System

Abstract

A system is provided.SOLUTION: Means for enabling a user to input a daily event in a natural language via a communication application, means for analyzing a text input in the natural language and extracting a keyword and emotional information, means for updating a profile database of the user based on the extracted keyword and emotional information, means for generating an appropriate response based on the updated profile database, means for transmitting the generated response to the user through the communication application, and means for searching for another user having a common taste and preference using the profile database, A system comprising: means for identifying a potential match; and means for sending a notification to a user based on the potential match.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, the problem of individual isolation, such as elderly people dying alone, social withdrawal, and school refusal, is becoming more serious. While the spread of the Internet may at first glance make it seem like anyone can connect with others, in reality, connections within communities are becoming weaker. Such isolation can have a negative impact on mental health and potentially lead to further social problems. The present invention aims to provide an effective system that alleviates this problem of isolation and allows individuals to maintain connections with society. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including: means for allowing a user to input daily events in natural language via a communication application; means for analyzing the input text and extracting keywords and emotional information; means for updating a profile database of the user based on the extracted information; means for generating an appropriate response based on the profile database; means for sending the generated response to the user via the communication application; means for searching for other users with common hobbies and preferences using the profile database to identify potential matches; and means for sending notifications to the user based on the potential matches. This system allows users to alleviate feelings of isolation through everyday conversations and strengthen connections with other users who share common interests.

[0006] A "communications application" is a software platform that allows users to send and receive messages using smartphones or computers.

[0007] "Natural language" refers to the text and speech forms of language used by users in their daily lives, including written and spoken forms.

[0008] "Natural language processing" refers to technology that enables computers to understand, analyze, and generate text and speech in natural language.

[0009] "Keywords" refer to words or phrases that have particular significance in the user's input text.

[0010] "Emotion information" refers to data indicating positive or negative emotions extracted from a user's input text.

[0011] A "profile database" is a database for storing and managing individual data such as a user's hobbies, preferences, behavioral history, and emotional information.

[0012] A "response" refers to a reply message that the system generates in response to an input from a user and sends to the user.

[0013] "Matching candidates" are other users who share common hobbies, tastes, and interests identified based on the profile database, and are potential users with whom you can connect.

[0014] "Notification" refers to a message sent by the system to convey specific information or offers to the user. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system is broadly divided into four main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," and the "matching phase."

[0037] Getting user input phase

[0038] Device: The user responds to the AI's question, "How was your day today?" in natural language via a communication application on a smartphone or computer. This response is then sent from the user's device to the LINE server via a communication platform such as LINE.

[0039] Server: The LINE server receives messages from users via the LINE API and then forwards them to the system's server.

[0040] Natural Language Processing Phase

[0041] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive, negative) are extracted.

[0042] Server: The analyzed information is stored in a profile database for each user. This database also stores past user input data and analysis results, providing a consistent record of the user's hobbies, preferences, and emotional state.

[0043] Response Generation Phase

[0044] Server: Based on the analysis results from the natural language processing engine, the AI ​​generates an appropriate response, such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[0045] Server: The generated responses are sent to the user through a communication application, allowing the user to share their experiences and find someone to talk to through natural responses.

[0046] Matching Phase

[0047] Server: Based on the profile database, searches for other users with the same interests and preferences. For example, find other users who share the hobby of "running."

[0048] Server: If a potential match is found, a match notification is generated based on that information. For example, a notification saying "I've found a user who shares my hobby of running. Would you like to connect?" is created and sent to the user.

[0049] Users: Users who receive the notification can form new connections based on the suggestions.

[0050] Specific examples

[0051] For example, suppose user A types, "I went for a run today and found a new route. It felt great!" This message is analyzed by the server, and keywords such as "running," "new route," and "felt great" as well as positive emotional information are extracted. Based on this, the server generates a response saying, "That's great! Finding a new route is like an adventure. Have fun on your next run!" and sends it to user A. Furthermore, based on the profile database, the server finds another user, user B, who also enjoys running, and sends user A a notification saying, "I found user B, who also enjoys running. Would you like to connect?"

[0052] In this way, the system provides an effective means of reducing users' feelings of isolation and supporting new social connections.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] User: Launches a communication application and responds to the message from the AI ​​account, "How was your day?" by typing in natural language. For example, "I went for a run today and found a new trail. It felt great!"

[0056] Step 2:

[0057] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[0058] Step 3:

[0059] Server: Receives user messages via the LINE API and temporarily stores them in storage.

[0060] Step 4:

[0061] Server: Passes the received message to a natural language processing engine, which analyzes the message and extracts keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive).

[0062] Step 5:

[0063] Server: Updates the user profile database based on the extracted keywords and emotion information. New items such as "running," "new roads," and "positive emotions" are added as updated items.

[0064] Step 6:

[0065] Server: Retrieves updated information from the profile database and uses a natural language processing engine to generate an appropriate response, such as "That's great! Finding a new route is an adventure. Have fun on your next run!"

[0066] Step 7:

[0067] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on a communication application on their smartphone or computer.

[0068] Step 8:

[0069] Server: Searches the profile database to find other users who share the same interests. For example, it finds other users who share the hobby of "running" in the database.

[0070] Step 9:

[0071] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0072] Step 10:

[0073] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[0074] This series of processes allows users to alleviate feelings of isolation through everyday conversation and connect with other users who share common interests and tastes.

[0075] Example 1

[0076] 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."

[0077] Currently, many users feel isolated in their daily lives and have difficulty making social connections. Conventional communication software does not provide appropriate matching based on the user's emotions, hobbies, and preferences, which often results in users feeling lonely. To solve this problem, a system is needed that understands the user's emotions, hobbies, and preferences and appropriately matches them with other users based on those preferences.

[0078] 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.

[0079] In this invention, the server includes: means for allowing a user to input daily events in natural language via communication software; means for analyzing the text input in natural language and extracting important words and emotional information; means for updating the user's attribute database based on the extracted important words and emotional information; means for generating an appropriate response based on the updated attribute database; means for sending the generated response to the user via the communication software; means for searching for other users with common interests and preferences using the attribute database to identify potential matching users; and means for sending a notification to the user based on the potential matching users. This allows the user to receive an appropriate response based on their emotions, interests, and preferences, and to connect with other users with common interests and preferences.

[0080] "Communications software" means programs that allow users to send and receive messages over the Internet or other networks.

[0081] "Natural language" refers to the language used by humans in everyday communication, including written and spoken language.

[0082] "Text" refers to character string data that a user inputs or transmits through communication software.

[0083] "Important words" refer to keywords with specific meanings or information extracted through natural language processing, and are words that express the user's emotions, hobbies, and preferences.

[0084] "Emotion information" is data that represents the type and intensity of emotions extracted from the text entered by the user. It includes emotion categories such as positive, negative, and neutral.

[0085] The "attribute database" is a database that stores individual user profile information, past input data, and analysis results, and consistently records the user's hobbies, preferences, and emotional state.

[0086] A "reply" is a response message in natural language that the server generates based on the user's input.

[0087] "Potential matching users" refer to other users who are determined to have common interests, preferences, and emotional states based on the attribute database.

[0088] "Notification" means a message sent by the server to a user based on information about potential matches, including a suggested match.

[0089] This invention provides a system to help users avoid isolation in their daily lives. The system receives input from users via communication software, generates appropriate responses, and matches users with other users based on their hobbies and preferences. The system is primarily composed of a network-connected server, a user terminal, and communication software.

[0090] Getting user input phase

[0091] Users use devices such as smartphones or computers to input information via communication software. This software (e.g., a messaging application) prompts users with questions such as "How was your day?", and the users then input responses in natural language.

[0092] Server Message Parsing Phase

[0093] Messages sent by users are sent over the Internet to a server, where they are first analyzed using a natural language processing engine. The program uses a natural language processing library (e.g., SpaCy or NLTK) to tokenize the message and extract key words and sentiment information. The results of this analysis are stored in an attribute database for each user.

[0094] Response Generation Phase

[0095] The server uses a generative AI model (e.g., GPT-4) based on the analysis results of the natural language processing engine to generate an appropriate response, which is then sent back to the user via communication software, allowing the user to receive a natural and empathetic response.

[0096] Matching Phase

[0097] The server searches the profiles of other users based on the user's attribute database to identify potential matches with users who share common interests. For example, it matches users who share the hobby of "running." When a match is found, the server sends a notification to the user based on that information.

[0098] Specific examples

[0099] If user A types "I went for a run today and found a new route. It felt great!", the server will analyze this message and extract important words such as "running," "new route," and "felt great," as well as positive emotional information. Based on this, the server will generate a response saying "That's great! Finding a new route is like an adventure. Have fun on your next run!" and send it to user A. Furthermore, based on the attribute database, the server will find another user B who also enjoys running, and send a notification to user A saying, "I found user B, who also enjoys running. Would you like to connect?"

[0100] Prompt Sentence Examples

[0101] User: "I went for a run today and discovered a new trail. It felt great!"

[0102] Example response: "That's great! Discovering new routes is an adventure. Have fun on your next run!"

[0103] In this way, the system can promote social connection and reduce feelings of isolation among users.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1: Getting User Input

[0106] Device: The user uses a smartphone or computer to launch the communication software. A chatbot within the software asks, "How was your day?" The user responds in natural language, such as, "I went for a run today and discovered a new path. It felt great!"

[0107] Input: The answer entered by the user in natural language

[0108] Output: Messages sent from the device to the communication software server

[0109] Step 2: Sending a message

[0110] Terminal: The message entered by the user is sent from the terminal to the communication software server, where it is encrypted to ensure security.

[0111] Input: Message from the user

[0112] Output: Message arriving at the server of the communication software

[0113] Step 3: Forward the message

[0114] Server: The server of the communication software transfers the received message to the server of this system using the API.

[0115] Input: Message that arrived at the communication software server

[0116] Output: Message forwarded to the system's server

[0117] Step 4: Natural Language Processing

[0118] Server: The server of this system passes the received message to a natural language processing engine (e.g., SpaCy or NLTK). The natural language processing engine tokenizes the message and extracts important words such as "running," "new road," and "felt good," as well as positive sentiment information.

[0119] Input: Received message

[0120] Output: Data with important words and sentiment information extracted

[0121] Step 5: Save your information

[0122] Server: The analyzed information is stored in an attribute database, which stores user profile information, past input data, and analysis results.

[0123] Input: Data with important words and sentiment information extracted

[0124] Output: Updated attribute database

[0125] Step 6: Generate a response

[0126] Server: Based on the analysis results of the natural language processing engine, a generative AI model (e.g., GPT-4) generates a specific response such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[0127] Input: Important words and sentiment information contained in the analysis results

[0128] Output: The generated response

[0129] Step 7: Sending a Response

[0130] Server: The generated responses are sent to the user through communication software, allowing the user to share their experiences and find someone to talk to through natural responses.

[0131] Input: Generated response

[0132] Output: The response sent to the user

[0133] Step 8: Finding potential matches

[0134] Server: Searches for other users who share common interests based on the attribute database. For example, it finds users whose hobby is "running."

[0135] Input: Attribute database information

[0136] Output: Found users with common interests

[0137] Step 9: Generate a Matching Notification

[0138] Server: If a potential match is found, the server generates a notification based on that information saying, "We've found user B, who also enjoys running. Would you like to connect?"

[0139] Input: Information of users with common interests

[0140] Output: The generated matching notification

[0141] Step 10: Sending a Match Notification

[0142] Server: The generated match notification is sent to the user via communication software. The user receives the notification and can form a new connection.

[0143] Input: Generated matching notification

[0144] Output: Match notification sent to user

[0145] In this way, the system can reduce users' feelings of isolation and provide new social connections.

[0146] (Application example 1)

[0147] 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."

[0148] Conventional user support systems using communication applications and information processing devices only provide interactive and matching functions to help users avoid isolation in their daily lives. However, these systems lack specific means for improving the customer experience in physical stores, and they do not adequately provide personalized product recommendations or services based on customers' hobbies, preferences, or emotional information. This results in customers not receiving services that meet their needs, leading to lower satisfaction. In addition, opportunities for customer interaction in physical stores are limited, and there is a lack of effective ways to connect with customers who share the same hobbies and preferences.

[0149] 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.

[0150] In this invention, the server includes: a means for allowing a user to input daily events in natural language via an information processing device; a means for analyzing the input text in natural language and extracting keywords and emotional information; a means for updating the user's profile database based on the extracted keywords and emotional information; a means for generating an appropriate response based on the updated profile database; a means for transmitting the generated response to the user via the information processing device; a means for searching for other users with common hobbies and preferences using the profile database and identifying potential matches; a means for providing personalized product suggestions and services in-store based on the customer's hobbies, preferences, and emotional information; and a means for sending notifications to the user based on the potential matches. This significantly improves the customer experience in physical stores and enables product suggestions and services tailored to customer needs. Furthermore, it also promotes interaction between customers and allows for the formation of new social connections.

[0151] An "information processing device" is a device that allows a user to perform information processing such as inputting natural language, sending and receiving data, and analyzing data.

[0152] "Natural language" refers to the language form that users use on a daily basis, and is used for dialogue and text input.

[0153] "Keywords" refer to words or phrases that are highly important in text entered in natural language, and are used for semantic analysis.

[0154] "Emotion information" is information that indicates the type and state of emotion extracted from the text entered by the user.

[0155] The "profile database" is a database for storing and managing individual data including information on the user's hobbies, preferences, and emotions.

[0156] A "response" is an appropriate reply message that is generated based on the user's input.

[0157] A "user" is a person who inputs and interacts in natural language using an information processing device.

[0158] "Matching candidates" are other related users selected based on common hobbies, preferences, and emotional information.

[0159] "Personalized product proposals" are the act of recommending products and services that suit specific customers based on their tastes, preferences, and emotional information.

[0160] "Notification" refers to information or messages sent to a user, including information about potential matches and product suggestions.

[0161] System Program

[0162] To realize this invention, the following system program is required. The system includes the following main components:

[0163] 1. Get user input phase:

[0164] Terminal: Users can input everyday events and questions in natural language via information processing devices such as smartphones and tablets.

[0165] Server: User input is sent to the server through a communication application, which receives this input data and passes it to the analysis engine.

[0166] 2. Natural Language Processing Phase:

[0167] Server: The received input data is analyzed using a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3). The analysis engine tokenizes the text and extracts keywords and sentiment information.

[0168] 3. Response generation phase:

[0169] Server: Generates an appropriate response based on the analysis results, such as "This is the section for new running shoes."

[0170] Server: Sends the generated response to the user's terminal through the communication application.

[0171] 4. Matching Phase:

[0172] Server: Searches for other users with common interests and preferences based on the profile database, taking into account the user's input data and past history.

[0173] Server: If a suitable match is found, it sends a notification to the user.

[0174] 5. Personalized product proposal phase:

[0175] In-store, specific products and services are suggested based on the customer's hobbies, preferences, and emotional information. This process is also carried out by the server, and personalized responses and product information are provided to the user.

[0176] Processing overview

[0177] Hardware and software used

[0178] Hardware

[0179] Smartphones, tablets, smart glasses, servers

[0180] software

[0181] Natural language processing engines (BERT and GPT-3)

[0182] Profile Database Management System

[0183] Communication applications (LINE API, etc.)

[0184] Data processing and calculation

[0185] Natural language text data received from the user is sent to the server. This text data is first tokenized to extract key keywords and emotional information. This information is then stored in a profile database, where the user's hobbies, preferences, and emotional information are managed in a unified manner. Based on this information, responses and product suggestions appropriate for each individual user are generated. Finally, the generated responses and suggestions are sent back to the user's device.

[0186] Example prompts to be input to the generative AI model

[0187] An example of actual usage is the prompt:

[0188] User: I ran today and it felt great!

[0189] AI response: The new running shoes section is here.

[0190] User: I'm looking to buy some new shoes. What do you recommend?

[0191] AI response: These are the latest recommended shoes!

[0192] Based on these prompts, the server uses a natural language processing engine to generate appropriate responses and provide them to the user, which not only reduces the user's sense of isolation and creates new social connections, but also improves the individual customer experience.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] Users input daily events in natural language via a communication application

[0196] A user opens a communication application on an information processing device such as a smartphone or tablet and inputs daily events or questions in natural language. The input data includes sentences such as "I felt great running today!" This input data is sent from the user's device to a server.

[0197] Input: User's natural language text

[0198] Output: Text data sent to the server via a communication application

[0199] Step 2:

[0200] Analyze the text data received by the server

[0201] The server processes the text data received from the communication application. Specifically, it uses a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3) to tokenize the text and extract key keywords and sentiment information. This analysis yields keywords such as "running" and "felt good" and sentiment information such as "positive."

[0202] Input: Natural language text sent to the server

[0203] Output: Extracted keywords and sentiment information

[0204] Step 3:

[0205] The server saves and updates the extracted information in the profile database

[0206] The server updates the user's profile database based on the extracted keywords and emotional information, including new information on hobbies and preferences and records of emotional states, allowing for consistent storage and management of the user's preferences and current moods.

[0207] Input: Extracted keywords and sentiment information

[0208] Output: Updated user profile database

[0209] Step 4:

[0210] The server generates an appropriate response and sends it to the user.

