system

Abstract

Provide a system. [Solution] A means for receiving text data of worries and feelings input by a user; A means for analyzing the received text data and extracting keywords and important phrases; using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and A means of obtaining information about specialist agencies and support services that respond to the advice generated; and A means to categorize users with related concerns and automatically generate communities where people in the same situation can gather, a means for providing generated advice, professional information, and community invitation links to users; A system including:

Description

[Technical Field]

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

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

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

[0004] While social media has increased connections in recent years, individual concerns have also become more isolated. Furthermore, the spread of remote work has exacerbated feelings of isolation, creating social issues such as a lack of access to appropriate advice and a lack of people around who understand one's thoughts and concerns. In particular, people with a variety of concerns in areas such as life, work, child-rearing, and romance find themselves in an environment where it is difficult to find psychological security. To solve these problems, a platform is needed where people can discuss their concerns, receive appropriate advice, and interact with others who share the same concerns. [Means for solving the problem]

[0005] The present invention has a function that first receives text data of worries and emotions entered by the user, analyzes the data, and extracts keywords and important phrases. Next, based on the extracted keywords and phrases, an artificial intelligence model is used to generate advice. The generated advice is provided to the user along with information on related specialist institutions and support services. The system also has a function that categorizes users with related worries and automatically generates a community where people in the same situation gather. Finally, the system provides users with an invitation link to join the generated community, promoting interaction between users. This system allows individual users with worries to receive accurate advice and psychological support by interacting with other people who share the same worries.

[0006] "User" refers to a person who uses this system to input their worries and feelings.

[0007] "Text data" refers to written information in natural language about worries and feelings entered by users.

[0008] "Analysis" refers to the process of dividing received text data and extracting keywords and important phrases.

[0009] "Keywords" refer to important words or phrases related to worries or emotions extracted from text data.

[0010] An "artificial intelligence model" refers to a computational model for generating advice based on input data, specifically one that uses natural language processing technology.

[0011] "Advice" refers to advice generated by an artificial intelligence model in response to analyzed concerns and emotions.

[0012] "Specialized agency" refers to a facility or organization that provides specialized support for specific concerns or problems.

[0013] "Support services" refers to various services provided by specialized institutions to resolve users' concerns and problems.

[0014] "Categorization" refers to the process of grouping users with similar circumstances by classifying user data with related concerns.

[0015] A "community" refers to a group where users with the same concerns gather to interact and support each other.

[0016] "Invitation Link" means the URL or guidance information used by a User to join a generated Community.

[0017] "Interaction" refers to activities in which users within the community share their concerns and experiences and support each other. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically creates a community where people in similar situations can gather. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described below.

[0040] System configuration

[0041] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server.

[0042] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0043] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[0044] Program processing

[0045] Entering and receiving text data

[0046] User: Enter their worries or feelings in natural language through the device. For example, they might enter, "I haven't been able to sleep lately because of work stress."

[0047] Terminal: Sends entered text data to the server.

[0048] Text data analysis

[0049] Server: Uses natural language processing techniques to analyze the received text data, including segmenting the text and extracting keywords and important phrases.

[0050] Server: For example, extract keywords such as "work," "stress," and "can't sleep."

[0051] Generating Advice

[0052] Server: Based on the extracted keywords and phrases, an artificial intelligence model (e.g., ChatGPT®) is used to generate advice.

[0053] Server: The generated advice is provided to the user as, for example, "methods for managing stress" or "how to relax before bed."

[0054] Providing information on specialized institutions

[0055] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[0056] Server: Converts the acquired information into a user-friendly format and provides it along with advice. For example, it may include information on clinics offering cognitive behavioral therapy.

[0057] Categorizing users with similar circumstances and creating communities

[0058] Server: Based on the user's problem data and advice, related users are categorized using a clustering algorithm.

[0059] Server: For example, it generates categories related to "work stress" and "sleep disorders" and automatically creates a community where users with the same concerns can gather.

[0060] Feedback and community engagement

[0061] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[0062] Terminal: Displays the received information to the user.

[0063] Users: Follow advice, consult professional organizations, and join communities.

[0064] Specific examples

[0065] Example: If the user enters "Sleep problems due to work stress"

[0066] 1. User: Enters "I can't sleep lately because of work stress" into the device.

[0067] 2. Terminal: Send this text data to the server.

[0068] 3. Server: Analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[0069] 4. Server: Queries the AI ​​model and generates "ways to reduce stress" and "advice for improving sleep."

[0070] 5. Server: Generates advice such as "take deep breaths and relax" or "maintain a bedtime routine."

[0071] 6. Server: Retrieves information about specialized institutions that provide cognitive behavioral therapy from a database and formats the information.

[0072] 7. Server: Categorizes users with concerns related to "work stress" and "sleep disorders" and generates a community of users with similar concerns.

[0073] 8. Server: Provides users with advice, information on professional organizations, and community invitation links.

[0074] 9. Terminal: Displays this information to the user.

[0075] 10. Users: Act on advice, consult with specialist authorities if necessary, and participate in the community.

[0076] This system allows users to receive appropriate advice on their concerns, and by interacting with other users who have the same concerns, they can reduce feelings of isolation and gain psychological security.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0080] Step 2:

[0081] The terminal transmits the input text data to the server.

[0082] Step 3:

[0083] The server stores the received text data and starts the text analysis module to begin analysis.

[0084] Step 4:

[0085] The server uses Natural Language Processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[0086] Step 5:

[0087] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and phrases.

[0088] Step 6:

[0089] The server receives the advice returned by the AI ​​model and formats it appropriately, generating specific advice such as "how to manage stress" or "how to relax before bed."

[0090] Step 7:

[0091] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[0092] Step 8:

[0093] Based on the user's worry data and advice, the server uses a clustering algorithm to categorize users who are in the same situation or have related worries.

[0094] Step 9:

[0095] For each cluster, the server automatically generates a community of users with related concerns. For example, it generates a community for "stress management and sleep improvement."

[0096] Step 10:

[0097] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[0098] Step 11:

[0099] The terminal displays the received feedback data to the user.

[0100] Step 12:

[0101] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[0102] Through this series of processes, users with concerns can receive specific and accurate advice and easily interact with other users who share the same concerns, which relieves users from feelings of isolation and provides a sense of psychological security.

[0103] Example 1

[0104] 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."

[0105] In modern society, individual users have a wide range of worries and emotions, but there is a lack of environments where they can receive appropriate advice or opportunities to interact with other users who share the same worries. There is also no system in place to provide appropriate information about specialized institutions and support services. As a result, users are unable to receive appropriate support, which leads to problems such as feelings of isolation and increased psychological stress.

[0106] 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.

[0107] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries using a clustering algorithm and automatically generating a community of people in the same situation, and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This makes it possible to provide appropriate advice for the worries and emotions that users have, quickly and appropriately provide information on specialist institutions and support services, and provide a place for users to interact with others who have the same worries.

[0108] A "user" is an entity that uses the system to input their worries and feelings and receive advice and access to the community.

[0109] "Text data" refers to information entered by a user in natural language to express their concerns or feelings.

[0110] A "server" is a device located at the center of a system that analyzes data sent by users and provides and manages information.

[0111] A "terminal" is a device that provides an interface for users to input text data, such as a PC, smartphone, or tablet.

[0112] "Natural language processing technology" is a technology for analyzing text data and extracting keywords and important phrases, and includes technologies such as SpaCy and NLTK.

[0113] A "keyword" is a particularly important word or phrase in the received text data.

[0114] A "phrase" is a series of significant word combinations within text data.

[0115] An "artificial intelligence model" is a learning model used to generate advice based on extracted keywords and phrases, such as ChatGPT.

[0116] A "clustering algorithm" is an algorithm for classifying users with related concerns and grouping people in the same situation.

[0117] A "community" is a virtual group where users with the same concerns and feelings can gather and interact.

[0118] A "specialized institution" is a facility or organization that provides specialized support and services for specific concerns or problems.

[0119] "Support services" is a general term for professional support and advice provided to users to address their concerns and emotions.

[0120] An "invitation link" is a URL or hyperlink that users can click to access in order to join a community.

[0121] "JSON format" is a data format used to structure text data and communicate between servers and devices.

[0122] "Tokenization" is an analytical method that divides text data into semantic units.

[0123] A "prompt sentence" is a text instruction sentence that is input to an artificial intelligence model to generate advice.

[0124] MODE FOR CARRYING OUT THE INVENTION

[0125] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically generates a community of people in similar situations. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described in detail below.

[0126] System configuration

[0127] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server. Specific technologies include SpaCy and NLTK for analyzing text data and ChatGPT for generating advice.

[0128] 2. Device: A device that provides an interface for users to input their worries and feelings, such as a PC, smartphone, or tablet. These devices provide a platform for users to input text data and send it to a server.

[0129] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[0130] Program processing

[0131] Entering and receiving text data

[0132] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I can't sleep lately because of work stress." The device then sends the input text data to the server.

[0133] Text data analysis

[0134] The server uses natural language processing techniques (SpaCy or NLTK) to analyze the received text data. This process involves tokenizing the text and extracting keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" can be extracted.

[0135] Generating Advice

[0136] The server constructs a prompt for the artificial intelligence model (ChatGPT) based on the extracted keywords and phrases, and generates advice. An example of a prompt is, "What should I do when I can't sleep because of work stress?" The generated advice is provided to the user, for example, "How to take deep breaths and relax" or "Maintain a bedtime routine."

[0137] Obtaining information on specialized institutions

[0138] The server retrieves information about specialist institutions and support services that can provide advice from a database. For example, it retrieves information about clinics that offer cognitive behavioral therapy. The retrieved information is formatted as the clinic's address, contact information, opening hours, etc.

[0139] Categorizing users with similar circumstances and creating communities

[0140] The server uses a clustering algorithm (e.g., k-means clustering) to categorize related users based on the user's concerns and advice. For example, it could create categories related to "work stress" and "sleep disorders," and automatically create a community of users with the same concerns.

[0141] Feedback and community engagement

[0142] The server sends the generated advice, information about the specialist organization, and an invitation link to the community to the user's device. The device displays the received information on a user interface. The user can then take action based on the displayed information. Specifically, the user can act on the advice, contact the specialist organization, or join the community to interact with other users.

[0143] Specific examples

[0144] If a user types, "Recently, I've been unable to sleep due to work stress," this text data is sent from the device to the server. The server analyzes this data and extracts the keywords "work," "stress," and "can't sleep." The generative AI model (ChatGPT) then receives a prompt such as, "What should I do when I can't sleep due to work stress?" and generates advice. For example, advice such as "Take deep breaths to relax" or "Maintain a bedtime routine" is provided. At the same time, information about related specialist institutions is retrieved and provided to the user. Users with the same problem are also categorized, a community is created, and an invitation link is sent. This system allows users to receive appropriate advice and connect with specialist institutions and other users with the same problem.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] User: Uses the device to input worries and feelings in natural language. For example, the user might input the text, "Recently, I've been unable to sleep due to work stress."

[0148] Input: Natural language text data entered by the user.

[0149] Output: Text data sent to the terminal.

[0150] Step 2:

[0151] Terminal: The entered text data is sent to the server. This data is generally converted to JSON format and sent using the HTTPS protocol.

[0152] Input: User text data.

[0153] Output: The text data sent to the server.

[0154] Step 3:

[0155] Server: Receives the incoming text data and analyzes it using natural language processing techniques (e.g., SpaCy or NLTK), which tokenizes the text and extracts keywords and key phrases.

[0156] Input: Text data received by the server.

[0157] Output: Extracted keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[0158] How it works: The server tokenizes the text and classifies it by parts of speech, such as nouns and verbs. It uses techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) to extract important phrases.

[0159] Step 4:

[0160] Server: Based on the extracted keywords, it generates prompts for the AI ​​model (e.g., ChatGPT) and generates advice.

[0161] Input: Extracted keywords and prompt sentences. For example, "What should I do when I can't sleep because of work stress?"

[0162] Output: Generated advice. For example, "Take deep breaths and relax" and "Follow a bedtime routine" are generated.

[0163] How it works: The server inputs a prompt into the generative AI model (ChatGPT), receives the text data returned by the model, formats the text data, and converts it into a format that can be provided to the user.

[0164] Step 5:

[0165] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[0166] Input: Generated advice and corresponding keywords.

[0167] Output: Details of professional and support services, such as clinic addresses, contact details, and opening hours.

[0168] How it works: The server uses a pre-configured database query to search for relevant professional organizations and retrieves the results in a formatted form.

[0169] Step 6:

[0170] Server: Based on the user's concerns and advice, related users are categorized using a clustering algorithm (e.g., k-means clustering), and a community of people in the same situation is automatically generated.

[0171] Input: User's trouble data and generated advice.

[0172] Output: Categorized user information and generated communities. For example, categories related to "work stress" and "sleep disorders" are generated.

[0173] How it works: The server runs a clustering algorithm to group user data, automatically forming communities of people with similar interests.

[0174] Step 7:

[0175] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[0176] Input: Generated advice, professional organization information, community invite link.

[0177] Output: Feedback information sent to the device.

[0178] How it works: The server packets information and sends it to the device, usually packing the data into JSON or XML format.

[0179] Step 8:

[0180] Terminal: Displays the received information in a user interface.

[0181] Input: Feedback information sent from the server (advice, professional information, community invite link).

[0182] Output: Feedback information that is displayed to the user.

[0183] How it works: The device's user interface displays the received information in the appropriate location, making it easily accessible to the user.

[0184] Step 9:

[0185] Users: Follow the advice provided, contact specialist organizations, and participate in communities.

[0186] Input: Feedback information displayed on the terminal.

[0187] Output: User action (following advice, contacting a specialist, joining a community).

[0188] Action: The user takes the necessary action based on the information provided, such as clicking a link to join a community or contact a specialist organization. This process provides the user with targeted support and interaction opportunities.

[0189] (Application example 1)

[0190] 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."

[0191] With conventional systems, it was difficult to provide accurate advice to users regarding their concerns and emotions, and it was also difficult to form a community of people in the same situation. In particular, since there was no system to address the specific concerns of store employees and customers, these users tended to feel isolated and were unable to obtain effective solutions or psychological support.

[0192] 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.

[0193] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries and automatically generating communities where people in similar situations gather, means for providing users with the generated advice, information on specialist institutions, and an invitation link to join the community, and means for generating appropriate advice for worries and emotions entered by store employees and customers and providing related community information. This makes it possible to quickly provide appropriate advice and automatically generate related communities even for the specific worries faced by store employees and customers.

[0194] A "user" is an entity that uses the system to input worries and feelings and receive advice and community information.

[0195] "Text data" is data in natural language format that includes worries and feelings entered by users.

[0196] "Extraction" is the process of extracting keywords and important phrases from the received text data.

[0197] An "artificial intelligence model" is a program or algorithm that uses natural language processing technology to understand the meaning of input data and generate appropriate advice.

[0198] A "specialized institution" is an institution such as a medical institution or counseling service that provides support and treatment for distress and emotions.

[0199] "Support services" refers to all services that provide specific support for users' concerns and emotions.

[0200] "Categorization" means classifying users with related concerns and grouping people in the same situation.

[0201] A "community" is a group of users who share the same concerns and feelings and come together to exchange information and interact with each other.

[0202] An "invitation link" is a reference URL or means of invitation to join that directs users to a newly created community.

[0203] "Physical store employees" refers to staff and sales associates who actually work in the store.

[0204] "Customers" refer to consumers who visit physical stores and use products and services.

[0205] MODE FOR CARRYING OUT THE INVENTION

[0206] This invention is a system that delves into the user's worries and feelings, provides appropriate advice, and automatically creates a community where people in the same situation can gather. This system is mainly composed of a server, terminals, and users.

[0207] System configuration

[0208] 1. Server:

[0209] Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology and artificial intelligence (AI) modules.

[0210] 2. Terminal:

[0211] A device that provides an interface for users to input their worries and feelings. Specifically, this applies to PCs, smartphones, tablets, etc.

[0212] 3. User:

[0213] These are the people who use the system to input their concerns and receive advice and access to the community. These include store employees and customers.

[0214] Program processing

[0215] The firmware and software configuration will be described.

[0216] Hardware:

[0217] Smartphones, tablets, etc. are used as input interfaces by users.

[0218] software:

[0219] This system uses the following software and services:

[0220] OpenAI® API: Used for natural language processing and advice generation.

[0221] HTTP request library (e.g. Requests): Used to retrieve community information.

[0222] Processing Description

[0223] The server performs a series of processes using the following means.

[0224] 1. Receive text data about worries and feelings:

[0225] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I've been having trouble with stress from customer service lately." This text data is sent to the server.

[0226] 2. Text data analysis:

[0227] The server uses OpenAI's API and natural language processing technology to analyze the received text data. This process segments the text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" are extracted.

[0228] 3. Generating Advice:

[0229] The server generates advice using an artificial intelligence model (e.g., GPT-3 (registered trademark)) based on the extracted keywords and phrases. The generated advice might be, for example, "Try practicing stress relief methods. For example, you could try breathing exercises or taking short breaks."

[0230] 4. Providing information on professional organizations:

[0231] The server retrieves information from a database about specialist institutions and support services that correspond to the generated advice, such as information about clinics offering cognitive behavioral therapy.

[0232] 5. Categorizing users with related concerns and creating a community:

[0233] The server uses a clustering algorithm to categorize related users based on their concerns and advice. For example, it could create a category related to "stress in customer service" and automatically create a community of users with the same concerns.

[0234] 6. Feedback and Community Involvement:

[0235] The generated advice, information about the expert organization, and an invitation link to the community are sent to the user's terminal, allowing the user to receive the advice, inquire about the expert organization, or join the community.

[0236] Specific examples

[0237] When a user inputs a concern such as "I've been suffering from stress lately when working with customers," the following prompt is sent to the generative AI model:

[0238] Prompt (for analysis): "Analyze your worries and feelings and extract keywords: I've been having trouble with stress from serving customers lately."

[0239] Prompt (for advice): "Generate appropriate advice based on the keywords 'customer service, stress'."

[0240] This makes it possible to quickly provide appropriate advice for the specific concerns of store employees and customers, and automatically generate related communities.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] The user uses a terminal to input their worries and feelings in natural language. For example, they might input text data such as, "Recently, I've been suffering from stress from customer service." This input text data is then sent from the terminal to the server.

[0244] Step 2:

[0245] The server uses OpenAI's API to analyze the received text data and performs natural language processing. This process tokenizes the input text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" can be extracted.

[0246] Input: Text data from the user: "Recently, I've been suffering from stress from customer service."

