-bound tasks

[1520] Step 1: A user says, "I have to submit my project report by next Monday."

[1521] Step 2: The device collects the voice data and sends it to the server.

[1522] Step 3: The server converts the audio data into text.

[1523] Step 4: The server parses the text data and generates a TODO list.

[1524] Step 5: Save the generated TODO list in the database.

[1525] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[1526] Step 7: Your device will notify you with an encouraging message: "The project report submission deadline has been added to your list. Good luck!"

[1527] Step 8: The server sends a reminder of the approaching deadline along with the location information.

[1528] Step 9: The device will announce, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[1529] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[1530] Example 2

[1531] 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."

[1532] In today's busy lives, many people need to efficiently manage numerous tasks and errands. However, manually managing these tasks and errands can be difficult, and people often forget to do them. Furthermore, conventional to-do list management systems cannot take into account the user's emotional state and therefore cannot reduce the psychological burden. The present invention aims to solve these problems by providing a system that collects and analyzes the user's daily conversations and automatically generates and manages to-do lists while recognizing their emotions.

[1533] 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.

[1534] In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, an emotion recognition means for analyzing the user's emotions from the analyzed text data, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, and an analysis means for performing statistical analysis based on the extracted information. This enables the user to efficiently manage their to-do list in a way that takes emotions into consideration, thereby reducing psychological burden.

[1535] The "voice input means" is a device that collects the user's everyday conversation and surrounding voices in real time.

[1536] A "voice recognition means" is a system that converts collected voice data into text data.

[1537] "Natural language processing means" is a technology that analyzes the converted text data and generates a to-do list.

[1538] "Emotion recognition means" is a technology that analyzes the user's emotions from the analyzed text data.

[1539] The "notification means" is a system that notifies the user of the generated TODO list.

[1540] The "keyword extraction means" is a technique for extracting important keywords and contexts from stored data.

[1541] The "analysis means" is a system that performs statistical analysis based on the extracted information.

[1542] The "reminding means" is a technology that tracks the user's location information and time and reminds them at the appropriate time.

[1543] The "audio output means" is a device that notifies the user of daily conversations and to-do lists by voice.

[1544] MODE FOR CARRYING OUT THE INVENTION

[1545] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. Furthermore, by combining this system with an emotion engine, it can provide notifications and reminders while taking the user's emotions into consideration. This system includes a voice input means, a voice recognition means, a natural language processing means, an emotion recognition means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[1546] Voice input means

[1547] Terminal

[1548] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. For example, if the user says, "I need to buy milk and bread at the supermarket tomorrow evening," the sound is recorded by the glasses-type device. The collected sound data is temporarily stored in the built-in memory.

[1549] Voice recognition means

[1550] server

[1551] The collected voice data is transferred to a server via Bluetooth or Wi-Fi. The server converts the received voice data into text data using a voice recognition system (e.g., a voice recognition API). This converted text data is then checked for initial misrecognition.

[1552] Natural language processing tools

[1553] server

[1554] The converted text data is analyzed using a natural language processing engine (e.g., a natural language processing API). During this analysis, the information required for the TODO list (action items, deadlines, and locations) is automatically extracted from the conversation. For example, keywords such as "tomorrow evening," "supermarket," "milk," and "bread" are analyzed.

[1555] emotion recognition means

[1556] server

[1557] The system analyzes user emotions from text data. This analysis uses an emotion recognition API. It determines the user's emotional state based on the tone of voice, the words used, and the context. For example, if the user is feeling stressed, that information can also be detected.

[1558] Notification means

[1559] Terminal

[1560] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[1561] Keyword extraction method

[1562] server

[1563] The process involves extracting keywords and context from the stored audio and text data. This process uses natural language processing techniques such as TF-IDF and BERT. The most important keywords are extracted from the user's everyday conversations and used for subsequent analysis.

[1564] analytical means

[1565] server

[1566] Statistical analysis is performed on the extracted keywords and phrases. This analysis uses statistical analysis tools and in-house developed models to understand user needs, preferences, and help propose new advertisements. For example, if a user repeatedly uses the words "supermarket" or "milk," data is generated to suggest related advertisements.

[1567] Reminder methods

[1568] server

[1569] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. For example, if there is a to-do list set for "tomorrow evening," a reminder notification is generated based on this information. Reminder notifications are also customized with emotions in mind.

[1570] Terminal

[1571] The generated reminder notification is sent to the user via audio through the glasses-type device. For example, the notification may say, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[1572] Audio output means

[1573] Terminal

[1574] Reminders and to-do list items are provided to the user via voice using a speech generation API, for example, "Buying milk and bread tomorrow evening has been added to your list."

[1575] Specific examples

[1576] Example 1: Shopping list generation and emotion-based notifications

[1577] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1578] Server: Receives the voice data and converts it into text using a speech recognition API. The text "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing API and added to the TODO list.

[1579] Emotion recognition: Recognizes anxiety from the user's tone of voice.

[1580] Device: The speech generation API notifies the user by saying, "Buying milk and bread tomorrow evening has been added to the list," in a gentle tone to calm the user.

[1581] Example 2: Managing deadline-bound tasks with consideration for emotions

[1582] User: "I have to turn in my project report by next Monday."

[1583] Server: Receives the voice data and converts it into text using a speech recognition API. The text "Submit the project report by next Monday" is analyzed using a natural language processing API and added to the TODO list.

[1584] Emotion recognition: Detects stress from the user's voice.

[1585] Device: The voice generation API sends encouraging words such as, "The project report submission deadline has been added to the list. Let's do our best!"

[1586] Server: When the deadline for submission approaches, the server sends a reminder to the user along with their location information obtained via GPS. The reminder also provides an encouraging message to the user.

[1587] Device: The voice generation API notifies the user, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[1588] In this way, the present invention can efficiently manage the user's daily to-do items, provide appropriate reminders at the necessary times, and provide a more comfortable user experience by taking the user's emotions into consideration.

[1589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1590] A detailed explanation of the program processing flow for this system

[1591] Step 1: Collecting audio data

[1592] Terminal

[1593] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time.

[1594] Input: User's everyday conversations and surrounding sounds

[1595] Output: Collected audio data (temporarily stored in internal memory)

[1596] Step 2: Transferring audio data

[1597] Terminal

[1598] The collected audio data is transferred to a server via Bluetooth or Wi-Fi.

[1599] Input: Collected voice data (voice data stored in the internal memory)

[1600] Output: Audio data sent to the server

[1601] Step 3: Voice Recognition

[1602] server

[1603] The received voice data is converted to text data using a speech recognition API, and this converted text data is checked for initial misrecognition.

[1604] Input: Audio data transferred to the server

[1605] Output: Text data (converted by speech recognition API)

[1606] Step 4: Natural Language Processing Analysis

[1607] server

[1608] The text data is analyzed using a natural language processing engine to extract the information needed for the TODO list (action items, deadlines, locations).

[1609] Input: Text data from step 3

[1610] Output: Extracted TODO list related information (analysis results using natural language processing API)

[1611] Step 5: Emotion Recognition

[1612] server

[1613] The emotion recognition API analyzes user emotions from text data, determining their emotional state based on the tone of voice, words used, and context.