[0211] The server generates an appropriate response for the user based on the updated profile database and the analysis results, and the response is sent to the user's device via a communication application. For example, the response may include a specific suggestion such as "Here's the section for new running shoes."

[0212] Input: Analysis results and updated profile database

[0213] Output: The generated response message

[0214] Step 5:

[0215] The server searches for other users with common interests and preferences.

[0216] The server uses a profile database to search for other users who share common interests, preferences, and emotional states, for example, to find users who share the hobby of "running" and identify potential matches.

[0217] Input: Updated profile database

[0218] Output: A list of possible matches

[0219] Step 6:

[0220] The server sends notifications to the user based on potential matches

[0221] The server then sends notifications to users based on the identified potential matches, such as a message like, "I've found a user who shares my interest in running. Would you like to connect with me?"

[0222] Input: A list of possible matches

[0223] Output: A notification message to the user

[0224] Step 7:

[0225] Servers make personalized product suggestions in-store

[0226] When a user is in a physical store, the server provides personalized product suggestions and services based on the hobbies, preferences, and emotional information entered by the customer in the store, allowing the user to receive product suggestions that meet their needs.

[0227] Input: User's tastes, preferences and emotional information

[0228] Output: Personalized product suggestions and service information

[0229] Step 8:

[0230] Forming new social connections with customers

[0231] The server provides users with opportunities to form new social connections through the responses and matching notifications they send, allowing them to exchange information and enjoy conversations about common interests with other customers.

[0232] Input: Generated response message and matching notification message

[0233] Output: Facilitating communication between users

[0234] 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.

[0235] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. This system is broadly divided into five main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," the "matching phase," and the "emotion recognition phase."

[0236] Getting user input phase

[0237] Device: The user responds to the AI's message "How was your day today?" in natural language via a communication application on a smartphone or computer. For example, the user might say, "I went for a run today and found a new trail. It felt great!"

[0238] Server: User messages are sent to the LINE server via a communication platform such as LINE, and then forwarded to the server of this system.

[0239] Natural Language Processing Phase

[0240] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[0241] Emotion Recognition Phase

[0242] Server: The text analyzed by the natural language processing engine is passed to the emotion engine, which extracts emotional information. Specifically, the emotion engine identifies emotion categories such as positive, negative, and neutral from the extracted text and quantifies their intensity to identify the type and intensity of the emotion.

[0243] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, it stores data such as "running," "new roads," and "intensity of positive emotions."

[0244] Response Generation Phase

[0245] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0246] Server: The server sends the generated response message to the user through a communication application. Users can share their experiences and find someone to talk to through natural responses.

[0247] Matching Phase

[0248] Server: Searches for other users with the same interests and preferences based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[0249] Server: When a potential match is found, a notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0250] Server: Sends the generated match notification to the user through a communication application. The user receives the notification and can form a new connection.

[0251] Specific examples

[0252] For example, suppose User A writes, "I went for a run today and discovered a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion engine then identifies positive emotions from the text and quantifies and analyzes the intensity of the emotion. The profile database is updated based on this information, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" The profile database is then searched for another User B with the same hobby, and a notification is sent to User A saying, "I found User B, who also enjoys running. Would you like to connect?" In this way, the system reduces users' feelings of isolation and promotes new social connections.

[0253] The processing flow will be explained below.

[0254] Step 1:

[0255] User: Launches a communication application and responds to a message from the AI ​​account, "How was your day?" by typing a natural language response. For example, the user might type, "I went for a run today and found a new trail. It felt great!"

[0256] Step 2:

[0257] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[0258] Step 3:

[0259] Server: Receives user messages via the LINE API. Temporarily saves the received messages as text data for analysis.

[0260] Step 4:

[0261] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (such as "running," "new road," and "felt good") are extracted.

[0262] Step 5:

[0263] Server: The extracted keywords are passed to the emotion engine to extract emotional information. Specifically, the engine identifies emotional categories (positive, negative, neutral, etc.) from the extracted text and quantifies their intensity.

[0264] Step 6:

[0265] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, the profile database stores data such as "running," "new roads," and "intensity of positive emotion."

[0266] Step 7:

[0267] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0268] Step 8:

[0269] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on the communication application.

[0270] Step 9:

[0271] Server: Searches for other users who share common interests based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[0272] Step 10:

[0273] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0274] Step 11:

[0275] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[0276] This series of processes allows users to alleviate feelings of isolation through everyday conversation, connect with other users who share common interests and tastes, and strengthen social connections.

[0277] Example 2

[0278] 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."

[0279] In modern society, people often feel isolated due to physical distance and time constraints. Isolation in daily life can cause mental stress and make it difficult to maintain social connections. Furthermore, conventional communication systems have difficulty providing personalized responses and matching based on users' emotions, hobbies, and preferences.

[0280] 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.

[0281] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication program; means for analyzing the input text in natural language and extracting keywords and emotional information; means for updating the user's characteristic database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated characteristic database; means for sending the generated response to the user via the communication program; means for searching for other users with common interests and preferences using the characteristic database to identify matching candidates; and means for sending a notification to the user based on the matching candidates. This allows the user to share their daily emotions, hobbies, and preferences, receive appropriate responses, and further form new connections with other users with common interests.

[0282] A "communication program" is software that allows users to input information about everyday events in natural language and exchange information with other users and systems.

[0283] "Natural language" is a language commonly used by humans in everyday conversation and writing, in a format that can be analyzed by machines.

[0284] "Text" refers to character string information such as sentences or messages entered by the user.

[0285] "Keywords" refer to particularly important words or phrases in the input text, and play a central role in the system's analysis.

[0286] "Emotion information" is data that indicates the user's emotion extracted from the input text, and includes the type of emotion (positive, negative, neutral, etc.) and its intensity.

[0287] The "characteristics database" is a database that stores and manages information about users' hobbies, preferences, and emotions, and is used by the system to respond to and match users.

[0288] A "response" is a reply message generated by the system in response to a user's input, and is expressed in natural language.

[0289] "Match Potential" means a person identified as another user with common interests or preferences based on information in the profile database.

[0290] A "notification" is an informational message sent by the system to the user, informing them of the existence of a potential match, etc.

[0291] A "mobile terminal" refers to an electronic device that a user can carry with them, such as a smartphone or tablet.

[0292] "Location data" refers to data relating to the geographical location of a user, such as GPS information obtained from a mobile terminal.

[0293] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication program, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system consists of five main phases: a "user input acquisition phase," a "natural language processing phase," an "emotion recognition phase," a "response generation phase," and a "matching phase."

[0294] Getting user input phase

[0295] Terminal: The user launches a communication program on their smartphone or computer. This program displays the message "How was your day?" to the user. The user responds in natural language, for example, "I went for a run today and found a new trail. It felt great!"

[0296] Natural Language Processing Phase

[0297] Server: The communication program server receives the user's message and forwards it to the system's server. The system's server passes the message to a natural language processing engine (e.g., SpaCy or NLTK) to analyze the text. During the analysis process, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[0298] Emotion Recognition Phase

[0299] Server: The emotion information is passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API) to analyze based on the keywords extracted by the natural language processing engine. The emotion recognition engine identifies emotion categories such as positive, negative, and neutral from these keywords and quantifies their intensity. For example, the expression "I felt good" is identified as a positive emotion with high intensity.

[0300] Profile database update

[0301] Server: Updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, adds and saves data such as "running," "new roads," and "intensity of positive emotions" to the user's characteristic database.

[0302] Response Generation Phase

[0303] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI's generative AI model). For example, generate the response text "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0304] Sending a Response

[0305] Server: The server sends the generated response message to the user via a communication program. The user can then view the message on their own device and share their individual experiences.

[0306] Matching Phase

[0307] Server: Searches for other users with the same hobbies and interests based on the characteristics database. For example, it finds other users who share the common hobby of "running" from the database.

[0308] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[0309] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections and reduce feelings of isolation.

[0310] Specific examples

[0311] For example, user A types, "I went for a run today and found a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion recognition engine identifies this as a positive emotion and quantifies the intensity of the emotion. The trait database is updated based on this information, and the generative AI model generates a response such as, "That's great! Finding a new route feels like an adventure. Have fun on your next run!" Furthermore, user B, who shares the same hobby, is found, and a notification is sent to user A saying, "I found user B, who also enjoys running. Would you like to connect?" Through this process, users can reduce their sense of isolation and form new social connections.

[0312] Example prompts for generative AI models

[0313] "User A entered, 'I went for a run today and found a new path. It felt great!' Extract keywords from this input, analyze the emotional information, and generate an appropriate response to User A and a matching notification with other users who have the same hobbies."

[0314] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0315] Step 1:

[0316] User: The user opens a communication program on their smartphone or computer and receives a message asking, "How was your day?" The user types a natural language response into a text box, such as, "I went for a run today and found a new trail. It felt great!"

[0317] Input: User's natural language text.

[0318] Output: Text data sent to the communications program.

[0319] Step 2:

[0320] Terminal: The terminal's communications program sends the user's message to the server.

[0321] Input: The text message entered by the user.

[0322] Output: The text data sent to the server.

[0323] Step 3:

[0324] Server: The server passes the received text data to a natural language processing engine (e.g., SpaCy or NLTK), tokenizes the text, and extracts key keywords (e.g., "running," "new road," "felt good").

[0325] Input: User's text data.

[0326] Output: Extracted keywords.

[0327] Step 4:

[0328] Server: Keywords extracted by the natural language processing engine are passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API), which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity.

[0329] Input: Extracted keywords.

[0330] Output: Sentiment category and its intensity.

[0331] Step 5:

[0332] Server: The server updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, it adds and saves the data "running," "new route," and "intensity of positive emotion" to the user's profile.

[0333] Input: sentiment information and keywords.

[0334] Output: Updated trait database.

[0335] Step 6:

[0336] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI GPT-3).

[0337] Input: Updated trait database.

[0338] Output: The generated response message.

[0339] Step 7:

[0340] Server: Sends the generated response message to the user via a communication program.

[0341] Input: The generated response message.

[0342] Output: The message sent to the user.

[0343] Step 8:

[0344] Server: Searches for other users with the same hobbies and interests based on the characteristics database. Specifically, it searches the database for other users who share the common hobby of "running."

[0345] Input: trait database.

[0346] Output: A list of potential matches.

[0347] Step 9:

[0348] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[0349] Input: A list of potential matches.

[0350] Output: The generated matching notification.

[0351] Step 10:

[0352] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections.

[0353] Input: The generated matching notification.

[0354] Output: Notification sent to the user.

[0355] (Application example 2)

[0356] 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."

[0357] The problem to be solved by this invention is to help users avoid isolation in their daily lives, promote interaction between customers in physical stores, and improve the customer experience in the store. In particular, the purpose is to reduce feelings of isolation and form new social connections by matching customers who share common hobbies, preferences, and emotional information in real time and recommending appropriate community spaces.

[0358] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0359] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication application; means for analyzing the text input in natural language and extracting keywords and emotional information; means for updating the user's profile database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated profile database; means for sending the generated response to the user via the communication application; means for searching for other users with common hobbies and preferences using the profile database and identifying match candidates; means for sending a notification to the user based on the match candidates; means for real-time matching to promote customer interaction in the store; and means for recommending the use of a specific community space based on the real-time matching means. This prevents users from feeling isolated in their daily lives, promotes customer interaction in the physical store, and improves the customer experience.

[0360] A "communications application" is software that enables communication between users over the Internet.

[0361] "Natural language" refers to language used by humans on a daily basis, excluding computer processing.

[0362] "Natural language processing" is a technology that allows computers to understand, analyze, and generate natural human language.

[0363] A "keyword extraction means" is a means for identifying and extracting key words and phrases from input text.

[0364] "Emotional information" refers to data that identifies emotional states, such as positive, negative, or neutral, from natural language text.

[0365] A "profile database" is a database for storing a user's hobbies, preferences, emotional information, and other related information.

[0366] The "response generation means" is a means for generating an appropriate response to a user's input.

[0367] "Matching means" is a method for discovering and presenting appropriate associations with other users based on common hobbies, preferences, and emotional information.

[0368] A "match notification means" is a means for notifying a user of a suitable match candidate that has been found.

[0369] "Real-time matching means" is a means of matching customers who are simultaneously in the store based on their hobbies and preferences.

[0370] The "community space recommendation means" is a means of recommending a space within the store where matched customers can interact with each other.

[0371] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. The system consists of five main phases: a user input acquisition phase, a natural language processing phase, a response generation phase, an emotion recognition phase, and a matching phase.

[0372] Getting user input phase

[0373] Device:

[0374] The user responds to the message "How was your day today?" from the AI ​​via a communication application on a smartphone or other device by inputting a natural language response. For example, the user might input "I went for a run today and found a new trail. It felt great!"

[0375] server:

[0376] The message is sent to the server through the communication platform and then forwarded to the server of the system.

[0377] Natural Language Processing Phase

[0378] server:

[0379] The received message is passed to a natural language processing engine and the text is analyzed. Specifically, the text is tokenized and key keywords such as "running," "new road," and "felt good" are extracted. The text analysis is also performed using the pipeline function of the transformers library.

[0380] Emotion Recognition Phase

[0381] server:

[0382] The parsed text is passed to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the Transformers library is used for emotion recognition. The extracted emotion information and keywords are stored in the user's profile database.

[0383] Response Generation Phase

[0384] server:

[0385] It uses a natural language processing engine to generate appropriate responses based on the user's profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0386] Matching Phase

[0387] server:

[0388] Search for other users with the same hobbies and interests based on the profile database. For example, find other users who share the hobby "running" from the database. When a potential match is found, generate a matching notification based on that information. For example, create a notification saying, "I've found user B, who also enjoys running. Would you like to connect?" and send it to the user.

[0389] Specific examples

[0390] For example, if User A types, "I went for a run today and found a new route. It felt great!", the message is analyzed by a natural language processing engine, and the keywords "running," "new route," and "felt great" are extracted. The emotion engine identifies positive emotions from these texts and quantifies and analyzes the intensity of the emotions. The profile database is updated, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" At the same time, the profile database is searched for another User B with the same hobby, and a notification is sent to User A saying, "We've found User B, who also enjoys running. Would you like to connect?"

[0391] This will prevent users from being isolated in their daily lives, promote customer interaction within physical stores, and improve the customer experience.

[0392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0393] Step 1:

[0394] User input

[0395] A user inputs a daily event in natural language via a communication application, for example, "I went running today and found a new path. It felt great!"

[0396] Step 2:

[0397] Sending a message

[0398] The terminal transmits the input text to the server through the communication platform, where the input is natural language text and the output is the same text data transmitted to the server.

[0399] Step 3:

[0400] Natural Language Processing

[0401] The server passes the received message to a natural language processing engine and analyzes the text. Specifically, it uses the pipeline function of the transformers library to tokenize the text and extract key keywords such as "running," "new road," and "felt good." The input is natural language text, and the output is a list of extracted keywords.

[0402] Step 4:

[0403] emotion recognition

[0404] The server passes the parsed text to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the transformers library is used for emotion recognition. The input is a list of extracted keywords, and the output is data with emotion categories and their intensities.

[0405] Step 5:

[0406] Profile database update

[0407] The server updates the user's profile database based on the extracted emotion information and keywords. The input is emotion data and a keyword list, and the output is the updated profile database.

[0408] Step 6:

[0409] Response Generation

[0410] The server uses a natural language processing engine to generate an appropriate response based on the updated profile database, for example, "That's great! Finding a new route sounds like an adventure. Have fun on your next run!" The input is the updated profile database, and the output is the generated response message.

[0411] Step 7:

[0412] Response Send

[0413] The server sends the generated response message to the user through the communication application, where the input is the generated response message and the output is the response message sent to the user terminal.

[0414] Step 8:

[0415] Searching for potential matches

[0416] The server searches for other users with the same hobbies and interests based on the profile database. Specifically, it finds other users who share the hobby "running" in the database. The input is the updated profile database, and the output is a list of match candidates.

[0417] Step 9:

[0418] Generate a matching notification

[0419] When the server finds a match candidate, it generates a match notification based on that information. For example, it creates a notification that reads, "I've found user B, who also enjoys running. Would you like to connect?" The input is a list of match candidates, and the output is the generated match notification.

[0420] Step 10:

[0421] Sending matching notifications

[0422] The server sends the generated match notification to the user through a communication application, where the input is the generated match notification and the output is the notification message sent to the user terminal.

[0423] Example prompt

[0424] User: I tried a new drink at the cafe today and it was delicious!

[0425] AI: That's great! Glad you had a great time.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Second embodiment]

[0430] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0441] 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."

[0442] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system is broadly divided into four main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," and the "matching phase."

[0443] Getting user input phase

[0444] Device: The user responds to the AI's question, "How was your day today?" in natural language via a communication application on a smartphone or computer. This response is then sent from the user's device to the LINE server via a communication platform such as LINE.

[0445] Server: The LINE server receives messages from users via the LINE API and then forwards them to the system's server.

[0446] Natural Language Processing Phase

[0447] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive, negative) are extracted.