[0247] Data processing: Text tokenization, keyword extraction

[0248] Output: Extracted keywords "customer service," "stress," and "troubled"

[0249] Step 3:

[0250] Based on the extracted keywords, the server uses a generative AI model (e.g., GPT-3) to generate appropriate advice, such as "Try practicing stress relief methods. For example, you could try breathing exercises or short breaks."

[0251] Input: Extracted keywords "customer service," "stress," "troubled"

[0252] Data Computation: Advice Generation with Generative AI Models

[0253] Output: Advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[0254] Step 4:

[0255] The server retrieves information on specialist institutions and support services that correspond to the generated advice from a database, for example, "information on clinics that provide cognitive behavioral therapy."

[0256] Input: Generated advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[0257] Data calculation: Retrieving professional agency information from databases

[0258] Output: Specialist information "Information on clinics offering cognitive behavioral therapy"

[0259] Step 5:

[0260] Based on the user's worry data and the generated advice, the server uses a clustering algorithm to categorize related users and automatically generate communities of users with the same worries. For example, it generates a community related to "stress in customer service."

[0261] Input: User's trouble data, generated advice

[0262] Data processing: Categorization using clustering algorithms

[0263] Output: Generated community "Community related to customer service stress"

[0264] Step 6:

[0265] The server sends the generated advice, information about the expert organization, and an invitation link to join the community to the user's device. The user receives this information and can then act on the advice, contact the expert organization, or join the community.

[0266] Input: Generated advice, professional information, community invite link

[0267] Data Computing: Formatting and Transmitting Information

[0268] Output: Display information on the user's device (advice, information on specialist institutions, community invitation links)

[0269] 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.

[0270] This invention is a system that delves deeply into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in similar situations. This system incorporates an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user. The operation of this system and the program processing are described below.

[0271] System configuration

[0272] 1. Server: Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine.

[0273] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0274] 3. User: The entity that uses the system to input their concerns and feelings and receive advice and access to the community.

[0275] Program processing

[0276] Entering and receiving text data

[0277] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0278] The terminal transmits the input text data to the server.

[0279] Text data analysis

[0280] The server stores the received text data and starts the text analysis module to begin analysis.

[0281] The server uses NLP technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[0282] Emotion recognition

[0283] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[0284] Generating Advice

[0285] The server generates advice based on the extracted keywords and sentiments using an artificial intelligence model (e.g., ChatGPT).

[0286] The server tailors the generated advice based on emotion, for example providing advice emphasizing relaxation techniques when stress levels are high.

[0287] Providing information on specialized institutions

[0288] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[0289] Categorizing users with similar circumstances and creating communities

[0290] The server categorizes related users using a clustering algorithm based on the user's concern data and emotion data.

[0291] For each cluster, the server automatically generates a community of users with related concerns or emotions. For example, it generates a community for "stress management and sleep improvement."

[0292] Feedback and community engagement

[0293] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[0294] The terminal displays the received feedback data to the user.

[0295] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[0296] Specific examples

[0297] Example: If the user enters "Sleep problems due to work stress"

[0298] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[0299] 2. The device sends this text data to the server.

[0300] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[0301] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[0302] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[0303] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[0304] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[0305] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[0306] 9. The server provides the user with generated advice, expert information, and community invitation links.

[0307] 10. The terminal displays this information to the user.

[0308] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[0309] This system allows users to receive appropriate advice regarding their worries and feelings, and they can receive psychological support by interacting with other users who have the same worries and feelings.

[0310] The processing flow will be explained below.

[0311] Step 1:

[0312] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0313] Step 2:

[0314] The terminal transmits the input text data to the server.

[0315] Step 3:

[0316] The server stores the received text data and starts the text analysis module to begin analysis.

[0317] Step 4:

[0318] The server uses natural language processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[0319] Step 5:

[0320] The server analyzes the extracted text data using an emotion engine to recognize the user's emotions, such as "stress" and "anxiety."

[0321] Step 6:

[0322] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and recognized emotions.

[0323] Step 7:

[0324] The server receives the advice returned by the AI ​​model and adjusts the advice to match the user's emotions, for example, emphasizing "ways to reduce stress" and "ways to relax."

[0325] Step 8:

[0326] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[0327] Step 9:

[0328] The server uses a clustering algorithm to categorize related users based on their worries and emotions, for example, creating a category for "stress management and sleep improvement."

[0329] Step 10:

[0330] Based on categorized user data, the server automatically generates a community where users with the same concerns and feelings can gather.

[0331] Step 11:

[0332] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[0333] Step 12:

[0334] The terminal displays the received feedback data to the user.

[0335] Step 13:

[0336] The user reviews the information received and takes action on the advice if necessary, contacts a specialist organization, or joins a community.

[0337] Through this series of processes, users with worries or feelings can receive specific and accurate advice and can easily interact with other users who share the same worries or feelings, thereby freeing users from feelings of isolation and providing a sense of psychological security.

[0338] Example 2

[0339] 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."

[0340] There is a need for a system that allows users to receive appropriate advice and support for the worries and emotions they experience in their daily lives. However, conventional systems have difficulty providing accurate advice tailored to the user's emotions and individual circumstances, and have not been able to quickly create a community where users can interact with people in the same situation. Therefore, there is an urgent need to develop a system that allows users to immediately feel reassured and receive advanced support.

[0341] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving text data of worries and emotions entered by a user; means for analyzing the received text data and extracting keywords and important phrases; means for recognizing the user's emotions based on the extracted emotion data; means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases; means for adjusting the generated advice based on the emotion data and keywords; means for acquiring information on specialist institutions and support services corresponding to the generated advice; means for categorizing users with related worries using a clustering method and automatically generating communities of people in the same situation; and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This allows users to receive quick and accurate advice on their worries and emotions and to obtain psychological support by interacting with people in the same situation.

[0342] A "user" is an entity that uses the system to input worries and feelings and receive advice and access to the community.

[0343] "Text data" refers to character strings of natural language data that express the user's worries and feelings and that the user enters into the device.

[0344] A "server" is a central system that processes data entered by users and manages and provides various information.

[0345] A "terminal" is a device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0346] "Natural language processing technology" is a technology that uses computers to analyze natural language and understand and generate human language.

[0347] "Keywords" refer to important words or phrases within text data, and are terms extracted for data analysis and advice generation.

[0348] The "emotion engine" is a system for recognizing and analyzing user emotions from text data.

[0349] An "artificial intelligence model" is an algorithm or machine learning model that generates advice based on extracted keywords and emotions.

[0350] A "clustering method" is an algorithm for classifying data into multiple clusters (groups), and is used to categorize data based on users' common concerns and emotions.

[0351] A "community" is an online group where users with the same circumstances or concerns gather, and is a place to exchange information and interact.

[0352] A "specialized agency" is an institution or facility that provides support or services for specific problems or concerns.

[0353] "Feedback Data" means data generated and formatted by the server, including advice, expert information, and community invitation links.

[0354] "Cluster" is a term that refers to a group of users who share similar characteristics or attributes.

[0355] This invention provides a system that delves into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in the same situation. This system is combined with an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user.

[0356] The system configuration is as follows:

[0357] Hardware and Software

[0358] 1. Server: Located centrally, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine. Specifically, it uses Spacy or NLTK as the NLP engine, Affectiva or IBM Watson (registered trademark) as the emotion engine, and ChatGPT as the generative AI model.

[0359] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0360] 3. User: The entity that uses the system to input their worries and feelings and receive advice and access to the community.

[0361] System Operation

[0362] The operation of the system is as follows.

[0363] Entering and receiving text data

[0364] The user inputs their worries and feelings into the device in natural language. For example, they might input, "I haven't been able to sleep lately because of work stress." The device then sends this input text data to the server.

[0365] Text data analysis

[0366] The server stores the received text data and starts the text analysis module to begin analysis. The server uses NLP technology to tokenize the text data and extract keywords and important phrases. For example, it extracts keywords such as "work," "stress," and "can't sleep."

[0367] Emotion recognition

[0368] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[0369] Generating Advice

[0370] The server generates advice using a generative AI model (such as ChatGPT) based on the extracted keywords and emotions. The server then adjusts the content of the advice based on the emotions. For example, if stress levels are high, the server will provide advice including relaxation techniques such as "take deep breaths to relax" and "maintain a bedtime routine."

[0371] Providing information on specialized institutions

[0372] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[0373] Categorizing users with similar circumstances and creating communities

[0374] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's worry and emotion data. Furthermore, for each cluster, the server automatically generates a community of users with related worries and emotions. For example, it generates a community for "stress management and sleep improvement."

[0375] Feedback and community engagement

[0376] The server prepares feedback data including the generated advice, information about the specialist organization, and an invitation link to join the community, and sends it to the user's terminal. The terminal displays the received feedback data to the user. The user checks the received information and, if necessary, follows the advice, contacts the specialist organization, or clicks the provided link to join the community.

[0377] Specific examples

[0378] Example: If the user enters "Sleep problems due to work stress"

[0379] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[0380] 2. The device sends this text data to the server.

[0381] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[0382] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[0383] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[0384] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[0385] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[0386] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[0387] 9. The server provides the user with generated advice, expert information, and community invitation links.

[0388] 10. The terminal displays this information to the user.

[0389] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[0390] An example of a prompt sentence is "Generate specific advice to reduce stress based on keywords entered by the user."

[0391] This system allows users to receive accurate and prompt advice on their worries and feelings, and they can also receive psychological support by interacting with other users who have the same worries and feelings.

[0392] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0393] Step 1:

[0394] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep due to work stress." This becomes the input data. The device receives this input text data and sends it to the server as an HTTP request. This becomes the output data.

[0395] Step 2:

[0396] The server receives text data and stores it in a database. This data becomes input data. Based on the stored data, a text analysis module (e.g., Spacy or NLTK) is invoked to tokenize the received text data. The tokenized data becomes output data.

[0397] Step 3:

[0398] The server analyzes the tokenized data and extracts keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted from text data. This is the input data, and the output data is a list of keywords as a result of the analysis.

[0399] Step 4:

[0400] The server uses an emotion engine (such as Affectiva or IBM Watson) to analyze the text data based on keywords and recognize the user's emotions. For example, emotions such as stress or anxiety are identified. This is the input data, and the emotion recognition results are the output data.

[0401] Step 5:

[0402] The server generates advice using a generative AI model (e.g., ChatGPT) based on the extracted keywords and the recognized emotions. Here, the server inputs prompt sentences, such as "How to reduce stress" or "Advice on improving sleep," into the AI ​​model. The generated advice is the output data.

[0403] Step 6:

[0404] The server adjusts the advice content based on the emotional data and the generated advice. For example, if stress is high, it will emphasize relaxation techniques such as "taking deep breaths to relax" and "maintaining a bedtime routine." The adjusted advice is the output data.

[0405] Step 7:

[0406] The server searches a database for information on specialist institutions and support services that correspond to the generated advice. For example, it searches for "information on clinics that provide cognitive behavioral therapy." The information on specialist institutions is the output data.

[0407] Step 8:

[0408] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's concern and emotion data. This is the input data. For each cluster, it automatically generates a community of users with related concerns and emotions. This is the output data.

[0409] Step 9:

[0410] The server prepares feedback data including the generated advice, information on the professional organization, and an invitation link to join the community, and sends it to the user's terminal, which is output data.

[0411] Step 10:

[0412] The device displays the received feedback data to the user. The user confirms the received information, for example by following the displayed advice, contacting a specialist, or clicking a provided link to join a community. This is input data.

[0413] Through this series of steps, users can receive accurate and prompt advice on their worries and feelings, and can also receive psychological support by interacting with other users who have the same worries and feelings.

[0414] (Application example 2)

[0415] 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."

[0416] In modern society, it is important to provide appropriate support to the many people suffering from stress and worries, and to form communities where people share similar problems. However, previous systems have struggled to accurately analyze users' worries and emotions and generate appropriate advice, and have been inadequate in automatically generating communities or directing users to specialized institutions. Furthermore, they lacked the functionality to record and analyze users' emotions and worries on a daily basis. The present invention aims to solve these problems and provide a more effective and comprehensive support system.

[0417] 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.

[0418] In this invention, the server includes a means for providing an interface for recording the user's emotions and worries on a daily basis, a means for analyzing text data entered by the user with an emotion analysis engine to recognize emotions, and a means for displaying generated advice on the smartphone screen. This allows users to continuously record their worries and emotions and receive appropriate advice based on that information. It also makes it easier for users to receive psychological support through interactions with other users with similar worries.

[0419] A "user" is an individual who uses the system to input their worries and feelings and receive advice and support.

[0420] "Text data" refers to data that includes character information entered by a user.

[0421] "Means for receiving" refers to a device or software that has the function of receiving data sent from a user and providing it for subsequent processing.

[0422] The "analyzing means" is a device or software that analyzes the received text data and extracts information such as meaning and emotion.

[0423] "Keywords and important phrases" are words or phrases in the text data that are recognized as having particularly important meanings.

[0424] An "artificial intelligence model" is an algorithm or program that allows a computer to automatically learn from data and generate judgments and advice.

[0425] "Advice" refers to advice or suggestions provided to users based on the results of the analysis.

[0426] "Specialized institutions and support services" are organizations and facilities that provide professional support to users regarding their worries and problems.

[0427] A "categorization tool" is software or algorithms used to group users with similar concerns or problems.

[0428] A "community" is a group of people in the same situation who come together to exchange information and provide support.

[0429] An "interface" is a screen or device that allows a user to input data into a system or receive output information.

[0430] An "emotion analysis engine" is software that analyzes and recognizes user emotions from text data.

[0431] "Means for displaying on the smartphone screen" refers to a function for visually presenting the generated advice or information on the smartphone display.

[0432] This invention is a system that analyzes text data entered by users about their worries and emotions, provides advice based on that data, and creates a community of people with the same worries. The system records users' emotions and worries daily and provides an interface for analyzing them. It also generates advice based on the analysis results and displays it on a smartphone screen.

[0433] The system consists of three main components:

[0434] 1. Server:

[0435] The server is centrally located and receives the data sent by users, analyzes it, and processes it.

[0436] The server is installed with a natural language processing (NLP) engine, a sentiment analysis engine, and artificial intelligence models for advice generation (e.g., OpenAI's GPT-3 and ChatGPT).

[0437] Specifically, the text data received by the server is analyzed using an NLP engine (e.g., SpaCy or Google (registered trademark) Cloud Natural Language API) to recognize emotions.

[0438] A sentiment analysis engine (e.g., Watson's Tone Analyzer) provides detailed analysis of the user's emotions.

[0439] An artificial intelligence model for generating advice generates advice based on the analysis results and provides data for displaying that advice on a smartphone screen.

[0440] 2. Terminal:

[0441] A terminal is a device that allows a user to input text data, including a smartphone, tablet, or PC.

[0442] The terminal transmits the input text data to the server and receives a response from the server.

[0443] Specifically, the user enters "I can't sleep lately because of work stress" into the text box and presses the send button. The device then sends this data to the server.

[0444] In response from the server, analysis results, advice, information on specialist institutions, and a link to join the community are displayed on the smartphone screen.

[0445] 3. User:

[0446] Users use this system to input their worries and feelings and receive support.

[0447] Users input their daily feelings and worries into the system, receive generated advice and information, and act based on it.

[0448] For example, if a user types, "Recently, I've been unable to sleep due to work stress," the server analyzes this text data, and the NLP engine extracts the keywords "work," "stress," and "can't sleep." The sentiment analysis engine recognizes the user's emotions as "anxiety" and "stress." The AI ​​model then generates advice based on these keywords and emotions, suggesting things like "taking deep breaths to relax" and "maintaining a bedtime routine." It also provides information about related specialist organizations and encourages users to join a community where users with the same concerns gather.

[0449] To further illustrate this, here are some example prompts:

[0450] User said: I can't sleep lately because of work stress.

[0451] Generate appropriate advice based on that emotion.

[0452] This will allow users to continuously record their worries and feelings and receive appropriate advice based on that information. It will also make it easier for them to receive psychological support through interactions with other users who have similar worries.

[0453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0454] Step 1:

[0455] A user inputs text data into a terminal.

[0456] Specifically, users use devices such as smartphones, tablets, and PCs to input their worries and feelings, such as "I haven't been able to sleep lately because of work stress," into the system's interface.

[0457] Input: Text data of worries and emotions

[0458] Output: The input text data

[0459] Step 2:

[0460] The terminal transmits the input text data to the server.

[0461] Specifically, the input text data (e.g., "I can't sleep lately because of work stress") is sent from the device to the server. This communication uses the Internet Protocol.

[0462] Input: Entered text data

[0463] Output: Text data sent to the server

[0464] Step 3:

[0465] The server stores the received text data and starts text analysis.

[0466] Specifically, the server uses an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to tokenize the received text data and extract keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[0467] Input: Transmitted text data

[0468] Output: Extracted keywords and phrases

[0469] Step 4:

[0470] The server analyzes the extracted keywords and phrases using a sentiment analysis engine to recognize the user's emotions.

[0471] Specifically, an emotion engine (e.g., Watson's Tone Analyzer) analyzes text data and identifies emotions such as anxiety and stress. For example, "anxiety" and "stress" are recognized.

[0472] Input: Extracted keywords or phrases

[0473] Output: Recognized emotion

[0474] Step 5:

[0475] The server generates advice using an artificial intelligence model based on the recognized emotions and keywords.

[0476] Specifically, a generative AI model (e.g., OpenAI's ChatGPT) generates advice based on the prompt, such as "take a deep breath and relax" or "maintain a bedtime routine."

[0477] Input: Recognized emotions and keywords

[0478] Output: Generated advice

[0479] Step 6:

[0480] The server formats the generated advice into a format suitable for display on the smartphone screen and sends it to the device.

[0481] Specifically, the server formats the advice content into an appropriate format, such as JSON, and sends it to the user's device.

[0482] Input: Generated advice

[0483] Output: Formatted advice data

[0484] Step 7:

[0485] The device will display on the screen the advice provided, information about specialist agencies, and an invitation to join the community.

[0486] Specifically, the device analyzes the data received from the server and visually displays it to the user. The user can then check the advice and information displayed on the screen and, if necessary, contact a specialist institution or join a community.

[0487] Input: formatted advice data, professional information, community links

[0488] Output: Advice and links displayed on the smartphone screen

[0489] 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.

[0490] 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 (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0491] 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.

[0492] [Second embodiment]

[0493] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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).

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0504] 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."

[0505] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically creates a community where people in similar situations can gather. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described below.

[0506] System configuration

[0507] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server.

[0508] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0509] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[0510] Program processing

[0511] Entering and receiving text data

[0512] User: Enter their worries or feelings in natural language through the device. For example, they might enter, "I haven't been able to sleep lately because of work stress."