[1614] Input: Text data from step 4

[1615] Output: User's emotional state (analysis results from emotion recognition API)

[1616] Step 6: Generate a TODO list

[1617] server

[1618] A TODO list is automatically generated based on the analyzed information and managed within the server.

[1619] Input: TODO list related information in step 4, user's emotional state in step 5

[1620] Output: Generated TODO list

[1621] Step 7: Notify users

[1622] Terminal

[1623] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[1624] Input: Generated TODO list, user's emotional state

[1625] Output: Audio notification to the user

[1626] Step 8: Keyword extraction

[1627] server

[1628] The process involves extracting keywords and context from the stored audio and text data using natural language processing techniques such as TF-IDF and BERT.

[1629] Input: Text data and its analysis results

[1630] Output: Extracted keywords and context information

[1631] Step 9: Statistical analysis

[1632] server

[1633] Statistical analysis is performed based on the extracted keywords and phrases, which can be used to understand user needs, interests, and preferences, and to suggest new advertisements.

[1634] Input: Extracted keywords and context information

[1635] Output: Statistical analysis results and proposed data

[1636] Step 10: Reminders

[1637] server

[1638] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, customizes the reminder content at the appropriate time, and sends the generated reminder notification to the user.

[1639] Input: User location, time, TODO list, user emotional state

[1640] Output: Generate reminder notification

[1641] Step 11: Audio output for reminder notifications

[1642] Terminal

[1643] The generated reminder notification is then provided to the user via audio through the glasses-type device. For example, the notification might say, "The deadline for submitting a project report is approaching. Please stay calm and work on it."

[1644] Input: Reminder notification

[1645] Output: Audio notification to the user

[1646] In this way, by performing specific processing at each step, users can efficiently manage their daily to-do items and receive reminders at the appropriate time.In addition, by linking the components, the system provides a more comfortable user experience by taking into account the user's emotions.

[1647] (Application example 2)

[1648] 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."

[1649] Modern brick-and-mortar shopping experiences lack sufficient consideration for users' individual needs and emotional states, making it difficult to provide an efficient and pleasant shopping experience. This problem is particularly pronounced when users experience emotional stress while shopping. This can lead to users overlooking necessary items or losing motivation to purchase. Therefore, it is important to develop a system that can automatically generate a to-do list based on the user's everyday conversations, provide reminders while taking their emotions into account, and provide an efficient and pleasant shopping experience.

[1650] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for customizing the generated to-do list based on the user's emotional state and notifying the user, a keyword extraction means for extracting keywords and context from the stored data, and an analysis means for performing statistical analysis on the extracted information and providing the data analysis results taking the user's emotional state into consideration. This enables automatic generation of a to-do list based on the user's daily conversations and emotional reminders, thereby making the user's shopping experience efficient and comfortable.

[1651] The "voice input means" is a means for collecting the user's everyday conversations and surrounding voices.

[1652] "Speech recognition means" is a means for converting voice data into text data.

[1653] The "natural language processing means" is a means for analyzing the converted text data, extracting necessary information, and processing it.

[1654] The "notification means" is a means for notifying the user of the generated data or information.

[1655] The "keyword extraction means" is a means for extracting important keywords and contexts from the stored data.

[1656] "Analysis means" refers to means for conducting statistical analysis or other data analysis based on the extracted information.

[1657] The "reminding means" is a means for notifying or reminding the user at an appropriate time by using the user's location information and time.

[1658] The "audio output means" is a means for providing information to the user by voice.

[1659] The "emotional state" is a psychological state determined from the user's tone of voice and speech content.

[1660] A "TODO list" is a list of tasks or action items that a user needs to accomplish.

[1661] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[1662] System configuration

[1663] Voice input means

[1664] Terminal

[1665] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory and later transmitted to a server.

[1666] Voice recognition means

[1667] server

[1668] The voice data sent from the terminal is converted into text data by a voice recognition system on the server, which can use, for example, the Google Speech Recognition API.

[1669] Natural language processing tools

[1670] server

[1671] The converted text data is analyzed by a natural language processing engine, which automatically extracts the information necessary for the TODO list from the user's everyday conversations.

[1672] emotion recognition means

[1673] server

[1674] Analyze user emotions from text data. This analysis uses machine learning techniques such as transformer models. For example, we use the Hugging Face transformers library to load an emotion model and recognize emotions from text.

[1675] Notification means

[1676] Terminal

[1677] The generated TODO list is then sent to the user via a glasses-type device, where the notification content is customized based on emotion recognition and provided as a voice notification.

[1678] Keyword extraction method

[1679] server

[1680] Important keywords and context are extracted from the stored data using natural language processing technology. The extracted keywords are used to understand user needs and interests.

[1681] analytical means

[1682] server

[1683] Statistical analysis is performed based on the extracted keywords and context, which allows us to analyze user preferences and behavioral patterns and provide data analysis results that take into account their emotional state.

[1684] Reminder methods

[1685] server

[1686] The system uses the user's location and time to send reminders at appropriate times, and the content of the reminders is also adjusted according to the user's emotional state.

[1687] Audio output means

[1688] Terminal

[1689] Reminders and to-do list items are provided to the user via voice, for example, using the pyttsx3 library for speech synthesis.

[1690] Specific examples

[1691] Example 1: Shopping list generation and emotion-based notifications

[1692] When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the server receives this as voice data and converts it into text. The natural language processing engine then registers this information as a "shopping list" in the TODO list. Furthermore, the emotion recognition function detects anxiousness in the user's voice tone, and the device issues a gentle voice notification saying, "Buying milk and bread tomorrow evening has been added to the list."

[1693] Example 2: Emotion-conscious management of deadline-bound tasks

[1694] When a user says, "I have to submit my project report by next Monday," the server receives this as voice data and converts it into text. A natural language processing engine registers this information as a "task with a deadline" in the TODO list. Furthermore, emotion recognition detects the user's stress and notifies them with encouraging words, such as, "The deadline for submitting the project report has been added to the list. Let's do our best!" Furthermore, when the deadline approaches, the server sends a reminder, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[1695] Example prompts to input to the generative AI model

[1696] User input:

[1697] "I need to buy some milk and bread."

[1698] Expected output:

[1699] Emotion: Stress

[1700] Notification message: "Milk and bread added to the list. Good luck!"

[1701] User input:

[1702] "I want a new book."

[1703] Expected output:

[1704] Emotion: Enjoyment

[1705] Notification message: "You've added a new book to your list. Keep it up!"

[1706] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1707] Step 1:

[1708] The user wears the glasses-type device and performs voice input.

[1709] Input: User's everyday conversation (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[1710] Specific operation: The built-in microphone of the glasses-type device collects surrounding sounds in real time and temporarily stores the sound data in the device's built-in memory.

[1711] Step 2:

[1712] The terminal transmits the collected voice data to the server.

[1713] Input: Audio data stored in the device's internal memory

[1714] Specific operation: The device sends voice data to the server via the Internet.

[1715] Step 3:

[1716] The server converts the voice data into text data.

[1717] Input: Audio data sent to the server

[1718] Output: Text data corresponding to the speech data (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[1719] Specific operation: The server uses the Google Speech Recognition API to convert the voice data into text data.