[0448] Server: The analyzed information is stored in a profile database for each user. This database also stores past user input data and analysis results, providing a consistent record of the user's hobbies, preferences, and emotional state.

[0449] Response Generation Phase

[0450] Server: Based on the analysis results from the natural language processing engine, the AI ​​generates an appropriate response, such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[0451] Server: The generated responses are sent to the user through a communication application, allowing the user to share their experiences and find someone to talk to through natural responses.

[0452] Matching Phase

[0453] Server: Based on the profile database, searches for other users with the same interests and preferences. For example, find other users who share the hobby of "running."

[0454] Server: If a potential match is found, a match notification is generated based on that information. For example, a notification saying "I've found a user who shares my hobby of running. Would you like to connect?" is created and sent to the user.

[0455] Users: Users who receive the notification can form new connections based on the suggestions.

[0456] Specific examples

[0457] For example, suppose user A types, "I went for a run today and found a new route. It felt great!" This message is analyzed by the server, and keywords such as "running," "new route," and "felt great" as well as positive emotional information are extracted. Based on this, the server generates a response saying, "That's great! Finding a new route is like an adventure. Have fun on your next run!" and sends it to user A. Furthermore, based on the profile database, the server finds another user, user B, who also enjoys running, and sends user A a notification saying, "I found user B, who also enjoys running. Would you like to connect?"

[0458] In this way, the system provides an effective means of reducing users' feelings of isolation and supporting new social connections.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] User: Launches a communication application and responds to the message from the AI ​​account, "How was your day?" by typing in natural language. For example, "I went for a run today and found a new trail. It felt great!"

[0462] Step 2:

[0463] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[0464] Step 3:

[0465] Server: Receives user messages via the LINE API and temporarily stores them in storage.

[0466] Step 4:

[0467] Server: Passes the received message to a natural language processing engine, which analyzes the message and extracts keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive).

[0468] Step 5:

[0469] Server: Updates the user profile database based on the extracted keywords and emotion information. New items such as "running," "new roads," and "positive emotions" are added as updated items.

[0470] Step 6:

[0471] Server: Retrieves updated information from the profile database and uses a natural language processing engine to generate an appropriate response, such as "That's great! Finding a new route is an adventure. Have fun on your next run!"

[0472] Step 7:

[0473] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on a communication application on their smartphone or computer.

[0474] Step 8:

[0475] Server: Searches the profile database to find other users who share the same interests. For example, it finds other users who share the hobby of "running" in the database.

[0476] Step 9:

[0477] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0478] Step 10:

[0479] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[0480] This series of processes allows users to alleviate feelings of isolation through everyday conversation and connect with other users who share common interests and tastes.

[0481] Example 1

[0482] 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."

[0483] Currently, many users feel isolated in their daily lives and have difficulty making social connections. Conventional communication software does not provide appropriate matching based on the user's emotions, hobbies, and preferences, which often results in users feeling lonely. To solve this problem, a system is needed that understands the user's emotions, hobbies, and preferences and appropriately matches them with other users based on those preferences.

[0484] 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.

[0485] In this invention, the server includes: means for allowing a user to input daily events in natural language via communication software; means for analyzing the text input in natural language and extracting important words and emotional information; means for updating the user's attribute database based on the extracted important words and emotional information; means for generating an appropriate response based on the updated attribute database; means for sending the generated response to the user via the communication software; means for searching for other users with common interests and preferences using the attribute database to identify potential matching users; and means for sending a notification to the user based on the potential matching users. This allows the user to receive an appropriate response based on their emotions, interests, and preferences, and to connect with other users with common interests and preferences.

[0486] "Communications software" means programs that allow users to send and receive messages over the Internet or other networks.

[0487] "Natural language" refers to the language used by humans in everyday communication, including written and spoken language.

[0488] "Text" refers to character string data that a user inputs or transmits through communication software.

[0489] "Important words" refer to keywords with specific meanings or information extracted through natural language processing, and are words that express the user's emotions, hobbies, and preferences.

[0490] "Emotion information" is data that represents the type and intensity of emotions extracted from the text entered by the user. It includes emotion categories such as positive, negative, and neutral.

[0491] The "attribute database" is a database that stores individual user profile information, past input data, and analysis results, and consistently records the user's hobbies, preferences, and emotional state.

[0492] A "reply" is a response message in natural language that the server generates based on the user's input.

[0493] "Potential matching users" refer to other users who are determined to have common interests, preferences, and emotional states based on the attribute database.

[0494] "Notification" means a message sent by the server to a user based on information about potential matches, including a suggested match.

[0495] This invention provides a system to help users avoid isolation in their daily lives. The system receives input from users via communication software, generates appropriate responses, and matches users with other users based on their hobbies and preferences. The system is primarily composed of a network-connected server, a user terminal, and communication software.

[0496] Getting user input phase

[0497] Users use devices such as smartphones or computers to input information via communication software. This software (e.g., a messaging application) prompts users with questions such as "How was your day?", and the users then input responses in natural language.

[0498] Server Message Parsing Phase

[0499] Messages sent by users are sent over the Internet to a server, where they are first analyzed using a natural language processing engine. The program uses a natural language processing library (e.g., SpaCy or NLTK) to tokenize the message and extract key words and sentiment information. The results of this analysis are stored in an attribute database for each user.

[0500] Response Generation Phase

[0501] The server uses a generative AI model (e.g., GPT-4) based on the analysis results of the natural language processing engine to generate an appropriate response, which is then sent back to the user via communication software, allowing the user to receive a natural and empathetic response.

[0502] Matching Phase

[0503] The server searches the profiles of other users based on the user's attribute database to identify potential matches with users who share common interests. For example, it matches users who share the hobby of "running." When a match is found, the server sends a notification to the user based on that information.

[0504] Specific examples

[0505] If user A types "I went for a run today and found a new route. It felt great!", the server will analyze this message and extract important words such as "running," "new route," and "felt great," as well as positive emotional information. Based on this, the server will generate a response saying "That's great! Finding a new route is like an adventure. Have fun on your next run!" and send it to user A. Furthermore, based on the attribute database, the server will find another user B who also enjoys running, and send a notification to user A saying, "I found user B, who also enjoys running. Would you like to connect?"

[0506] Prompt Sentence Examples

[0507] User: "I went for a run today and discovered a new trail. It felt great!"

[0508] Example response: "That's great! Discovering new routes is an adventure. Have fun on your next run!"

[0509] In this way, the system can promote social connection and reduce feelings of isolation among users.

[0510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0511] Step 1: Getting User Input

[0512] Device: The user uses a smartphone or computer to launch the communication software. A chatbot within the software asks, "How was your day?" The user responds in natural language, such as, "I went for a run today and discovered a new path. It felt great!"

[0513] Input: The answer entered by the user in natural language

[0514] Output: Messages sent from the device to the communication software server

[0515] Step 2: Sending a message

[0516] Terminal: The message entered by the user is sent from the terminal to the communication software server, where it is encrypted to ensure security.

[0517] Input: Message from the user

[0518] Output: Message arriving at the server of the communication software

[0519] Step 3: Forward the message

[0520] Server: The server of the communication software transfers the received message to the server of this system using the API.

[0521] Input: Message that arrived at the communication software server

[0522] Output: Message forwarded to the system's server

[0523] Step 4: Natural Language Processing

[0524] Server: The server of this system passes the received message to a natural language processing engine (e.g., SpaCy or NLTK). The natural language processing engine tokenizes the message and extracts important words such as "running," "new road," and "felt good," as well as positive sentiment information.

[0525] Input: Received message

[0526] Output: Data with important words and sentiment information extracted

[0527] Step 5: Save your information

[0528] Server: The analyzed information is stored in an attribute database, which stores user profile information, past input data, and analysis results.

[0529] Input: Data with important words and sentiment information extracted

[0530] Output: Updated attribute database

[0531] Step 6: Generate a response

[0532] Server: Based on the analysis results of the natural language processing engine, a generative AI model (e.g., GPT-4) generates a specific response such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[0533] Input: Important words and sentiment information contained in the analysis results

[0534] Output: The generated response

[0535] Step 7: Sending a Response

[0536] Server: The generated responses are sent to the user through communication software, allowing the user to share their experiences and find someone to talk to through natural responses.

[0537] Input: Generated response

[0538] Output: The response sent to the user

[0539] Step 8: Finding potential matches

[0540] Server: Searches for other users who share common interests based on the attribute database. For example, it finds users whose hobby is "running."

[0541] Input: Attribute database information

[0542] Output: Found users with common interests

[0543] Step 9: Generate a Matching Notification

[0544] Server: If a potential match is found, the server generates a notification based on that information saying, "We've found user B, who also enjoys running. Would you like to connect?"

[0545] Input: Information of users with common interests

[0546] Output: The generated matching notification

[0547] Step 10: Sending a Match Notification

[0548] Server: The generated match notification is sent to the user via communication software. The user receives the notification and can form a new connection.

[0549] Input: Generated matching notification

[0550] Output: Match notification sent to user

[0551] In this way, the system can reduce users' feelings of isolation and provide new social connections.

[0552] (Application example 1)

[0553] 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."

[0554] Conventional user support systems using communication applications and information processing devices only provide interactive and matching functions to help users avoid isolation in their daily lives. However, these systems lack specific means for improving the customer experience in physical stores, and they do not adequately provide personalized product recommendations or services based on customers' hobbies, preferences, or emotional information. This results in customers not receiving services that meet their needs, leading to lower satisfaction. In addition, opportunities for customer interaction in physical stores are limited, and there is a lack of effective ways to connect with customers who share the same hobbies and preferences.

[0555] 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.

[0556] In this invention, the server includes: a means for allowing a user to input daily events in natural language via an information processing device; a means for analyzing the input text in natural language and extracting keywords and emotional information; a means for updating the user's profile database based on the extracted keywords and emotional information; a means for generating an appropriate response based on the updated profile database; a means for transmitting the generated response to the user via the information processing device; a means for searching for other users with common hobbies and preferences using the profile database and identifying potential matches; a means for providing personalized product suggestions and services in-store based on the customer's hobbies, preferences, and emotional information; and a means for sending notifications to the user based on the potential matches. This significantly improves the customer experience in physical stores and enables product suggestions and services tailored to customer needs. Furthermore, it also promotes interaction between customers and allows for the formation of new social connections.

[0557] An "information processing device" is a device that allows a user to perform information processing such as inputting natural language, sending and receiving data, and analyzing data.

[0558] "Natural language" refers to the language form that users use on a daily basis, and is used for dialogue and text input.

[0559] "Keywords" refer to words or phrases that are highly important in text entered in natural language, and are used for semantic analysis.

[0560] "Emotion information" is information that indicates the type and state of emotion extracted from the text entered by the user.

[0561] The "profile database" is a database for storing and managing individual data including information on the user's hobbies, preferences, and emotions.

[0562] A "response" is an appropriate reply message that is generated based on the user's input.

[0563] A "user" is a person who inputs and interacts in natural language using an information processing device.

[0564] "Matching candidates" are other related users selected based on common hobbies, preferences, and emotional information.

[0565] "Personalized product proposals" are the act of recommending products and services that suit specific customers based on their tastes, preferences, and emotional information.

[0566] "Notification" refers to information or messages sent to a user, including information about potential matches and product suggestions.

[0567] System Program

[0568] To realize this invention, the following system program is required. The system includes the following main components:

[0569] 1. Get user input phase:

[0570] Terminal: Users can input everyday events and questions in natural language via information processing devices such as smartphones and tablets.

[0571] Server: User input is sent to the server through a communication application, which receives this input data and passes it to the analysis engine.

[0572] 2. Natural Language Processing Phase:

[0573] Server: The received input data is analyzed using a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3). The analysis engine tokenizes the text and extracts keywords and sentiment information.

[0574] 3. Response generation phase:

[0575] Server: Generates an appropriate response based on the analysis results, such as "This is the section for new running shoes."

[0576] Server: Sends the generated response to the user's terminal through the communication application.

[0577] 4. Matching Phase:

[0578] Server: Searches for other users with common interests and preferences based on the profile database, taking into account the user's input data and past history.

[0579] Server: If a suitable match is found, it sends a notification to the user.

[0580] 5. Personalized product proposal phase:

[0581] In-store, specific products and services are suggested based on the customer's hobbies, preferences, and emotional information. This process is also carried out by the server, and personalized responses and product information are provided to the user.

[0582] Processing overview

[0583] Hardware and software used

[0584] Hardware

[0585] Smartphones, tablets, smart glasses, servers

[0586] software

[0587] Natural language processing engines (BERT and GPT-3)

[0588] Profile Database Management System

[0589] Communication applications (LINE API, etc.)

[0590] Data processing and calculation

[0591] Natural language text data received from the user is sent to the server. This text data is first tokenized to extract key keywords and emotional information. This information is then stored in a profile database, where the user's hobbies, preferences, and emotional information are managed in a unified manner. Based on this information, responses and product suggestions appropriate for each individual user are generated. Finally, the generated responses and suggestions are sent back to the user's device.

[0592] Example prompts to be input to the generative AI model

[0593] An example of actual usage is the prompt:

[0594] User: I ran today and it felt great!

[0595] AI response: The new running shoes section is here.

[0596] User: I'm looking to buy some new shoes. What do you recommend?

[0597] AI response: These are the latest recommended shoes!

[0598] Based on these prompts, the server uses a natural language processing engine to generate appropriate responses and provide them to the user, which not only reduces the user's sense of isolation and creates new social connections, but also improves the individual customer experience.

[0599] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0600] Step 1:

[0601] Users input daily events in natural language via a communication application

[0602] A user opens a communication application on an information processing device such as a smartphone or tablet and inputs daily events or questions in natural language. The input data includes sentences such as "I felt great running today!" This input data is sent from the user's device to a server.

[0603] Input: User's natural language text

[0604] Output: Text data sent to the server via a communication application

[0605] Step 2:

[0606] Analyze the text data received by the server

[0607] The server processes the text data received from the communication application. Specifically, it uses a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3) to tokenize the text and extract key keywords and sentiment information. This analysis yields keywords such as "running" and "felt good" and sentiment information such as "positive."

[0608] Input: Natural language text sent to the server

[0609] Output: Extracted keywords and sentiment information

[0610] Step 3:

[0611] The server saves and updates the extracted information in the profile database

[0612] The server updates the user's profile database based on the extracted keywords and emotional information, including new information on hobbies and preferences and records of emotional states, allowing for consistent storage and management of the user's preferences and current moods.

[0613] Input: Extracted keywords and sentiment information

[0614] Output: Updated user profile database

[0615] Step 4:

[0616] The server generates an appropriate response and sends it to the user.

[0617] The server generates an appropriate response for the user based on the updated profile database and the analysis results, and the response is sent to the user's device via a communication application. For example, the response may include a specific suggestion such as "Here's the section for new running shoes."

[0618] Input: Analysis results and updated profile database

[0619] Output: The generated response message

[0620] Step 5:

[0621] The server searches for other users with common interests and preferences.

[0622] The server uses a profile database to search for other users who share common interests, preferences, and emotional states, for example, to find users who share the hobby of "running" and identify potential matches.

[0623] Input: Updated profile database

[0624] Output: A list of possible matches

[0625] Step 6:

[0626] The server sends notifications to the user based on potential matches

[0627] The server then sends notifications to users based on the identified potential matches, such as a message like, "I've found a user who shares my interest in running. Would you like to connect with me?"

[0628] Input: A list of possible matches

[0629] Output: A notification message to the user

[0630] Step 7:

[0631] Servers make personalized product suggestions in-store

[0632] When a user is in a physical store, the server provides personalized product suggestions and services based on the hobbies, preferences, and emotional information entered by the customer in the store, allowing the user to receive product suggestions that meet their needs.

[0633] Input: User's tastes, preferences and emotional information

[0634] Output: Personalized product suggestions and service information

[0635] Step 8:

[0636] Forming new social connections with customers

[0637] The server provides users with opportunities to form new social connections through the responses and matching notifications they send, allowing them to exchange information and enjoy conversations about common interests with other customers.

[0638] Input: Generated response message and matching notification message

[0639] Output: Facilitating communication between users

[0640] 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.

[0641] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. This system is broadly divided into five main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," the "matching phase," and the "emotion recognition phase."

[0642] Getting user input phase

[0643] Device: The user responds to the AI's message "How was your day today?" in natural language via a communication application on a smartphone or computer. For example, the user might say, "I went for a run today and found a new trail. It felt great!"

[0644] Server: User messages are sent to the LINE server via a communication platform such as LINE, and then forwarded to the server of this system.

[0645] Natural Language Processing Phase

[0646] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[0647] Emotion Recognition Phase

[0648] Server: The text analyzed by the natural language processing engine is passed to the emotion engine, which extracts emotional information. Specifically, the emotion engine identifies emotion categories such as positive, negative, and neutral from the extracted text and quantifies their intensity to identify the type and intensity of the emotion.

[0649] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, it stores data such as "running," "new roads," and "intensity of positive emotions."

[0650] Response Generation Phase

[0651] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0652] Server: The server sends the generated response message to the user through a communication application. Users can share their experiences and find someone to talk to through natural responses.