[0513] Terminal: Sends entered text data to the server.

[0514] Text data analysis

[0515] Server: Uses natural language processing techniques to analyze the received text data, including segmenting the text and extracting keywords and important phrases.

[0516] Server: For example, extract keywords such as "work," "stress," and "can't sleep."

[0517] Generating Advice

[0518] Server: Generates advice using an artificial intelligence model (e.g., ChatGPT) based on the extracted keywords and phrases.

[0519] Server: The generated advice is provided to the user as, for example, "methods for managing stress" or "how to relax before bed."

[0520] Providing information on specialized institutions

[0521] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[0522] Server: Converts the acquired information into a user-friendly format and provides it along with advice. For example, it may include information on clinics offering cognitive behavioral therapy.

[0523] Categorizing users with similar circumstances and creating communities

[0524] Server: Based on the user's problem data and advice, related users are categorized using a clustering algorithm.

[0525] Server: For example, it generates categories related to "work stress" and "sleep disorders" and automatically creates a community where users with the same concerns can gather.

[0526] Feedback and community engagement

[0527] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[0528] Terminal: Displays the received information to the user.

[0529] Users: Follow advice, consult professional organizations, and join communities.

[0530] Specific examples

[0531] Example: If the user enters "Sleep problems due to work stress"

[0532] 1. User: Enters "I can't sleep lately because of work stress" into the device.

[0533] 2. Terminal: Send this text data to the server.

[0534] 3. Server: Analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[0535] 4. Server: Queries the AI ​​model and generates "ways to reduce stress" and "advice for improving sleep."

[0536] 5. Server: Generates advice such as "take deep breaths and relax" or "maintain a bedtime routine."

[0537] 6. Server: Retrieves information about specialized institutions that provide cognitive behavioral therapy from a database and formats the information.

[0538] 7. Server: Categorizes users with concerns related to "work stress" and "sleep disorders" and generates a community of users with similar concerns.

[0539] 8. Server: Provides users with advice, information on professional organizations, and community invitation links.

[0540] 9. Terminal: Displays this information to the user.

[0541] 10. Users: Act on advice, consult with specialist authorities if necessary, and participate in the community.

[0542] This system allows users to receive appropriate advice on their concerns, and by interacting with other users who have the same concerns, they can reduce feelings of isolation and gain psychological security.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0546] Step 2:

[0547] The terminal transmits the input text data to the server.

[0548] Step 3:

[0549] The server stores the received text data and starts the text analysis module to begin analysis.

[0550] Step 4:

[0551] The server uses Natural Language Processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[0552] Step 5:

[0553] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and phrases.

[0554] Step 6:

[0555] The server receives the advice returned by the AI ​​model and formats it appropriately, generating specific advice such as "how to manage stress" or "how to relax before bed."

[0556] Step 7:

[0557] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[0558] Step 8:

[0559] Based on the user's worry data and advice, the server uses a clustering algorithm to categorize users who are in the same situation or have related worries.

[0560] Step 9:

[0561] For each cluster, the server automatically generates a community of users with related concerns. For example, it generates a community for "stress management and sleep improvement."

[0562] Step 10:

[0563] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[0564] Step 11:

[0565] The terminal displays the received feedback data to the user.

[0566] Step 12:

[0567] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[0568] Through this series of processes, users with concerns can receive specific and accurate advice and easily interact with other users who share the same concerns, which relieves users from feelings of isolation and provides a sense of psychological security.

[0569] Example 1

[0570] 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."

[0571] In modern society, individual users have a wide range of worries and emotions, but there is a lack of environments where they can receive appropriate advice or opportunities to interact with other users who share the same worries. There is also no system in place to provide appropriate information about specialized institutions and support services. As a result, users are unable to receive appropriate support, which leads to problems such as feelings of isolation and increased psychological stress.

[0572] 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.

[0573] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries using a clustering algorithm and automatically generating a community of people in the same situation, and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This makes it possible to provide appropriate advice for the worries and emotions that users have, quickly and appropriately provide information on specialist institutions and support services, and provide a place for users to interact with others who have the same worries.

[0574] A "user" is an entity that uses the system to input their worries and feelings and receive advice and access to the community.

[0575] "Text data" refers to information entered by a user in natural language to express their concerns or feelings.

[0576] A "server" is a device located at the center of a system that analyzes data sent by users and provides and manages information.

[0577] A "terminal" is a device that provides an interface for users to input text data, such as a PC, smartphone, or tablet.

[0578] "Natural language processing technology" is a technology for analyzing text data and extracting keywords and important phrases, and includes technologies such as SpaCy and NLTK.

[0579] A "keyword" is a particularly important word or phrase in the received text data.

[0580] A "phrase" is a series of significant word combinations within text data.

[0581] An "artificial intelligence model" is a learning model used to generate advice based on extracted keywords and phrases, such as ChatGPT.

[0582] A "clustering algorithm" is an algorithm for classifying users with related concerns and grouping people in the same situation.

[0583] A "community" is a virtual group where users with the same concerns and feelings can gather and interact.

[0584] A "specialized institution" is a facility or organization that provides specialized support and services for specific concerns or problems.

[0585] "Support services" is a general term for professional support and advice provided to users to address their concerns and emotions.

[0586] An "invitation link" is a URL or hyperlink that users can click to access in order to join a community.

[0587] "JSON format" is a data format used to structure text data and communicate between servers and devices.

[0588] "Tokenization" is an analytical method that divides text data into semantic units.

[0589] A "prompt sentence" is a text instruction sentence that is input to an artificial intelligence model to generate advice.

[0590] MODE FOR CARRYING OUT THE INVENTION

[0591] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically generates a community of people in similar situations. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described in detail below.

[0592] System configuration

[0593] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server. Specific technologies include SpaCy and NLTK for analyzing text data and ChatGPT for generating advice.

[0594] 2. Device: A device that provides an interface for users to input their worries and feelings, such as a PC, smartphone, or tablet. These devices provide a platform for users to input text data and send it to a server.

[0595] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[0596] Program processing

[0597] Entering and receiving text data

[0598] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I can't sleep lately because of work stress." The device then sends the input text data to the server.

[0599] Text data analysis

[0600] The server uses natural language processing techniques (SpaCy or NLTK) to analyze the received text data. This process involves tokenizing the text and extracting keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" can be extracted.

[0601] Generating Advice

[0602] The server constructs a prompt for the artificial intelligence model (ChatGPT) based on the extracted keywords and phrases, and generates advice. An example of a prompt is, "What should I do when I can't sleep because of work stress?" The generated advice is provided to the user, for example, "How to take deep breaths and relax" or "Maintain a bedtime routine."

[0603] Obtaining information on specialized institutions

[0604] The server retrieves information about specialist institutions and support services that can provide advice from a database. For example, it retrieves information about clinics that offer cognitive behavioral therapy. The retrieved information is formatted as the clinic's address, contact information, opening hours, etc.

[0605] Categorizing users with similar circumstances and creating communities

[0606] The server uses a clustering algorithm (e.g., k-means clustering) to categorize related users based on the user's concerns and advice. For example, it could create categories related to "work stress" and "sleep disorders," and automatically create a community of users with the same concerns.

[0607] Feedback and community engagement

[0608] The server sends the generated advice, information about the specialist organization, and an invitation link to the community to the user's device. The device displays the received information on a user interface. The user can then take action based on the displayed information. Specifically, the user can act on the advice, contact the specialist organization, or join the community to interact with other users.

[0609] Specific examples

[0610] If a user types, "Recently, I've been unable to sleep due to work stress," this text data is sent from the device to the server. The server analyzes this data and extracts the keywords "work," "stress," and "can't sleep." The generative AI model (ChatGPT) then receives a prompt such as, "What should I do when I can't sleep due to work stress?" and generates advice. For example, advice such as "Take deep breaths to relax" or "Maintain a bedtime routine" is provided. At the same time, information about related specialist institutions is retrieved and provided to the user. Users with the same problem are also categorized, a community is created, and an invitation link is sent. This system allows users to receive appropriate advice and connect with specialist institutions and other users with the same problem.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] User: Uses the device to input worries and feelings in natural language. For example, the user might input the text, "Recently, I've been unable to sleep due to work stress."

[0614] Input: Natural language text data entered by the user.

[0615] Output: Text data sent to the terminal.

[0616] Step 2:

[0617] Terminal: The entered text data is sent to the server. This data is generally converted to JSON format and sent using the HTTPS protocol.

[0618] Input: User text data.

[0619] Output: The text data sent to the server.

[0620] Step 3:

[0621] Server: Receives the incoming text data and analyzes it using natural language processing techniques (e.g., SpaCy or NLTK), which tokenizes the text and extracts keywords and key phrases.

[0622] Input: Text data received by the server.

[0623] Output: Extracted keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[0624] How it works: The server tokenizes the text and classifies it by parts of speech, such as nouns and verbs. It uses techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) to extract important phrases.

[0625] Step 4:

[0626] Server: Based on the extracted keywords, it generates prompts for the AI ​​model (e.g., ChatGPT) and generates advice.

[0627] Input: Extracted keywords and prompt sentences. For example, "What should I do when I can't sleep because of work stress?"

[0628] Output: Generated advice. For example, "Take deep breaths and relax" and "Follow a bedtime routine" are generated.

[0629] How it works: The server inputs a prompt into the generative AI model (ChatGPT), receives the text data returned by the model, formats the text data, and converts it into a format that can be provided to the user.

[0630] Step 5:

[0631] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[0632] Input: Generated advice and corresponding keywords.

[0633] Output: Details of professional and support services, such as clinic addresses, contact details, and opening hours.

[0634] How it works: The server uses a pre-configured database query to search for relevant professional organizations and retrieves the results in a formatted form.

[0635] Step 6:

[0636] Server: Based on the user's concerns and advice, related users are categorized using a clustering algorithm (e.g., k-means clustering), and a community of people in the same situation is automatically generated.

[0637] Input: User's trouble data and generated advice.

[0638] Output: Categorized user information and generated communities. For example, categories related to "work stress" and "sleep disorders" are generated.

[0639] How it works: The server runs a clustering algorithm to group user data, automatically forming communities of people with similar interests.

[0640] Step 7:

[0641] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[0642] Input: Generated advice, professional organization information, community invite link.

[0643] Output: Feedback information sent to the device.

[0644] How it works: The server packets information and sends it to the device, usually packing the data into JSON or XML format.

[0645] Step 8:

[0646] Terminal: Displays the received information in a user interface.

[0647] Input: Feedback information sent from the server (advice, professional information, community invite link).

[0648] Output: Feedback information that is displayed to the user.

[0649] How it works: The device's user interface displays the received information in the appropriate location, making it easily accessible to the user.

[0650] Step 9:

[0651] Users: Follow the advice provided, contact specialist organizations, and participate in communities.

[0652] Input: Feedback information displayed on the terminal.

[0653] Output: User action (following advice, contacting a specialist, joining a community).

[0654] Action: The user takes the necessary action based on the information provided, such as clicking a link to join a community or contact a specialist organization. This process provides the user with targeted support and interaction opportunities.

[0655] (Application example 1)

[0656] 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."

[0657] With conventional systems, it was difficult to provide accurate advice to users regarding their concerns and emotions, and it was also difficult to form a community of people in the same situation. In particular, since there was no system to address the specific concerns of store employees and customers, these users tended to feel isolated and were unable to obtain effective solutions or psychological support.

[0658] 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.

[0659] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries and automatically generating communities where people in similar situations gather, means for providing users with the generated advice, information on specialist institutions, and an invitation link to join the community, and means for generating appropriate advice for worries and emotions entered by store employees and customers and providing related community information. This makes it possible to quickly provide appropriate advice and automatically generate related communities even for the specific worries faced by store employees and customers.

[0660] A "user" is an entity that uses the system to input worries and feelings and receive advice and community information.

[0661] "Text data" is data in natural language format that includes worries and feelings entered by users.

[0662] "Extraction" is the process of extracting keywords and important phrases from the received text data.

[0663] An "artificial intelligence model" is a program or algorithm that uses natural language processing technology to understand the meaning of input data and generate appropriate advice.

[0664] A "specialized institution" is an institution such as a medical institution or counseling service that provides support and treatment for distress and emotions.

[0665] "Support services" refers to all services that provide specific support for users' concerns and emotions.

[0666] "Categorization" means classifying users with related concerns and grouping people in the same situation.

[0667] A "community" is a group of users who share the same concerns and feelings and come together to exchange information and interact with each other.

[0668] An "invitation link" is a reference URL or means of invitation to join that directs users to a newly created community.

[0669] "Physical store employees" refers to staff and sales associates who actually work in the store.

[0670] "Customers" refer to consumers who visit physical stores and use products and services.

[0671] MODE FOR CARRYING OUT THE INVENTION

[0672] This invention is a system that delves into the user's worries and feelings, provides appropriate advice, and automatically creates a community where people in the same situation can gather. This system is mainly composed of a server, terminals, and users.

[0673] System configuration

[0674] 1. Server:

[0675] Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology and artificial intelligence (AI) modules.

[0676] 2. Terminal:

[0677] A device that provides an interface for users to input their worries and feelings. Specifically, this applies to PCs, smartphones, tablets, etc.

[0678] 3. User:

[0679] These are the people who use the system to input their concerns and receive advice and access to the community. These include store employees and customers.

[0680] Program processing

[0681] The firmware and software configuration will be described.

[0682] Hardware:

[0683] Smartphones, tablets, etc. are used as input interfaces by users.

[0684] software:

[0685] This system uses the following software and services:

[0686] OpenAI's API: Used for natural language processing and advice generation.

[0687] HTTP request library (e.g. Requests): Used to retrieve community information.

[0688] Processing Description

[0689] The server performs a series of processes using the following means.

[0690] 1. Receive text data about worries and feelings:

[0691] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I've been having trouble with stress from customer service lately." This text data is sent to the server.

[0692] 2. Text data analysis:

[0693] The server uses OpenAI's API and natural language processing technology to analyze the received text data. This process segments the text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" are extracted.

[0694] 3. Generating Advice:

[0695] The server generates advice using an artificial intelligence model (e.g., GPT-3) based on the extracted keywords and phrases. The advice generated might be, for example, "Try practicing stress relief techniques. For example, you could try breathing exercises or taking short breaks."

[0696] 4. Providing information on professional organizations:

[0697] The server retrieves information from a database about specialist institutions and support services that correspond to the generated advice, such as information about clinics offering cognitive behavioral therapy.

[0698] 5. Categorizing users with related concerns and creating a community:

[0699] The server uses a clustering algorithm to categorize related users based on their concerns and advice. For example, it could create a category related to "stress in customer service" and automatically create a community of users with the same concerns.

[0700] 6. Feedback and Community Involvement:

[0701] The generated advice, information about the expert organization, and an invitation link to the community are sent to the user's terminal, allowing the user to receive the advice, inquire about the expert organization, or join the community.

[0702] Specific examples

[0703] When a user inputs a concern such as "I've been suffering from stress lately when working with customers," the following prompt is sent to the generative AI model:

[0704] Prompt (for analysis): "Analyze your worries and feelings and extract keywords: I've been having trouble with stress from serving customers lately."

[0705] Prompt (for advice): "Generate appropriate advice based on the keywords 'customer service, stress'."

[0706] This makes it possible to quickly provide appropriate advice for the specific concerns of store employees and customers, and automatically generate related communities.

[0707] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0708] Step 1:

[0709] The user uses a terminal to input their worries and feelings in natural language. For example, they might input text data such as, "Recently, I've been suffering from stress from customer service." This input text data is then sent from the terminal to the server.

[0710] Step 2:

[0711] The server uses OpenAI's API to analyze the received text data and performs natural language processing. This process tokenizes the input text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" can be extracted.

[0712] Input: Text data from the user: "Recently, I've been suffering from stress from customer service."

[0713] Data processing: Text tokenization, keyword extraction

[0714] Output: Extracted keywords "customer service," "stress," and "troubled"

[0715] Step 3:

[0716] Based on the extracted keywords, the server uses a generative AI model (e.g., GPT-3) to generate appropriate advice, such as "Try practicing stress relief methods. For example, you could try breathing exercises or short breaks."

[0717] Input: Extracted keywords "customer service," "stress," "troubled"

[0718] Data Computation: Advice Generation with Generative AI Models

[0719] Output: Advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[0720] Step 4:

[0721] The server retrieves information on specialist institutions and support services that correspond to the generated advice from a database, for example, "information on clinics that provide cognitive behavioral therapy."

[0722] Input: Generated advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[0723] Data calculation: Retrieving professional agency information from databases

[0724] Output: Specialist information "Information on clinics offering cognitive behavioral therapy"

[0725] Step 5:

[0726] Based on the user's worry data and the generated advice, the server uses a clustering algorithm to categorize related users and automatically generate communities of users with the same worries. For example, it generates a community related to "stress in customer service."

[0727] Input: User's trouble data, generated advice

[0728] Data processing: Categorization using clustering algorithms

[0729] Output: Generated community "Community related to customer service stress"

[0730] Step 6:

[0731] The server sends the generated advice, information about the expert organization, and an invitation link to join the community to the user's device. The user receives this information and can then act on the advice, contact the expert organization, or join the community.

[0732] Input: Generated advice, professional information, community invite link

[0733] Data Computing: Formatting and Transmitting Information

[0734] Output: Display information on the user's device (advice, information on specialist institutions, community invitation links)

[0735] 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.

[0736] This invention is a system that delves deeply into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in similar situations. This system incorporates an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user. The operation of this system and the program processing are described below.

[0737] System configuration

[0738] 1. Server: Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine.

[0739] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0740] 3. User: The entity that uses the system to input their concerns and feelings and receive advice and access to the community.

[0741] Program processing

[0742] Entering and receiving text data

[0743] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0744] The terminal transmits the input text data to the server.

[0745] Text data analysis

[0746] The server stores the received text data and starts the text analysis module to begin analysis.

[0747] The server uses NLP technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[0748] Emotion recognition

[0749] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[0750] Generating Advice

[0751] The server generates advice based on the extracted keywords and sentiments using an artificial intelligence model (e.g., ChatGPT).

[0752] The server tailors the generated advice based on emotion, for example providing advice emphasizing relaxation techniques when stress levels are high.

[0753] Providing information on specialized institutions

[0754] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[0755] Categorizing users with similar circumstances and creating communities

[0756] The server categorizes related users using a clustering algorithm based on the user's concern data and emotion data.

[0757] For each cluster, the server automatically generates a community of users with related concerns or emotions. For example, it generates a community for "stress management and sleep improvement."