[1720] Step 4:

[1721] The server analyzes the text data using a natural language processing engine and extracts the information necessary for the TODO list.

[1722] Input: Text data generated by a speech recognition tool

[1723] Output: TODO list items (e.g. "Buy milk and bread at the supermarket tomorrow evening")

[1724] Specific operation: The server's natural language processing engine analyzes the text data and extracts the information necessary for the TODO list (date, time, location, action items, etc.).

[1725] Step 5:

[1726] The server uses an emotion recognition engine to analyze the user's emotions from the text data.

[1727] Input: Text data processed by natural language processing means

[1728] Output: User's emotional state (e.g., anxious)

[1729] Specific operation: The server loads the emotion model using the Hugging Face transformers library and recognizes emotions from text data.

[1730] Step 6:

[1731] The device will notify the user of the TODO list based on their emotions.

[1732] Input: TODO list items and emotional state sent from the server

[1733] Output: A notification message to the user (e.g. "Buying milk and bread tomorrow evening has been added to your list")

[1734] Specific behavior: The device generates voice notifications using a speech synthesis engine (e.g., the pyttsx3 library) and adjusts the tone and content depending on the user's emotional state.

[1735] Step 7:

[1736] The server extracts keywords and context from the stored data.

[1737] Input: Text and audio data

[1738] Output: Extracted keywords and context

[1739] Specific operation: The server uses natural language processing techniques to extract important keywords and context from the stored data.

[1740] Step 8:

[1741] The server performs statistical analysis based on the extracted keywords and context.

[1742] Input: Extracted keywords and context

[1743] Output: Analysis results (user preferences and behavioral patterns)

[1744] Specific operation: The server uses statistical analysis techniques to analyze the user's preferences and behavioral patterns.

[1745] Step 9:

[1746] The server uses the user's location and time to generate appropriate reminders.

[1747] Input: User's location, time, and TODO list information

[1748] Output: Reminder notification

[1749] Specific operation: The server generates a reminder notification at the appropriate time based on GPS data and timer information, comparing it with the contents of the TODO list.

[1750] Step 10:

[1751] The device provides the user with a voice reminder notification.

[1752] Input: Reminder notification information sent from the server

[1753] Output: Reminder audio notification to the user

[1754] Specific operation: The device uses a speech synthesis engine to generate a reminder notification and provides it to the user aloud.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] [Fourth embodiment]

[1759] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1760] 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.

[1761] 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).

[1762] 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.

[1763] 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.

[1764] 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).

[1765] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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."

[1772] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[1773] System configuration and program processing

[1774] Voice input means

[1775] Terminal

[1776] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and this sound data is temporarily stored in the built-in memory.

[1777] Voice recognition means

[1778] server

[1779] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[1780] Natural language processing tools

[1781] server

[1782] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1783] Notification means

[1784] Terminal

[1785] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[1786] Keyword extraction method

[1787] server

[1788] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[1789] analytical means

[1790] server

[1791] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[1792] Reminder methods

[1793] server

[1794] The system tracks the user's location and time, and sends reminders at appropriate times. This reminder information is sent to the user's device and provided to them.

[1795] Audio output means

[1796] Terminal

[1797] Reminders and to-do list items are provided to the user via audio.

[1798] Specific examples

[1799] Example 1: Creating a Shopping List

[1800] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1801] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[1802] Device: "Milk and bread have been added to your list for tomorrow evening."

[1803] Example 2: Managing tasks with deadlines

[1804] User: "I have to turn in my project report by next Monday."

[1805] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[1806] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[1807] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[1808] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[1809] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[1810] The processing flow will be explained below.

[1811] Step 1:

[1812] Terminal

[1813] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[1814] Step 2:

[1815] Terminal

[1816] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[1817] Step 3:

[1818] server

[1819] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[1820] Step 4:

[1821] server

[1822] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[1823] Step 5:

[1824] server

[1825] The generated TODO list items are organized for each user and saved in a database. The saved TODO list contents are associated with the user's profile information.

[1826] Step 6:

[1827] Terminal

[1828] Notify the user when a new to-do list item is created, and read the to-do list contents aloud if the user requests it.

[1829] Step 7:

[1830] server

[1831] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate time for a reminder, and generates a reminder notification.

[1832] Step 8:

[1833] Terminal

[1834] Provide users with voice reminders, for example, a voice reminder to "buy milk and bread" when the user arrives near a supermarket.

[1835] Step 9:

[1836] server

[1837] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[1838] Step 10:

[1839] server

[1840] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[1841] This processing step allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs.

[1842] Example 1

[1843] 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."

[1844] In modern society, users need to manage numerous to-do lists in their busy daily lives, but doing so manually is cumbersome and increases the risk of forgetting. Furthermore, few systems have the functionality to provide reminders at appropriate times, taking into account the user's location and time. Furthermore, it is important to analyze collected data and understand the user's hobbies, preferences, and behavioral patterns in order to provide personalized services and advertising suggestions.

[1845] 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.

[1846] In this invention, the server includes a speech recognition means, a natural language processing means for analyzing the converted text data and generating a to-do list, and a keyword extraction means for extracting keywords and contexts from the saved data. This allows users to automatically generate to-do lists through everyday conversations, receive reminders based on location information and time, and analyze the collected data to understand the user's behavioral patterns and provide personalized services.

[1847] "Speech input means" refers to a device for collecting the user's everyday conversation, and specifically refers to a terminal with a built-in microphone.

[1848] "Speech recognition means" refers to a technology or device that converts collected voice data into text data.

[1849] "Natural language processing means" refers to the processing technology used to analyze the converted text data and generate a TODO list.

[1850] The "notification means" refers to a method or device for notifying the user of the generated TODO list.

[1851] "Keyword extraction means" refers to a technique or device that extracts important keywords and contexts from stored data.

[1852] "Analysis means" refers to a technology or device that performs statistical analysis based on extracted keywords and context.

[1853] "Reminding means" refers to a method or device for tracking the user's location information and time and reminding the user at an appropriate time.

[1854] "Audio output means" refers to technology or equipment that provides reminder notifications and TODO list contents in audio format.

[1855] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[1856] System configuration and program processing

[1857] Voice input means

[1858] Terminal

[1859] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. This sound data is temporarily stored in the built-in memory. The high-quality microphone also has a function to remove surrounding noise.

[1860] Voice recognition means

[1861] server

[1862] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Cloud Speech-to-Text). Basic recognition error checks are also performed at this stage.

[1863] Natural language processing tools

[1864] server

[1865] The converted text data is then analyzed by a natural language processing engine (e.g., OpenAI GPT-4), which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1866] Notification means

[1867] Terminal

[1868] The generated TODO list is notified to the user through the glasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[1869] Keyword extraction method

[1870] server

[1871] Natural language processing techniques (e.g., spaCy) are used to extract keywords and context from the stored text data, and this information is used for subsequent analysis.

[1872] analytical means

[1873] server

[1874] The extracted keywords and contexts are used for statistical analysis (e.g., using Python's Pandas library). Based on the results of this analysis, it is possible to understand users' interests, preferences, and behavioral patterns.