[0653] Matching Phase

[0654] Server: Searches for other users with the same interests and preferences based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[0655] Server: When a potential match is found, a notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0656] Server: Sends the generated match notification to the user through a communication application. The user receives the notification and can form a new connection.

[0657] Specific examples

[0658] For example, suppose User A writes, "I went for a run today and discovered a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion engine then identifies positive emotions from the text and quantifies and analyzes the intensity of the emotion. The profile database is updated based on this information, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" The profile database is then searched for another User B with the same hobby, and a notification is sent to User A saying, "I found User B, who also enjoys running. Would you like to connect?" In this way, the system reduces users' feelings of isolation and promotes new social connections.

[0659] The processing flow will be explained below.

[0660] Step 1:

[0661] User: Launches a communication application and responds to a message from the AI ​​account, "How was your day?" by typing a natural language response. For example, the user might type, "I went for a run today and found a new trail. It felt great!"

[0662] Step 2:

[0663] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[0664] Step 3:

[0665] Server: Receives user messages via the LINE API. Temporarily saves the received messages as text data for analysis.

[0666] Step 4:

[0667] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (such as "running," "new road," and "felt good") are extracted.

[0668] Step 5:

[0669] Server: The extracted keywords are passed to the emotion engine to extract emotional information. Specifically, the engine identifies emotional categories (positive, negative, neutral, etc.) from the extracted text and quantifies their intensity.

[0670] Step 6:

[0671] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, the profile database stores data such as "running," "new roads," and "intensity of positive emotion."

[0672] Step 7:

[0673] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0674] Step 8:

[0675] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on the communication application.

[0676] Step 9:

[0677] Server: Searches for other users who share common interests based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[0678] Step 10:

[0679] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0680] Step 11:

[0681] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[0682] This series of processes allows users to alleviate feelings of isolation through everyday conversation, connect with other users who share common interests and tastes, and strengthen social connections.

[0683] Example 2

[0684] 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."

[0685] In modern society, people often feel isolated due to physical distance and time constraints. Isolation in daily life can cause mental stress and make it difficult to maintain social connections. Furthermore, conventional communication systems have difficulty providing personalized responses and matching based on users' emotions, hobbies, and preferences.

[0686] 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.

[0687] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication program; means for analyzing the input text in natural language and extracting keywords and emotional information; means for updating the user's characteristic database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated characteristic database; means for sending the generated response to the user via the communication program; means for searching for other users with common interests and preferences using the characteristic database to identify matching candidates; and means for sending a notification to the user based on the matching candidates. This allows the user to share their daily emotions, hobbies, and preferences, receive appropriate responses, and further form new connections with other users with common interests.

[0688] A "communication program" is software that allows users to input information about everyday events in natural language and exchange information with other users and systems.

[0689] "Natural language" is a language commonly used by humans in everyday conversation and writing, in a format that can be analyzed by machines.

[0690] "Text" refers to character string information such as sentences or messages entered by the user.

[0691] "Keywords" refer to particularly important words or phrases in the input text, and play a central role in the system's analysis.

[0692] "Emotion information" is data that indicates the user's emotion extracted from the input text, and includes the type of emotion (positive, negative, neutral, etc.) and its intensity.

[0693] The "characteristics database" is a database that stores and manages information about users' hobbies, preferences, and emotions, and is used by the system to respond to and match users.

[0694] A "response" is a reply message generated by the system in response to a user's input, and is expressed in natural language.

[0695] "Match Potential" means a person identified as another user with common interests or preferences based on information in the profile database.

[0696] A "notification" is an informational message sent by the system to the user, informing them of the existence of a potential match, etc.

[0697] A "mobile terminal" refers to an electronic device that a user can carry with them, such as a smartphone or tablet.

[0698] "Location data" refers to data relating to the geographical location of a user, such as GPS information obtained from a mobile terminal.

[0699] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication program, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system consists of five main phases: a "user input acquisition phase," a "natural language processing phase," an "emotion recognition phase," a "response generation phase," and a "matching phase."

[0700] Getting user input phase

[0701] Terminal: The user launches a communication program on their smartphone or computer. This program displays the message "How was your day?" to the user. The user responds in natural language, for example, "I went for a run today and found a new trail. It felt great!"

[0702] Natural Language Processing Phase

[0703] Server: The communication program server receives the user's message and forwards it to the system's server. The system's server passes the message to a natural language processing engine (e.g., SpaCy or NLTK) to analyze the text. During the analysis process, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[0704] Emotion Recognition Phase

[0705] Server: The emotion information is passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API) to analyze based on the keywords extracted by the natural language processing engine. The emotion recognition engine identifies emotion categories such as positive, negative, and neutral from these keywords and quantifies their intensity. For example, the expression "I felt good" is identified as a positive emotion with high intensity.

[0706] Profile database update

[0707] Server: Updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, adds and saves data such as "running," "new roads," and "intensity of positive emotions" to the user's characteristic database.

[0708] Response Generation Phase

[0709] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI's generative AI model). For example, generate the response text "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0710] Sending a Response

[0711] Server: The server sends the generated response message to the user via a communication program. The user can then view the message on their own device and share their individual experiences.

[0712] Matching Phase

[0713] Server: Searches for other users with the same hobbies and interests based on the characteristics database. For example, it finds other users who share the common hobby of "running" from the database.

[0714] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[0715] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections and reduce feelings of isolation.

[0716] Specific examples

[0717] For example, user A types, "I went for a run today and found a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion recognition engine identifies this as a positive emotion and quantifies the intensity of the emotion. The trait database is updated based on this information, and the generative AI model generates a response such as, "That's great! Finding a new route feels like an adventure. Have fun on your next run!" Furthermore, user B, who shares the same hobby, is found, and a notification is sent to user A saying, "I found user B, who also enjoys running. Would you like to connect?" Through this process, users can reduce their sense of isolation and form new social connections.

[0718] Example prompts for generative AI models

[0719] "User A entered, 'I went for a run today and found a new path. It felt great!' Extract keywords from this input, analyze the emotional information, and generate an appropriate response to User A and a matching notification with other users who have the same hobbies."

[0720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0721] Step 1:

[0722] User: The user opens a communication program on their smartphone or computer and receives a message asking, "How was your day?" The user types a natural language response into a text box, such as, "I went for a run today and found a new trail. It felt great!"

[0723] Input: User's natural language text.

[0724] Output: Text data sent to the communications program.

[0725] Step 2:

[0726] Terminal: The terminal's communications program sends the user's message to the server.

[0727] Input: The text message entered by the user.

[0728] Output: The text data sent to the server.

[0729] Step 3:

[0730] Server: The server passes the received text data to a natural language processing engine (e.g., SpaCy or NLTK), tokenizes the text, and extracts key keywords (e.g., "running," "new road," "felt good").

[0731] Input: User's text data.

[0732] Output: Extracted keywords.

[0733] Step 4:

[0734] Server: Keywords extracted by the natural language processing engine are passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API), which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity.

[0735] Input: Extracted keywords.

[0736] Output: Sentiment category and its intensity.

[0737] Step 5:

[0738] Server: The server updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, it adds and saves the data "running," "new route," and "intensity of positive emotion" to the user's profile.

[0739] Input: sentiment information and keywords.

[0740] Output: Updated trait database.

[0741] Step 6:

[0742] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI GPT-3).

[0743] Input: Updated trait database.

[0744] Output: The generated response message.

[0745] Step 7:

[0746] Server: Sends the generated response message to the user via a communication program.

[0747] Input: The generated response message.

[0748] Output: The message sent to the user.

[0749] Step 8:

[0750] Server: Searches for other users with the same hobbies and interests based on the characteristics database. Specifically, it searches the database for other users who share the common hobby of "running."

[0751] Input: trait database.

[0752] Output: A list of potential matches.

[0753] Step 9:

[0754] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[0755] Input: A list of potential matches.

[0756] Output: The generated matching notification.

[0757] Step 10:

[0758] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections.

[0759] Input: The generated matching notification.

[0760] Output: Notification sent to the user.

[0761] (Application example 2)

[0762] 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."

[0763] The problem to be solved by this invention is to help users avoid isolation in their daily lives, promote interaction between customers in physical stores, and improve the customer experience in the store. In particular, the purpose is to reduce feelings of isolation and form new social connections by matching customers who share common hobbies, preferences, and emotional information in real time and recommending appropriate community spaces.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0765] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication application; means for analyzing the text input in natural language and extracting keywords and emotional information; means for updating the user's profile database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated profile database; means for sending the generated response to the user via the communication application; means for searching for other users with common hobbies and preferences using the profile database and identifying match candidates; means for sending a notification to the user based on the match candidates; means for real-time matching to promote customer interaction in the store; and means for recommending the use of a specific community space based on the real-time matching means. This prevents users from feeling isolated in their daily lives, promotes customer interaction in the physical store, and improves the customer experience.

[0766] A "communications application" is software that enables communication between users over the Internet.

[0767] "Natural language" refers to language used by humans on a daily basis, excluding computer processing.

[0768] "Natural language processing" is a technology that allows computers to understand, analyze, and generate natural human language.

[0769] A "keyword extraction means" is a means for identifying and extracting key words and phrases from input text.

[0770] "Emotional information" refers to data that identifies emotional states, such as positive, negative, or neutral, from natural language text.

[0771] A "profile database" is a database for storing a user's hobbies, preferences, emotional information, and other related information.

[0772] The "response generation means" is a means for generating an appropriate response to a user's input.

[0773] "Matching means" is a method for discovering and presenting appropriate associations with other users based on common hobbies, preferences, and emotional information.

[0774] A "match notification means" is a means for notifying a user of a suitable match candidate that has been found.

[0775] "Real-time matching means" is a means of matching customers who are simultaneously in the store based on their hobbies and preferences.

[0776] The "community space recommendation means" is a means of recommending a space within the store where matched customers can interact with each other.

[0777] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. The system consists of five main phases: a user input acquisition phase, a natural language processing phase, a response generation phase, an emotion recognition phase, and a matching phase.

[0778] Getting user input phase

[0779] Device:

[0780] The user responds to the message "How was your day today?" from the AI ​​via a communication application on a smartphone or other device by inputting a natural language response. For example, the user might input "I went for a run today and found a new trail. It felt great!"

[0781] server:

[0782] The message is sent to the server through the communication platform and then forwarded to the server of the system.

[0783] Natural Language Processing Phase

[0784] server:

[0785] The received message is passed to a natural language processing engine and the text is analyzed. Specifically, the text is tokenized and key keywords such as "running," "new road," and "felt good" are extracted. The text analysis is also performed using the pipeline function of the transformers library.

[0786] Emotion Recognition Phase

[0787] server:

[0788] The parsed text is passed to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the Transformers library is used for emotion recognition. The extracted emotion information and keywords are stored in the user's profile database.

[0789] Response Generation Phase

[0790] server:

[0791] It uses a natural language processing engine to generate appropriate responses based on the user's profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[0792] Matching Phase

[0793] server:

[0794] Search for other users with the same hobbies and interests based on the profile database. For example, find other users who share the hobby "running" from the database. When a potential match is found, generate a matching notification based on that information. For example, create a notification saying, "I've found user B, who also enjoys running. Would you like to connect?" and send it to the user.

[0795] Specific examples

[0796] For example, if User A types, "I went for a run today and found a new route. It felt great!", the message is analyzed by a natural language processing engine, and the keywords "running," "new route," and "felt great" are extracted. The emotion engine identifies positive emotions from these texts and quantifies and analyzes the intensity of the emotions. The profile database is updated, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" At the same time, the profile database is searched for another User B with the same hobby, and a notification is sent to User A saying, "We've found User B, who also enjoys running. Would you like to connect?"

[0797] This will prevent users from being isolated in their daily lives, promote customer interaction within physical stores, and improve the customer experience.

[0798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0799] Step 1:

[0800] User input

[0801] A user inputs a daily event in natural language via a communication application, for example, "I went running today and found a new path. It felt great!"

[0802] Step 2:

[0803] Sending a message

[0804] The terminal transmits the input text to the server through the communication platform, where the input is natural language text and the output is the same text data transmitted to the server.

[0805] Step 3:

[0806] Natural Language Processing

[0807] The server passes the received message to a natural language processing engine and analyzes the text. Specifically, it uses the pipeline function of the transformers library to tokenize the text and extract key keywords such as "running," "new road," and "felt good." The input is natural language text, and the output is a list of extracted keywords.

[0808] Step 4:

[0809] emotion recognition

[0810] The server passes the parsed text to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the transformers library is used for emotion recognition. The input is a list of extracted keywords, and the output is data with emotion categories and their intensities.

[0811] Step 5:

[0812] Profile database update

[0813] The server updates the user's profile database based on the extracted emotion information and keywords. The input is emotion data and a keyword list, and the output is the updated profile database.

[0814] Step 6:

[0815] Response Generation

[0816] The server uses a natural language processing engine to generate an appropriate response based on the updated profile database, for example, "That's great! Finding a new route sounds like an adventure. Have fun on your next run!" The input is the updated profile database, and the output is the generated response message.

[0817] Step 7:

[0818] Response Send

[0819] The server sends the generated response message to the user through the communication application, where the input is the generated response message and the output is the response message sent to the user terminal.

[0820] Step 8:

[0821] Searching for potential matches

[0822] The server searches for other users with the same hobbies and interests based on the profile database. Specifically, it finds other users who share the hobby "running" in the database. The input is the updated profile database, and the output is a list of match candidates.

[0823] Step 9:

[0824] Generate a matching notification

[0825] When the server finds a match candidate, it generates a match notification based on that information. For example, it creates a notification that reads, "I've found user B, who also enjoys running. Would you like to connect?" The input is a list of match candidates, and the output is the generated match notification.

[0826] Step 10:

[0827] Sending matching notifications

[0828] The server sends the generated match notification to the user through a communication application, where the input is the generated match notification and the output is the notification message sent to the user terminal.

[0829] Example prompt

[0830] User: I tried a new drink at the cafe today and it was delicious!

[0831] AI: That's great! Glad you had a great time.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] [Third embodiment]

[0836] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0837] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0838] 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).

[0839] 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.

[0840] 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.

[0841] 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).

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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."

[0848] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system is broadly divided into four main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," and the "matching phase."

[0849] Getting user input phase

[0850] Device: The user responds to the AI's question, "How was your day today?" in natural language via a communication application on a smartphone or computer. This response is then sent from the user's device to the LINE server via a communication platform such as LINE.

[0851] Server: The LINE server receives messages from users via the LINE API and then forwards them to the system's server.

[0852] Natural Language Processing Phase

[0853] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive, negative) are extracted.

[0854] Server: The analyzed information is stored in a profile database for each user. This database also stores past user input data and analysis results, providing a consistent record of the user's hobbies, preferences, and emotional state.

[0855] Response Generation Phase

[0856] Server: Based on the analysis results from the natural language processing engine, the AI ​​generates an appropriate response, such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[0857] Server: The generated responses are sent to the user through a communication application, allowing the user to share their experiences and find someone to talk to through natural responses.

[0858] Matching Phase

[0859] Server: Based on the profile database, searches for other users with the same interests and preferences. For example, find other users who share the hobby of "running."

[0860] Server: If a potential match is found, a match notification is generated based on that information. For example, a notification saying "I've found a user who shares my hobby of running. Would you like to connect?" is created and sent to the user.

[0861] Users: Users who receive the notification can form new connections based on the suggestions.

[0862] Specific examples

[0863] For example, suppose user A types, "I went for a run today and found a new route. It felt great!" This message is analyzed by the server, and keywords such as "running," "new route," and "felt great" as well as positive emotional information are extracted. Based on this, the server generates a response saying, "That's great! Finding a new route is like an adventure. Have fun on your next run!" and sends it to user A. Furthermore, based on the profile database, the server finds another user, user B, who also enjoys running, and sends user A a notification saying, "I found user B, who also enjoys running. Would you like to connect?"

[0864] In this way, the system provides an effective means of reducing users' feelings of isolation and supporting new social connections.

[0865] The processing flow will be explained below.

[0866] Step 1:

[0867] User: Launches a communication application and responds to the message from the AI ​​account, "How was your day?" by typing in natural language. For example, "I went for a run today and found a new trail. It felt great!"

[0868] Step 2:

[0869] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[0870] Step 3:

[0871] Server: Receives user messages via the LINE API and temporarily stores them in storage.

[0872] Step 4:

[0873] Server: Passes the received message to a natural language processing engine, which analyzes the message and extracts keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive).

[0874] Step 5:

[0875] Server: Updates the user profile database based on the extracted keywords and emotion information. New items such as "running," "new roads," and "positive emotions" are added as updated items.

[0876] Step 6:

[0877] Server: Retrieves updated information from the profile database and uses a natural language processing engine to generate an appropriate response, such as "That's great! Finding a new route is an adventure. Have fun on your next run!"

[0878] Step 7:

[0879] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on a communication application on their smartphone or computer.

[0880] Step 8:

[0881] Server: Searches the profile database to find other users who share the same interests. For example, it finds other users who share the hobby of "running" in the database.

[0882] Step 9:

[0883] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[0884] Step 10:

[0885] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[0886] This series of processes allows users to alleviate feelings of isolation through everyday conversation and connect with other users who share common interests and tastes.

