system

Abstract

We provide the system. [Solution] A means of receiving the content of the consultation, A means of analyzing the received consultation content using natural language processing, A means for generating a primary response based on the analysis results and providing it to the user, A means of determining the severity of the consultation, When it is determined that expert assistance is necessary, a means of providing expert information and guiding the user to the appropriate expert is provided. A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, social problems such as social withdrawal and suicide are worsening, and rapid and appropriate responses are required to solve these problems. However, in many current systems, there is no mechanism to appropriately analyze the problems faced by users and quickly involve experts when necessary. As a result, individuals with problems cannot receive appropriate support, and the problems often become more serious.

Means for Solving the Problems

[0005] This invention provides a system that receives inquiries from users, analyzes them using natural language processing, and generates an initial response. Furthermore, it determines the severity of the inquiry and, if it is determined that professional assistance is necessary, provides the user with information on experts and guides them to the appropriate specialist. It also manages consultation appointments with experts and collects fees when an appointment is confirmed. This allows users to receive prompt and appropriate support, preventing the problem from escalating. In addition, collecting feedback from users after the consultation can be used to improve the system and evaluate the experts.

[0006] "Consultation details" refer to information describing the problems or concerns that the user is facing.

[0007] "Natural language processing" refers to the technology of analyzing, understanding, and generating human language.

[0008] "Initial response" refers to the solutions and advice provided in response to an initial consultation.

[0009] "Severity" is an indicator used to assess the seriousness and urgency of the problem being discussed.

[0010] An "expert" refers to a person who possesses advanced knowledge and experience in a specific field and can provide appropriate responses and advice.

[0011] "Guidance" refers to the process of directing users to the information and resources they need.

[0012] "Reservation management" refers to a system function that coordinates and confirms the date and time of consultation with an expert.

[0013] "Fee collection" refers to the process of collecting payment from users for consultation services provided by experts.

[0014] "Feedback" refers to information collected from users, including their opinions and evaluations, that is used to improve systems and services. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The program's processing is described in detail below.

[0037] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[0038] The server analyzes the received inquiry using natural language processing technology and generates an initial response. This initial response is provided to the user in the form of specific advice and solutions. This allows the user to obtain initial information to resolve the issue themselves.

[0039] Furthermore, the server determines the severity of the consultation. If the severity is high and professional assistance is deemed necessary, the server provides the user with a list of specialists and information on available appointment times. The user can then select an appropriate specialist from the provided list and confirm the appointment.

[0040] The server collects a monthly fee from the user once the reservation is confirmed. This fee collection process is based on payment methods such as credit card information. Expert fees are distributed appropriately after the consultation has taken place.

[0041] At the scheduled time, the expert will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This will allow the user to receive concrete support for resolving their problems.

[0042] After the consultation ends, the server sends the user a feedback form. Through this form, the user submits their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0043] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." However, if the user replies, "I still think that's difficult," the server determines that professional help is needed and directs the user to a specialist.

[0044] Next, let's consider the case of someone contemplating suicide. If a user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It provides information on the most readily available specialists, and the user can quickly schedule a consultation with one of them.

[0045] As a result, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] User:

[0049] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[0050] Step 2:

[0051] Terminal:

[0052] The user's inputted consultation details are sent to the server.

[0053] Step 3:

[0054] server:

[0055] The received consultation content is passed to a natural language processing engine for analysis.

[0056] Step 4:

[0057] server:

[0058] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[0059] Step 5:

[0060] server:

[0061] The generated primary response is formatted and sent to the terminal for the user to receive.

[0062] Step 6:

[0063] User:

[0064] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[0065] Step 7:

[0066] server:

[0067] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[0068] Step 8:

[0069] server:

[0070] If the situation is deemed serious and requires professional assistance, the user will be provided with a list of available specialists and information on their available appointment times.

[0071] Step 9:

[0072] User:

[0073] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[0074] Step 10:

[0075] server:

[0076] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[0077] Step 11:

[0078] server:

[0079] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[0080] Step 12:

[0081] server:

[0082] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[0083] Step 13:

[0084] Experts:

[0085] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[0086] Step 14:

[0087] User:

[0088] You will receive a consultation from an expert at the scheduled time.