[0758] Feedback and community engagement

[0759] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[0760] The terminal displays the received feedback data to the user.

[0761] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[0762] Specific examples

[0763] Example: If the user enters "Sleep problems due to work stress"

[0764] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[0765] 2. The device sends this text data to the server.

[0766] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[0767] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[0768] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[0769] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[0770] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[0771] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[0772] 9. The server provides the user with generated advice, expert information, and community invitation links.

[0773] 10. The terminal displays this information to the user.

[0774] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[0775] This system allows users to receive appropriate advice regarding their worries and feelings, and they can receive psychological support by interacting with other users who have the same worries and feelings.

[0776] The processing flow will be explained below.

[0777] Step 1:

[0778] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0779] Step 2:

[0780] The terminal transmits the input text data to the server.

[0781] Step 3:

[0782] The server stores the received text data and starts the text analysis module to begin analysis.

[0783] Step 4:

[0784] The server uses natural language processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[0785] Step 5:

[0786] The server analyzes the extracted text data using an emotion engine to recognize the user's emotions, such as "stress" and "anxiety."

[0787] Step 6:

[0788] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and recognized emotions.

[0789] Step 7:

[0790] The server receives the advice returned by the AI ​​model and adjusts the advice to match the user's emotions, for example, emphasizing "ways to reduce stress" and "ways to relax."

[0791] Step 8:

[0792] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[0793] Step 9:

[0794] The server uses a clustering algorithm to categorize related users based on their worries and emotions, for example, creating a category for "stress management and sleep improvement."

[0795] Step 10:

[0796] Based on categorized user data, the server automatically generates a community where users with the same concerns and feelings can gather.

[0797] Step 11:

[0798] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[0799] Step 12:

[0800] The terminal displays the received feedback data to the user.

[0801] Step 13:

[0802] The user reviews the information received and takes action on the advice if necessary, contacts a specialist organization, or joins a community.

[0803] Through this series of processes, users with worries or feelings can receive specific and accurate advice and can easily interact with other users who share the same worries or feelings, thereby freeing users from feelings of isolation and providing a sense of psychological security.

[0804] Example 2

[0805] 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."

[0806] There is a need for a system that allows users to receive appropriate advice and support for the worries and emotions they experience in their daily lives. However, conventional systems have difficulty providing accurate advice tailored to the user's emotions and individual circumstances, and have not been able to quickly create a community where users can interact with people in the same situation. Therefore, there is an urgent need to develop a system that allows users to immediately feel reassured and receive advanced support.

[0807] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving text data of worries and emotions entered by a user; means for analyzing the received text data and extracting keywords and important phrases; means for recognizing the user's emotions based on the extracted emotion data; means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases; means for adjusting the generated advice based on the emotion data and keywords; means for acquiring information on specialist institutions and support services corresponding to the generated advice; means for categorizing users with related worries using a clustering method and automatically generating communities of people in the same situation; and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This allows users to receive quick and accurate advice on their worries and emotions and to obtain psychological support by interacting with people in the same situation.

[0808] A "user" is an entity that uses the system to input worries and feelings and receive advice and access to the community.

[0809] "Text data" refers to character strings of natural language data that express the user's worries and feelings and that the user enters into the device.

[0810] A "server" is a central system that processes data entered by users and manages and provides various information.

[0811] A "terminal" is a device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0812] "Natural language processing technology" is a technology that uses computers to analyze natural language and understand and generate human language.

[0813] "Keywords" refer to important words or phrases within text data, and are terms extracted for data analysis and advice generation.

[0814] The "emotion engine" is a system for recognizing and analyzing user emotions from text data.

[0815] An "artificial intelligence model" is an algorithm or machine learning model that generates advice based on extracted keywords and emotions.

[0816] A "clustering method" is an algorithm for classifying data into multiple clusters (groups), and is used to categorize data based on users' common concerns and emotions.

[0817] A "community" is an online group where users with the same circumstances or concerns gather, and is a place to exchange information and interact.

[0818] A "specialized agency" is an institution or facility that provides support or services for specific problems or concerns.

[0819] "Feedback Data" means data generated and formatted by the server, including advice, expert information, and community invitation links.

[0820] "Cluster" is a term that refers to a group of users who share similar characteristics or attributes.

[0821] This invention provides a system that delves into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in the same situation. This system is combined with an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user.

[0822] The system configuration is as follows:

[0823] Hardware and Software

[0824] 1. Server: Located centrally, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine. Specifically, it uses Spacy or NLTK as the NLP engine, Affectiva or IBM Watson as the emotion engine, and ChatGPT as the generative AI model.

[0825] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0826] 3. User: The entity that uses the system to input their worries and feelings and receive advice and access to the community.

[0827] System Operation

[0828] The operation of the system is as follows.

[0829] Entering and receiving text data

[0830] The user inputs their worries and feelings into the device in natural language. For example, they might input, "I haven't been able to sleep lately because of work stress." The device then sends this input text data to the server.

[0831] Text data analysis

[0832] The server stores the received text data and starts the text analysis module to begin analysis. The server uses NLP technology to tokenize the text data and extract keywords and important phrases. For example, it extracts keywords such as "work," "stress," and "can't sleep."

[0833] Emotion recognition

[0834] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[0835] Generating Advice

[0836] The server generates advice using a generative AI model (such as ChatGPT) based on the extracted keywords and emotions. The server then adjusts the content of the advice based on the emotions. For example, if stress levels are high, the server will provide advice including relaxation techniques such as "take deep breaths to relax" and "maintain a bedtime routine."

[0837] Providing information on specialized institutions

[0838] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[0839] Categorizing users with similar circumstances and creating communities

[0840] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's worry and emotion data. Furthermore, for each cluster, the server automatically generates a community of users with related worries and emotions. For example, it generates a community for "stress management and sleep improvement."

[0841] Feedback and community engagement

[0842] The server prepares feedback data including the generated advice, information about the specialist organization, and an invitation link to join the community, and sends it to the user's terminal. The terminal displays the received feedback data to the user. The user checks the received information and, if necessary, follows the advice, contacts the specialist organization, or clicks the provided link to join the community.

[0843] Specific examples

[0844] Example: If the user enters "Sleep problems due to work stress"

[0845] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[0846] 2. The device sends this text data to the server.

[0847] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[0848] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[0849] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[0850] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[0851] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[0852] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[0853] 9. The server provides the user with generated advice, expert information, and community invitation links.

[0854] 10. The terminal displays this information to the user.

[0855] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[0856] An example of a prompt sentence is "Generate specific advice to reduce stress based on keywords entered by the user."

[0857] This system allows users to receive accurate and prompt advice on their worries and feelings, and they can also receive psychological support by interacting with other users who have the same worries and feelings.

[0858] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0859] Step 1:

[0860] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep due to work stress." This becomes the input data. The device receives this input text data and sends it to the server as an HTTP request. This becomes the output data.

[0861] Step 2:

[0862] The server receives text data and stores it in a database. This data becomes input data. Based on the stored data, a text analysis module (e.g., Spacy or NLTK) is invoked to tokenize the received text data. The tokenized data becomes output data.

[0863] Step 3:

[0864] The server analyzes the tokenized data and extracts keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted from text data. This is the input data, and the output data is a list of keywords as a result of the analysis.

[0865] Step 4:

[0866] The server uses an emotion engine (such as Affectiva or IBM Watson) to analyze the text data based on keywords and recognize the user's emotions. For example, emotions such as stress or anxiety are identified. This is the input data, and the emotion recognition results are the output data.

[0867] Step 5:

[0868] The server generates advice using a generative AI model (e.g., ChatGPT) based on the extracted keywords and the recognized emotions. Here, the server inputs prompt sentences, such as "How to reduce stress" or "Advice on improving sleep," into the AI ​​model. The generated advice is the output data.

[0869] Step 6:

[0870] The server adjusts the advice content based on the emotional data and the generated advice. For example, if stress is high, it will emphasize relaxation techniques such as "taking deep breaths to relax" and "maintaining a bedtime routine." The adjusted advice is the output data.

[0871] Step 7:

[0872] The server searches a database for information on specialist institutions and support services that correspond to the generated advice. For example, it searches for "information on clinics that provide cognitive behavioral therapy." The information on specialist institutions is the output data.

[0873] Step 8:

[0874] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's concern and emotion data. This is the input data. For each cluster, it automatically generates a community of users with related concerns and emotions. This is the output data.

[0875] Step 9:

[0876] The server prepares feedback data including the generated advice, information on the professional organization, and an invitation link to join the community, and sends it to the user's terminal, which is output data.

[0877] Step 10:

[0878] The device displays the received feedback data to the user. The user confirms the received information, for example by following the displayed advice, contacting a specialist, or clicking a provided link to join a community. This is input data.

[0879] Through this series of steps, users can receive accurate and prompt advice on their worries and feelings, and can also receive psychological support by interacting with other users who have the same worries and feelings.

[0880] (Application example 2)

[0881] 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."

[0882] In modern society, it is important to provide appropriate support to the many people suffering from stress and worries, and to form communities where people share similar problems. However, previous systems have struggled to accurately analyze users' worries and emotions and generate appropriate advice, and have been inadequate in automatically generating communities or directing users to specialized institutions. Furthermore, they lacked the functionality to record and analyze users' emotions and worries on a daily basis. The present invention aims to solve these problems and provide a more effective and comprehensive support system.

[0883] 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.

[0884] In this invention, the server includes a means for providing an interface for recording the user's emotions and worries on a daily basis, a means for analyzing text data entered by the user with an emotion analysis engine to recognize emotions, and a means for displaying generated advice on the smartphone screen. This allows users to continuously record their worries and emotions and receive appropriate advice based on that information. It also makes it easier for users to receive psychological support through interactions with other users with similar worries.

[0885] A "user" is an individual who uses the system to input their worries and feelings and receive advice and support.

[0886] "Text data" refers to data that includes character information entered by a user.

[0887] "Means for receiving" refers to a device or software that has the function of receiving data sent from a user and providing it for subsequent processing.

[0888] The "analyzing means" is a device or software that analyzes the received text data and extracts information such as meaning and emotion.

[0889] "Keywords and important phrases" are words or phrases in the text data that are recognized as having particularly important meanings.

[0890] An "artificial intelligence model" is an algorithm or program that allows a computer to automatically learn from data and generate judgments and advice.

[0891] "Advice" refers to advice or suggestions provided to users based on the results of the analysis.

[0892] "Specialized institutions and support services" are organizations and facilities that provide professional support to users regarding their worries and problems.

[0893] A "categorization tool" is software or algorithms used to group users with similar concerns or problems.

[0894] A "community" is a group of people in the same situation who come together to exchange information and provide support.

[0895] An "interface" is a screen or device that allows a user to input data into a system or receive output information.

[0896] An "emotion analysis engine" is software that analyzes and recognizes user emotions from text data.

[0897] "Means for displaying on the smartphone screen" refers to a function for visually presenting the generated advice or information on the smartphone display.

[0898] This invention is a system that analyzes text data entered by users about their worries and emotions, provides advice based on that data, and creates a community of people with the same worries. The system records users' emotions and worries daily and provides an interface for analyzing them. It also generates advice based on the analysis results and displays it on a smartphone screen.

[0899] The system consists of three main components:

[0900] 1. Server:

[0901] The server is centrally located and receives the data sent by users, analyzes it, and processes it.

[0902] The server is installed with a natural language processing (NLP) engine, a sentiment analysis engine, and artificial intelligence models for advice generation (e.g., OpenAI's GPT-3 and ChatGPT).

[0903] Specifically, the text data received by the server is analyzed using an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to recognize emotions.

[0904] A sentiment analysis engine (e.g., Watson's Tone Analyzer) provides detailed analysis of the user's emotions.

[0905] An artificial intelligence model for generating advice generates advice based on the analysis results and provides data for displaying that advice on a smartphone screen.

[0906] 2. Terminal:

[0907] A terminal is a device that allows a user to input text data, including a smartphone, tablet, or PC.

[0908] The terminal transmits the input text data to the server and receives a response from the server.

[0909] Specifically, the user enters "I can't sleep lately because of work stress" into the text box and presses the send button. The device then sends this data to the server.

[0910] In response from the server, analysis results, advice, information on specialist institutions, and a link to join the community are displayed on the smartphone screen.

[0911] 3. User:

[0912] Users use this system to input their worries and feelings and receive support.

[0913] Users input their daily feelings and worries into the system, receive generated advice and information, and act based on it.

[0914] For example, if a user types, "Recently, I've been unable to sleep due to work stress," the server analyzes this text data, and the NLP engine extracts the keywords "work," "stress," and "can't sleep." The sentiment analysis engine recognizes the user's emotions as "anxiety" and "stress." The AI ​​model then generates advice based on these keywords and emotions, suggesting things like "taking deep breaths to relax" and "maintaining a bedtime routine." It also provides information about related specialist organizations and encourages users to join a community where users with the same concerns gather.

[0915] To further illustrate this, here are some example prompts:

[0916] User said: I can't sleep lately because of work stress.

[0917] Generate appropriate advice based on that emotion.

[0918] This will allow users to continuously record their worries and feelings and receive appropriate advice based on that information. It will also make it easier for them to receive psychological support through interactions with other users who have similar worries.

[0919] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0920] Step 1:

[0921] A user inputs text data into a terminal.

[0922] Specifically, users use devices such as smartphones, tablets, and PCs to input their worries and feelings, such as "I haven't been able to sleep lately because of work stress," into the system's interface.

[0923] Input: Text data of worries and emotions

[0924] Output: The input text data

[0925] Step 2:

[0926] The terminal transmits the input text data to the server.

[0927] Specifically, the input text data (e.g., "I can't sleep lately because of work stress") is sent from the device to the server. This communication uses the Internet Protocol.

[0928] Input: Entered text data

[0929] Output: Text data sent to the server

[0930] Step 3:

[0931] The server stores the received text data and starts text analysis.

[0932] Specifically, the server uses an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to tokenize the received text data and extract keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[0933] Input: Transmitted text data

[0934] Output: Extracted keywords and phrases

[0935] Step 4:

[0936] The server analyzes the extracted keywords and phrases using a sentiment analysis engine to recognize the user's emotions.

[0937] Specifically, an emotion engine (e.g., Watson's Tone Analyzer) analyzes text data and identifies emotions such as anxiety and stress. For example, "anxiety" and "stress" are recognized.

[0938] Input: Extracted keywords or phrases

[0939] Output: Recognized emotion

[0940] Step 5:

[0941] The server generates advice using an artificial intelligence model based on the recognized emotions and keywords.

[0942] Specifically, a generative AI model (e.g., OpenAI's ChatGPT) generates advice based on the prompt, such as "take a deep breath and relax" or "maintain a bedtime routine."

[0943] Input: Recognized emotions and keywords

[0944] Output: Generated advice

[0945] Step 6:

[0946] The server formats the generated advice into a format suitable for display on the smartphone screen and sends it to the device.

[0947] Specifically, the server formats the advice content into an appropriate format, such as JSON, and sends it to the user's device.

[0948] Input: Generated advice

[0949] Output: Formatted advice data

[0950] Step 7:

[0951] The device will display on the screen the advice provided, information about specialist agencies, and an invitation to join the community.

[0952] Specifically, the device analyzes the data received from the server and visually displays it to the user. The user can then check the advice and information displayed on the screen and, if necessary, contact a specialist institution or join a community.

[0953] Input: formatted advice data, professional information, community links

[0954] Output: Advice and links displayed on the smartphone screen

[0955] 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.

[0956] 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.

[0957] 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.

[0958] [Third embodiment]

[0959] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0960] 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.

[0961] 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).

[0962] 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.

[0963] 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.

[0964] 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).

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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."

[0971] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically creates a community where people in similar situations can gather. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described below.

[0972] System configuration

[0973] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server.

[0974] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[0975] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[0976] Program processing

[0977] Entering and receiving text data

[0978] User: Enter their worries or feelings in natural language through the device. For example, they might enter, "I haven't been able to sleep lately because of work stress."

[0979] Terminal: Sends entered text data to the server.

[0980] Text data analysis

[0981] Server: Uses natural language processing techniques to analyze the received text data, including segmenting the text and extracting keywords and important phrases.

[0982] Server: For example, extract keywords such as "work," "stress," and "can't sleep."

[0983] Generating Advice

[0984] Server: Generates advice using an artificial intelligence model (e.g., ChatGPT) based on the extracted keywords and phrases.

[0985] Server: The generated advice is provided to the user as, for example, "methods for managing stress" or "how to relax before bed."

[0986] Providing information on specialized institutions

[0987] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[0988] Server: Converts the acquired information into a user-friendly format and provides it along with advice. For example, it may include information on clinics offering cognitive behavioral therapy.

[0989] Categorizing users with similar circumstances and creating communities

[0990] Server: Based on the user's problem data and advice, related users are categorized using a clustering algorithm.

[0991] Server: For example, it generates categories related to "work stress" and "sleep disorders" and automatically creates a community where users with the same concerns can gather.

[0992] Feedback and community engagement

[0993] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[0994] Terminal: Displays the received information to the user.

[0995] Users: Follow advice, consult professional organizations, and join communities.

[0996] Specific examples

[0997] Example: If the user enters "Sleep problems due to work stress"

[0998] 1. User: Enters "I can't sleep lately because of work stress" into the device.

[0999] 2. Terminal: Send this text data to the server.

[1000] 3. Server: Analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[1001] 4. Server: Queries the AI ​​model and generates "ways to reduce stress" and "advice for improving sleep."

[1002] 5. Server: Generates advice such as "take deep breaths and relax" or "maintain a bedtime routine."

[1003] 6. Server: Retrieves information about specialized institutions that provide cognitive behavioral therapy from a database and formats the information.

[1004] 7. Server: Categorizes users with concerns related to "work stress" and "sleep disorders" and generates a community of users with similar concerns.

[1005] 8. Server: Provides users with advice, information on professional organizations, and community invitation links.

[1006] 9. Terminal: Displays this information to the user.

[1007] 10. Users: Act on advice, consult with specialist authorities if necessary, and participate in the community.

[1008] This system allows users to receive appropriate advice on their concerns, and by interacting with other users who have the same concerns, they can reduce feelings of isolation and gain psychological security.

[1009] The processing flow will be explained below.

[1010] Step 1:

[1011] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1012] Step 2:

[1013] The terminal transmits the input text data to the server.

[1014] Step 3:

[1015] The server stores the received text data and starts the text analysis module to begin analysis.

[1016] Step 4:

[1017] The server uses Natural Language Processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[1018] Step 5:

[1019] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and phrases.