[0887] Example 1

[0888] 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."

[0889] Currently, many users feel isolated in their daily lives and have difficulty making social connections. Conventional communication software does not provide appropriate matching based on the user's emotions, hobbies, and preferences, which often results in users feeling lonely. To solve this problem, a system is needed that understands the user's emotions, hobbies, and preferences and appropriately matches them with other users based on those preferences.

[0890] 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.

[0891] In this invention, the server includes: means for allowing a user to input daily events in natural language via communication software; means for analyzing the text input in natural language and extracting important words and emotional information; means for updating the user's attribute database based on the extracted important words and emotional information; means for generating an appropriate response based on the updated attribute database; means for sending the generated response to the user via the communication software; means for searching for other users with common interests and preferences using the attribute database to identify potential matching users; and means for sending a notification to the user based on the potential matching users. This allows the user to receive an appropriate response based on their emotions, interests, and preferences, and to connect with other users with common interests and preferences.

[0892] "Communications software" means programs that allow users to send and receive messages over the Internet or other networks.

[0893] "Natural language" refers to the language used by humans in everyday communication, including written and spoken language.

[0894] "Text" refers to character string data that a user inputs or transmits through communication software.

[0895] "Important words" refer to keywords with specific meanings or information extracted through natural language processing, and are words that express the user's emotions, hobbies, and preferences.

[0896] "Emotion information" is data that represents the type and intensity of emotions extracted from the text entered by the user. It includes emotion categories such as positive, negative, and neutral.

[0897] The "attribute database" is a database that stores individual user profile information, past input data, and analysis results, and consistently records the user's hobbies, preferences, and emotional state.

[0898] A "reply" is a response message in natural language that the server generates based on the user's input.

[0899] "Potential matching users" refer to other users who are determined to have common interests, preferences, and emotional states based on the attribute database.

[0900] "Notification" means a message sent by the server to a user based on information about potential matches, including a suggested match.

[0901] This invention provides a system to help users avoid isolation in their daily lives. The system receives input from users via communication software, generates appropriate responses, and matches users with other users based on their hobbies and preferences. The system is primarily composed of a network-connected server, a user terminal, and communication software.

[0902] Getting user input phase

[0903] Users use devices such as smartphones or computers to input information via communication software. This software (e.g., a messaging application) prompts users with questions such as "How was your day?", and the users then input responses in natural language.

[0904] Server Message Parsing Phase

[0905] Messages sent by users are sent over the Internet to a server, where they are first analyzed using a natural language processing engine. The program uses a natural language processing library (e.g., SpaCy or NLTK) to tokenize the message and extract key words and sentiment information. The results of this analysis are stored in an attribute database for each user.

[0906] Response Generation Phase

[0907] The server uses a generative AI model (e.g., GPT-4) based on the analysis results of the natural language processing engine to generate an appropriate response, which is then sent back to the user via communication software, allowing the user to receive a natural and empathetic response.

[0908] Matching Phase

[0909] The server searches the profiles of other users based on the user's attribute database to identify potential matches with users who share common interests. For example, it matches users who share the hobby of "running." When a match is found, the server sends a notification to the user based on that information.

[0910] Specific examples

[0911] If user A types "I went for a run today and found a new route. It felt great!", the server will analyze this message and extract important words such as "running," "new route," and "felt great," as well as positive emotional information. Based on this, the server will generate a response saying "That's great! Finding a new route is like an adventure. Have fun on your next run!" and send it to user A. Furthermore, based on the attribute database, the server will find another user B who also enjoys running, and send a notification to user A saying, "I found user B, who also enjoys running. Would you like to connect?"

[0912] Prompt Sentence Examples

[0913] User: "I went for a run today and discovered a new trail. It felt great!"

[0914] Example response: "That's great! Discovering new routes is an adventure. Have fun on your next run!"

[0915] In this way, the system can promote social connection and reduce feelings of isolation among users.

[0916] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0917] Step 1: Getting User Input

[0918] Device: The user uses a smartphone or computer to launch the communication software. A chatbot within the software asks, "How was your day?" The user responds in natural language, such as, "I went for a run today and discovered a new path. It felt great!"

[0919] Input: The answer entered by the user in natural language

[0920] Output: Messages sent from the device to the communication software server

[0921] Step 2: Sending a message

[0922] Terminal: The message entered by the user is sent from the terminal to the communication software server, where it is encrypted to ensure security.

[0923] Input: Message from the user

[0924] Output: Message arriving at the server of the communication software

[0925] Step 3: Forward the message

[0926] Server: The server of the communication software transfers the received message to the server of this system using the API.

[0927] Input: Message that arrived at the communication software server

[0928] Output: Message forwarded to the system's server

[0929] Step 4: Natural Language Processing

[0930] Server: The server of this system passes the received message to a natural language processing engine (e.g., SpaCy or NLTK). The natural language processing engine tokenizes the message and extracts important words such as "running," "new road," and "felt good," as well as positive sentiment information.

[0931] Input: Received message

[0932] Output: Data with important words and sentiment information extracted

[0933] Step 5: Save your information

[0934] Server: The analyzed information is stored in an attribute database, which stores user profile information, past input data, and analysis results.

[0935] Input: Data with important words and sentiment information extracted

[0936] Output: Updated attribute database

[0937] Step 6: Generate a response

[0938] Server: Based on the analysis results of the natural language processing engine, a generative AI model (e.g., GPT-4) generates a specific response such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[0939] Input: Important words and sentiment information contained in the analysis results

[0940] Output: The generated response

[0941] Step 7: Sending a Response

[0942] Server: The generated responses are sent to the user through communication software, allowing the user to share their experiences and find someone to talk to through natural responses.

[0943] Input: Generated response

[0944] Output: The response sent to the user

[0945] Step 8: Finding potential matches

[0946] Server: Searches for other users who share common interests based on the attribute database. For example, it finds users whose hobby is "running."

[0947] Input: Attribute database information

[0948] Output: Found users with common interests

[0949] Step 9: Generate a Matching Notification

[0950] Server: If a potential match is found, the server generates a notification based on that information saying, "We've found user B, who also enjoys running. Would you like to connect?"

[0951] Input: Information of users with common interests

[0952] Output: The generated matching notification

[0953] Step 10: Sending a Match Notification

[0954] Server: The generated match notification is sent to the user via communication software. The user receives the notification and can form a new connection.

[0955] Input: Generated matching notification

[0956] Output: Match notification sent to user

[0957] In this way, the system can reduce users' feelings of isolation and provide new social connections.

[0958] (Application example 1)

[0959] 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."

[0960] Conventional user support systems using communication applications and information processing devices only provide interactive and matching functions to help users avoid isolation in their daily lives. However, these systems lack specific means for improving the customer experience in physical stores, and they do not adequately provide personalized product recommendations or services based on customers' hobbies, preferences, or emotional information. This results in customers not receiving services that meet their needs, leading to lower satisfaction. In addition, opportunities for customer interaction in physical stores are limited, and there is a lack of effective ways to connect with customers who share the same hobbies and preferences.

[0961] 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.

[0962] In this invention, the server includes: a means for allowing a user to input daily events in natural language via an information processing device; a means for analyzing the input text in natural language and extracting keywords and emotional information; a means for updating the user's profile database based on the extracted keywords and emotional information; a means for generating an appropriate response based on the updated profile database; a means for transmitting the generated response to the user via the information processing device; a means for searching for other users with common hobbies and preferences using the profile database and identifying potential matches; a means for providing personalized product suggestions and services in-store based on the customer's hobbies, preferences, and emotional information; and a means for sending notifications to the user based on the potential matches. This significantly improves the customer experience in physical stores and enables product suggestions and services tailored to customer needs. Furthermore, it also promotes interaction between customers and allows for the formation of new social connections.

[0963] An "information processing device" is a device that allows a user to perform information processing such as inputting natural language, sending and receiving data, and analyzing data.

[0964] "Natural language" refers to the language form that users use on a daily basis, and is used for dialogue and text input.

[0965] "Keywords" refer to words or phrases that are highly important in text entered in natural language, and are used for semantic analysis.

[0966] "Emotion information" is information that indicates the type and state of emotion extracted from the text entered by the user.

[0967] The "profile database" is a database for storing and managing individual data including information on the user's hobbies, preferences, and emotions.

[0968] A "response" is an appropriate reply message that is generated based on the user's input.

[0969] A "user" is a person who inputs and interacts in natural language using an information processing device.

[0970] "Matching candidates" are other related users selected based on common hobbies, preferences, and emotional information.

[0971] "Personalized product proposals" are the act of recommending products and services that suit specific customers based on their tastes, preferences, and emotional information.

[0972] "Notification" refers to information or messages sent to a user, including information about potential matches and product suggestions.

[0973] System Program

[0974] To realize this invention, the following system program is required. The system includes the following main components:

[0975] 1. Get user input phase:

[0976] Terminal: Users can input everyday events and questions in natural language via information processing devices such as smartphones and tablets.

[0977] Server: User input is sent to the server through a communication application, which receives this input data and passes it to the analysis engine.

[0978] 2. Natural Language Processing Phase:

[0979] Server: The received input data is analyzed using a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3). The analysis engine tokenizes the text and extracts keywords and sentiment information.

[0980] 3. Response generation phase:

[0981] Server: Generates an appropriate response based on the analysis results, such as "This is the section for new running shoes."

[0982] Server: Sends the generated response to the user's terminal through the communication application.

[0983] 4. Matching Phase:

[0984] Server: Searches for other users with common interests and preferences based on the profile database, taking into account the user's input data and past history.

[0985] Server: If a suitable match is found, it sends a notification to the user.

[0986] 5. Personalized product proposal phase:

[0987] In-store, specific products and services are suggested based on the customer's hobbies, preferences, and emotional information. This process is also carried out by the server, and personalized responses and product information are provided to the user.

[0988] Processing overview

[0989] Hardware and software used

[0990] Hardware

[0991] Smartphones, tablets, smart glasses, servers

[0992] software

[0993] Natural language processing engines (BERT and GPT-3)

[0994] Profile Database Management System

[0995] Communication applications (LINE API, etc.)

[0996] Data processing and calculation

[0997] Natural language text data received from the user is sent to the server. This text data is first tokenized to extract key keywords and emotional information. This information is then stored in a profile database, where the user's hobbies, preferences, and emotional information are managed in a unified manner. Based on this information, responses and product suggestions appropriate for each individual user are generated. Finally, the generated responses and suggestions are sent back to the user's device.

[0998] Example prompts to be input to the generative AI model

[0999] An example of actual usage is the prompt:

[1000] User: I ran today and it felt great!

[1001] AI response: The new running shoes section is here.

[1002] User: I'm looking to buy some new shoes. What do you recommend?

[1003] AI response: These are the latest recommended shoes!

[1004] Based on these prompts, the server uses a natural language processing engine to generate appropriate responses and provide them to the user, which not only reduces the user's sense of isolation and creates new social connections, but also improves the individual customer experience.

[1005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1006] Step 1:

[1007] Users input daily events in natural language via a communication application

[1008] A user opens a communication application on an information processing device such as a smartphone or tablet and inputs daily events or questions in natural language. The input data includes sentences such as "I felt great running today!" This input data is sent from the user's device to a server.

[1009] Input: User's natural language text

[1010] Output: Text data sent to the server via a communication application

[1011] Step 2:

[1012] Analyze the text data received by the server

[1013] The server processes the text data received from the communication application. Specifically, it uses a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3) to tokenize the text and extract key keywords and sentiment information. This analysis yields keywords such as "running" and "felt good" and sentiment information such as "positive."

[1014] Input: Natural language text sent to the server

[1015] Output: Extracted keywords and sentiment information

[1016] Step 3:

[1017] The server saves and updates the extracted information in the profile database

[1018] The server updates the user's profile database based on the extracted keywords and emotional information, including new information on hobbies and preferences and records of emotional states, allowing for consistent storage and management of the user's preferences and current moods.

[1019] Input: Extracted keywords and sentiment information

[1020] Output: Updated user profile database

[1021] Step 4:

[1022] The server generates an appropriate response and sends it to the user.

[1023] The server generates an appropriate response for the user based on the updated profile database and the analysis results, and the response is sent to the user's device via a communication application. For example, the response may include a specific suggestion such as "Here's the section for new running shoes."

[1024] Input: Analysis results and updated profile database

[1025] Output: The generated response message

[1026] Step 5:

[1027] The server searches for other users with common interests and preferences.

[1028] The server uses a profile database to search for other users who share common interests, preferences, and emotional states, for example, to find users who share the hobby of "running" and identify potential matches.

[1029] Input: Updated profile database

[1030] Output: A list of possible matches

[1031] Step 6:

[1032] The server sends notifications to the user based on potential matches

[1033] The server then sends notifications to users based on the identified potential matches, such as a message like, "I've found a user who shares my interest in running. Would you like to connect with me?"

[1034] Input: A list of possible matches

[1035] Output: A notification message to the user

[1036] Step 7:

[1037] Servers make personalized product suggestions in-store

[1038] When a user is in a physical store, the server provides personalized product suggestions and services based on the hobbies, preferences, and emotional information entered by the customer in the store, allowing the user to receive product suggestions that meet their needs.

[1039] Input: User's tastes, preferences and emotional information

[1040] Output: Personalized product suggestions and service information

[1041] Step 8:

[1042] Forming new social connections with customers

[1043] The server provides users with opportunities to form new social connections through the responses and matching notifications they send, allowing them to exchange information and enjoy conversations about common interests with other customers.

[1044] Input: Generated response message and matching notification message

[1045] Output: Facilitating communication between users

[1046] 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.

[1047] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. This system is broadly divided into five main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," the "matching phase," and the "emotion recognition phase."

[1048] Getting user input phase

[1049] Device: The user responds to the AI's message "How was your day today?" in natural language via a communication application on a smartphone or computer. For example, the user might say, "I went for a run today and found a new trail. It felt great!"

[1050] Server: User messages are sent to the LINE server via a communication platform such as LINE, and then forwarded to the server of this system.

[1051] Natural Language Processing Phase

[1052] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[1053] Emotion Recognition Phase

[1054] Server: The text analyzed by the natural language processing engine is passed to the emotion engine, which extracts emotional information. Specifically, the emotion engine identifies emotion categories such as positive, negative, and neutral from the extracted text and quantifies their intensity to identify the type and intensity of the emotion.

[1055] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, it stores data such as "running," "new roads," and "intensity of positive emotions."

[1056] Response Generation Phase

[1057] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1058] Server: The server sends the generated response message to the user through a communication application. Users can share their experiences and find someone to talk to through natural responses.

[1059] Matching Phase

[1060] Server: Searches for other users with the same interests and preferences based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[1061] Server: When a potential match is found, a notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[1062] Server: Sends the generated match notification to the user through a communication application. The user receives the notification and can form a new connection.

[1063] Specific examples

[1064] For example, suppose User A writes, "I went for a run today and discovered a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion engine then identifies positive emotions from the text and quantifies and analyzes the intensity of the emotion. The profile database is updated based on this information, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" The profile database is then searched for another User B with the same hobby, and a notification is sent to User A saying, "I found User B, who also enjoys running. Would you like to connect?" In this way, the system reduces users' feelings of isolation and promotes new social connections.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] User: Launches a communication application and responds to a message from the AI ​​account, "How was your day?" by typing a natural language response. For example, the user might type, "I went for a run today and found a new trail. It felt great!"

[1068] Step 2:

[1069] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[1070] Step 3:

[1071] Server: Receives user messages via the LINE API. Temporarily saves the received messages as text data for analysis.

[1072] Step 4:

[1073] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (such as "running," "new road," and "felt good") are extracted.

[1074] Step 5:

[1075] Server: The extracted keywords are passed to the emotion engine to extract emotional information. Specifically, the engine identifies emotional categories (positive, negative, neutral, etc.) from the extracted text and quantifies their intensity.

[1076] Step 6:

[1077] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, the profile database stores data such as "running," "new roads," and "intensity of positive emotion."

[1078] Step 7:

[1079] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1080] Step 8:

[1081] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on the communication application.

[1082] Step 9:

[1083] Server: Searches for other users who share common interests based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[1084] Step 10:

[1085] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[1086] Step 11:

[1087] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[1088] This series of processes allows users to alleviate feelings of isolation through everyday conversation, connect with other users who share common interests and tastes, and strengthen social connections.

[1089] Example 2

[1090] 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."

[1091] In modern society, people often feel isolated due to physical distance and time constraints. Isolation in daily life can cause mental stress and make it difficult to maintain social connections. Furthermore, conventional communication systems have difficulty providing personalized responses and matching based on users' emotions, hobbies, and preferences.

[1092] 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.

[1093] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication program; means for analyzing the input text in natural language and extracting keywords and emotional information; means for updating the user's characteristic database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated characteristic database; means for sending the generated response to the user via the communication program; means for searching for other users with common interests and preferences using the characteristic database to identify matching candidates; and means for sending a notification to the user based on the matching candidates. This allows the user to share their daily emotions, hobbies, and preferences, receive appropriate responses, and further form new connections with other users with common interests.

[1094] A "communication program" is software that allows users to input information about everyday events in natural language and exchange information with other users and systems.