[1875] Reminder methods

[1876] server

[1877] The system tracks the user's location information (e.g., GPS data) and time, and sends reminders at appropriate times. This reminder information is sent to the device and provided to the user.

[1878] Audio output means

[1879] Terminal

[1880] Reminders and to-do list items are provided to the user via voice.

[1881] Specific examples

[1882] Example 1: Creating a Shopping List

[1883] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[1884] Server: Receives the voice data and converts it into text using a speech recognition system. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[1885] Device: "Milk and bread have been added to your list for tomorrow evening."

[1886] Example 2: Managing tasks with deadlines

[1887] User: "I have to turn in my project report by next Monday."

[1888] Server: Receives the voice data and converts it into text using a speech recognition system. The information "Submit the project report by next Monday" is analyzed using a natural language processing engine and added to the TODO list as a "task with a deadline."

[1889] On your device: You hear a voice announcement saying, "Your project report due date has been added to your list."

[1890] Server: When the submission deadline approaches, send a reminder along with the user's location information.

[1891] On your device: Receive a voice notification saying, "Your project report deadline is approaching."

[1892] This system allows users to remember their daily to-do items and improve their quality of life. Furthermore, analyzing the accumulated data can be useful for developing new advertising markets and providing new services.

[1893] Examples of prompt statements

[1894] If the user is creating a shopping list, the prompt might read: "I need to buy milk and bread at the supermarket tomorrow evening."

[1895] If the user manages due tasks, the prompt reads: "I need to turn in my project report by next Monday."

[1896] The above is the "Mode for carrying out the invention", and by combining these components, the present invention realizes efficient generation of a TODO list from a user's everyday conversations and appropriate management.

[1897] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1898] System program processing flow

[1899] Step 1: Speech input from the user

[1900] Terminal

[1901] The user speaks everyday conversation into the glasses-type device. The device collects the voice using the built-in microphone (input). The collected voice data is temporarily stored in the built-in memory (output).

[1902] How it works: When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the audio is instantly captured by the glasses-type device and stored in its internal memory.

[1903] Step 2: Sending audio data to the server

[1904] Terminal

[1905] The device transmits the collected voice data to a server over the internet in real time (input), where the data is encrypted to ensure secure communication (output).

[1906] What it does: The stored audio data is split into packets in JSON format and sent to the server using the HTTPS protocol.

[1907] Step 3: Convert audio data to text

[1908] server

[1909] The voice data received by the server is passed to a voice recognition system (e.g., Google Cloud Speech-to-Text) (input). The system converts the voice data into text data and checks for misrecognition (output).

[1910] Specific operation: The server analyzes the voice data, obtains the text data "I need to buy milk and bread at the supermarket tomorrow evening," and automatically adds punctuation.

[1911] Step 4: Natural Language Processing of Text Data

[1912] server

[1913] The converted text data is sent to a natural language processing engine (e.g., OpenAI GPT-4) (input), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation (output).

[1914] Specific operation: Keywords and context such as "tomorrow evening," "supermarket," and "buy milk and bread" are extracted from the text data and formatted as TODO list entries.

[1915] Step 5: Generate a TODO list

[1916] server

[1917] Based on the extracted information, a TODO list is automatically generated and saved in a database (input and output).

[1918] Specific behavior: An entry "Buy milk and bread at the supermarket tomorrow evening" is added to the database.

[1919] Step 6: Notify users of the TODO list

[1920] Terminal

[1921] The generated TODO list is sent to the device (glasses-type device) to notify the user (input), and the notification is made via the device's built-in speaker or display (output).

[1922] What it does: The glasses announce, "Buying milk and bread tomorrow evening has been added to your list."

[1923] Step 7: Keyword extraction and analysis

[1924] server

[1925] Extract keywords and context from stored text data and create datasets for statistical analysis (input and output).

[1926] Specific operation: The server reviews the text data and extracts frequently occurring keywords, which are then used to analyze the user's behavioral patterns and preferences.

[1927] Step 8: Set reminders and notifications

[1928] Server and Device

[1929] It tracks the user's location (e.g. GPS data) and time, and sets reminders based on that (input and output). This reminder information is sent to the device and notified to the user.

[1930] Specific behavior: For example, when the user arrives near a supermarket, the glasses-type device will remind the user by voice, "There is a supermarket nearby. Let's buy some milk and bread."

[1931] The above is the flow of processing in the program for this system, and a detailed explanation of the specific operations and data processing performed at each step.

[1932] (Application example 1)

[1933] 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."

[1934] Modern consumers must manage numerous to-do lists in their busy daily lives. However, doing so manually is time-consuming and can lead to forgetting or missing out on purchases. Furthermore, locating products in many stores can be difficult and time-consuming, making the shopping experience stressful. Therefore, there is a need for a system that can automatically generate to-do lists based on the user's everyday conversations and provide product location information within the store based on those lists.

[1935] 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.

[1936] In this invention, the server includes a voice input means for collecting the user's daily conversation, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, an analysis means for performing statistical analysis based on the extracted information, a product location information acquisition means for providing product location information based on the extracted information, and a product location notification means for notifying the user of the product location information. This enables the user to shop efficiently and avoid missing out on purchases.

[1937] The "voice input means" is a device or system that collects the user's everyday conversation in real time.

[1938] A "voice recognition means" is a device or system that converts collected voice data into text data.

[1939] The "natural language processing means" is a device or system that analyzes the converted text data and generates a TODO list.

[1940] The "notification means" is a device or system that notifies the user of the generated TODO list.

[1941] A "keyword extractor" is a device or system that extracts keywords and contexts from stored data.

[1942] "Analysis means" refers to a device or system that performs statistical analysis based on the extracted information.

[1943] The "product location information acquisition means" is a device or system that provides product location information based on the extracted information.

[1944] The "product location notification means" is a device or system that notifies the user of product location information.

[1945] The present invention is a system that collects and analyzes a user's everyday conversations to automatically generate and manage a to-do list, and also provides product location information. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a product location information acquisition means, and a product location notification means. Specific embodiments of this system are described below.

[1946] System configuration and program processing

[1947] Voice input means

[1948] Terminal

[1949] The glasses-type device worn by the user is equipped with a built-in microphone that collects surrounding sounds in real time. This sound data is temporarily stored in the built-in memory and sent to a server as needed.

[1950] Voice recognition means

[1951] server

[1952] The voice data sent from the device is converted into text data by a speech recognition system on the server (for example, Google Speech-to-Text API). The text data undergoes basic checks to prevent misrecognition of the voice.

[1953] Natural language processing tools

[1954] server

[1955] The converted text data is then analyzed by a natural language processing engine (e.g., SpaCy), which automatically extracts the information needed for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[1956] Notification means

[1957] Terminal

[1958] The generated TODO list is notified to the user through the eyeglasses-type device, and if the user requests, the contents of the TODO list can be confirmed by voice.

[1959] Keyword extraction method

[1960] server

[1961] The stored data is processed to extract keywords and context. Natural language processing techniques are used to accurately extract keywords and context.

[1962] analytical means

[1963] server

[1964] The extracted keywords and context are used for statistical analysis (for example, using Python's pandas and scikit-learn libraries). The results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[1965] Product location information acquisition means

[1966] server

[1967] The extracted information is used to provide product location information, which is obtained from the physical store's inventory management system or through REST API calls.