[0089] Step 15:

[0090] server:

[0091] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[0092] Step 16:

[0093] User:

[0094] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[0095] Step 17:

[0096] server:

[0097] Based on the feedback, we will improve the system and conduct expert evaluations.

[0098] The above outlines the specific processing steps and the actions performed at each step. Through these steps, users can receive efficient and appropriate support.

[0099] (Example 1)

[0100] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] The present invention aims to provide a system that offers appropriate and prompt support to users when they face major social problems. In particular, it aims to provide a seamless process for appropriately assessing the severity of the consultation, promptly referring users to experts when necessary, and conducting online or offline consultations.

[0102] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0103] In this invention, the server includes means for the user to input and transmit consultation content using a terminal, means for analyzing the received consultation content using natural language processing technology, and means for generating an initial response based on the analysis results and providing it to the user. This makes it possible to quickly provide initial support to the problems faced by the user, appropriately assess the severity of the problem, and provide expert support in a timely manner.

[0104] A "user" refers to an individual who accesses the system and enters their problems or concerns into the consultation form.

[0105] A "terminal" refers to an electronic device, such as a computer or smartphone, that a user uses to access a system.

[0106] "Consultation content" refers to the text data of the user's problems or concerns that they input and submit to the system.

[0107] A "server" refers to a remote computer system that receives user inquiries, analyzes them using natural language processing technology, and executes various functions.

[0108] "Natural language processing technology" is a general term for algorithms and programs that enable computers to understand human language, and is used for analysis and generating first-line responses.

[0109] "Initial response" refers to the initial response provided by the server to the user after analyzing the inquiry, including the first advice and solutions.

[0110] "Severity" refers to the criteria used to determine the importance and urgency of the matter being discussed.

[0111] A "specialist" refers to a professional who possesses the qualifications and knowledge to provide support and advice based on the user's inquiries.

[0112] "Guidance" refers to the process by which a server provides a user with information about the appropriate expert and encourages the user to consult with that expert.

[0113] "Reservation" refers to the procedure for users to confirm the date and time of a consultation with an expert, as well as the information confirming that reservation.

[0114] "Fee collection" refers to the process of requiring users to pay a fee for consultations with experts.

[0115] "Feedback" refers to the opinions and evaluations collected from users after a consultation has taken place.

[0116] Modes for carrying out the invention

[0117] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The specific configuration and operation of the system will be described below.

[0118] System Configuration

[0119] The system mainly consists of the following elements:

[0120] 1. The device the user will be using (e.g., PC, smartphone)

[0121] 2. Server

[0122] 3. Natural language processing technology (specifically, OpenAI's GPT-4®)

[0123] 4. Payment services (e.g., PayPal, Stripe)

[0124] 5. Video conferencing systems (e.g., Zoom, MICROSOFT® TEAMS®)

[0125] Hardware and software usage

[0126] terminal

[0127] A terminal is a device used by a user to access the system, and is not dependent on any particular type as long as it has an internet connection. The terminal has a web browser or a dedicated application installed, through which the user accesses the system's consultation form.

[0128] server

[0129] The server is the central computing resource for receiving, analyzing, and generating responses to inquiries submitted by users. The server has the following main functions:

[0130] Data reception: Receives consultation content sent by the user from their device.

[0131] Natural Language Processing: Received text data is analyzed using natural language processing techniques (such as GPT-4).

[0132] Primary response generation: Generates a primary response based on the analysis results and provides it to the user.

[0133] Severity Assessment: Evaluate the severity of the consultation content.

[0134] Expert Information Provision: If the severity is determined to be high, the user will be provided with a list of experts and an appointment will be scheduled with them.

[0135] Payment Collection: After the reservation is confirmed, the fee will be collected based on the user's payment information.

[0136] Feedback Collection: Collect feedback from users after the consultation is complete.

[0137] Natural Language Processing Technology

[0138] The server uses natural language processing technology to analyze the user's inquiry and generate an appropriate initial response. Specifically, OpenAI's GPT-4 is used. This allows for the automatic generation of appropriate responses to user input.