[1095] "Natural language" is a language commonly used by humans in everyday conversation and writing, in a format that can be analyzed by machines.

[1096] "Text" refers to character string information such as sentences or messages entered by the user.

[1097] "Keywords" refer to particularly important words or phrases in the input text, and play a central role in the system's analysis.

[1098] "Emotion information" is data that indicates the user's emotion extracted from the input text, and includes the type of emotion (positive, negative, neutral, etc.) and its intensity.

[1099] The "characteristics database" is a database that stores and manages information about users' hobbies, preferences, and emotions, and is used by the system to respond to and match users.

[1100] A "response" is a reply message generated by the system in response to a user's input, and is expressed in natural language.

[1101] "Match Potential" means a person identified as another user with common interests or preferences based on information in the profile database.

[1102] A "notification" is an informational message sent by the system to the user, informing them of the existence of a potential match, etc.

[1103] A "mobile terminal" refers to an electronic device that a user can carry with them, such as a smartphone or tablet.

[1104] "Location data" refers to data relating to the geographical location of a user, such as GPS information obtained from a mobile terminal.

[1105] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication program, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system consists of five main phases: a "user input acquisition phase," a "natural language processing phase," an "emotion recognition phase," a "response generation phase," and a "matching phase."

[1106] Getting user input phase

[1107] Terminal: The user launches a communication program on their smartphone or computer. This program displays the message "How was your day?" to the user. The user responds in natural language, for example, "I went for a run today and found a new trail. It felt great!"

[1108] Natural Language Processing Phase

[1109] Server: The communication program server receives the user's message and forwards it to the system's server. The system's server passes the message to a natural language processing engine (e.g., SpaCy or NLTK) to analyze the text. During the analysis process, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[1110] Emotion Recognition Phase

[1111] Server: The emotion information is passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API) to analyze based on the keywords extracted by the natural language processing engine. The emotion recognition engine identifies emotion categories such as positive, negative, and neutral from these keywords and quantifies their intensity. For example, the expression "I felt good" is identified as a positive emotion with high intensity.

[1112] Profile database update

[1113] Server: Updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, adds and saves data such as "running," "new roads," and "intensity of positive emotions" to the user's characteristic database.

[1114] Response Generation Phase

[1115] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI's generative AI model). For example, generate the response text "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1116] Sending a Response

[1117] Server: The server sends the generated response message to the user via a communication program. The user can then view the message on their own device and share their individual experiences.

[1118] Matching Phase

[1119] Server: Searches for other users with the same hobbies and interests based on the characteristics database. For example, it finds other users who share the common hobby of "running" from the database.

[1120] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[1121] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections and reduce feelings of isolation.

[1122] Specific examples

[1123] For example, user A types, "I went for a run today and found a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion recognition engine identifies this as a positive emotion and quantifies the intensity of the emotion. The trait database is updated based on this information, and the generative AI model generates a response such as, "That's great! Finding a new route feels like an adventure. Have fun on your next run!" Furthermore, user B, who shares the same hobby, is found, and a notification is sent to user A saying, "I found user B, who also enjoys running. Would you like to connect?" Through this process, users can reduce their sense of isolation and form new social connections.

[1124] Example prompts for generative AI models

[1125] "User A entered, 'I went for a run today and found a new path. It felt great!' Extract keywords from this input, analyze the emotional information, and generate an appropriate response to User A and a matching notification with other users who have the same hobbies."

[1126] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1127] Step 1:

[1128] User: The user opens a communication program on their smartphone or computer and receives a message asking, "How was your day?" The user types a natural language response into a text box, such as, "I went for a run today and found a new trail. It felt great!"

[1129] Input: User's natural language text.

[1130] Output: Text data sent to the communications program.

[1131] Step 2:

[1132] Terminal: The terminal's communications program sends the user's message to the server.

[1133] Input: The text message entered by the user.

[1134] Output: The text data sent to the server.

[1135] Step 3:

[1136] Server: The server passes the received text data to a natural language processing engine (e.g., SpaCy or NLTK), tokenizes the text, and extracts key keywords (e.g., "running," "new road," "felt good").

[1137] Input: User's text data.

[1138] Output: Extracted keywords.

[1139] Step 4:

[1140] Server: Keywords extracted by the natural language processing engine are passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API), which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity.

[1141] Input: Extracted keywords.

[1142] Output: Sentiment category and its intensity.

[1143] Step 5:

[1144] Server: The server updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, it adds and saves the data "running," "new route," and "intensity of positive emotion" to the user's profile.

[1145] Input: sentiment information and keywords.

[1146] Output: Updated trait database.

[1147] Step 6:

[1148] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI GPT-3).

[1149] Input: Updated trait database.

[1150] Output: The generated response message.

[1151] Step 7:

[1152] Server: Sends the generated response message to the user via a communication program.

[1153] Input: The generated response message.

[1154] Output: The message sent to the user.

[1155] Step 8:

[1156] Server: Searches for other users with the same hobbies and interests based on the characteristics database. Specifically, it searches the database for other users who share the common hobby of "running."

[1157] Input: trait database.

[1158] Output: A list of potential matches.

[1159] Step 9:

[1160] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[1161] Input: A list of potential matches.

[1162] Output: The generated matching notification.

[1163] Step 10:

[1164] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections.

[1165] Input: The generated matching notification.

[1166] Output: Notification sent to the user.

[1167] (Application example 2)

[1168] 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."

[1169] The problem to be solved by this invention is to help users avoid isolation in their daily lives, promote interaction between customers in physical stores, and improve the customer experience in the store. In particular, the purpose is to reduce feelings of isolation and form new social connections by matching customers who share common hobbies, preferences, and emotional information in real time and recommending appropriate community spaces.

[1170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1171] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication application; means for analyzing the text input in natural language and extracting keywords and emotional information; means for updating the user's profile database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated profile database; means for sending the generated response to the user via the communication application; means for searching for other users with common hobbies and preferences using the profile database and identifying match candidates; means for sending a notification to the user based on the match candidates; means for real-time matching to promote customer interaction in the store; and means for recommending the use of a specific community space based on the real-time matching means. This prevents users from feeling isolated in their daily lives, promotes customer interaction in the physical store, and improves the customer experience.

[1172] A "communications application" is software that enables communication between users over the Internet.

[1173] "Natural language" refers to language used by humans on a daily basis, excluding computer processing.

[1174] "Natural language processing" is a technology that allows computers to understand, analyze, and generate natural human language.

[1175] A "keyword extraction means" is a means for identifying and extracting key words and phrases from input text.

[1176] "Emotional information" refers to data that identifies emotional states, such as positive, negative, or neutral, from natural language text.

[1177] A "profile database" is a database for storing a user's hobbies, preferences, emotional information, and other related information.

[1178] The "response generation means" is a means for generating an appropriate response to a user's input.

[1179] "Matching means" is a method for discovering and presenting appropriate associations with other users based on common hobbies, preferences, and emotional information.

[1180] A "match notification means" is a means for notifying a user of a suitable match candidate that has been found.

[1181] "Real-time matching means" is a means of matching customers who are simultaneously in the store based on their hobbies and preferences.

[1182] The "community space recommendation means" is a means of recommending a space within the store where matched customers can interact with each other.

[1183] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. The system consists of five main phases: a user input acquisition phase, a natural language processing phase, a response generation phase, an emotion recognition phase, and a matching phase.

[1184] Getting user input phase

[1185] Device:

[1186] The user responds to the message "How was your day today?" from the AI ​​via a communication application on a smartphone or other device by inputting a natural language response. For example, the user might input "I went for a run today and found a new trail. It felt great!"

[1187] server:

[1188] The message is sent to the server through the communication platform and then forwarded to the server of the system.

[1189] Natural Language Processing Phase

[1190] server:

[1191] The received message is passed to a natural language processing engine and the text is analyzed. Specifically, the text is tokenized and key keywords such as "running," "new road," and "felt good" are extracted. The text analysis is also performed using the pipeline function of the transformers library.

[1192] Emotion Recognition Phase

[1193] server:

[1194] The parsed text is passed to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the Transformers library is used for emotion recognition. The extracted emotion information and keywords are stored in the user's profile database.

[1195] Response Generation Phase

[1196] server:

[1197] It uses a natural language processing engine to generate appropriate responses based on the user's profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1198] Matching Phase

[1199] server:

[1200] Search for other users with the same hobbies and interests based on the profile database. For example, find other users who share the hobby "running" from the database. When a potential match is found, generate a matching notification based on that information. For example, create a notification saying, "I've found user B, who also enjoys running. Would you like to connect?" and send it to the user.

[1201] Specific examples

[1202] For example, if User A types, "I went for a run today and found a new route. It felt great!", the message is analyzed by a natural language processing engine, and the keywords "running," "new route," and "felt great" are extracted. The emotion engine identifies positive emotions from these texts and quantifies and analyzes the intensity of the emotions. The profile database is updated, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" At the same time, the profile database is searched for another User B with the same hobby, and a notification is sent to User A saying, "We've found User B, who also enjoys running. Would you like to connect?"

[1203] This will prevent users from being isolated in their daily lives, promote customer interaction within physical stores, and improve the customer experience.

[1204] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1205] Step 1:

[1206] User input

[1207] A user inputs a daily event in natural language via a communication application, for example, "I went running today and found a new path. It felt great!"

[1208] Step 2:

[1209] Sending a message

[1210] The terminal transmits the input text to the server through the communication platform, where the input is natural language text and the output is the same text data transmitted to the server.

[1211] Step 3:

[1212] Natural Language Processing

[1213] The server passes the received message to a natural language processing engine and analyzes the text. Specifically, it uses the pipeline function of the transformers library to tokenize the text and extract key keywords such as "running," "new road," and "felt good." The input is natural language text, and the output is a list of extracted keywords.

[1214] Step 4:

[1215] emotion recognition

[1216] The server passes the parsed text to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the transformers library is used for emotion recognition. The input is a list of extracted keywords, and the output is data with emotion categories and their intensities.

[1217] Step 5:

[1218] Profile database update

[1219] The server updates the user's profile database based on the extracted emotion information and keywords. The input is emotion data and a keyword list, and the output is the updated profile database.

[1220] Step 6:

[1221] Response Generation

[1222] The server uses a natural language processing engine to generate an appropriate response based on the updated profile database, for example, "That's great! Finding a new route sounds like an adventure. Have fun on your next run!" The input is the updated profile database, and the output is the generated response message.

[1223] Step 7:

[1224] Response Send

[1225] The server sends the generated response message to the user through the communication application, where the input is the generated response message and the output is the response message sent to the user terminal.

[1226] Step 8:

[1227] Searching for potential matches

[1228] The server searches for other users with the same hobbies and interests based on the profile database. Specifically, it finds other users who share the hobby "running" in the database. The input is the updated profile database, and the output is a list of match candidates.

[1229] Step 9:

[1230] Generate a matching notification

[1231] When the server finds a match candidate, it generates a match notification based on that information. For example, it creates a notification that reads, "I've found user B, who also enjoys running. Would you like to connect?" The input is a list of match candidates, and the output is the generated match notification.

[1232] Step 10:

[1233] Sending matching notifications

[1234] The server sends the generated match notification to the user through a communication application, where the input is the generated match notification and the output is the notification message sent to the user terminal.

[1235] Example prompt

[1236] User: I tried a new drink at the cafe today and it was delicious!

[1237] AI: That's great! Glad you had a great time.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] [Fourth embodiment]

[1242] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1243] 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.

[1244] 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).

[1245] 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.

[1246] 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.

[1247] 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).

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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."

[1255] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system is broadly divided into four main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," and the "matching phase."

[1256] Getting user input phase

[1257] Device: The user responds to the AI's question, "How was your day today?" in natural language via a communication application on a smartphone or computer. This response is then sent from the user's device to the LINE server via a communication platform such as LINE.

[1258] Server: The LINE server receives messages from users via the LINE API and then forwards them to the system's server.

[1259] Natural Language Processing Phase

[1260] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive, negative) are extracted.

[1261] Server: The analyzed information is stored in a profile database for each user. This database also stores past user input data and analysis results, providing a consistent record of the user's hobbies, preferences, and emotional state.

[1262] Response Generation Phase

[1263] Server: Based on the analysis results from the natural language processing engine, the AI ​​generates an appropriate response, such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[1264] Server: The generated responses are sent to the user through a communication application, allowing the user to share their experiences and find someone to talk to through natural responses.

[1265] Matching Phase

[1266] Server: Based on the profile database, searches for other users with the same interests and preferences. For example, find other users who share the hobby of "running."

[1267] Server: If a potential match is found, a match notification is generated based on that information. For example, a notification saying "I've found a user who shares my hobby of running. Would you like to connect?" is created and sent to the user.

[1268] Users: Users who receive the notification can form new connections based on the suggestions.

[1269] Specific examples

[1270] For example, suppose user A types, "I went for a run today and found a new route. It felt great!" This message is analyzed by the server, and keywords such as "running," "new route," and "felt great" as well as positive emotional information are extracted. Based on this, the server generates a response saying, "That's great! Finding a new route is like an adventure. Have fun on your next run!" and sends it to user A. Furthermore, based on the profile database, the server finds another user, user B, who also enjoys running, and sends user A a notification saying, "I found user B, who also enjoys running. Would you like to connect?"

[1271] In this way, the system provides an effective means of reducing users' feelings of isolation and supporting new social connections.

[1272] The processing flow will be explained below.

[1273] Step 1:

[1274] User: Launches a communication application and responds to the message from the AI ​​account, "How was your day?" by typing in natural language. For example, "I went for a run today and found a new trail. It felt great!"

[1275] Step 2:

[1276] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[1277] Step 3:

[1278] Server: Receives user messages via the LINE API and temporarily stores them in storage.

[1279] Step 4:

[1280] Server: Passes the received message to a natural language processing engine, which analyzes the message and extracts keywords (e.g., "running," "new road," "felt good") and emotional information (e.g., positive).

[1281] Step 5:

[1282] Server: Updates the user profile database based on the extracted keywords and emotion information. New items such as "running," "new roads," and "positive emotions" are added as updated items.

[1283] Step 6:

[1284] Server: Retrieves updated information from the profile database and uses a natural language processing engine to generate an appropriate response, such as "That's great! Finding a new route is an adventure. Have fun on your next run!"

[1285] Step 7:

[1286] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on a communication application on their smartphone or computer.

[1287] Step 8:

[1288] Server: Searches the profile database to find other users who share the same interests. For example, it finds other users who share the hobby of "running" in the database.

[1289] Step 9:

[1290] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[1291] Step 10:

[1292] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[1293] This series of processes allows users to alleviate feelings of isolation through everyday conversation and connect with other users who share common interests and tastes.

[1294] Example 1

[1295] 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."

[1296] Currently, many users feel isolated in their daily lives and have difficulty making social connections. Conventional communication software does not provide appropriate matching based on the user's emotions, hobbies, and preferences, which often results in users feeling lonely. To solve this problem, a system is needed that understands the user's emotions, hobbies, and preferences and appropriately matches them with other users based on those preferences.

[1297] 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.

[1298] In this invention, the server includes: means for allowing a user to input daily events in natural language via communication software; means for analyzing the text input in natural language and extracting important words and emotional information; means for updating the user's attribute database based on the extracted important words and emotional information; means for generating an appropriate response based on the updated attribute database; means for sending the generated response to the user via the communication software; means for searching for other users with common interests and preferences using the attribute database to identify potential matching users; and means for sending a notification to the user based on the potential matching users. This allows the user to receive an appropriate response based on their emotions, interests, and preferences, and to connect with other users with common interests and preferences.

[1299] "Communications software" means programs that allow users to send and receive messages over the Internet or other networks.

[1300] "Natural language" refers to the language used by humans in everyday communication, including written and spoken language.

[1301] "Text" refers to character string data that a user inputs or transmits through communication software.

[1302] "Important words" refer to keywords with specific meanings or information extracted through natural language processing, and are words that express the user's emotions, hobbies, and preferences.

[1303] "Emotion information" is data that represents the type and intensity of emotions extracted from the text entered by the user. It includes emotion categories such as positive, negative, and neutral.

[1304] The "attribute database" is a database that stores individual user profile information, past input data, and analysis results, and consistently records the user's hobbies, preferences, and emotional state.

[1305] A "reply" is a response message in natural language that the server generates based on the user's input.

[1306] "Potential matching users" refer to other users who are determined to have common interests, preferences, and emotional states based on the attribute database.

[1307] "Notification" means a message sent by the server to a user based on information about potential matches, including a suggested match.

[1308] This invention provides a system to help users avoid isolation in their daily lives. The system receives input from users via communication software, generates appropriate responses, and matches users with other users based on their hobbies and preferences. The system is primarily composed of a network-connected server, a user terminal, and communication software.

[1309] Getting user input phase

[1310] Users use devices such as smartphones or computers to input information via communication software. This software (e.g., a messaging application) prompts users with questions such as "How was your day?", and the users then input responses in natural language.

[1311] Server Message Parsing Phase

[1312] Messages sent by users are sent over the Internet to a server, where they are first analyzed using a natural language processing engine. The program uses a natural language processing library (e.g., SpaCy or NLTK) to tokenize the message and extract key words and sentiment information. The results of this analysis are stored in an attribute database for each user.