[1968] Product location notification means

[1969] Terminal

[1970] The user is notified of product location information. This notification is displayed on the HUD (head-up display) of the eyeglass-type device and is also guided to the user by voice.

[1971] Specific examples

[1972] Example 1: Creating a Shopping List

[1973] If a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the system converts the speech to text, extracts the information "milk" and "bread," and generates a shopping list based on this information and notifies the user.

[1974] Example 2: Managing tasks with deadlines

[1975] If a user says, "I have to submit my project report by next Monday," the system converts the speech to text and extracts the task "Submit project report" and the deadline "Next Monday." Based on this, it generates a task with a deadline and notifies the user at the appropriate time using the reminder function.

[1976] Prompt Sentence Examples

[1977] Create an assistant app that informs users of the location of desired products while shopping. The app must receive voice input from the microphone on the smart glasses, extract the product name using natural language processing, and display the product's location information. Specifically, you will use the speech_recognition library for speech recognition and the spacy library for natural language processing, and incorporate a REST API call to obtain product location information.

[1978] The system allows users to improve shopping efficiency, remember to purchase necessary items, and ensures important tasks are completed with reminders.

[1979] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1980] Step 1:

[1981] Audio collection

[1982] When a user is having a normal conversation, the built-in microphone of the device collects the conversation in real time. The collected voice data is temporarily stored in the built-in memory. The input here is the user's voice, and the output is voice data.

[1983] Step 2:

[1984] Sending audio to a server

[1985] The collected voice data is sent to the server periodically or under certain conditions. For example, sending can start when a certain amount of voice data has been reached or when the user manually instructs it. The input here is the voice data, and the output is the voice data sent to the server.

[1986] Step 3:

[1987] Converting audio data to text

[1988] The server converts the received voice data into text data using a speech recognition system. This conversion is performed using services such as the Google Speech-to-Text API. The input here is voice data, and the output is the converted text data.

[1989] Step 4:

[1990] Text data analysis

[1991] The server analyzes the converted text data using a natural language processing engine (e.g., SpaCy). This analysis extracts the information needed for the TODO list (action items, deadlines, locations, etc.). The input here is the text data, and the output is the analyzed information.

[1992] Step 5:

[1993] Generate a TODO list

[1994] The server automatically generates a TODO list based on the parsed information. The generated TODO list is used in the following notification step. The input here is the parsed information, and the output is the generated TODO list.

[1995] Step 6:

[1996] Generate notifications

[1997] The server sends the generated TODO list to the device. The device displays the received TODO list on the HUD (Heads-Up Display) and notifies the user. It is also possible to confirm by voice. The input here is the TODO list, and the output is a notification to the user.

[1998] Step 7:

[1999] Keyword and context extraction

[2000] The server extracts keywords and context from the stored data. This process uses natural language processing techniques. The extracted information is used in subsequent analysis steps. The input here is the stored text data, and the output is the extracted keywords and context.

[2001] Step 8:

[2002] Performing statistical analysis

[2003] The server performs statistical analysis based on the extracted keywords and context. The analysis is performed using Python's pandas and scikit-learn libraries. The results are used to understand the user's interests, preferences, and needs. The input here is the extracted keywords and context, and the output is the analysis results.

[2004] Step 9:

[2005] Obtaining product location information

[2006] The server then uses the extracted information to obtain product location information from the physical store's inventory management system or by calling a REST API. The input here is keywords and context, and the output is product location information.

[2007] Step 10:

[2008] Product location notification

[2009] The server sends the acquired product location information to the terminal. The terminal displays the received location information on the HUD and, in some cases, guides the user by voice. The input here is the product location information, and the output is a notification to the user.

[2010] These are the processing steps of the system according to the present invention, which allows users to shop efficiently and remember to purchase necessary items.

[2011] 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.

[2012] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[2013] System configuration and program processing

[2014] Voice input means

[2015] Terminal

[2016] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory.

[2017] Voice recognition means

[2018] server

[2019] The voice data sent from the device is converted into text data by a voice recognition system on the server, and basic checks are performed on this text data to prevent misrecognition of the voice.

[2020] Natural language processing tools

[2021] server

[2022] The converted text data is then analyzed by a natural language processing engine, which automatically extracts the necessary information for the TODO list (action items, deadlines, locations, etc.) from the conversation.

[2023] emotion recognition means

[2024] server

[2025] The system analyzes user emotions from text data using technology that determines emotions based on tone of voice, words used, and context.

[2026] Notification means

[2027] Terminal

[2028] The generated to-do list is notified to the user through the glasses-type device. The notification content is customized taking into account the user's emotional state. If the user requests, the contents of the to-do list can be confirmed by voice.

[2029] Keyword extraction method

[2030] server

[2031] The stored data is then processed to extract keywords and context, a process that is carried out using natural language processing techniques.

[2032] analytical means

[2033] server

[2034] The extracted keywords and contexts are used for statistical analysis, and the results of this analysis are used to understand users' interests, preferences, and needs, which can be used to propose new advertisements and provide new services.

[2035] Reminder methods

[2036] server

[2037] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. It determines the appropriate time for a reminder and generates a reminder notification. The content of the reminder is also adjusted taking into account the user's emotions.

[2038] Audio output means

[2039] Terminal

[2040] Reminders and to-do list items are provided to the user via audio.

[2041] Specific examples

[2042] Example 1: Shopping list generation and emotion-based notifications

[2043] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[2044] Server: Receives the voice data and converts it into text. The information "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing engine and added to the TODO list as a "shopping list."

[2045] Emotion recognition: Detects anxiety from the user's tone of voice.

[2046] On the device: The voice announces, "Buying milk and bread tomorrow evening has been added to your list," with a gentle tone to calm the user.

[2047] Example 2: Emotion-conscious management of deadline-bound tasks

[2048] User: "I have to turn in my project report by next Monday."

[2049] Server: Receives the voice data and converts it into text. The natural language processing engine analyzes the information, such as "Submit the project report by next Monday," and registers it in the TODO list as a "task with a deadline."

[2050] Emotion recognition: Detects stress from the user's voice.

[2051] On your device: You'll be notified with an encouraging message: "The project report deadline has been added to your list. Good luck!"

[2052] Server: When the deadline approaches, the server will send a reminder with the user's location. The reminder will also be provided with an inspiring message.

[2053] Device: Receive a voice notification saying, "The deadline for submitting your project report is approaching. Please stay calm and work hard."

[2054] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[2055] The processing flow will be explained below.

[2056] Step 1:

[2057] Terminal

[2058] When the user wears the glasses, the built-in microphone collects surrounding sounds in real time, and the sound data is temporarily stored in the built-in memory.

[2059] Step 2:

[2060] Terminal

[2061] The temporarily stored audio data is batched at regular intervals (e.g., every minute) and then sent to a server using a secure communication protocol (e.g., HTTPS).

[2062] Step 3:

[2063] server

[2064] The received voice data is input into a voice recognition system and converted into text data. The voice recognition system performs basic consistency checks to prevent misrecognition.

[2065] Step 4:

[2066] server

[2067] The converted text data is input into a natural language processing engine, which analyzes the conversation context and automatically extracts the information needed for the TODO list (actions, deadlines, locations, etc.).