[1020] Step 6:

[1021] The server receives the advice returned by the AI ​​model and formats it appropriately, generating specific advice such as "how to manage stress" or "how to relax before bed."

[1022] Step 7:

[1023] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[1024] Step 8:

[1025] Based on the user's worry data and advice, the server uses a clustering algorithm to categorize users who are in the same situation or have related worries.

[1026] Step 9:

[1027] For each cluster, the server automatically generates a community of users with related concerns. For example, it generates a community for "stress management and sleep improvement."

[1028] Step 10:

[1029] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[1030] Step 11:

[1031] The terminal displays the received feedback data to the user.

[1032] Step 12:

[1033] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[1034] Through this series of processes, users with concerns can receive specific and accurate advice and easily interact with other users who share the same concerns, which relieves users from feelings of isolation and provides a sense of psychological security.

[1035] Example 1

[1036] 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."

[1037] In modern society, individual users have a wide range of worries and emotions, but there is a lack of environments where they can receive appropriate advice or opportunities to interact with other users who share the same worries. There is also no system in place to provide appropriate information about specialized institutions and support services. As a result, users are unable to receive appropriate support, which leads to problems such as feelings of isolation and increased psychological stress.

[1038] 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.

[1039] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries using a clustering algorithm and automatically generating a community of people in the same situation, and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This makes it possible to provide appropriate advice for the worries and emotions that users have, quickly and appropriately provide information on specialist institutions and support services, and provide a place for users to interact with others who have the same worries.

[1040] A "user" is an entity that uses the system to input their worries and feelings and receive advice and access to the community.

[1041] "Text data" refers to information entered by a user in natural language to express their concerns or feelings.

[1042] A "server" is a device located at the center of a system that analyzes data sent by users and provides and manages information.

[1043] A "terminal" is a device that provides an interface for users to input text data, such as a PC, smartphone, or tablet.

[1044] "Natural language processing technology" is a technology for analyzing text data and extracting keywords and important phrases, and includes technologies such as SpaCy and NLTK.

[1045] A "keyword" is a particularly important word or phrase in the received text data.

[1046] A "phrase" is a series of significant word combinations within text data.

[1047] An "artificial intelligence model" is a learning model used to generate advice based on extracted keywords and phrases, such as ChatGPT.

[1048] A "clustering algorithm" is an algorithm for classifying users with related concerns and grouping people in the same situation.

[1049] A "community" is a virtual group where users with the same concerns and feelings can gather and interact.

[1050] A "specialized institution" is a facility or organization that provides specialized support and services for specific concerns or problems.

[1051] "Support services" is a general term for professional support and advice provided to users to address their concerns and emotions.

[1052] An "invitation link" is a URL or hyperlink that users can click to access in order to join a community.

[1053] "JSON format" is a data format used to structure text data and communicate between servers and devices.

[1054] "Tokenization" is an analytical method that divides text data into semantic units.

[1055] A "prompt sentence" is a text instruction sentence that is input to an artificial intelligence model to generate advice.

[1056] MODE FOR CARRYING OUT THE INVENTION

[1057] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically generates a community of people in similar situations. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described in detail below.

[1058] System configuration

[1059] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server. Specific technologies include SpaCy and NLTK for analyzing text data and ChatGPT for generating advice.

[1060] 2. Device: A device that provides an interface for users to input their worries and feelings, such as a PC, smartphone, or tablet. These devices provide a platform for users to input text data and send it to a server.

[1061] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[1062] Program processing

[1063] Entering and receiving text data

[1064] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I can't sleep lately because of work stress." The device then sends the input text data to the server.

[1065] Text data analysis

[1066] The server uses natural language processing techniques (SpaCy or NLTK) to analyze the received text data. This process involves tokenizing the text and extracting keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" can be extracted.

[1067] Generating Advice

[1068] The server constructs a prompt for the artificial intelligence model (ChatGPT) based on the extracted keywords and phrases, and generates advice. An example of a prompt is, "What should I do when I can't sleep because of work stress?" The generated advice is provided to the user, for example, "How to take deep breaths and relax" or "Maintain a bedtime routine."

[1069] Obtaining information on specialized institutions

[1070] The server retrieves information about specialist institutions and support services that can provide advice from a database. For example, it retrieves information about clinics that offer cognitive behavioral therapy. The retrieved information is formatted as the clinic's address, contact information, opening hours, etc.

[1071] Categorizing users with similar circumstances and creating communities

[1072] The server uses a clustering algorithm (e.g., k-means clustering) to categorize related users based on the user's concerns and advice. For example, it could create categories related to "work stress" and "sleep disorders," and automatically create a community of users with the same concerns.

[1073] Feedback and community engagement

[1074] The server sends the generated advice, information about the specialist organization, and an invitation link to the community to the user's device. The device displays the received information on a user interface. The user can then take action based on the displayed information. Specifically, the user can act on the advice, contact the specialist organization, or join the community to interact with other users.

[1075] Specific examples

[1076] If a user types, "Recently, I've been unable to sleep due to work stress," this text data is sent from the device to the server. The server analyzes this data and extracts the keywords "work," "stress," and "can't sleep." The generative AI model (ChatGPT) then receives a prompt such as, "What should I do when I can't sleep due to work stress?" and generates advice. For example, advice such as "Take deep breaths to relax" or "Maintain a bedtime routine" is provided. At the same time, information about related specialist institutions is retrieved and provided to the user. Users with the same problem are also categorized, a community is created, and an invitation link is sent. This system allows users to receive appropriate advice and connect with specialist institutions and other users with the same problem.

[1077] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1078] Step 1:

[1079] User: Uses the device to input worries and feelings in natural language. For example, the user might input the text, "Recently, I've been unable to sleep due to work stress."

[1080] Input: Natural language text data entered by the user.

[1081] Output: Text data sent to the terminal.

[1082] Step 2:

[1083] Terminal: The entered text data is sent to the server. This data is generally converted to JSON format and sent using the HTTPS protocol.

[1084] Input: User text data.

[1085] Output: The text data sent to the server.

[1086] Step 3:

[1087] Server: Receives the incoming text data and analyzes it using natural language processing techniques (e.g., SpaCy or NLTK), which tokenizes the text and extracts keywords and key phrases.

[1088] Input: Text data received by the server.

[1089] Output: Extracted keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[1090] How it works: The server tokenizes the text and classifies it by parts of speech, such as nouns and verbs. It uses techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) to extract important phrases.

[1091] Step 4:

[1092] Server: Based on the extracted keywords, it generates prompts for the AI ​​model (e.g., ChatGPT) and generates advice.

[1093] Input: Extracted keywords and prompt sentences. For example, "What should I do when I can't sleep because of work stress?"

[1094] Output: Generated advice. For example, "Take deep breaths and relax" and "Follow a bedtime routine" are generated.

[1095] How it works: The server inputs a prompt into the generative AI model (ChatGPT), receives the text data returned by the model, formats the text data, and converts it into a format that can be provided to the user.

[1096] Step 5:

[1097] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[1098] Input: Generated advice and corresponding keywords.

[1099] Output: Details of professional and support services, such as clinic addresses, contact details, and opening hours.

[1100] How it works: The server uses a pre-configured database query to search for relevant professional organizations and retrieves the results in a formatted form.

[1101] Step 6:

[1102] Server: Based on the user's concerns and advice, related users are categorized using a clustering algorithm (e.g., k-means clustering), and a community of people in the same situation is automatically generated.

[1103] Input: User's trouble data and generated advice.

[1104] Output: Categorized user information and generated communities. For example, categories related to "work stress" and "sleep disorders" are generated.

[1105] How it works: The server runs a clustering algorithm to group user data, automatically forming communities of people with similar interests.

[1106] Step 7:

[1107] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[1108] Input: Generated advice, professional organization information, community invite link.

[1109] Output: Feedback information sent to the device.

[1110] How it works: The server packets information and sends it to the device, usually packing the data into JSON or XML format.

[1111] Step 8:

[1112] Terminal: Displays the received information in a user interface.

[1113] Input: Feedback information sent from the server (advice, professional information, community invite link).

[1114] Output: Feedback information that is displayed to the user.

[1115] How it works: The device's user interface displays the received information in the appropriate location, making it easily accessible to the user.

[1116] Step 9:

[1117] Users: Follow the advice provided, contact specialist organizations, and participate in communities.

[1118] Input: Feedback information displayed on the terminal.

[1119] Output: User action (following advice, contacting a specialist, joining a community).

[1120] Action: The user takes the necessary action based on the information provided, such as clicking a link to join a community or contact a specialist organization. This process provides the user with targeted support and interaction opportunities.

[1121] (Application example 1)

[1122] 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."

[1123] With conventional systems, it was difficult to provide accurate advice to users regarding their concerns and emotions, and it was also difficult to form a community of people in the same situation. In particular, since there was no system to address the specific concerns of store employees and customers, these users tended to feel isolated and were unable to obtain effective solutions or psychological support.

[1124] 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.

[1125] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries and automatically generating communities where people in similar situations gather, means for providing users with the generated advice, information on specialist institutions, and an invitation link to join the community, and means for generating appropriate advice for worries and emotions entered by store employees and customers and providing related community information. This makes it possible to quickly provide appropriate advice and automatically generate related communities even for the specific worries faced by store employees and customers.

[1126] A "user" is an entity that uses the system to input worries and feelings and receive advice and community information.

[1127] "Text data" is data in natural language format that includes worries and feelings entered by users.

[1128] "Extraction" is the process of extracting keywords and important phrases from the received text data.

[1129] An "artificial intelligence model" is a program or algorithm that uses natural language processing technology to understand the meaning of input data and generate appropriate advice.

[1130] A "specialized institution" is an institution such as a medical institution or counseling service that provides support and treatment for distress and emotions.

[1131] "Support services" refers to all services that provide specific support for users' concerns and emotions.

[1132] "Categorization" means classifying users with related concerns and grouping people in the same situation.

[1133] A "community" is a group of users who share the same concerns and feelings and come together to exchange information and interact with each other.

[1134] An "invitation link" is a reference URL or means of invitation to join that directs users to a newly created community.

[1135] "Physical store employees" refers to staff and sales associates who actually work in the store.

[1136] "Customers" refer to consumers who visit physical stores and use products and services.

[1137] MODE FOR CARRYING OUT THE INVENTION

[1138] This invention is a system that delves into the user's worries and feelings, provides appropriate advice, and automatically creates a community where people in the same situation can gather. This system is mainly composed of a server, terminals, and users.

[1139] System configuration

[1140] 1. Server:

[1141] Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology and artificial intelligence (AI) modules.

[1142] 2. Terminal:

[1143] A device that provides an interface for users to input their worries and feelings. Specifically, this applies to PCs, smartphones, tablets, etc.

[1144] 3. User:

[1145] These are the people who use the system to input their concerns and receive advice and access to the community. These include store employees and customers.

[1146] Program processing

[1147] The firmware and software configuration will be described.

[1148] Hardware:

[1149] Smartphones, tablets, etc. are used as input interfaces by users.

[1150] software:

[1151] This system uses the following software and services:

[1152] OpenAI's API: Used for natural language processing and advice generation.

[1153] HTTP request library (e.g. Requests): Used to retrieve community information.

[1154] Processing Description

[1155] The server performs a series of processes using the following means.

[1156] 1. Receive text data about worries and feelings:

[1157] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I've been having trouble with stress from customer service lately." This text data is sent to the server.

[1158] 2. Text data analysis:

[1159] The server uses OpenAI's API and natural language processing technology to analyze the received text data. This process segments the text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" are extracted.

[1160] 3. Generating Advice:

[1161] The server generates advice using an artificial intelligence model (e.g., GPT-3) based on the extracted keywords and phrases. The advice generated might be, for example, "Try practicing stress relief techniques. For example, you could try breathing exercises or taking short breaks."

[1162] 4. Providing information on professional organizations:

[1163] The server retrieves information from a database about specialist institutions and support services that correspond to the generated advice, such as information about clinics offering cognitive behavioral therapy.

[1164] 5. Categorizing users with related concerns and creating a community:

[1165] The server uses a clustering algorithm to categorize related users based on their concerns and advice. For example, it could create a category related to "stress in customer service" and automatically create a community of users with the same concerns.

[1166] 6. Feedback and Community Involvement:

[1167] The generated advice, information about the expert organization, and an invitation link to the community are sent to the user's terminal, allowing the user to receive the advice, inquire about the expert organization, or join the community.

[1168] Specific examples

[1169] When a user inputs a concern such as "I've been suffering from stress lately when working with customers," the following prompt is sent to the generative AI model:

[1170] Prompt (for analysis): "Analyze your worries and feelings and extract keywords: I've been having trouble with stress from serving customers lately."

[1171] Prompt (for advice): "Generate appropriate advice based on the keywords 'customer service, stress'."

[1172] This makes it possible to quickly provide appropriate advice for the specific concerns of store employees and customers, and automatically generate related communities.

[1173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1174] Step 1:

[1175] The user uses a terminal to input their worries and feelings in natural language. For example, they might input text data such as, "Recently, I've been suffering from stress from customer service." This input text data is then sent from the terminal to the server.

[1176] Step 2:

[1177] The server uses OpenAI's API to analyze the received text data and performs natural language processing. This process tokenizes the input text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" can be extracted.

[1178] Input: Text data from the user: "Recently, I've been suffering from stress from customer service."

[1179] Data processing: Text tokenization, keyword extraction

[1180] Output: Extracted keywords "customer service," "stress," and "troubled"

[1181] Step 3:

[1182] Based on the extracted keywords, the server uses a generative AI model (e.g., GPT-3) to generate appropriate advice, such as "Try practicing stress relief methods. For example, you could try breathing exercises or short breaks."

[1183] Input: Extracted keywords "customer service," "stress," "troubled"

[1184] Data Computation: Advice Generation with Generative AI Models

[1185] Output: Advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[1186] Step 4:

[1187] The server retrieves information on specialist institutions and support services that correspond to the generated advice from a database, for example, "information on clinics that provide cognitive behavioral therapy."

[1188] Input: Generated advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[1189] Data calculation: Retrieving professional agency information from databases

[1190] Output: Specialist information "Information on clinics offering cognitive behavioral therapy"

[1191] Step 5:

[1192] Based on the user's worry data and the generated advice, the server uses a clustering algorithm to categorize related users and automatically generate communities of users with the same worries. For example, it generates a community related to "stress in customer service."

[1193] Input: User's trouble data, generated advice

[1194] Data processing: Categorization using clustering algorithms

[1195] Output: Generated community "Community related to customer service stress"

[1196] Step 6:

[1197] The server sends the generated advice, information about the expert organization, and an invitation link to join the community to the user's device. The user receives this information and can then act on the advice, contact the expert organization, or join the community.

[1198] Input: Generated advice, professional information, community invite link

[1199] Data Computing: Formatting and Transmitting Information

[1200] Output: Display information on the user's device (advice, information on specialist institutions, community invitation links)

[1201] 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.

[1202] This invention is a system that delves deeply into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in similar situations. This system incorporates an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user. The operation of this system and the program processing are described below.

[1203] System configuration

[1204] 1. Server: Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine.

[1205] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1206] 3. User: The entity that uses the system to input their concerns and feelings and receive advice and access to the community.

[1207] Program processing

[1208] Entering and receiving text data

[1209] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1210] The terminal transmits the input text data to the server.

[1211] Text data analysis

[1212] The server stores the received text data and starts the text analysis module to begin analysis.

[1213] The server uses NLP technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[1214] Emotion recognition

[1215] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[1216] Generating Advice

[1217] The server generates advice based on the extracted keywords and sentiments using an artificial intelligence model (e.g., ChatGPT).

[1218] The server tailors the generated advice based on emotion, for example providing advice emphasizing relaxation techniques when stress levels are high.

[1219] Providing information on specialized institutions

[1220] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[1221] Categorizing users with similar circumstances and creating communities

[1222] The server categorizes related users using a clustering algorithm based on the user's concern data and emotion data.

[1223] For each cluster, the server automatically generates a community of users with related concerns or emotions. For example, it generates a community for "stress management and sleep improvement."

[1224] Feedback and community engagement

[1225] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[1226] The terminal displays the received feedback data to the user.

[1227] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[1228] Specific examples

[1229] Example: If the user enters "Sleep problems due to work stress"

[1230] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[1231] 2. The device sends this text data to the server.

[1232] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[1233] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[1234] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[1235] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[1236] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[1237] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[1238] 9. The server provides the user with generated advice, expert information, and community invitation links.

[1239] 10. The terminal displays this information to the user.

[1240] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[1241] This system allows users to receive appropriate advice regarding their worries and feelings, and they can receive psychological support by interacting with other users who have the same worries and feelings.

[1242] The processing flow will be explained below.

[1243] Step 1:

[1244] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1245] Step 2:

[1246] The terminal transmits the input text data to the server.

[1247] Step 3:

[1248] The server stores the received text data and starts the text analysis module to begin analysis.

[1249] Step 4:

[1250] The server uses natural language processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[1251] Step 5:

[1252] The server analyzes the extracted text data using an emotion engine to recognize the user's emotions, such as "stress" and "anxiety."

[1253] Step 6:

[1254] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and recognized emotions.

[1255] Step 7:

[1256] The server receives the advice returned by the AI ​​model and adjusts the advice to match the user's emotions, for example, emphasizing "ways to reduce stress" and "ways to relax."

[1257] Step 8:

[1258] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[1259] Step 9:

[1260] The server uses a clustering algorithm to categorize related users based on their worries and emotions, for example, creating a category for "stress management and sleep improvement."

[1261] Step 10:

[1262] Based on categorized user data, the server automatically generates a community where users with the same concerns and feelings can gather.

[1263] Step 11:

[1264] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[1265] Step 12:

[1266] The terminal displays the received feedback data to the user.

[1267] Step 13:

[1268] The user reviews the information received and takes action on the advice if necessary, contacts a specialist organization, or joins a community.

[1269] Through this series of processes, users with worries or feelings can receive specific and accurate advice and can easily interact with other users who share the same worries or feelings, thereby freeing users from feelings of isolation and providing a sense of psychological security.

[1270] Example 2

[1271] 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."

[1272] There is a need for a system that allows users to receive appropriate advice and support for the worries and emotions they experience in their daily lives. However, conventional systems have difficulty providing accurate advice tailored to the user's emotions and individual circumstances, and have not been able to quickly create a community where users can interact with people in the same situation. Therefore, there is an urgent need to develop a system that allows users to immediately feel reassured and receive advanced support.