[1313] Response Generation Phase

[1314] The server uses a generative AI model (e.g., GPT-4) based on the analysis results of the natural language processing engine to generate an appropriate response, which is then sent back to the user via communication software, allowing the user to receive a natural and empathetic response.

[1315] Matching Phase

[1316] The server searches the profiles of other users based on the user's attribute database to identify potential matches with users who share common interests. For example, it matches users who share the hobby of "running." When a match is found, the server sends a notification to the user based on that information.

[1317] Specific examples

[1318] If user A types "I went for a run today and found a new route. It felt great!", the server will analyze this message and extract important words such as "running," "new route," and "felt great," as well as positive emotional information. Based on this, the server will generate a response saying "That's great! Finding a new route is like an adventure. Have fun on your next run!" and send it to user A. Furthermore, based on the attribute database, the server will find another user B who also enjoys running, and send a notification to user A saying, "I found user B, who also enjoys running. Would you like to connect?"

[1319] Prompt Sentence Examples

[1320] User: "I went for a run today and discovered a new trail. It felt great!"

[1321] Example response: "That's great! Discovering new routes is an adventure. Have fun on your next run!"

[1322] In this way, the system can promote social connection and reduce feelings of isolation among users.

[1323] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1324] Step 1: Getting User Input

[1325] Device: The user uses a smartphone or computer to launch the communication software. A chatbot within the software asks, "How was your day?" The user responds in natural language, such as, "I went for a run today and discovered a new path. It felt great!"

[1326] Input: The answer entered by the user in natural language

[1327] Output: Messages sent from the device to the communication software server

[1328] Step 2: Sending a message

[1329] Terminal: The message entered by the user is sent from the terminal to the communication software server, where it is encrypted to ensure security.

[1330] Input: Message from the user

[1331] Output: Message arriving at the server of the communication software

[1332] Step 3: Forward the message

[1333] Server: The server of the communication software transfers the received message to the server of this system using the API.

[1334] Input: Message that arrived at the communication software server

[1335] Output: Message forwarded to the system's server

[1336] Step 4: Natural Language Processing

[1337] Server: The server of this system passes the received message to a natural language processing engine (e.g., SpaCy or NLTK). The natural language processing engine tokenizes the message and extracts important words such as "running," "new road," and "felt good," as well as positive sentiment information.

[1338] Input: Received message

[1339] Output: Data with important words and sentiment information extracted

[1340] Step 5: Save your information

[1341] Server: The analyzed information is stored in an attribute database, which stores user profile information, past input data, and analysis results.

[1342] Input: Data with important words and sentiment information extracted

[1343] Output: Updated attribute database

[1344] Step 6: Generate a response

[1345] Server: Based on the analysis results of the natural language processing engine, a generative AI model (e.g., GPT-4) generates a specific response such as, "That's great! Finding a new route is like an adventure. Have fun on your next run!"

[1346] Input: Important words and sentiment information contained in the analysis results

[1347] Output: The generated response

[1348] Step 7: Sending a Response

[1349] Server: The generated responses are sent to the user through communication software, allowing the user to share their experiences and find someone to talk to through natural responses.

[1350] Input: Generated response

[1351] Output: The response sent to the user

[1352] Step 8: Finding potential matches

[1353] Server: Searches for other users who share common interests based on the attribute database. For example, it finds users whose hobby is "running."

[1354] Input: Attribute database information

[1355] Output: Found users with common interests

[1356] Step 9: Generate a Matching Notification

[1357] Server: If a potential match is found, the server generates a notification based on that information saying, "We've found user B, who also enjoys running. Would you like to connect?"

[1358] Input: Information of users with common interests

[1359] Output: The generated matching notification

[1360] Step 10: Sending a Match Notification

[1361] Server: The generated match notification is sent to the user via communication software. The user receives the notification and can form a new connection.

[1362] Input: Generated matching notification

[1363] Output: Match notification sent to user

[1364] In this way, the system can reduce users' feelings of isolation and provide new social connections.

[1365] (Application example 1)

[1366] 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."

[1367] Conventional user support systems using communication applications and information processing devices only provide interactive and matching functions to help users avoid isolation in their daily lives. However, these systems lack specific means for improving the customer experience in physical stores, and they do not adequately provide personalized product recommendations or services based on customers' hobbies, preferences, or emotional information. This results in customers not receiving services that meet their needs, leading to lower satisfaction. In addition, opportunities for customer interaction in physical stores are limited, and there is a lack of effective ways to connect with customers who share the same hobbies and preferences.

[1368] 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.

[1369] In this invention, the server includes: a means for allowing a user to input daily events in natural language via an information processing device; a means for analyzing the input text in natural language and extracting keywords and emotional information; a means for updating the user's profile database based on the extracted keywords and emotional information; a means for generating an appropriate response based on the updated profile database; a means for transmitting the generated response to the user via the information processing device; a means for searching for other users with common hobbies and preferences using the profile database and identifying potential matches; a means for providing personalized product suggestions and services in-store based on the customer's hobbies, preferences, and emotional information; and a means for sending notifications to the user based on the potential matches. This significantly improves the customer experience in physical stores and enables product suggestions and services tailored to customer needs. Furthermore, it also promotes interaction between customers and allows for the formation of new social connections.

[1370] An "information processing device" is a device that allows a user to perform information processing such as inputting natural language, sending and receiving data, and analyzing data.

[1371] "Natural language" refers to the language form that users use on a daily basis, and is used for dialogue and text input.

[1372] "Keywords" refer to words or phrases that are highly important in text entered in natural language, and are used for semantic analysis.

[1373] "Emotion information" is information that indicates the type and state of emotion extracted from the text entered by the user.

[1374] The "profile database" is a database for storing and managing individual data including information on the user's hobbies, preferences, and emotions.

[1375] A "response" is an appropriate reply message that is generated based on the user's input.

[1376] A "user" is a person who inputs and interacts in natural language using an information processing device.

[1377] "Matching candidates" are other related users selected based on common hobbies, preferences, and emotional information.

[1378] "Personalized product proposals" are the act of recommending products and services that suit specific customers based on their tastes, preferences, and emotional information.

[1379] "Notification" refers to information or messages sent to a user, including information about potential matches and product suggestions.

[1380] System Program

[1381] To realize this invention, the following system program is required. The system includes the following main components:

[1382] 1. Get user input phase:

[1383] Terminal: Users can input everyday events and questions in natural language via information processing devices such as smartphones and tablets.

[1384] Server: User input is sent to the server through a communication application, which receives this input data and passes it to the analysis engine.

[1385] 2. Natural Language Processing Phase:

[1386] Server: The received input data is analyzed using a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3). The analysis engine tokenizes the text and extracts keywords and sentiment information.

[1387] 3. Response generation phase:

[1388] Server: Generates an appropriate response based on the analysis results, such as "This is the section for new running shoes."

[1389] Server: Sends the generated response to the user's terminal through the communication application.

[1390] 4. Matching Phase:

[1391] Server: Searches for other users with common interests and preferences based on the profile database, taking into account the user's input data and past history.

[1392] Server: If a suitable match is found, it sends a notification to the user.

[1393] 5. Personalized product proposal phase:

[1394] In-store, specific products and services are suggested based on the customer's hobbies, preferences, and emotional information. This process is also carried out by the server, and personalized responses and product information are provided to the user.

[1395] Processing overview

[1396] Hardware and software used

[1397] Hardware

[1398] Smartphones, tablets, smart glasses, servers

[1399] software

[1400] Natural language processing engines (BERT and GPT-3)

[1401] Profile Database Management System

[1402] Communication applications (LINE API, etc.)

[1403] Data processing and calculation

[1404] Natural language text data received from the user is sent to the server. This text data is first tokenized to extract key keywords and emotional information. This information is then stored in a profile database, where the user's hobbies, preferences, and emotional information are managed in a unified manner. Based on this information, responses and product suggestions appropriate for each individual user are generated. Finally, the generated responses and suggestions are sent back to the user's device.

[1405] Example prompts to be input to the generative AI model

[1406] An example of actual usage is the prompt:

[1407] User: I ran today and it felt great!

[1408] AI response: The new running shoes section is here.

[1409] User: I'm looking to buy some new shoes. What do you recommend?

[1410] AI response: These are the latest recommended shoes!

[1411] Based on these prompts, the server uses a natural language processing engine to generate appropriate responses and provide them to the user, which not only reduces the user's sense of isolation and creates new social connections, but also improves the individual customer experience.

[1412] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1413] Step 1:

[1414] Users input daily events in natural language via a communication application

[1415] A user opens a communication application on an information processing device such as a smartphone or tablet and inputs daily events or questions in natural language. The input data includes sentences such as "I felt great running today!" This input data is sent from the user's device to a server.

[1416] Input: User's natural language text

[1417] Output: Text data sent to the server via a communication application

[1418] Step 2:

[1419] Analyze the text data received by the server

[1420] The server processes the text data received from the communication application. Specifically, it uses a natural language processing engine (e.g., a generative AI model such as BERT or GPT-3) to tokenize the text and extract key keywords and sentiment information. This analysis yields keywords such as "running" and "felt good" and sentiment information such as "positive."

[1421] Input: Natural language text sent to the server

[1422] Output: Extracted keywords and sentiment information

[1423] Step 3:

[1424] The server saves and updates the extracted information in the profile database

[1425] The server updates the user's profile database based on the extracted keywords and emotional information, including new information on hobbies and preferences and records of emotional states, allowing for consistent storage and management of the user's preferences and current moods.

[1426] Input: Extracted keywords and sentiment information

[1427] Output: Updated user profile database

[1428] Step 4:

[1429] The server generates an appropriate response and sends it to the user.

[1430] The server generates an appropriate response for the user based on the updated profile database and the analysis results, and the response is sent to the user's device via a communication application. For example, the response may include a specific suggestion such as "Here's the section for new running shoes."

[1431] Input: Analysis results and updated profile database

[1432] Output: The generated response message

[1433] Step 5:

[1434] The server searches for other users with common interests and preferences.

[1435] The server uses a profile database to search for other users who share common interests, preferences, and emotional states, for example, to find users who share the hobby of "running" and identify potential matches.

[1436] Input: Updated profile database

[1437] Output: A list of possible matches

[1438] Step 6:

[1439] The server sends notifications to the user based on potential matches

[1440] The server then sends notifications to users based on the identified potential matches, such as a message like, "I've found a user who shares my interest in running. Would you like to connect with me?"

[1441] Input: A list of possible matches

[1442] Output: A notification message to the user

[1443] Step 7:

[1444] Servers make personalized product suggestions in-store

[1445] When a user is in a physical store, the server provides personalized product suggestions and services based on the hobbies, preferences, and emotional information entered by the customer in the store, allowing the user to receive product suggestions that meet their needs.

[1446] Input: User's tastes, preferences and emotional information

[1447] Output: Personalized product suggestions and service information

[1448] Step 8:

[1449] Forming new social connections with customers

[1450] The server provides users with opportunities to form new social connections through the responses and matching notifications they send, allowing them to exchange information and enjoy conversations about common interests with other customers.

[1451] Input: Generated response message and matching notification message

[1452] Output: Facilitating communication between users

[1453] 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.

[1454] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. This system is broadly divided into five main phases: the "user input acquisition phase," the "natural language processing phase," the "response generation phase," the "matching phase," and the "emotion recognition phase."

[1455] Getting user input phase

[1456] Device: The user responds to the AI's message "How was your day today?" in natural language via a communication application on a smartphone or computer. For example, the user might say, "I went for a run today and found a new trail. It felt great!"

[1457] Server: User messages are sent to the LINE server via a communication platform such as LINE, and then forwarded to the server of this system.

[1458] Natural Language Processing Phase

[1459] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[1460] Emotion Recognition Phase

[1461] Server: The text analyzed by the natural language processing engine is passed to the emotion engine, which extracts emotional information. Specifically, the emotion engine identifies emotion categories such as positive, negative, and neutral from the extracted text and quantifies their intensity to identify the type and intensity of the emotion.

[1462] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, it stores data such as "running," "new roads," and "intensity of positive emotions."

[1463] Response Generation Phase

[1464] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1465] Server: The server sends the generated response message to the user through a communication application. Users can share their experiences and find someone to talk to through natural responses.

[1466] Matching Phase

[1467] Server: Searches for other users with the same interests and preferences based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[1468] Server: When a potential match is found, a notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[1469] Server: Sends the generated match notification to the user through a communication application. The user receives the notification and can form a new connection.

[1470] Specific examples

[1471] For example, suppose User A writes, "I went for a run today and discovered a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion engine then identifies positive emotions from the text and quantifies and analyzes the intensity of the emotion. The profile database is updated based on this information, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" The profile database is then searched for another User B with the same hobby, and a notification is sent to User A saying, "I found User B, who also enjoys running. Would you like to connect?" In this way, the system reduces users' feelings of isolation and promotes new social connections.

[1472] The processing flow will be explained below.

[1473] Step 1:

[1474] User: Launches a communication application and responds to a message from the AI ​​account, "How was your day?" by typing a natural language response. For example, the user might type, "I went for a run today and found a new trail. It felt great!"

[1475] Step 2:

[1476] Terminal: The message entered by the user is sent to the server via a communication application (e.g., LINE). The message is transferred to the LINE server using a communication protocol.

[1477] Step 3:

[1478] Server: Receives user messages via the LINE API. Temporarily saves the received messages as text data for analysis.

[1479] Step 4:

[1480] Server: The received message is passed to a natural language processing engine for text analysis. Specifically, the text is tokenized and key keywords (such as "running," "new road," and "felt good") are extracted.

[1481] Step 5:

[1482] Server: The extracted keywords are passed to the emotion engine to extract emotional information. Specifically, the engine identifies emotional categories (positive, negative, neutral, etc.) from the extracted text and quantifies their intensity.

[1483] Step 6:

[1484] Server: Updates the user profile database based on the extracted emotion information and keywords. For example, the profile database stores data such as "running," "new roads," and "intensity of positive emotion."

[1485] Step 7:

[1486] Server: Utilizes a natural language processing engine to generate an appropriate response based on the updated profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1487] Step 8:

[1488] Server: The server sends the generated response message to the user via the LINE API. The user receives the response message on the communication application.

[1489] Step 9:

[1490] Server: Searches for other users who share common interests based on the profile database. For example, it finds other users who share the hobby of "running" in the database.

[1491] Step 10:

[1492] Server: When a potential match is found, a matching notification is generated based on that information. For example, a notification is created that reads, "I've found User B, who also enjoys running. Would you like to connect?"

[1493] Step 11:

[1494] Server: The generated matching notification is sent to the user via the LINE API. The user receives the notification and has a new opportunity for communication.

[1495] This series of processes allows users to alleviate feelings of isolation through everyday conversation, connect with other users who share common interests and tastes, and strengthen social connections.

[1496] Example 2

[1497] 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."

[1498] In modern society, people often feel isolated due to physical distance and time constraints. Isolation in daily life can cause mental stress and make it difficult to maintain social connections. Furthermore, conventional communication systems have difficulty providing personalized responses and matching based on users' emotions, hobbies, and preferences.

[1499] 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.

[1500] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication program; means for analyzing the input text in natural language and extracting keywords and emotional information; means for updating the user's characteristic database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated characteristic database; means for sending the generated response to the user via the communication program; means for searching for other users with common interests and preferences using the characteristic database to identify matching candidates; and means for sending a notification to the user based on the matching candidates. This allows the user to share their daily emotions, hobbies, and preferences, receive appropriate responses, and further form new connections with other users with common interests.

[1501] A "communication program" is software that allows users to input information about everyday events in natural language and exchange information with other users and systems.

[1502] "Natural language" is a language commonly used by humans in everyday conversation and writing, in a format that can be analyzed by machines.

[1503] "Text" refers to character string information such as sentences or messages entered by the user.

[1504] "Keywords" refer to particularly important words or phrases in the input text, and play a central role in the system's analysis.

[1505] "Emotion information" is data that indicates the user's emotion extracted from the input text, and includes the type of emotion (positive, negative, neutral, etc.) and its intensity.

[1506] The "characteristics database" is a database that stores and manages information about users' hobbies, preferences, and emotions, and is used by the system to respond to and match users.

[1507] A "response" is a reply message generated by the system in response to a user's input, and is expressed in natural language.

[1508] "Match Potential" means a person identified as another user with common interests or preferences based on information in the profile database.

[1509] A "notification" is an informational message sent by the system to the user, informing them of the existence of a potential match, etc.

[1510] A "mobile terminal" refers to an electronic device that a user can carry with them, such as a smartphone or tablet.

[1511] "Location data" refers to data relating to the geographical location of a user, such as GPS information obtained from a mobile terminal.

[1512] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication program, collecting information on their hobbies, preferences, and emotions, and then matching them appropriately with other users based on that information. This system consists of five main phases: a "user input acquisition phase," a "natural language processing phase," an "emotion recognition phase," a "response generation phase," and a "matching phase."