[0139] Payment services

[0140] External payment services (e.g., PayPal, Stripe) are used for fee collection, providing a secure payment environment.

[0141] Video conferencing system

[0142] When experts provide online consultations to users, video conferencing systems (e.g., Zoom, Microsoft Teams) are used. This allows for real-time consultations with experts located in remote areas.

[0143] Specific example

[0144] Example 1: Consultation regarding social withdrawal

[0145] A user submits a question saying, "I've been a hikikomori (social recluse) for the past year. How can I get myself to go outside?" The server uses natural language processing technology to analyze the question and generates an initial response: "Let's start with small steps. Try taking a walk around your neighborhood." If the user replies, "I still think that's difficult," the server determines the situation is serious and provides a list of experts.

[0146] Example 2: If you are considering suicide

[0147] If a user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with them.

[0148] Example of a prompt

[0149] "I've been a recluse for the past year. How can I get myself to go outside?"

[0150] "I'm thinking of committing suicide. Please help me."

[0151] In this way, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[0152] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0153] Processing steps:

[0154] Step 1:

[0155] Users access the system's consultation form using their device, enter their problems or concerns, and submit it. The input includes the user's text data, and the output is the device sending this text data to the server.

[0156] Step 2:

[0157] The server receives the consultation content sent by the user. The input includes the user's text data, and the received text data is stored on the server as output.

[0158] Step 3:

[0159] The server analyzes the received consultation content using natural language processing technology. Specifically, a natural language processing model (e.g., GPT-4) is used for the analysis. The input includes the user's text data, and the output is a data structure generated based on the analysis results.

[0160] Step 4:

[0161] The server generates a primary response based on the analysis results and provides it to the user. The primary response is generated using a generative AI model. The input includes the analysis results, and the output is the text data of the primary response. The server sends this text data to the user's terminal.

[0162] Step 5:

[0163] The server determines the severity of the consultation. Specifically, a natural language processing model evaluates the severity. The input includes the analysis results of the consultation content, and the output generates the severity determination result.

[0164] Step 6:

[0165] If the server determines the situation is serious, it will provide expert information and guide the user to a specialist. The input includes the severity assessment result, and the output generates a list of specialists and available appointment times. The server then sends this information to the user's terminal.

[0166] Step 7:

[0167] The user selects the appropriate expert from the presented list and confirms the reservation. The input includes the expert list and the user's selection, and the output generates reservation information. The user then sends this information to the server.

[0168] Step 8:

[0169] The server collects a monthly fee from the user once the reservation is confirmed. Input includes reservation information and the user's payment information, and output is a confirmation of the fee collection. An external payment service is used for fee collection.

[0170] Step 9:

[0171] Experts conduct online or offline consultations with users. Input includes appointment information, and output is a consultation report. The expert then sends this report to the server.

[0172] Step 10:

[0173] After the consultation ends, the server sends a feedback form to the user. The input includes the consultation report, and the output is the result of submitting the feedback form. The user fills out the feedback and submits it to the server.

[0174] Step 11:

[0175] The server collects user feedback to help improve the system and for expert evaluation. Input includes user feedback data, and output generates improvement suggestions and evaluation results.

[0176] (Application Example 1)

[0177] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] When users face urgent security issues, it is difficult for them to receive prompt and appropriate assistance. Existing systems take time to immediately find the right expert based on the severity of the problem and to schedule a consultation, increasing user anxiety and risk. Therefore, there is a need for a system that can respond quickly to security issues and ensure user safety.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0180] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for determining the severity of the consultation content, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, means for managing reservations for consultations with experts and collecting fees when reservations are confirmed, and means for immediately providing expert information and facilitating a rapid response in the case of security-related emergencies. As a result, the user can receive prompt and appropriate support even in emergencies.

[0181] "Consultation details" refer to information describing the problems or concerns that the user is facing.

[0182] A "means of receiving information" refers to an interface that allows users to input their inquiries into the system and retrieve that information.

[0183] "Natural language processing" is a technology that analyzes text data entered by users to understand its meaning and intent.

[0184] "Initial response" refers to a response that includes initial advice or solutions, and is information provided to the user.