[1273] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving text data of worries and emotions entered by a user; means for analyzing the received text data and extracting keywords and important phrases; means for recognizing the user's emotions based on the extracted emotion data; means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases; means for adjusting the generated advice based on the emotion data and keywords; means for acquiring information on specialist institutions and support services corresponding to the generated advice; means for categorizing users with related worries using a clustering method and automatically generating communities of people in the same situation; and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This allows users to receive quick and accurate advice on their worries and emotions and to obtain psychological support by interacting with people in the same situation.

[1274] A "user" is an entity that uses the system to input worries and feelings and receive advice and access to the community.

[1275] "Text data" refers to character strings of natural language data that express the user's worries and feelings and that the user enters into the device.

[1276] A "server" is a central system that processes data entered by users and manages and provides various information.

[1277] A "terminal" is a device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1278] "Natural language processing technology" is a technology that uses computers to analyze natural language and understand and generate human language.

[1279] "Keywords" refer to important words or phrases within text data, and are terms extracted for data analysis and advice generation.

[1280] The "emotion engine" is a system for recognizing and analyzing user emotions from text data.

[1281] An "artificial intelligence model" is an algorithm or machine learning model that generates advice based on extracted keywords and emotions.

[1282] A "clustering method" is an algorithm for classifying data into multiple clusters (groups), and is used to categorize data based on users' common concerns and emotions.

[1283] A "community" is an online group where users with the same circumstances or concerns gather, and is a place to exchange information and interact.

[1284] A "specialized agency" is an institution or facility that provides support or services for specific problems or concerns.

[1285] "Feedback Data" means data generated and formatted by the server, including advice, expert information, and community invitation links.

[1286] "Cluster" is a term that refers to a group of users who share similar characteristics or attributes.

[1287] This invention provides a system that delves into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in the same situation. This system is combined with an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user.

[1288] The system configuration is as follows:

[1289] Hardware and Software

[1290] 1. Server: Located centrally, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine. Specifically, it uses Spacy or NLTK as the NLP engine, Affectiva or IBM Watson as the emotion engine, and ChatGPT as the generative AI model.

[1291] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1292] 3. User: The entity that uses the system to input their worries and feelings and receive advice and access to the community.

[1293] System Operation

[1294] The operation of the system is as follows.

[1295] Entering and receiving text data

[1296] The user inputs their worries and feelings into the device in natural language. For example, they might input, "I haven't been able to sleep lately because of work stress." The device then sends this input text data to the server.

[1297] Text data analysis

[1298] The server stores the received text data and starts the text analysis module to begin analysis. The server uses NLP technology to tokenize the text data and extract keywords and important phrases. For example, it extracts keywords such as "work," "stress," and "can't sleep."

[1299] Emotion recognition

[1300] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[1301] Generating Advice

[1302] The server generates advice using a generative AI model (such as ChatGPT) based on the extracted keywords and emotions. The server then adjusts the content of the advice based on the emotions. For example, if stress levels are high, the server will provide advice including relaxation techniques such as "take deep breaths to relax" and "maintain a bedtime routine."

[1303] Providing information on specialized institutions

[1304] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[1305] Categorizing users with similar circumstances and creating communities

[1306] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's worry and emotion data. Furthermore, for each cluster, the server automatically generates a community of users with related worries and emotions. For example, it generates a community for "stress management and sleep improvement."

[1307] Feedback and community engagement

[1308] The server prepares feedback data including the generated advice, information about the specialist organization, and an invitation link to join the community, and sends it to the user's terminal. The terminal displays the received feedback data to the user. The user checks the received information and, if necessary, follows the advice, contacts the specialist organization, or clicks the provided link to join the community.

[1309] Specific examples

[1310] Example: If the user enters "Sleep problems due to work stress"

[1311] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[1312] 2. The device sends this text data to the server.

[1313] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[1314] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[1315] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[1316] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[1317] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[1318] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[1319] 9. The server provides the user with generated advice, expert information, and community invitation links.

[1320] 10. The terminal displays this information to the user.

[1321] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[1322] An example of a prompt sentence is "Generate specific advice to reduce stress based on keywords entered by the user."

[1323] This system allows users to receive accurate and prompt advice on their worries and feelings, and they can also receive psychological support by interacting with other users who have the same worries and feelings.

[1324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1325] Step 1:

[1326] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep due to work stress." This becomes the input data. The device receives this input text data and sends it to the server as an HTTP request. This becomes the output data.

[1327] Step 2:

[1328] The server receives text data and stores it in a database. This data becomes input data. Based on the stored data, a text analysis module (e.g., Spacy or NLTK) is invoked to tokenize the received text data. The tokenized data becomes output data.

[1329] Step 3:

[1330] The server analyzes the tokenized data and extracts keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted from text data. This is the input data, and the output data is a list of keywords as a result of the analysis.

[1331] Step 4:

[1332] The server uses an emotion engine (such as Affectiva or IBM Watson) to analyze the text data based on keywords and recognize the user's emotions. For example, emotions such as stress or anxiety are identified. This is the input data, and the emotion recognition results are the output data.

[1333] Step 5:

[1334] The server generates advice using a generative AI model (e.g., ChatGPT) based on the extracted keywords and the recognized emotions. Here, the server inputs prompt sentences, such as "How to reduce stress" or "Advice on improving sleep," into the AI ​​model. The generated advice is the output data.

[1335] Step 6:

[1336] The server adjusts the advice content based on the emotional data and the generated advice. For example, if stress is high, it will emphasize relaxation techniques such as "taking deep breaths to relax" and "maintaining a bedtime routine." The adjusted advice is the output data.

[1337] Step 7:

[1338] The server searches a database for information on specialist institutions and support services that correspond to the generated advice. For example, it searches for "information on clinics that provide cognitive behavioral therapy." The information on specialist institutions is the output data.

[1339] Step 8:

[1340] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's concern and emotion data. This is the input data. For each cluster, it automatically generates a community of users with related concerns and emotions. This is the output data.

[1341] Step 9:

[1342] The server prepares feedback data including the generated advice, information on the professional organization, and an invitation link to join the community, and sends it to the user's terminal, which is output data.

[1343] Step 10:

[1344] The device displays the received feedback data to the user. The user confirms the received information, for example by following the displayed advice, contacting a specialist, or clicking a provided link to join a community. This is input data.

[1345] Through this series of steps, users can receive accurate and prompt advice on their worries and feelings, and can also receive psychological support by interacting with other users who have the same worries and feelings.

[1346] (Application example 2)

[1347] 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."

[1348] In modern society, it is important to provide appropriate support to the many people suffering from stress and worries, and to form communities where people share similar problems. However, previous systems have struggled to accurately analyze users' worries and emotions and generate appropriate advice, and have been inadequate in automatically generating communities or directing users to specialized institutions. Furthermore, they lacked the functionality to record and analyze users' emotions and worries on a daily basis. The present invention aims to solve these problems and provide a more effective and comprehensive support system.

[1349] 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.

[1350] In this invention, the server includes a means for providing an interface for recording the user's emotions and worries on a daily basis, a means for analyzing text data entered by the user with an emotion analysis engine to recognize emotions, and a means for displaying generated advice on the smartphone screen. This allows users to continuously record their worries and emotions and receive appropriate advice based on that information. It also makes it easier for users to receive psychological support through interactions with other users with similar worries.

[1351] A "user" is an individual who uses the system to input their worries and feelings and receive advice and support.

[1352] "Text data" refers to data that includes character information entered by a user.

[1353] "Means for receiving" refers to a device or software that has the function of receiving data sent from a user and providing it for subsequent processing.

[1354] The "analyzing means" is a device or software that analyzes the received text data and extracts information such as meaning and emotion.

[1355] "Keywords and important phrases" are words or phrases in the text data that are recognized as having particularly important meanings.

[1356] An "artificial intelligence model" is an algorithm or program that allows a computer to automatically learn from data and generate judgments and advice.

[1357] "Advice" refers to advice or suggestions provided to users based on the results of the analysis.

[1358] "Specialized institutions and support services" are organizations and facilities that provide professional support to users regarding their worries and problems.

[1359] A "categorization tool" is software or algorithms used to group users with similar concerns or problems.

[1360] A "community" is a group of people in the same situation who come together to exchange information and provide support.

[1361] An "interface" is a screen or device that allows a user to input data into a system or receive output information.

[1362] An "emotion analysis engine" is software that analyzes and recognizes user emotions from text data.

[1363] "Means for displaying on the smartphone screen" refers to a function for visually presenting the generated advice or information on the smartphone display.

[1364] This invention is a system that analyzes text data entered by users about their worries and emotions, provides advice based on that data, and creates a community of people with the same worries. The system records users' emotions and worries daily and provides an interface for analyzing them. It also generates advice based on the analysis results and displays it on a smartphone screen.

[1365] The system consists of three main components:

[1366] 1. Server:

[1367] The server is centrally located and receives the data sent by users, analyzes it, and processes it.

[1368] The server is installed with a natural language processing (NLP) engine, a sentiment analysis engine, and artificial intelligence models for advice generation (e.g., OpenAI's GPT-3 and ChatGPT).

[1369] Specifically, the text data received by the server is analyzed using an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to recognize emotions.

[1370] A sentiment analysis engine (e.g., Watson's Tone Analyzer) provides detailed analysis of the user's emotions.

[1371] An artificial intelligence model for generating advice generates advice based on the analysis results and provides data for displaying that advice on a smartphone screen.

[1372] 2. Terminal:

[1373] A terminal is a device that allows a user to input text data, including a smartphone, tablet, or PC.

[1374] The terminal transmits the input text data to the server and receives a response from the server.

[1375] Specifically, the user enters "I can't sleep lately because of work stress" into the text box and presses the send button. The device then sends this data to the server.

[1376] In response from the server, analysis results, advice, information on specialist institutions, and a link to join the community are displayed on the smartphone screen.

[1377] 3. User:

[1378] Users use this system to input their worries and feelings and receive support.

[1379] Users input their daily feelings and worries into the system, receive generated advice and information, and act based on it.

[1380] For example, if a user types, "Recently, I've been unable to sleep due to work stress," the server analyzes this text data, and the NLP engine extracts the keywords "work," "stress," and "can't sleep." The sentiment analysis engine recognizes the user's emotions as "anxiety" and "stress." The AI ​​model then generates advice based on these keywords and emotions, suggesting things like "taking deep breaths to relax" and "maintaining a bedtime routine." It also provides information about related specialist organizations and encourages users to join a community where users with the same concerns gather.

[1381] To further illustrate this, here are some example prompts:

[1382] User said: I can't sleep lately because of work stress.

[1383] Generate appropriate advice based on that emotion.

[1384] This will allow users to continuously record their worries and feelings and receive appropriate advice based on that information. It will also make it easier for them to receive psychological support through interactions with other users who have similar worries.

[1385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1386] Step 1:

[1387] A user inputs text data into a terminal.

[1388] Specifically, users use devices such as smartphones, tablets, and PCs to input their worries and feelings, such as "I haven't been able to sleep lately because of work stress," into the system's interface.

[1389] Input: Text data of worries and emotions

[1390] Output: The input text data

[1391] Step 2:

[1392] The terminal transmits the input text data to the server.

[1393] Specifically, the input text data (e.g., "I can't sleep lately because of work stress") is sent from the device to the server. This communication uses the Internet Protocol.

[1394] Input: Entered text data

[1395] Output: Text data sent to the server

[1396] Step 3:

[1397] The server stores the received text data and starts text analysis.

[1398] Specifically, the server uses an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to tokenize the received text data and extract keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[1399] Input: Transmitted text data

[1400] Output: Extracted keywords and phrases

[1401] Step 4:

[1402] The server analyzes the extracted keywords and phrases using a sentiment analysis engine to recognize the user's emotions.

[1403] Specifically, an emotion engine (e.g., Watson's Tone Analyzer) analyzes text data and identifies emotions such as anxiety and stress. For example, "anxiety" and "stress" are recognized.

[1404] Input: Extracted keywords or phrases

[1405] Output: Recognized emotion

[1406] Step 5:

[1407] The server generates advice using an artificial intelligence model based on the recognized emotions and keywords.

[1408] Specifically, a generative AI model (e.g., OpenAI's ChatGPT) generates advice based on the prompt, such as "take a deep breath and relax" or "maintain a bedtime routine."

[1409] Input: Recognized emotions and keywords

[1410] Output: Generated advice

[1411] Step 6:

[1412] The server formats the generated advice into a format suitable for display on the smartphone screen and sends it to the device.

[1413] Specifically, the server formats the advice content into an appropriate format, such as JSON, and sends it to the user's device.

[1414] Input: Generated advice

[1415] Output: Formatted advice data

[1416] Step 7:

[1417] The device will display on the screen the advice provided, information about specialist agencies, and an invitation to join the community.

[1418] Specifically, the device analyzes the data received from the server and visually displays it to the user. The user can then check the advice and information displayed on the screen and, if necessary, contact a specialist institution or join a community.

[1419] Input: formatted advice data, professional information, community links

[1420] Output: Advice and links displayed on the smartphone screen

[1421] 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.

[1422] 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.

[1423] 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.

[1424] [Fourth embodiment]

[1425] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1426] 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.

[1427] 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).

[1428] 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.

[1429] 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.

[1430] 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).

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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."

[1438] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically creates a community where people in similar situations can gather. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described below.

[1439] System configuration

[1440] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server.

[1441] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1442] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[1443] Program processing

[1444] Entering and receiving text data

[1445] User: Enter their worries or feelings in natural language through the device. For example, they might enter, "I haven't been able to sleep lately because of work stress."

[1446] Terminal: Sends entered text data to the server.

[1447] Text data analysis

[1448] Server: Uses natural language processing techniques to analyze the received text data, including segmenting the text and extracting keywords and important phrases.

[1449] Server: For example, extract keywords such as "work," "stress," and "can't sleep."

[1450] Generating Advice

[1451] Server: Generates advice using an artificial intelligence model (e.g., ChatGPT) based on the extracted keywords and phrases.

[1452] Server: The generated advice is provided to the user as, for example, "methods for managing stress" or "how to relax before bed."

[1453] Providing information on specialized institutions

[1454] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[1455] Server: Converts the acquired information into a user-friendly format and provides it along with advice. For example, it may include information on clinics offering cognitive behavioral therapy.

[1456] Categorizing users with similar circumstances and creating communities

[1457] Server: Based on the user's problem data and advice, related users are categorized using a clustering algorithm.

[1458] Server: For example, it generates categories related to "work stress" and "sleep disorders" and automatically creates a community where users with the same concerns can gather.

[1459] Feedback and community engagement

[1460] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[1461] Terminal: Displays the received information to the user.

[1462] Users: Follow advice, consult professional organizations, and join communities.

[1463] Specific examples

[1464] Example: If the user enters "Sleep problems due to work stress"

[1465] 1. User: Enters "I can't sleep lately because of work stress" into the device.

[1466] 2. Terminal: Send this text data to the server.

[1467] 3. Server: Analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[1468] 4. Server: Queries the AI ​​model and generates "ways to reduce stress" and "advice for improving sleep."

[1469] 5. Server: Generates advice such as "take deep breaths and relax" or "maintain a bedtime routine."

[1470] 6. Server: Retrieves information about specialized institutions that provide cognitive behavioral therapy from a database and formats the information.

[1471] 7. Server: Categorizes users with concerns related to "work stress" and "sleep disorders" and generates a community of users with similar concerns.

[1472] 8. Server: Provides users with advice, information on professional organizations, and community invitation links.

[1473] 9. Terminal: Displays this information to the user.

[1474] 10. Users: Act on advice, consult with specialist authorities if necessary, and participate in the community.

[1475] This system allows users to receive appropriate advice on their concerns, and by interacting with other users who have the same concerns, they can reduce feelings of isolation and gain psychological security.

[1476] The processing flow will be explained below.

[1477] Step 1:

[1478] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1479] Step 2:

[1480] The terminal transmits the input text data to the server.

[1481] Step 3:

[1482] The server stores the received text data and starts the text analysis module to begin analysis.

[1483] Step 4:

[1484] The server uses Natural Language Processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[1485] Step 5:

[1486] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and phrases.

[1487] Step 6:

[1488] The server receives the advice returned by the AI ​​model and formats it appropriately, generating specific advice such as "how to manage stress" or "how to relax before bed."

[1489] Step 7:

[1490] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[1491] Step 8:

[1492] Based on the user's worry data and advice, the server uses a clustering algorithm to categorize users who are in the same situation or have related worries.

[1493] Step 9:

[1494] For each cluster, the server automatically generates a community of users with related concerns. For example, it generates a community for "stress management and sleep improvement."

[1495] Step 10:

[1496] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[1497] Step 11:

[1498] The terminal displays the received feedback data to the user.

[1499] Step 12:

[1500] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[1501] Through this series of processes, users with concerns can receive specific and accurate advice and easily interact with other users who share the same concerns, which relieves users from feelings of isolation and provides a sense of psychological security.

[1502] Example 1

[1503] 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."

[1504] In modern society, individual users have a wide range of worries and emotions, but there is a lack of environments where they can receive appropriate advice or opportunities to interact with other users who share the same worries. There is also no system in place to provide appropriate information about specialized institutions and support services. As a result, users are unable to receive appropriate support, which leads to problems such as feelings of isolation and increased psychological stress.

[1505] 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.

[1506] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries using a clustering algorithm and automatically generating a community of people in the same situation, and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This makes it possible to provide appropriate advice for the worries and emotions that users have, quickly and appropriately provide information on specialist institutions and support services, and provide a place for users to interact with others who have the same worries.

[1507] A "user" is an entity that uses the system to input their worries and feelings and receive advice and access to the community.

[1508] "Text data" refers to information entered by a user in natural language to express their concerns or feelings.

[1509] A "server" is a device located at the center of a system that analyzes data sent by users and provides and manages information.

[1510] A "terminal" is a device that provides an interface for users to input text data, such as a PC, smartphone, or tablet.

[1511] "Natural language processing technology" is a technology for analyzing text data and extracting keywords and important phrases, and includes technologies such as SpaCy and NLTK.

[1512] A "keyword" is a particularly important word or phrase in the received text data.

[1513] A "phrase" is a series of significant word combinations within text data.

[1514] An "artificial intelligence model" is a learning model used to generate advice based on extracted keywords and phrases, such as ChatGPT.

[1515] A "clustering algorithm" is an algorithm for classifying users with related concerns and grouping people in the same situation.

[1516] A "community" is a virtual group where users with the same concerns and feelings can gather and interact.

[1517] A "specialized institution" is a facility or organization that provides specialized support and services for specific concerns or problems.

[1518] "Support services" is a general term for professional support and advice provided to users to address their concerns and emotions.

[1519] An "invitation link" is a URL or hyperlink that users can click to access in order to join a community.

[1520] "JSON format" is a data format used to structure text data and communicate between servers and devices.