[1513] Getting user input phase

[1514] Terminal: The user launches a communication program on their smartphone or computer. This program displays the message "How was your day?" to the user. The user responds in natural language, for example, "I went for a run today and found a new trail. It felt great!"

[1515] Natural Language Processing Phase

[1516] Server: The communication program server receives the user's message and forwards it to the system's server. The system's server passes the message to a natural language processing engine (e.g., SpaCy or NLTK) to analyze the text. During the analysis process, the text is tokenized and key keywords (e.g., "running," "new road," "felt good," etc.) are extracted.

[1517] Emotion Recognition Phase

[1518] Server: The emotion information is passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API) to analyze based on the keywords extracted by the natural language processing engine. The emotion recognition engine identifies emotion categories such as positive, negative, and neutral from these keywords and quantifies their intensity. For example, the expression "I felt good" is identified as a positive emotion with high intensity.

[1519] Profile database update

[1520] Server: Updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, adds and saves data such as "running," "new roads," and "intensity of positive emotions" to the user's characteristic database.

[1521] Response Generation Phase

[1522] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI's generative AI model). For example, generate the response text "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1523] Sending a Response

[1524] Server: The server sends the generated response message to the user via a communication program. The user can then view the message on their own device and share their individual experiences.

[1525] Matching Phase

[1526] Server: Searches for other users with the same hobbies and interests based on the characteristics database. For example, it finds other users who share the common hobby of "running" from the database.

[1527] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[1528] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections and reduce feelings of isolation.

[1529] Specific examples

[1530] For example, user A types, "I went for a run today and found a new route. It felt great!" The message is received by the server, and the natural language processing engine extracts the keywords "running," "new route," and "felt great." The emotion recognition engine identifies this as a positive emotion and quantifies the intensity of the emotion. The trait database is updated based on this information, and the generative AI model generates a response such as, "That's great! Finding a new route feels like an adventure. Have fun on your next run!" Furthermore, user B, who shares the same hobby, is found, and a notification is sent to user A saying, "I found user B, who also enjoys running. Would you like to connect?" Through this process, users can reduce their sense of isolation and form new social connections.

[1531] Example prompts for generative AI models

[1532] "User A entered, 'I went for a run today and found a new path. It felt great!' Extract keywords from this input, analyze the emotional information, and generate an appropriate response to User A and a matching notification with other users who have the same hobbies."

[1533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1534] Step 1:

[1535] User: The user opens a communication program on their smartphone or computer and receives a message asking, "How was your day?" The user types a natural language response into a text box, such as, "I went for a run today and found a new trail. It felt great!"

[1536] Input: User's natural language text.

[1537] Output: Text data sent to the communications program.

[1538] Step 2:

[1539] Terminal: The terminal's communications program sends the user's message to the server.

[1540] Input: The text message entered by the user.

[1541] Output: The text data sent to the server.

[1542] Step 3:

[1543] Server: The server passes the received text data to a natural language processing engine (e.g., SpaCy or NLTK), tokenizes the text, and extracts key keywords (e.g., "running," "new road," "felt good").

[1544] Input: User's text data.

[1545] Output: Extracted keywords.

[1546] Step 4:

[1547] Server: Keywords extracted by the natural language processing engine are passed to an emotion recognition engine (e.g., IBM Watson or Google Cloud Natural Language API), which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity.

[1548] Input: Extracted keywords.

[1549] Output: Sentiment category and its intensity.

[1550] Step 5:

[1551] Server: The server updates the user's characteristic database based on the extracted emotion information and keywords. Specifically, it adds and saves the data "running," "new route," and "intensity of positive emotion" to the user's profile.

[1552] Input: sentiment information and keywords.

[1553] Output: Updated trait database.

[1554] Step 6:

[1555] Server: Based on the updated feature database, generate an appropriate response message using a natural language generation engine (e.g., OpenAI GPT-3).

[1556] Input: Updated trait database.

[1557] Output: The generated response message.

[1558] Step 7:

[1559] Server: Sends the generated response message to the user via a communication program.

[1560] Input: The generated response message.

[1561] Output: The message sent to the user.

[1562] Step 8:

[1563] Server: Searches for other users with the same hobbies and interests based on the characteristics database. Specifically, it searches the database for other users who share the common hobby of "running."

[1564] Input: trait database.

[1565] Output: A list of potential matches.

[1566] Step 9:

[1567] Server: Generates a matching notification based on the match candidates found. For example, a notification with the content "I found User B, who also enjoys running. Would you like to connect?"

[1568] Input: A list of potential matches.

[1569] Output: The generated matching notification.

[1570] Step 10:

[1571] Server: The server sends the generated match notification to the user via a communication program, allowing the user to gain new connections.

[1572] Input: The generated matching notification.

[1573] Output: Notification sent to the user.

[1574] (Application example 2)

[1575] 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."

[1576] The problem to be solved by this invention is to help users avoid isolation in their daily lives, promote interaction between customers in physical stores, and improve the customer experience in the store. In particular, the purpose is to reduce feelings of isolation and form new social connections by matching customers who share common hobbies, preferences, and emotional information in real time and recommending appropriate community spaces.

[1577] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1578] In this invention, the server includes: means for allowing a user to input daily events in natural language via a communication application; means for analyzing the text input in natural language and extracting keywords and emotional information; means for updating the user's profile database based on the extracted keywords and emotional information; means for generating an appropriate response based on the updated profile database; means for sending the generated response to the user via the communication application; means for searching for other users with common hobbies and preferences using the profile database and identifying match candidates; means for sending a notification to the user based on the match candidates; means for real-time matching to promote customer interaction in the store; and means for recommending the use of a specific community space based on the real-time matching means. This prevents users from feeling isolated in their daily lives, promotes customer interaction in the physical store, and improves the customer experience.

[1579] A "communications application" is software that enables communication between users over the Internet.

[1580] "Natural language" refers to language used by humans on a daily basis, excluding computer processing.

[1581] "Natural language processing" is a technology that allows computers to understand, analyze, and generate natural human language.

[1582] A "keyword extraction means" is a means for identifying and extracting key words and phrases from input text.

[1583] "Emotional information" refers to data that identifies emotional states, such as positive, negative, or neutral, from natural language text.

[1584] A "profile database" is a database for storing a user's hobbies, preferences, emotional information, and other related information.

[1585] The "response generation means" is a means for generating an appropriate response to a user's input.

[1586] "Matching means" is a method for discovering and presenting appropriate associations with other users based on common hobbies, preferences, and emotional information.

[1587] A "match notification means" is a means for notifying a user of a suitable match candidate that has been found.

[1588] "Real-time matching means" is a means of matching customers who are simultaneously in the store based on their hobbies and preferences.

[1589] The "community space recommendation means" is a means of recommending a space within the store where matched customers can interact with each other.

[1590] This invention is a system that helps users avoid isolation in their daily lives by interacting with them via a communication application, collecting information on their hobbies, preferences, and emotions, and then matching them with other users appropriately based on that information. The system consists of five main phases: a user input acquisition phase, a natural language processing phase, a response generation phase, an emotion recognition phase, and a matching phase.

[1591] Getting user input phase

[1592] Device:

[1593] The user responds to the message "How was your day today?" from the AI ​​via a communication application on a smartphone or other device by inputting a natural language response. For example, the user might input "I went for a run today and found a new trail. It felt great!"

[1594] server:

[1595] The message is sent to the server through the communication platform and then forwarded to the server of the system.

[1596] Natural Language Processing Phase

[1597] server:

[1598] The received message is passed to a natural language processing engine and the text is analyzed. Specifically, the text is tokenized and key keywords such as "running," "new road," and "felt good" are extracted. The text analysis is also performed using the pipeline function of the transformers library.

[1599] Emotion Recognition Phase

[1600] server:

[1601] The parsed text is passed to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the Transformers library is used for emotion recognition. The extracted emotion information and keywords are stored in the user's profile database.

[1602] Response Generation Phase

[1603] server:

[1604] It uses a natural language processing engine to generate appropriate responses based on the user's profile database, such as "That's great! Finding a new route sounds like an adventure. Have fun on your next run!"

[1605] Matching Phase

[1606] server:

[1607] Search for other users with the same hobbies and interests based on the profile database. For example, find other users who share the hobby "running" from the database. When a potential match is found, generate a matching notification based on that information. For example, create a notification saying, "I've found user B, who also enjoys running. Would you like to connect?" and send it to the user.

[1608] Specific examples

[1609] For example, if User A types, "I went for a run today and found a new route. It felt great!", the message is analyzed by a natural language processing engine, and the keywords "running," "new route," and "felt great" are extracted. The emotion engine identifies positive emotions from these texts and quantifies and analyzes the intensity of the emotions. The profile database is updated, and a response message is generated: "That's great! Finding a new route feels like an adventure. Have fun on your next run!" At the same time, the profile database is searched for another User B with the same hobby, and a notification is sent to User A saying, "We've found User B, who also enjoys running. Would you like to connect?"

[1610] This will prevent users from being isolated in their daily lives, promote customer interaction within physical stores, and improve the customer experience.

[1611] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1612] Step 1:

[1613] User input

[1614] A user inputs a daily event in natural language via a communication application, for example, "I went running today and found a new path. It felt great!"

[1615] Step 2:

[1616] Sending a message

[1617] The terminal transmits the input text to the server through the communication platform, where the input is natural language text and the output is the same text data transmitted to the server.

[1618] Step 3:

[1619] Natural Language Processing

[1620] The server passes the received message to a natural language processing engine and analyzes the text. Specifically, it uses the pipeline function of the transformers library to tokenize the text and extract key keywords such as "running," "new road," and "felt good." The input is natural language text, and the output is a list of extracted keywords.

[1621] Step 4:

[1622] emotion recognition

[1623] The server passes the parsed text to an emotion engine, which identifies emotion categories such as positive, negative, and neutral, and quantifies their intensity. The emotion recognition model from the transformers library is used for emotion recognition. The input is a list of extracted keywords, and the output is data with emotion categories and their intensities.

[1624] Step 5:

[1625] Profile database update

[1626] The server updates the user's profile database based on the extracted emotion information and keywords. The input is emotion data and a keyword list, and the output is the updated profile database.

[1627] Step 6:

[1628] Response Generation

[1629] The server uses a natural language processing engine to generate an appropriate response based on the updated profile database, for example, "That's great! Finding a new route sounds like an adventure. Have fun on your next run!" The input is the updated profile database, and the output is the generated response message.

[1630] Step 7:

[1631] Response Send

[1632] The server sends the generated response message to the user through the communication application, where the input is the generated response message and the output is the response message sent to the user terminal.

[1633] Step 8:

[1634] Searching for potential matches

[1635] The server searches for other users with the same hobbies and interests based on the profile database. Specifically, it finds other users who share the hobby "running" in the database. The input is the updated profile database, and the output is a list of match candidates.

[1636] Step 9:

[1637] Generate a matching notification

[1638] When the server finds a match candidate, it generates a match notification based on that information. For example, it creates a notification that reads, "I've found user B, who also enjoys running. Would you like to connect?" The input is a list of match candidates, and the output is the generated match notification.

[1639] Step 10:

[1640] Sending matching notifications

[1641] The server sends the generated match notification to the user through a communication application, where the input is the generated match notification and the output is the notification message sent to the user terminal.

[1642] Example prompt

[1643] User: I tried a new drink at the cafe today and it was delicious!

[1644] AI: That's great! Glad you had a great time.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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).

[1652] 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.

[1653] 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."

[1654] 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.

[1655] 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).

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1661] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1662] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1663] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1664] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1665] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1666] The following is further disclosed regarding the above embodiment.

[1667] (Claim 1)

[1668] means for enabling a user to input daily occurrences in natural language via a communication application;

[1669] means for analyzing the text input in the natural language and extracting keywords and emotion information;

[1670] means for updating a user profile database based on the extracted keywords and emotion information;

[1671] means for generating an appropriate response based on the updated profile database;

[1672] means for transmitting the generated response to a user through the communication application;

[1673] A means for searching for other users who have common interests and preferences using the profile database and identifying potential matches;

[1674] means for sending a notification to a user based on the match candidates;

[1675] A system including:

[1676] (Claim 2)

[1677] 2. The system of claim 1, wherein the means for generating a response is configured to generate a positive response or a negative response based on the extracted emotional information.

[1678] (Claim 3)

[1679] 10. The system of claim 1, wherein the communication application operates on a mobile device and further comprises means for ascertaining a user's activity location using GPS data received from the mobile device and storing the location as additional information in a profile database.

[1680] "Example 1"

[1681] (Claim 1)

[1682] means for enabling a user to input daily occurrences in natural language via the communications software;

[1683] means for analyzing the input text in the natural language and extracting important words and sentiment information;

[1684] means for updating a user attribute database based on the extracted important words and emotion information;

[1685] means for generating an appropriate response based on the updated attribute database;

[1686] means for transmitting said generated response to a user through said communications software;

[1687] A means for searching for other users who have common interests and preferences using the attribute database and identifying potential matching users;

[1688] means for sending notifications to users based on the potential matching users;

[1689] A system including:

[1690] (Claim 2)

[1691] 2. The system of claim 1, wherein the means for generating a response is configured to generate a positive response or a negative response based on the extracted emotion information.

[1692] (Claim 3)

[1693] 10. The system of claim 1, wherein the communications software operates on the mobile device and further comprises means for ascertaining the user's activity location using location data received from the mobile device and storing the location as additional information in an attribute database.

[1694] "Application Example 1"

[1695] (Claim 1)

[1696] means for enabling a user to input daily events in natural language via an information processing device;

[1697] means for analyzing the text input in the natural language and extracting keywords and emotion information;

[1698] means for updating a user profile database based on the extracted keywords and emotion information;

[1699] means for generating an appropriate response based on the updated profile database;

[1700] means for transmitting the generated response to a user through the information processing device;

[1701] A means for searching for other users who have common interests and preferences using the profile database and identifying potential matches;

[1702] A means for providing personalized product suggestions and services in-store based on customer preferences and emotional information;

[1703] means for sending a notification to a user based on the match candidates;

[1704] A system including:

[1705] (Claim 2)

[1706] 2. The system of claim 1, wherein the means for generating a response is configured to generate a positive response or a negative response based on the extracted emotional information.

[1707] (Claim 3)

[1708] 10. The system of claim 1, wherein the information processing device operates on a portable electronic device and further comprises means for ascertaining the user's activity location using location data received from the portable electronic device and storing the location as additional information in a profile database.

[1709] "Example 2: Combining Emotion Engines"

[1710] (Claim 1)

[1711] means for enabling a user to input daily occurrences in natural language via a communication program;

[1712] means for analyzing the text input in the natural language and extracting keywords and emotion information;

[1713] means for updating a user characteristic database based on the extracted keywords and emotion information;

[1714] means for generating an appropriate response based on the updated characteristic database;

[1715] means for transmitting the generated response to a user through the communication program;

[1716] A means for searching for other users who share common interests and preferences using the characteristic database and identifying potential matches;

[1717] means for sending a notification to a user based on the match candidates;

[1718] A system including:

[1719] (Claim 2)

[1720] 2. The system of claim 1, wherein the means for generating a response is configured to generate a positive response or a negative response based on the extracted emotional information.

[1721] (Claim 3)

[1722] 2. The system of claim 1, wherein the communication program runs on the mobile terminal and further comprises means for ascertaining the user's activity location using location data received from the mobile terminal and storing the location as additional information in a characteristic database.

[1723] "Application example 2 when combining emotion engines"

[1724] (Claim 1)

[1725] means for enabling a user to input daily occurrences in natural language via a communication application;

[1726] means for analyzing the text input in the natural language and extracting keywords and emotion information;

[1727] means for updating a user profile database based on the extracted keywords and emotion information;

[1728] means for generating an appropriate response based on the updated profile database;

[1729] means for transmitting the generated response to a user through the communication application;

[1730] A means for searching for other users who have common interests and preferences using the profile database and identifying potential matches;

[1731] means for sending a notification to a user based on the match candidates;

[1732] A real-time matching method to promote customer interaction in stores;

[1733] A means for recommending the use of a specific community space based on the real-time matching means;

[1734] A system including:

[1735] (Claim 2)

[1736] 2. The system of claim 1, wherein the means for generating a response is configured to generate a positive response or a negative response based on the extracted emotional information.

[1737] (Claim 3)

[1738] 10. The system of claim 1, wherein the communication application operates on a mobile device and further comprises means for ascertaining a user's activity location using location data received from the mobile device and storing the location data as additional information in a profile database. [Explanation of symbols]

[1739] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for enabling a user to input daily occurrences in natural language via a communication application; means for analyzing the text input in the natural language and extracting keywords and emotion information; means for updating a user profile database based on the extracted keywords and emotion information; means for generating an appropriate response based on the updated profile database; means for transmitting the generated response to a user through the communication application; A means for searching for other users who have common interests and preferences using the profile database and identifying potential matches; means for sending a notification to a user based on the match candidates; A system including:

2. The system of claim 1 , wherein the means for generating a response is configured to generate a positive or negative response based on the extracted emotional information.

3. 10. The system of claim 1, wherein the communication application operates on a mobile device and further comprises means for ascertaining a user's activity location using GPS data received from the mobile device and storing the location as additional information in a profile database.

Images