[2068] Step 5:

[2069] server

[2070] The generated TODO list items are organized by user and saved in a database.

[2071] Step 6:

[2072] server

[2073] Text data is input into an emotion recognition engine to analyze the user's emotions, using technology that determines emotions based on tone of voice, words used, and context.

[2074] Step 7:

[2075] Terminal

[2076] A new to-do list item is created and the user is notified, with the notification content customized to take into account the user's emotional state - for example, if the user is stressed, the notification will be delivered in a comforting tone.

[2077] Step 8:

[2078] server

[2079] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, determines the appropriate reminder timing, and generates a reminder notification, which is also adjusted based on the user's emotional state.

[2080] Step 9:

[2081] Terminal

[2082] Reminders are provided to users through voice. For example, a gentle reminder to "buy milk and bread at the supermarket" will be given the next evening.

[2083] Step 10:

[2084] server

[2085] The text data and summaries of conversations are stored in a database as a life log, and keywords and contexts are extracted from the stored data and statistically analyzed.

[2086] Step 11:

[2087] server

[2088] Based on statistical analysis of keywords and context, data is generated for new advertising proposals and service provision. The analysis results are used to understand user needs by gender and age group.

[2089] Specific examples

[2090] Example 1: Shopping list generation and emotion-based notifications

[2091] Step 1: The user puts on the glasses and says, "I need to buy milk and bread at the supermarket tomorrow evening."

[2092] Step 2: The device collects the voice data and sends it to the server.

[2093] Step 3: The server converts the audio data into text.

[2094] Step 4: The server parses the text data and generates a TODO list.

[2095] Step 5: Save the generated TODO list in the database.

[2096] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[2097] Step 7: Your device will announce in a gentle tone, "Buying milk and bread tomorrow evening has been added to your list."

[2098] Example 2: Emotion-conscious management of deadline-bound tasks

[2099] Step 1: A user says, "I have to submit my project report by next Monday."

[2100] Step 2: The device collects the voice data and sends it to the server.

[2101] Step 3: The server converts the audio data into text.

[2102] Step 4: The server parses the text data and generates a TODO list.

[2103] Step 5: Save the generated TODO list in the database.

[2104] Step 6: The server analyzes emotions from the text data and detects that the user is feeling stressed.

[2105] Step 7: Your device will notify you with an encouraging message: "The project report submission deadline has been added to your list. Good luck!"

[2106] Step 8: The server sends a reminder of the approaching deadline along with the location information.

[2107] Step 9: The device will announce, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[2108] This system allows users to efficiently manage their daily to-do items and receive reminders when needed. Furthermore, by utilizing the accumulated data, it is possible to develop new advertising markets and understand user needs. In particular, notifications and reminders that take emotions into consideration can reduce the psychological burden on users and provide a more comfortable user experience.

[2109] Example 2

[2110] 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."

[2111] In today's busy lives, many people need to efficiently manage numerous tasks and errands. However, manually managing these tasks and errands can be difficult, and people often forget to do them. Furthermore, conventional to-do list management systems cannot take into account the user's emotional state and therefore cannot reduce the psychological burden. The present invention aims to solve these problems by providing a system that collects and analyzes the user's daily conversations and automatically generates and manages to-do lists while recognizing their emotions.

[2112] 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.

[2113] In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, an emotion recognition means for analyzing the user's emotions from the analyzed text data, a notification means for notifying the user of the generated to-do list, a keyword extraction means for extracting keywords and contexts from the saved data, and an analysis means for performing statistical analysis based on the extracted information. This enables the user to efficiently manage their to-do list in a way that takes emotions into consideration, thereby reducing psychological burden.

[2114] The "voice input means" is a device that collects the user's everyday conversation and surrounding voices in real time.

[2115] A "voice recognition means" is a system that converts collected voice data into text data.

[2116] "Natural language processing means" is a technology that analyzes the converted text data and generates a to-do list.

[2117] "Emotion recognition means" is a technology that analyzes the user's emotions from the analyzed text data.

[2118] The "notification means" is a system that notifies the user of the generated TODO list.

[2119] The "keyword extraction means" is a technique for extracting important keywords and contexts from stored data.

[2120] The "analysis means" is a system that performs statistical analysis based on the extracted information.

[2121] The "reminding means" is a technology that tracks the user's location information and time and reminds them at the appropriate time.

[2122] The "audio output means" is a device that notifies the user of daily conversations and to-do lists by voice.

[2123] MODE FOR CARRYING OUT THE INVENTION

[2124] This invention is a system that automatically generates and manages a to-do list by collecting and analyzing a user's daily conversations. Furthermore, by combining this system with an emotion engine, it can provide notifications and reminders while taking the user's emotions into consideration. This system includes a voice input means, a voice recognition means, a natural language processing means, an emotion recognition means, a notification means, a keyword extraction means, an analysis means, a reminder means, and a voice output means.

[2125] Voice input means

[2126] Terminal

[2127] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time. For example, if the user says, "I need to buy milk and bread at the supermarket tomorrow evening," the sound is recorded by the glasses-type device. The collected sound data is temporarily stored in the built-in memory.

[2128] Voice recognition means

[2129] server

[2130] The collected voice data is transferred to a server via Bluetooth or Wi-Fi. The server converts the received voice data into text data using a voice recognition system (e.g., a voice recognition API). This converted text data is then checked for initial misrecognition.

[2131] Natural language processing tools

[2132] server

[2133] The converted text data is analyzed using a natural language processing engine (e.g., a natural language processing API). During this analysis, the information required for the TODO list (action items, deadlines, and locations) is automatically extracted from the conversation. For example, keywords such as "tomorrow evening," "supermarket," "milk," and "bread" are analyzed.

[2134] emotion recognition means

[2135] server

[2136] The system analyzes user emotions from text data. This analysis uses an emotion recognition API. It determines the user's emotional state based on the tone of voice, the words used, and the context. For example, if the user is feeling stressed, that information can also be detected.

[2137] Notification means

[2138] Terminal

[2139] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[2140] Keyword extraction method

[2141] server

[2142] The process involves extracting keywords and context from the stored audio and text data. This process uses natural language processing techniques such as TF-IDF and BERT. The most important keywords are extracted from the user's everyday conversations and used for subsequent analysis.

[2143] analytical means

[2144] server

[2145] Statistical analysis is performed on the extracted keywords and phrases. This analysis uses statistical analysis tools and in-house developed models to understand user needs, preferences, and help propose new advertisements. For example, if a user repeatedly uses the words "supermarket" or "milk," data is generated to suggest related advertisements.

[2146] Reminder methods

[2147] server

[2148] The system obtains the user's location and time from GPS and timers and compares them with the current to-do list. For example, if there is a to-do list set for "tomorrow evening," a reminder notification is generated based on this information. Reminder notifications are also customized with emotions in mind.

[2149] Terminal

[2150] The generated reminder notification is sent to the user via audio through the glasses-type device. For example, the notification may say, "The deadline for submitting your project report is approaching. Please stay calm and work on it."

[2151] Audio output means

[2152] Terminal

[2153] Reminders and to-do list items are provided to the user via voice using a speech generation API, for example, "Buying milk and bread tomorrow evening has been added to your list."