[1521] "Tokenization" is an analytical method that divides text data into semantic units.

[1522] A "prompt sentence" is a text instruction sentence that is input to an artificial intelligence model to generate advice.

[1523] MODE FOR CARRYING OUT THE INVENTION

[1524] This invention is a system that delves into the various worries and feelings of users, provides appropriate advice, and automatically generates a community of people in similar situations. This system is composed of a server, terminals, and users. The operation of this system and the processing of the program are described in detail below.

[1525] System configuration

[1526] 1. Server: Located in a central location, it processes data entered by users and manages and provides various information. Natural language processing (NLP) technology and artificial intelligence (AI) modules are installed on the server. Specific technologies include SpaCy and NLTK for analyzing text data and ChatGPT for generating advice.

[1527] 2. Device: A device that provides an interface for users to input their worries and feelings, such as a PC, smartphone, or tablet. These devices provide a platform for users to input text data and send it to a server.

[1528] 3. User: The entity who uses the system to input their concerns and receive advice and access to the community.

[1529] Program processing

[1530] Entering and receiving text data

[1531] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I can't sleep lately because of work stress." The device then sends the input text data to the server.

[1532] Text data analysis

[1533] The server uses natural language processing techniques (SpaCy or NLTK) to analyze the received text data. This process involves tokenizing the text and extracting keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" can be extracted.

[1534] Generating Advice

[1535] The server constructs a prompt for the artificial intelligence model (ChatGPT) based on the extracted keywords and phrases, and generates advice. An example of a prompt is, "What should I do when I can't sleep because of work stress?" The generated advice is provided to the user, for example, "How to take deep breaths and relax" or "Maintain a bedtime routine."

[1536] Obtaining information on specialized institutions

[1537] The server retrieves information about specialist institutions and support services that can provide advice from a database. For example, it retrieves information about clinics that offer cognitive behavioral therapy. The retrieved information is formatted as the clinic's address, contact information, opening hours, etc.

[1538] Categorizing users with similar circumstances and creating communities

[1539] The server uses a clustering algorithm (e.g., k-means clustering) to categorize related users based on the user's concerns and advice. For example, it could create categories related to "work stress" and "sleep disorders," and automatically create a community of users with the same concerns.

[1540] Feedback and community engagement

[1541] The server sends the generated advice, information about the specialist organization, and an invitation link to the community to the user's device. The device displays the received information on a user interface. The user can then take action based on the displayed information. Specifically, the user can act on the advice, contact the specialist organization, or join the community to interact with other users.

[1542] Specific examples

[1543] If a user types, "Recently, I've been unable to sleep due to work stress," this text data is sent from the device to the server. The server analyzes this data and extracts the keywords "work," "stress," and "can't sleep." The generative AI model (ChatGPT) then receives a prompt such as, "What should I do when I can't sleep due to work stress?" and generates advice. For example, advice such as "Take deep breaths to relax" or "Maintain a bedtime routine" is provided. At the same time, information about related specialist institutions is retrieved and provided to the user. Users with the same problem are also categorized, a community is created, and an invitation link is sent. This system allows users to receive appropriate advice and connect with specialist institutions and other users with the same problem.

[1544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1545] Step 1:

[1546] User: Uses the device to input worries and feelings in natural language. For example, the user might input the text, "Recently, I've been unable to sleep due to work stress."

[1547] Input: Natural language text data entered by the user.

[1548] Output: Text data sent to the terminal.

[1549] Step 2:

[1550] Terminal: The entered text data is sent to the server. This data is generally converted to JSON format and sent using the HTTPS protocol.

[1551] Input: User text data.

[1552] Output: The text data sent to the server.

[1553] Step 3:

[1554] Server: Receives the incoming text data and analyzes it using natural language processing techniques (e.g., SpaCy or NLTK), which tokenizes the text and extracts keywords and key phrases.

[1555] Input: Text data received by the server.

[1556] Output: Extracted keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[1557] How it works: The server tokenizes the text and classifies it by parts of speech, such as nouns and verbs. It uses techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) to extract important phrases.

[1558] Step 4:

[1559] Server: Based on the extracted keywords, it generates prompts for the AI ​​model (e.g., ChatGPT) and generates advice.

[1560] Input: Extracted keywords and prompt sentences. For example, "What should I do when I can't sleep because of work stress?"

[1561] Output: Generated advice. For example, "Take deep breaths and relax" and "Follow a bedtime routine" are generated.

[1562] How it works: The server inputs a prompt into the generative AI model (ChatGPT), receives the text data returned by the model, formats the text data, and converts it into a format that can be provided to the user.

[1563] Step 5:

[1564] Server: Retrieves information from a database about specialist institutions and support services that can respond to advice.

[1565] Input: Generated advice and corresponding keywords.

[1566] Output: Details of professional and support services, such as clinic addresses, contact details, and opening hours.

[1567] How it works: The server uses a pre-configured database query to search for relevant professional organizations and retrieves the results in a formatted form.

[1568] Step 6:

[1569] Server: Based on the user's concerns and advice, related users are categorized using a clustering algorithm (e.g., k-means clustering), and a community of people in the same situation is automatically generated.

[1570] Input: User's trouble data and generated advice.

[1571] Output: Categorized user information and generated communities. For example, categories related to "work stress" and "sleep disorders" are generated.

[1572] How it works: The server runs a clustering algorithm to group user data, automatically forming communities of people with similar interests.

[1573] Step 7:

[1574] Server: Sends generated advice, information on expert organizations, and an invitation link to the community to the user's device.

[1575] Input: Generated advice, professional organization information, community invite link.

[1576] Output: Feedback information sent to the device.

[1577] How it works: The server packets information and sends it to the device, usually packing the data into JSON or XML format.

[1578] Step 8:

[1579] Terminal: Displays the received information in a user interface.

[1580] Input: Feedback information sent from the server (advice, professional information, community invite link).

[1581] Output: Feedback information that is displayed to the user.

[1582] How it works: The device's user interface displays the received information in the appropriate location, making it easily accessible to the user.

[1583] Step 9:

[1584] Users: Follow the advice provided, contact specialist organizations, and participate in communities.

[1585] Input: Feedback information displayed on the terminal.

[1586] Output: User action (following advice, contacting a specialist, joining a community).

[1587] Action: The user takes the necessary action based on the information provided, such as clicking a link to join a community or contact a specialist organization. This process provides the user with targeted support and interaction opportunities.

[1588] (Application example 1)

[1589] 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."

[1590] With conventional systems, it was difficult to provide accurate advice to users regarding their concerns and emotions, and it was also difficult to form a community of people in the same situation. In particular, since there was no system to address the specific concerns of store employees and customers, these users tended to feel isolated and were unable to obtain effective solutions or psychological support.

[1591] 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.

[1592] In this invention, the server includes means for receiving text data of worries and emotions entered by a user, means for analyzing the received text data and extracting keywords and important phrases, means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases, means for acquiring information on specialist institutions and support services corresponding to the generated advice, means for categorizing users with related worries and automatically generating communities where people in similar situations gather, means for providing users with the generated advice, information on specialist institutions, and an invitation link to join the community, and means for generating appropriate advice for worries and emotions entered by store employees and customers and providing related community information. This makes it possible to quickly provide appropriate advice and automatically generate related communities even for the specific worries faced by store employees and customers.

[1593] A "user" is an entity that uses the system to input worries and feelings and receive advice and community information.

[1594] "Text data" is data in natural language format that includes worries and feelings entered by users.

[1595] "Extraction" is the process of extracting keywords and important phrases from the received text data.

[1596] An "artificial intelligence model" is a program or algorithm that uses natural language processing technology to understand the meaning of input data and generate appropriate advice.

[1597] A "specialized institution" is an institution such as a medical institution or counseling service that provides support and treatment for distress and emotions.

[1598] "Support services" refers to all services that provide specific support for users' concerns and emotions.

[1599] "Categorization" means classifying users with related concerns and grouping people in the same situation.

[1600] A "community" is a group of users who share the same concerns and feelings and come together to exchange information and interact with each other.

[1601] An "invitation link" is a reference URL or means of invitation to join that directs users to a newly created community.

[1602] "Physical store employees" refers to staff and sales associates who actually work in the store.

[1603] "Customers" refer to consumers who visit physical stores and use products and services.

[1604] MODE FOR CARRYING OUT THE INVENTION

[1605] This invention is a system that delves into the user's worries and feelings, provides appropriate advice, and automatically creates a community where people in the same situation can gather. This system is mainly composed of a server, terminals, and users.

[1606] System configuration

[1607] 1. Server:

[1608] Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology and artificial intelligence (AI) modules.

[1609] 2. Terminal:

[1610] A device that provides an interface for users to input their worries and feelings. Specifically, this applies to PCs, smartphones, tablets, etc.

[1611] 3. User:

[1612] These are the people who use the system to input their concerns and receive advice and access to the community. These include store employees and customers.

[1613] Program processing

[1614] The firmware and software configuration will be described.

[1615] Hardware:

[1616] Smartphones, tablets, etc. are used as input interfaces by users.

[1617] software:

[1618] This system uses the following software and services:

[1619] OpenAI's API: Used for natural language processing and advice generation.

[1620] HTTP request library (e.g. Requests): Used to retrieve community information.

[1621] Processing Description

[1622] The server performs a series of processes using the following means.

[1623] 1. Receive text data about worries and feelings:

[1624] The user inputs their "concerns" or "feelings" in natural language through the device. For example, they might input "I've been having trouble with stress from customer service lately." This text data is sent to the server.

[1625] 2. Text data analysis:

[1626] The server uses OpenAI's API and natural language processing technology to analyze the received text data. This process segments the text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" are extracted.

[1627] 3. Generating Advice:

[1628] The server generates advice using an artificial intelligence model (e.g., GPT-3) based on the extracted keywords and phrases. The advice generated might be, for example, "Try practicing stress relief techniques. For example, you could try breathing exercises or taking short breaks."

[1629] 4. Providing information on professional organizations:

[1630] The server retrieves information from a database about specialist institutions and support services that correspond to the generated advice, such as information about clinics offering cognitive behavioral therapy.

[1631] 5. Categorizing users with related concerns and creating a community:

[1632] The server uses a clustering algorithm to categorize related users based on their concerns and advice. For example, it could create a category related to "stress in customer service" and automatically create a community of users with the same concerns.

[1633] 6. Feedback and Community Involvement:

[1634] The generated advice, information about the expert organization, and an invitation link to the community are sent to the user's terminal, allowing the user to receive the advice, inquire about the expert organization, or join the community.

[1635] Specific examples

[1636] When a user inputs a concern such as "I've been suffering from stress lately when working with customers," the following prompt is sent to the generative AI model:

[1637] Prompt (for analysis): "Analyze your worries and feelings and extract keywords: I've been having trouble with stress from serving customers lately."

[1638] Prompt (for advice): "Generate appropriate advice based on the keywords 'customer service, stress'."

[1639] This makes it possible to quickly provide appropriate advice for the specific concerns of store employees and customers, and automatically generate related communities.

[1640] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1641] Step 1:

[1642] The user uses a terminal to input their worries and feelings in natural language. For example, they might input text data such as, "Recently, I've been suffering from stress from customer service." This input text data is then sent from the terminal to the server.

[1643] Step 2:

[1644] The server uses OpenAI's API to analyze the received text data and performs natural language processing. This process tokenizes the input text and extracts keywords and important phrases. For example, keywords such as "customer service," "stress," and "troubled" can be extracted.

[1645] Input: Text data from the user: "Recently, I've been suffering from stress from customer service."

[1646] Data processing: Text tokenization, keyword extraction

[1647] Output: Extracted keywords "customer service," "stress," and "troubled"

[1648] Step 3:

[1649] Based on the extracted keywords, the server uses a generative AI model (e.g., GPT-3) to generate appropriate advice, such as "Try practicing stress relief methods. For example, you could try breathing exercises or short breaks."

[1650] Input: Extracted keywords "customer service," "stress," "troubled"

[1651] Data Computation: Advice Generation with Generative AI Models

[1652] Output: Advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[1653] Step 4:

[1654] The server retrieves information on specialist institutions and support services that correspond to the generated advice from a database, for example, "information on clinics that provide cognitive behavioral therapy."

[1655] Input: Generated advice: "Try practicing stress reduction techniques, such as breathing exercises and taking short breaks."

[1656] Data calculation: Retrieving professional agency information from databases

[1657] Output: Specialist information "Information on clinics offering cognitive behavioral therapy"

[1658] Step 5:

[1659] Based on the user's worry data and the generated advice, the server uses a clustering algorithm to categorize related users and automatically generate communities of users with the same worries. For example, it generates a community related to "stress in customer service."

[1660] Input: User's trouble data, generated advice

[1661] Data processing: Categorization using clustering algorithms

[1662] Output: Generated community "Community related to customer service stress"

[1663] Step 6:

[1664] The server sends the generated advice, information about the expert organization, and an invitation link to join the community to the user's device. The user receives this information and can then act on the advice, contact the expert organization, or join the community.

[1665] Input: Generated advice, professional information, community invite link

[1666] Data Computing: Formatting and Transmitting Information

[1667] Output: Display information on the user's device (advice, information on specialist institutions, community invitation links)

[1668] 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.

[1669] This invention is a system that delves deeply into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in similar situations. This system incorporates an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user. The operation of this system and the program processing are described below.

[1670] System configuration

[1671] 1. Server: Located in the center, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine.

[1672] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1673] 3. User: The entity that uses the system to input their concerns and feelings and receive advice and access to the community.

[1674] Program processing

[1675] Entering and receiving text data

[1676] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1677] The terminal transmits the input text data to the server.

[1678] Text data analysis

[1679] The server stores the received text data and starts the text analysis module to begin analysis.

[1680] The server uses NLP technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[1681] Emotion recognition

[1682] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[1683] Generating Advice

[1684] The server generates advice based on the extracted keywords and sentiments using an artificial intelligence model (e.g., ChatGPT).

[1685] The server tailors the generated advice based on emotion, for example providing advice emphasizing relaxation techniques when stress levels are high.

[1686] Providing information on specialized institutions

[1687] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[1688] Categorizing users with similar circumstances and creating communities

[1689] The server categorizes related users using a clustering algorithm based on the user's concern data and emotion data.

[1690] For each cluster, the server automatically generates a community of users with related concerns or emotions. For example, it generates a community for "stress management and sleep improvement."

[1691] Feedback and community engagement

[1692] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[1693] The terminal displays the received feedback data to the user.

[1694] The user reviews the information received and, if necessary, acts on the advice, contacts a specialist, or joins the community by clicking on the provided link.

[1695] Specific examples

[1696] Example: If the user enters "Sleep problems due to work stress"

[1697] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[1698] 2. The device sends this text data to the server.

[1699] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[1700] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[1701] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[1702] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[1703] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[1704] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[1705] 9. The server provides the user with generated advice, expert information, and community invitation links.

[1706] 10. The terminal displays this information to the user.

[1707] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[1708] This system allows users to receive appropriate advice regarding their worries and feelings, and they can receive psychological support by interacting with other users who have the same worries and feelings.

[1709] The processing flow will be explained below.

[1710] Step 1:

[1711] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1712] Step 2:

[1713] The terminal transmits the input text data to the server.

[1714] Step 3:

[1715] The server stores the received text data and starts the text analysis module to begin analysis.

[1716] Step 4:

[1717] The server uses natural language processing (NLP) technology to tokenize the text data and extract keywords and important phrases, such as "work," "stress," and "can't sleep."

[1718] Step 5:

[1719] The server analyzes the extracted text data using an emotion engine to recognize the user's emotions, such as "stress" and "anxiety."

[1720] Step 6:

[1721] The server sends a request to an artificial intelligence model (e.g., ChatGPT) to generate advice based on the extracted keywords and recognized emotions.

[1722] Step 7:

[1723] The server receives the advice returned by the AI ​​model and adjusts the advice to match the user's emotions, for example, emphasizing "ways to reduce stress" and "ways to relax."

[1724] Step 8:

[1725] The server searches a database for information on specialist institutions and support services that correspond to the generated advice, for example, obtaining information on clinics that offer cognitive behavioral therapy.

[1726] Step 9:

[1727] The server uses a clustering algorithm to categorize related users based on their worries and emotions, for example, creating a category for "stress management and sleep improvement."

[1728] Step 10:

[1729] Based on categorized user data, the server automatically generates a community where users with the same concerns and feelings can gather.

[1730] Step 11:

[1731] The server prepares feedback data including the generated advice, information about the professional organization, and an invitation link to join the community, and transmits the feedback data to the user's terminal.

[1732] Step 12:

[1733] The terminal displays the received feedback data to the user.

[1734] Step 13:

[1735] The user reviews the information received and takes action on the advice if necessary, contacts a specialist organization, or joins a community.

[1736] Through this series of processes, users with worries or feelings can receive specific and accurate advice and can easily interact with other users who share the same worries or feelings, thereby freeing users from feelings of isolation and providing a sense of psychological security.

[1737] Example 2

[1738] 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."

[1739] There is a need for a system that allows users to receive appropriate advice and support for the worries and emotions they experience in their daily lives. However, conventional systems have difficulty providing accurate advice tailored to the user's emotions and individual circumstances, and have not been able to quickly create a community where users can interact with people in the same situation. Therefore, there is an urgent need to develop a system that allows users to immediately feel reassured and receive advanced support.

[1740] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving text data of worries and emotions entered by a user; means for analyzing the received text data and extracting keywords and important phrases; means for recognizing the user's emotions based on the extracted emotion data; means for using an artificial intelligence model to generate advice based on the extracted keywords and phrases; means for adjusting the generated advice based on the emotion data and keywords; means for acquiring information on specialist institutions and support services corresponding to the generated advice; means for categorizing users with related worries using a clustering method and automatically generating communities of people in the same situation; and means for providing the generated advice, information on specialist institutions, and an invitation link to join the community to the user. This allows users to receive quick and accurate advice on their worries and emotions and to obtain psychological support by interacting with people in the same situation.

[1741] A "user" is an entity that uses the system to input worries and feelings and receive advice and access to the community.

[1742] "Text data" refers to character strings of natural language data that express the user's worries and feelings and that the user enters into the device.

[1743] A "server" is a central system that processes data entered by users and manages and provides various information.

[1744] A "terminal" is a device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1745] "Natural language processing technology" is a technology that uses computers to analyze natural language and understand and generate human language.

[1746] "Keywords" refer to important words or phrases within text data, and are terms extracted for data analysis and advice generation.

[1747] The "emotion engine" is a system for recognizing and analyzing user emotions from text data.