[0185] A "means for determining severity" refers to an algorithm that evaluates the urgency and importance of the consultation content and determines the need for appropriate action.

[0186] "Expert information" refers to data regarding the contact information and available times of experts who possess the skills and knowledge to address a specific problem.

[0187] "Means of guidance" refers to functions that guide users to appropriate specialists and establish a process for receiving support.

[0188] "Methods for managing reservations" refers to a system that sets and adjusts consultation dates and times with experts, and retains that information once the time is confirmed.

[0189] "Means of collecting fees" refers to the process of collecting appropriate fees from users once a consultation is confirmed.

[0190] "Means for collecting feedback" refers to a function that obtains opinions and impressions from users after a consultation and uses them to improve the system and evaluate experts.

[0191] "A method for recommending the most suitable expert from multiple experts" refers to an algorithm that selects and recommends the most appropriate expert based on the user's consultation content.

[0192] "Security-related urgent issues" refer to safety-related problems that require a swift response, such as intruder intrusions or stalking incidents.

[0193] "Means to facilitate rapid response" refers to a function that provides immediate access to expert information regarding urgent security issues, enabling a swift response.

[0194] To implement this invention, the process begins with the user launching the "Emergency Support Help" application on their smartphone. The user first enters their problem into the consultation form within the application. This problem is entered in text format and sent to the server.

[0195] The server receives this input and analyzes its content using natural language processing (NLP) techniques. It utilizes SpaCy software and a Japanese NLP model. Based on the analysis, it generates a preliminary response and sends it back to the user. This preliminary response provides quick initial advice and simple solutions.

[0196] Next, the server executes an algorithm to determine the severity of the consultation. This severity assessment analyzes the keywords and context of the entered consultation and scores its importance. Based on the results, if the severity is high, expert information is provided and the user is guided to a specialist.

[0197] Expert information includes the expert's contact details and available times. Users select an expert they wish to consult from the presented list and make a consultation appointment. The server manages the appointments and collects fees using the user's credit card information once the appointment is confirmed. This ensures that experts receive timely and appropriate compensation, and users receive prompt and professional assistance.

[0198] Furthermore, in the event of a security-related emergency (such as an intruder's visit or stalking), this system immediately provides expert information to facilitate a rapid response. This allows users to deal with problems with peace of mind even in emergencies.

[0199] After the consultation ends, the server sends the user a feedback form. The user uses this form to provide feedback on the consultation and opinions on application features. This feedback helps improve the system and contributes to a better user experience.

[0200] As a concrete example, consider a scenario where a user enters a message stating, "An intruder has entered my home." The server instantly analyzes this message and determines its severity. As a result, the server responds with, "Please call 110 immediately. The following specialists are available to assist you," and provides a list of specialists. This entire process allows the user to receive quick and appropriate assistance.

[0201] An example of a prompt sentence to input into the generating AI model is, "How should an emergency message regarding an intruder be handled, and what advice should be provided?"

[0202] Based on the above, this invention provides a system that enables a rapid and appropriate response to urgent security-related issues.

[0203] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0204] Step 1:

[0205] The user launches the "Emergency Support Help" application on their smartphone and enters their problem into the consultation form. The entered information is sent from the device to the server in text format. Specifically, when the user enters their problem and presses the submit button, the information is sent to the server via an HTTP request.

[0206] Step 2:

[0207] The server analyzes the received consultation content using natural language processing technology. Here, natural language processing libraries such as SpaCy are used to analyze the input text data. The input is the text data of the user's consultation, and the output is the analysis results, including the meaning and importance of that content.

[0208] Step 3:

[0209] Based on the analysis results, the server generates an initial response and provides it to the user. Using a generation AI model, it outputs appropriate advice and solutions. The input is the analysis results obtained in the previous step, and the output is the text message of the initial response provided to the user. Specifically, the generated response message is sent to the terminal in JSON format and displayed on the terminal.

[0210] Step 4:

[0211] The server executes an algorithm to determine the severity of the consultation. For example, it calculates a severity score based on keyword frequency and context. The input is the analysis result, and the output is the severity score. Specifically, it uses a numerical engine that determines urgency based on whether a particular keyword is included and from the context.