[2154] Specific examples

[2155] Example 1: Shopping list generation and emotion-based notifications

[2156] User: "I need to buy some milk and bread at the supermarket tomorrow evening."

[2157] Server: Receives the voice data and converts it into text using a speech recognition API. The text "I will buy milk and bread at the supermarket tomorrow evening" is analyzed using a natural language processing API and added to the TODO list.

[2158] Emotion recognition: Recognizes anxiety from the user's tone of voice.

[2159] Device: The speech generation API notifies the user by saying, "Buying milk and bread tomorrow evening has been added to the list," in a gentle tone to calm the user.

[2160] Example 2: Managing deadline-bound tasks with consideration for emotions

[2161] User: "I have to turn in my project report by next Monday."

[2162] Server: Receives the voice data and converts it into text using a speech recognition API. The text "Submit the project report by next Monday" is analyzed using a natural language processing API and added to the TODO list.

[2163] Emotion recognition: Detects stress from the user's voice.

[2164] Device: The voice generation API sends encouraging words such as, "The project report submission deadline has been added to the list. Let's do our best!"

[2165] Server: When the deadline for submission approaches, the server sends a reminder to the user along with their location information obtained via GPS. The reminder also provides an encouraging message to the user.

[2166] Device: The voice generation API notifies the user, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[2167] In this way, the present invention can efficiently manage the user's daily to-do items, provide appropriate reminders at the necessary times, and provide a more comfortable user experience by taking the user's emotions into consideration.

[2168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2169] A detailed explanation of the program processing flow for this system

[2170] Step 1: Collecting audio data

[2171] Terminal

[2172] The glasses-type device worn by the user uses a built-in microphone to collect surrounding sounds in real time.

[2173] Input: User's everyday conversations and surrounding sounds

[2174] Output: Collected audio data (temporarily stored in internal memory)

[2175] Step 2: Transferring audio data

[2176] Terminal

[2177] The collected audio data is transferred to a server via Bluetooth or Wi-Fi.

[2178] Input: Collected voice data (voice data stored in the internal memory)

[2179] Output: Audio data sent to the server

[2180] Step 3: Voice Recognition

[2181] server

[2182] The received voice data is converted to text data using a speech recognition API, and this converted text data is checked for initial misrecognition.

[2183] Input: Audio data transferred to the server

[2184] Output: Text data (converted by speech recognition API)

[2185] Step 4: Natural Language Processing Analysis

[2186] server

[2187] The text data is analyzed using a natural language processing engine to extract the information needed for the TODO list (action items, deadlines, locations).

[2188] Input: Text data from step 3

[2189] Output: Extracted TODO list related information (analysis results using natural language processing API)

[2190] Step 5: Emotion Recognition

[2191] server

[2192] The emotion recognition API analyzes user emotions from text data, determining their emotional state based on the tone of voice, words used, and context.

[2193] Input: Text data from step 4

[2194] Output: User's emotional state (analysis results from emotion recognition API)

[2195] Step 6: Generate a TODO list

[2196] server

[2197] A TODO list is automatically generated based on the analyzed information and managed within the server.

[2198] Input: TODO list related information in step 4, user's emotional state in step 5

[2199] Output: Generated TODO list

[2200] Step 7: Notify users

[2201] Terminal

[2202] The generated to-do list is notified to the user through the glasses-type device. The notification is made audibly using a speech generation API. For example, the notification may say, "Buying milk and bread tomorrow evening has been added to the list." The tone of the notification is adjusted taking into account the user's emotional state.

[2203] Input: Generated TODO list, user's emotional state

[2204] Output: Audio notification to the user

[2205] Step 8: Keyword extraction

[2206] server

[2207] The process involves extracting keywords and context from the stored audio and text data using natural language processing techniques such as TF-IDF and BERT.

[2208] Input: Text data and its analysis results

[2209] Output: Extracted keywords and context information

[2210] Step 9: Statistical analysis

[2211] server

[2212] Statistical analysis is performed based on the extracted keywords and phrases, which can be used to understand user needs, interests, and preferences, and to suggest new advertisements.

[2213] Input: Extracted keywords and context information

[2214] Output: Statistical analysis results and proposed data

[2215] Step 10: Reminders

[2216] server

[2217] It obtains the user's location and time from GPS and timers, compares them with the current to-do list, customizes the reminder content at the appropriate time, and sends the generated reminder notification to the user.

[2218] Input: User location, time, TODO list, user emotional state

[2219] Output: Generate reminder notification

[2220] Step 11: Audio output for reminder notifications

[2221] Terminal

[2222] The generated reminder notification is then provided to the user via audio through the glasses-type device. For example, the notification might say, "The deadline for submitting a project report is approaching. Please stay calm and work on it."

[2223] Input: Reminder notification

[2224] Output: Audio notification to the user

[2225] In this way, by performing specific processing at each step, users can efficiently manage their daily to-do items and receive reminders at the appropriate time.In addition, by linking the components, the system provides a more comfortable user experience by taking into account the user's emotions.

[2226] (Application example 2)

[2227] 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."

[2228] Modern brick-and-mortar shopping experiences lack sufficient consideration for users' individual needs and emotional states, making it difficult to provide an efficient and pleasant shopping experience. This problem is particularly pronounced when users experience emotional stress while shopping. This can lead to users overlooking necessary items or losing motivation to purchase. Therefore, it is important to develop a system that can automatically generate a to-do list based on the user's everyday conversations, provide reminders while taking their emotions into account, and provide an efficient and pleasant shopping experience.

[2229] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a voice input means for collecting the user's daily conversations, a voice recognition means for converting the collected voice data into text data, a natural language processing means for analyzing the converted text data and generating a to-do list, a notification means for customizing the generated to-do list based on the user's emotional state and notifying the user, a keyword extraction means for extracting keywords and context from the stored data, and an analysis means for performing statistical analysis on the extracted information and providing the data analysis results taking the user's emotional state into consideration. This enables automatic generation of a to-do list based on the user's daily conversations and emotional reminders, thereby making the user's shopping experience efficient and comfortable.

[2230] The "voice input means" is a means for collecting the user's everyday conversations and surrounding voices.

[2231] "Speech recognition means" is a means for converting voice data into text data.

[2232] The "natural language processing means" is a means for analyzing the converted text data, extracting necessary information, and processing it.

[2233] The "notification means" is a means for notifying the user of the generated data or information.

[2234] The "keyword extraction means" is a means for extracting important keywords and contexts from the stored data.

[2235] "Analysis means" refers to means for conducting statistical analysis or other data analysis based on the extracted information.

[2236] The "reminding means" is a means for notifying or reminding the user at an appropriate time by using the user's location information and time.

[2237] The "audio output means" is a means for providing information to the user by voice.

[2238] The "emotional state" is a psychological state determined from the user's tone of voice and speech content.

[2239] A "TODO list" is a list of tasks or action items that a user needs to accomplish.

[2240] This invention combines an emotion engine with a system that automatically generates and manages to-do lists by collecting and analyzing users' daily conversations. This system includes a voice input means, a voice recognition means, a natural language processing means, a notification means, a keyword extraction means, an analysis means, a reminder means, a voice output means, and an emotion recognition means.