[1748] An "artificial intelligence model" is an algorithm or machine learning model that generates advice based on extracted keywords and emotions.

[1749] A "clustering method" is an algorithm for classifying data into multiple clusters (groups), and is used to categorize data based on users' common concerns and emotions.

[1750] A "community" is an online group where users with the same circumstances or concerns gather, and is a place to exchange information and interact.

[1751] A "specialized agency" is an institution or facility that provides support or services for specific problems or concerns.

[1752] "Feedback Data" means data generated and formatted by the server, including advice, expert information, and community invitation links.

[1753] "Cluster" is a term that refers to a group of users who share similar characteristics or attributes.

[1754] This invention provides a system that delves into the user's worries and emotions, provides appropriate advice, and automatically generates a community of people in the same situation. This system is combined with an emotion engine to provide comprehensive support, including recognizing the emotions entered by the user.

[1755] The system configuration is as follows:

[1756] Hardware and Software

[1757] 1. Server: Located centrally, it processes data entered by users and manages and provides various information. The server is equipped with natural language processing (NLP) technology, artificial intelligence (AI) modules, and an emotion engine. Specifically, it uses Spacy or NLTK as the NLP engine, Affectiva or IBM Watson as the emotion engine, and ChatGPT as the generative AI model.

[1758] 2. Terminal: A device that provides an interface for users to input their concerns and feelings, such as a PC, smartphone, or tablet.

[1759] 3. User: The entity that uses the system to input their worries and feelings and receive advice and access to the community.

[1760] System Operation

[1761] The operation of the system is as follows.

[1762] Entering and receiving text data

[1763] The user inputs their worries and feelings into the device in natural language. For example, they might input, "I haven't been able to sleep lately because of work stress." The device then sends this input text data to the server.

[1764] Text data analysis

[1765] The server stores the received text data and starts the text analysis module to begin analysis. The server uses NLP technology to tokenize the text data and extract keywords and important phrases. For example, it extracts keywords such as "work," "stress," and "can't sleep."

[1766] Emotion recognition

[1767] The server analyzes the text data using an emotion engine to recognize the user's emotions, such as stress or anxiety.

[1768] Generating Advice

[1769] The server generates advice using a generative AI model (such as ChatGPT) based on the extracted keywords and emotions. The server then adjusts the content of the advice based on the emotions. For example, if stress levels are high, the server will provide advice including relaxation techniques such as "take deep breaths to relax" and "maintain a bedtime routine."

[1770] Providing information on specialized institutions

[1771] The server searches the database for information on specialist institutions and support services that can provide advice. For example, it retrieves information on clinics that offer cognitive behavioral therapy.

[1772] Categorizing users with similar circumstances and creating communities

[1773] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's worry and emotion data. Furthermore, for each cluster, the server automatically generates a community of users with related worries and emotions. For example, it generates a community for "stress management and sleep improvement."

[1774] Feedback and community engagement

[1775] The server prepares feedback data including the generated advice, information about the specialist organization, and an invitation link to join the community, and sends it to the user's terminal. The terminal displays the received feedback data to the user. The user checks the received information and, if necessary, follows the advice, contacts the specialist organization, or clicks the provided link to join the community.

[1776] Specific examples

[1777] Example: If the user enters "Sleep problems due to work stress"

[1778] 1. The user types into the terminal, "I haven't been able to sleep lately because of work stress."

[1779] 2. The device sends this text data to the server.

[1780] 3. The server analyzes the received text data and extracts the keywords "work," "stress," and "can't sleep."

[1781] 4. The server analyzes the text data using an emotion engine to recognize emotions such as anxiety and stress.

[1782] 5. The server queries the AI ​​model to generate "stress reduction methods" and "sleep improvement advice," adjusting them based on emotions.

[1783] 6. The server generates advice such as "take a deep breath and relax" or "follow a bedtime routine."

[1784] 7. The server retrieves information about specialized institutions that provide cognitive behavioral therapy from the database and formats the information.

[1785] 8. The server categorizes users who have worries or feelings related to "work stress" or "sleep disorders" and generates a community of people with similar worries.

[1786] 9. The server provides the user with generated advice, expert information, and community invitation links.

[1787] 10. The terminal displays this information to the user.

[1788] 11. The user follows the advice, consults with specialist authorities if necessary, and participates in the community.

[1789] An example of a prompt sentence is "Generate specific advice to reduce stress based on keywords entered by the user."

[1790] This system allows users to receive accurate and prompt advice on their worries and feelings, and they can also receive psychological support by interacting with other users who have the same worries and feelings.

[1791] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1792] Step 1:

[1793] The user inputs their worries and feelings into the device in natural language. For example, they might input, "Recently, I've been unable to sleep due to work stress." This becomes the input data. The device receives this input text data and sends it to the server as an HTTP request. This becomes the output data.

[1794] Step 2:

[1795] The server receives text data and stores it in a database. This data becomes input data. Based on the stored data, a text analysis module (e.g., Spacy or NLTK) is invoked to tokenize the received text data. The tokenized data becomes output data.

[1796] Step 3:

[1797] The server analyzes the tokenized data and extracts keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted from text data. This is the input data, and the output data is a list of keywords as a result of the analysis.

[1798] Step 4:

[1799] The server uses an emotion engine (such as Affectiva or IBM Watson) to analyze the text data based on keywords and recognize the user's emotions. For example, emotions such as stress or anxiety are identified. This is the input data, and the emotion recognition results are the output data.

[1800] Step 5:

[1801] The server generates advice using a generative AI model (e.g., ChatGPT) based on the extracted keywords and the recognized emotions. Here, the server inputs prompt sentences, such as "How to reduce stress" or "Advice on improving sleep," into the AI ​​model. The generated advice is the output data.

[1802] Step 6:

[1803] The server adjusts the advice content based on the emotional data and the generated advice. For example, if stress is high, it will emphasize relaxation techniques such as "taking deep breaths to relax" and "maintaining a bedtime routine." The adjusted advice is the output data.

[1804] Step 7:

[1805] The server searches a database for information on specialist institutions and support services that correspond to the generated advice. For example, it searches for "information on clinics that provide cognitive behavioral therapy." The information on specialist institutions is the output data.

[1806] Step 8:

[1807] The server categorizes related users using a clustering algorithm (e.g., K-means or DBSCAN) based on the user's concern and emotion data. This is the input data. For each cluster, it automatically generates a community of users with related concerns and emotions. This is the output data.

[1808] Step 9:

[1809] The server prepares feedback data including the generated advice, information on the professional organization, and an invitation link to join the community, and sends it to the user's terminal, which is output data.

[1810] Step 10:

[1811] The device displays the received feedback data to the user. The user confirms the received information, for example by following the displayed advice, contacting a specialist, or clicking a provided link to join a community. This is input data.

[1812] Through this series of steps, users can receive accurate and prompt advice on their worries and feelings, and can also receive psychological support by interacting with other users who have the same worries and feelings.

[1813] (Application example 2)

[1814] 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."

[1815] In modern society, it is important to provide appropriate support to the many people suffering from stress and worries, and to form communities where people share similar problems. However, previous systems have struggled to accurately analyze users' worries and emotions and generate appropriate advice, and have been inadequate in automatically generating communities or directing users to specialized institutions. Furthermore, they lacked the functionality to record and analyze users' emotions and worries on a daily basis. The present invention aims to solve these problems and provide a more effective and comprehensive support system.

[1816] 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.

[1817] In this invention, the server includes a means for providing an interface for recording the user's emotions and worries on a daily basis, a means for analyzing text data entered by the user with an emotion analysis engine to recognize emotions, and a means for displaying generated advice on the smartphone screen. This allows users to continuously record their worries and emotions and receive appropriate advice based on that information. It also makes it easier for users to receive psychological support through interactions with other users with similar worries.

[1818] A "user" is an individual who uses the system to input their worries and feelings and receive advice and support.

[1819] "Text data" refers to data that includes character information entered by a user.

[1820] "Means for receiving" refers to a device or software that has the function of receiving data sent from a user and providing it for subsequent processing.

[1821] The "analyzing means" is a device or software that analyzes the received text data and extracts information such as meaning and emotion.

[1822] "Keywords and important phrases" are words or phrases in the text data that are recognized as having particularly important meanings.

[1823] An "artificial intelligence model" is an algorithm or program that allows a computer to automatically learn from data and generate judgments and advice.

[1824] "Advice" refers to advice or suggestions provided to users based on the results of the analysis.

[1825] "Specialized institutions and support services" are organizations and facilities that provide professional support to users regarding their worries and problems.

[1826] A "categorization tool" is software or algorithms used to group users with similar concerns or problems.

[1827] A "community" is a group of people in the same situation who come together to exchange information and provide support.

[1828] An "interface" is a screen or device that allows a user to input data into a system or receive output information.

[1829] An "emotion analysis engine" is software that analyzes and recognizes user emotions from text data.

[1830] "Means for displaying on the smartphone screen" refers to a function for visually presenting the generated advice or information on the smartphone display.

[1831] This invention is a system that analyzes text data entered by users about their worries and emotions, provides advice based on that data, and creates a community of people with the same worries. The system records users' emotions and worries daily and provides an interface for analyzing them. It also generates advice based on the analysis results and displays it on a smartphone screen.

[1832] The system consists of three main components:

[1833] 1. Server:

[1834] The server is centrally located and receives the data sent by users, analyzes it, and processes it.

[1835] The server is installed with a natural language processing (NLP) engine, a sentiment analysis engine, and artificial intelligence models for advice generation (e.g., OpenAI's GPT-3 and ChatGPT).

[1836] Specifically, the text data received by the server is analyzed using an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to recognize emotions.

[1837] A sentiment analysis engine (e.g., Watson's Tone Analyzer) provides detailed analysis of the user's emotions.

[1838] An artificial intelligence model for generating advice generates advice based on the analysis results and provides data for displaying that advice on a smartphone screen.

[1839] 2. Terminal:

[1840] A terminal is a device that allows a user to input text data, including a smartphone, tablet, or PC.

[1841] The terminal transmits the input text data to the server and receives a response from the server.

[1842] Specifically, the user enters "I can't sleep lately because of work stress" into the text box and presses the send button. The device then sends this data to the server.

[1843] In response from the server, analysis results, advice, information on specialist institutions, and a link to join the community are displayed on the smartphone screen.

[1844] 3. User:

[1845] Users use this system to input their worries and feelings and receive support.

[1846] Users input their daily feelings and worries into the system, receive generated advice and information, and act based on it.

[1847] For example, if a user types, "Recently, I've been unable to sleep due to work stress," the server analyzes this text data, and the NLP engine extracts the keywords "work," "stress," and "can't sleep." The sentiment analysis engine recognizes the user's emotions as "anxiety" and "stress." The AI ​​model then generates advice based on these keywords and emotions, suggesting things like "taking deep breaths to relax" and "maintaining a bedtime routine." It also provides information about related specialist organizations and encourages users to join a community where users with the same concerns gather.

[1848] To further illustrate this, here are some example prompts:

[1849] User said: I can't sleep lately because of work stress.

[1850] Generate appropriate advice based on that emotion.

[1851] This will allow users to continuously record their worries and feelings and receive appropriate advice based on that information. It will also make it easier for them to receive psychological support through interactions with other users who have similar worries.

[1852] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1853] Step 1:

[1854] A user inputs text data into a terminal.

[1855] Specifically, users use devices such as smartphones, tablets, and PCs to input their worries and feelings, such as "I haven't been able to sleep lately because of work stress," into the system's interface.

[1856] Input: Text data of worries and emotions

[1857] Output: The input text data

[1858] Step 2:

[1859] The terminal transmits the input text data to the server.

[1860] Specifically, the input text data (e.g., "I can't sleep lately because of work stress") is sent from the device to the server. This communication uses the Internet Protocol.

[1861] Input: Entered text data

[1862] Output: Text data sent to the server

[1863] Step 3:

[1864] The server stores the received text data and starts text analysis.

[1865] Specifically, the server uses an NLP engine (e.g., SpaCy or Google Cloud Natural Language API) to tokenize the received text data and extract keywords and important phrases. For example, keywords such as "work," "stress," and "can't sleep" are extracted.

[1866] Input: Transmitted text data

[1867] Output: Extracted keywords and phrases

[1868] Step 4:

[1869] The server analyzes the extracted keywords and phrases using a sentiment analysis engine to recognize the user's emotions.

[1870] Specifically, an emotion engine (e.g., Watson's Tone Analyzer) analyzes text data and identifies emotions such as anxiety and stress. For example, "anxiety" and "stress" are recognized.

[1871] Input: Extracted keywords or phrases

[1872] Output: Recognized emotion

[1873] Step 5:

[1874] The server generates advice using an artificial intelligence model based on the recognized emotions and keywords.

[1875] Specifically, a generative AI model (e.g., OpenAI's ChatGPT) generates advice based on the prompt, such as "take a deep breath and relax" or "maintain a bedtime routine."

[1876] Input: Recognized emotions and keywords

[1877] Output: Generated advice

[1878] Step 6:

[1879] The server formats the generated advice into a format suitable for display on the smartphone screen and sends it to the device.

[1880] Specifically, the server formats the advice content into an appropriate format, such as JSON, and sends it to the user's device.

[1881] Input: Generated advice

[1882] Output: Formatted advice data

[1883] Step 7:

[1884] The device will display on the screen the advice provided, information about specialist agencies, and an invitation to join the community.

[1885] Specifically, the device analyzes the data received from the server and visually displays it to the user. The user can then check the advice and information displayed on the screen and, if necessary, contact a specialist institution or join a community.

[1886] Input: formatted advice data, professional information, community links

[1887] Output: Advice and links displayed on the smartphone screen

[1888] 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.

[1889] 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.

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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.

[1894] 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).

[1895] 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.

[1896] 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."

[1897] 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.

[1898] 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).

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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.

[1909] The following is further disclosed regarding the above embodiment.

[1910] (Claim 1)

[1911] A means for receiving text data of worries and feelings input by a user;

[1912] A means for analyzing the received text data and extracting keywords and important phrases;

[1913] using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and

[1914] A means of obtaining information about specialist agencies and support services that respond to the advice generated; and

[1915] A means to categorize users with related concerns and automatically generate communities where people in the same situation can gather,

[1916] a means for providing generated advice, professional information, and community invitation links to users;

[1917] A system including:

[1918] (Claim 2)

[1919] 2. The system according to claim 1, wherein the input of the user's worries and emotions is analyzed using natural language processing technology.

[1920] (Claim 3)

[1921] 10. The system according to claim 1, further comprising means for promoting interaction between users in the generated community.

[1922] "Example 1"

[1923] (Claim 1)

[1924] A means for receiving text data of worries and feelings input by a user;

[1925] A means for analyzing the received text data and extracting keywords and important phrases;

[1926] using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and

[1927] A means of obtaining information about specialist agencies and support services that respond to the advice generated; and

[1928] A clustering algorithm is used to categorize users with related concerns, and a means of automatically generating communities where people in similar situations gather.

[1929] a means for providing generated advice, professional information, and community invitation links to users;

[1930] A system including:

[1931] (Claim 2)

[1932] 2. The system according to claim 1, wherein the input of the user's worries and emotions is analyzed using natural language processing technology.

[1933] (Claim 3)

[1934] 10. The system according to claim 1, further comprising means for promoting interaction between users in the generated community.

[1935] "Application Example 1"

[1936] (Claim 1)

[1937] A means for receiving text data of worries and feelings input by a user;

[1938] A means for analyzing the received text data and extracting keywords and important phrases;

[1939] using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and

[1940] A means of obtaining information about specialist agencies and support services that respond to the advice generated; and

[1941] A means to categorize users with related concerns and automatically generate communities where people in the same situation can gather,

[1942] a means for providing generated advice, professional information, and community invitation links to users;

[1943] A means to generate appropriate advice for the concerns and feelings entered by store employees and customers, and to provide related community information;

[1944] A system including:

[1945] (Claim 2)

[1946] 2. The system according to claim 1, wherein the input of the user's worries and emotions is analyzed using natural language processing technology.

[1947] (Claim 3)

[1948] 10. The system according to claim 1, further comprising means for promoting interaction between users in the generated community.

[1949] "Example 2: Combining Emotion Engines"

[1950] (Claim 1)

[1951] A means for receiving text data of worries and feelings input by a user;

[1952] A means for analyzing the received text data and extracting keywords and important phrases;

[1953] using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and

[1954] A means of obtaining information about specialist agencies and support services that respond to the advice generated; and

[1955] A means for recognizing a user's emotion based on the extracted emotion data;

[1956] A means for adjusting the generated advice content based on the emotion data and keywords;

[1957] A method for automatically generating communities of people with similar concerns by categorizing them using clustering techniques, and

[1958] a means for providing generated advice, professional information, and community invitation links to users;

[1959] A system including:

[1960] (Claim 2)

[1961] 2. The system according to claim 1, wherein the input of the user's worries and emotions is analyzed using natural language processing technology.

[1962] (Claim 3)

[1963] 10. The system according to claim 1, further comprising means for promoting interaction between users in the generated community.

[1964] "Application example 2 when combining emotion engines"

[1965] (Claim 1)

[1966] A means for receiving text data of worries and feelings input by a user;

[1967] A means for analyzing the received text data and extracting keywords and important phrases;

[1968] using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and

[1969] A means of obtaining information about specialist agencies and support services that respond to the advice generated; and

[1970] A means to categorize users with related concerns and automatically generate communities where people in the same situation can gather,

[1971] a means for providing generated advice, professional information, and community invitation links to users;

[1972] A means to provide an interface for users to record their daily emotions and worries, and

[1973] A means for analyzing text data entered by a user using a sentiment analysis engine and recognizing emotions;

[1974] a means for displaying the generated advice on a smartphone screen;

[1975] A system including:

[1976] (Claim 2)

[1977] The system according to claim 1, wherein the system analyzes the user's input of worries and emotions using natural language processing technology and optimizes the generated advice.

[1978] (Claim 3)

[1979] 10. The system according to claim 1, further comprising a communication means for promoting interaction between users in the generated community. [Explanation of symbols]

[1980] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving text data of worries and feelings input by a user; A means for analyzing the received text data and extracting keywords and important phrases; using an artificial intelligence model to generate recommendations based on the extracted keywords and phrases; and A means of obtaining information about specialist agencies and support services that respond to the advice generated; and A means to categorize users with related concerns and automatically generate communities where people in the same situation can gather, a means for providing generated advice, professional information, and community invitation links to users; A system including:

2. 2. The system according to claim 1, wherein the input of the user's worries and feelings is analyzed using natural language processing technology.

3. The system according to claim 1 , further comprising means for promoting interaction between users in the created community.

Images