[0212] Step 5:

[0213] If the severity level is high, the server provides expert information and directs the user to an expert. Expert information includes contact details and available time slots. The input is the severity score, and the output is a list of experts provided to the user. Specifically, it queries the database for available experts and retrieves information about the relevant experts.

[0214] Step 6:

[0215] The user selects a specialist from a presented list and makes a consultation appointment. The terminal displays the options and accepts the user's selection. The input is the user's selection, and the output is the appointment information. Specifically, when the user selects a specialist, the selection is sent to the server and the appointment is recorded.

[0216] Step 7:

[0217] The server collects payment using the user's credit card information once the reservation is confirmed. Payment is made using an online payment gateway. Inputs are reservation information and payment information, and output is a notification of payment completion. Specifically, it calls a payment API to execute the payment process and notifies the user of the result.

[0218] Step 8:

[0219] After the consultation ends, the server sends a feedback form to the user. The terminal displays the form and accepts the user's feedback. The input is the consultation completion event, and the output is the feedback information. Specifically, the feedback form is sent to the user's terminal in JSON format, and the user's input is returned to the server.

[0220] Step 9:

[0221] The collected feedback is analyzed on the server and used to improve the system. The input is feedback data, and the output is improvement suggestions and evaluation reports. Specifically, the feedback data is statistically analyzed to identify areas for system improvement.

[0222] This series of steps ensures that users receive prompt and appropriate assistance even in emergencies.

[0223] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0224] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with an emotion engine that recognizes the user's emotions, a more personalized response can be achieved.

[0225] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[0226] The server passes the received inquiry to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[0227] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is also used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong sadness or despair, the severity will be set high.

[0228] If the situation is deemed serious and requires professional intervention, the server provides the user with a list of specialists and information on available appointment times. A feature is also included that recommends the most suitable specialist based on emotions recognized by an emotion engine. This allows users to select the most appropriate specialist who is emotionally supportive.

[0229] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects a monthly fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is conducted.

[0230] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for resolving their problems.

[0231] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0232] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[0233] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[0234] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] User:

[0238] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[0239] Step 2:

[0240] Terminal:

[0241] The user's inputted consultation details are sent to the server.

[0242] Step 3:

[0243] server:

[0244] The received consultation content is passed to a natural language processing engine for analysis.

[0245] Step 4:

[0246] server:

[0247] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[0248] Step 5:

[0249] server:

[0250] The generated primary response is formatted and sent to the terminal for the user to receive.

[0251] Step 6:

[0252] User:

[0253] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[0254] Step 7:

[0255] server:

[0256] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[0257] Step 8:

[0258] server:

[0259] Furthermore, the consultation content is passed to an emotion engine to recognize the user's emotions.

[0260] Step 9:

[0261] server:

[0262] The severity level is adjusted based on the results of the emotion engine. For example, if the user is showing strong sadness or despair, the severity level is set higher.

[0263] Step 10:

[0264] server:

[0265] If the situation is deemed serious and requires professional intervention, the user will be provided with a list of available specialists and information on their available appointment times. Furthermore, based on the results of the emotion engine, the most suitable specialist will be recommended.

[0266] Step 11:

[0267] User:

[0268] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[0269] Step 12:

[0270] server:

[0271] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[0272] Step 13:

[0273] server:

[0274] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[0275] Step 14:

[0276] server:

[0277] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[0278] Step 15:

[0279] Experts:

[0280] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[0281] Step 16:

[0282] User:

[0283] Receive consultation from an expert at the reserved time.

[0284] Step 17:

[0285] Server:

[0286] After the consultation ends, send a feedback form to the user and collect information on the consultation content and satisfaction.

[0287] Step 18:

[0288] User:

[0289] Submit feelings about the consultation and opinions on the system through the feedback form.

[0290] Step 19:

[0291] Server:

[0292] Improve the system and evaluate the experts based on the feedback.

[0293] As a specific example, consider a consultation about hikikomori. Assume a case where a user sends a consultation saying, "I have been shut-in for the past year. How can I go outside?" The server analyzes it using natural language processing and generates a primary response such as "Let's start with small steps. Try starting by taking a walk around your house." On the other hand, if the emotion engine senses strong anxiety or fear from the user's input, it determines a higher level of severity and prioritizes referral to an expert.