[2241] System configuration

[2242] Voice input means

[2243] Terminal

[2244] The glasses-type device worn by the user collects surrounding sounds in real time using a built-in microphone, and the sound data is temporarily stored in the built-in memory and later transmitted to a server.

[2245] Voice recognition means

[2246] server

[2247] The voice data sent from the terminal is converted into text data by a voice recognition system on the server, which can use, for example, the Google Speech Recognition API.

[2248] Natural language processing tools

[2249] server

[2250] The converted text data is analyzed by a natural language processing engine, which automatically extracts the information necessary for the TODO list from the user's everyday conversations.

[2251] emotion recognition means

[2252] server

[2253] Analyze user emotions from text data. This analysis uses machine learning techniques such as transformer models. For example, we use the Hugging Face transformers library to load an emotion model and recognize emotions from text.

[2254] Notification means

[2255] Terminal

[2256] The generated TODO list is then sent to the user via a glasses-type device, where the notification content is customized based on emotion recognition and provided as a voice notification.

[2257] Keyword extraction method

[2258] server

[2259] Important keywords and context are extracted from the stored data using natural language processing technology. The extracted keywords are used to understand user needs and interests.

[2260] analytical means

[2261] server

[2262] Statistical analysis is performed based on the extracted keywords and context, which allows us to analyze user preferences and behavioral patterns and provide data analysis results that take into account their emotional state.

[2263] Reminder methods

[2264] server

[2265] The system uses the user's location and time to send reminders at appropriate times, and the content of the reminders is also adjusted according to the user's emotional state.

[2266] Audio output means

[2267] Terminal

[2268] Reminders and to-do list items are provided to the user via voice, for example, using the pyttsx3 library for speech synthesis.

[2269] Specific examples

[2270] Example 1: Shopping list generation and emotion-based notifications

[2271] When a user says, "I need to buy milk and bread at the supermarket tomorrow evening," the server receives this as voice data and converts it into text. The natural language processing engine then registers this information as a "shopping list" in the TODO list. Furthermore, the emotion recognition function detects anxiousness in the user's voice tone, and the device issues a gentle voice notification saying, "Buying milk and bread tomorrow evening has been added to the list."

[2272] Example 2: Emotion-conscious management of deadline-bound tasks

[2273] When a user says, "I have to submit my project report by next Monday," the server receives this as voice data and converts it into text. A natural language processing engine registers this information as a "task with a deadline" in the TODO list. Furthermore, emotion recognition detects the user's stress and notifies them with encouraging words, such as, "The deadline for submitting the project report has been added to the list. Let's do our best!" Furthermore, when the deadline approaches, the server sends a reminder, "The deadline for submitting the project report is approaching. Please stay calm and work on it."

[2274] Example prompts to input to the generative AI model

[2275] User input:

[2276] "I need to buy some milk and bread."

[2277] Expected output:

[2278] Emotion: Stress

[2279] Notification message: "Milk and bread added to the list. Good luck!"

[2280] User input:

[2281] "I want a new book."

[2282] Expected output:

[2283] Emotion: Enjoyment

[2284] Notification message: "You've added a new book to your list. Keep it up!"

[2285] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2286] Step 1:

[2287] The user wears the glasses-type device and performs voice input.

[2288] Input: User's everyday conversation (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[2289] Specific operation: The built-in microphone of the glasses-type device collects surrounding sounds in real time and temporarily stores the sound data in the device's built-in memory.

[2290] Step 2:

[2291] The terminal transmits the collected voice data to the server.

[2292] Input: Audio data stored in the device's internal memory

[2293] Specific operation: The device sends voice data to the server via the Internet.

[2294] Step 3:

[2295] The server converts the voice data into text data.

[2296] Input: Audio data sent to the server

[2297] Output: Text data corresponding to the speech data (e.g., "I need to buy milk and bread at the supermarket tomorrow evening.")

[2298] Specific operation: The server uses the Google Speech Recognition API to convert the voice data into text data.

[2299] Step 4:

[2300] The server analyzes the text data using a natural language processing engine and extracts the information necessary for the TODO list.

[2301] Input: Text data generated by a speech recognition tool

[2302] Output: TODO list items (e.g. "Buy milk and bread at the supermarket tomorrow evening")

[2303] Specific operation: The server's natural language processing engine analyzes the text data and extracts the information necessary for the TODO list (date, time, location, action items, etc.).

[2304] Step 5:

[2305] The server uses an emotion recognition engine to analyze the user's emotions from the text data.

[2306] Input: Text data processed by natural language processing means

[2307] Output: User's emotional state (e.g., anxious)

[2308] Specific operation: The server loads the emotion model using the Hugging Face transformers library and recognizes emotions from text data.

[2309] Step 6:

[2310] The device will notify the user of the TODO list based on their emotions.

[2311] Input: TODO list items and emotional state sent from the server

[2312] Output: A notification message to the user (e.g. "Buying milk and bread tomorrow evening has been added to your list")

[2313] Specific behavior: The device generates voice notifications using a speech synthesis engine (e.g., the pyttsx3 library) and adjusts the tone and content depending on the user's emotional state.

[2314] Step 7:

[2315] The server extracts keywords and context from the stored data.

[2316] Input: Text and audio data

[2317] Output: Extracted keywords and context

[2318] Specific operation: The server uses natural language processing techniques to extract important keywords and context from the stored data.

[2319] Step 8:

[2320] The server performs statistical analysis based on the extracted keywords and context.

[2321] Input: Extracted keywords and context

[2322] Output: Analysis results (user preferences and behavioral patterns)

[2323] Specific operation: The server uses statistical analysis techniques to analyze the user's preferences and behavioral patterns.

[2324] Step 9:

[2325] The server uses the user's location and time to generate appropriate reminders.

[2326] Input: User's location, time, and TODO list information

[2327] Output: Reminder notification

[2328] Specific operation: The server generates a reminder notification at the appropriate time based on GPS data and timer information, comparing it with the contents of the TODO list.

[2329] Step 10:

[2330] The device provides the user with a voice reminder notification.

[2331] Input: Reminder notification information sent from the server

[2332] Output: Reminder audio notification to the user

[2333] Specific operation: The device uses a speech synthesis engine to generate a reminder notification and provides it to the user aloud.

[2334] 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.

[2335] 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.

[2336] 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.

[2337] 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.

[2338] 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.

[2339] 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.

[2340] 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).

[2341] 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.

[2342] 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."

[2343] 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.

[2344] 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).

[2345] 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.

[2346] 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.

[2347] 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.

[2348] 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.

[2349] 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), whic...

Claims

1. A voice input means for collecting daily conversations of a user; a speech recognition means for converting collected speech data into text data; natural language processing means for analyzing the converted text data and generating a TODO list; A notification means for notifying a user of the generated TODO list; a keyword extraction means for extracting keywords and contexts from the stored data; an analysis means for performing statistical analysis based on the extracted information; A system including:

2. The system according to claim 1, further comprising a reminding means for tracking the user's location information and time and for reminding the user at an appropriate time.

3. 2. The system according to claim 1, further comprising a voice output means for outputting the user's daily conversation by voice.

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