[0294] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[0295] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[0296] (Example 2)

[0297] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0298] There is a challenge in providing prompt and appropriate support for the various social problems faced by those seeking help. Conventional systems often lack sufficient analysis of consultation content and assessment of severity, making personalized responses difficult. Furthermore, they lack the ability to recognize and respond to emotions, making it difficult to provide empathetic support. Therefore, there is a need for a system that can quickly match individuals with appropriate professionals and provide effective support.

[0299] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0300] In this invention, the server includes means for receiving consultation content from a user, means for analyzing the received consultation content through natural language processing, means for generating a primary response based on the analysis result and providing it to the user, an emotion recognition means for recognizing the emotion of the consultation content, means for determining the severity of the consultation content based on the recognized emotion, and means for providing expert information and guiding the user to an expert when it is determined that specialized handling is necessary according to the severity, and means for managing the reservation of consultations with experts and collecting fees when the reservation is confirmed. Thereby, it becomes possible to provide prompt and appropriate support that conforms to the emotions of the person seeking consultation.

[0301] The "user" refers to an individual or a corporation that inputs consultation content using the system and seeks support.

[0302] The "consultation content" refers to the text and information about the troubles and problems that the user inputs through the system.

[0303] "Natural language processing" is a technology that analyzes the consultation content input by the user and extracts its intention and important information.

[0304] The "primary response" refers to the initial advice or solution generated by the server based on the result of natural language processing.

[0305] "Emotion recognition" is a technology for identifying emotions such as anger, sadness, fear, surprise, and happiness from the consultation content of the user.

[0306] The "severity" refers to the degree of importance of the problem judged based on the consultation content of the user and the recognized emotion.

[0307] The "expert" refers to an individual or a group having specialized knowledge and experience corresponding to the consultation content, and refers to a person who provides specific support and advice to the user.

[0308] The "expert information" refers to information such as the name, qualifications, contact information, and available consultation time of the expert.

[0309] A "reservation" is the process of securing a designated date and time for a user to consult with an expert.

[0310] "Fee collection" refers to the process of receiving payment from the user once a consultation with an expert has been confirmed, and generally involves using payment methods such as credit cards.

[0311] "Feedback" refers to the opinions and impressions that users provide after a consultation, and this information is used to improve the system and evaluate experts.

[0312] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with a function that recognizes the user's emotions, it achieves a more personalized response.

[0313] First, the user accesses the system's consultation form using their device and enters their problem or concern. The device then sends this consultation information to the server. The device can be a typical personal computer, smartphone, or tablet.

[0314] The server passes the received inquiry to a natural language processing engine (for example, Google's Cloud Natural Language API, a common API) for analysis. Based on the analysis results, the server generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[0315] Furthermore, the server determines the severity of the consultation. In this process, it utilizes an emotion recognition engine (for example, IBM Watson® Tone Analyzer, a common API) to recognize emotions from the user's consultation. The emotion recognition engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and uses the results to determine the severity. For example, if the user expresses strong sadness or despair, the severity level will be set high.

[0316] If the situation is deemed serious and requires professional intervention, the server will provide the user with a list of specialists and information on available appointment times. The system also incorporates a feature that recommends the most suitable specialist based on emotions recognized by an emotion recognition engine. This allows users to select the most appropriate specialist who can empathize with their situation.

[0317] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects the fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is completed.

[0318] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. For online consultations, common video conferencing tools (such as Zoom) can be used. This allows the user to receive concrete support for problem-solving.

[0319] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0320] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation request saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the request and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion recognition engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[0321] Furthermore, let's consider the case of someone contemplating suicide. If the user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion recognition engine highly values ​​the user's sense of despair. The server then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with one of them.

[0322] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] The processing flow of this system's program

[0325] Step 1: User inquiry input

[0326] The user accesses the system's consultation form using their device and enters their concerns or problems. This input is entered into the text box on the device, and the user proceeds to the next step when the submit button is pressed.

[0327] Input: The content of the consultation entered by the user (e.g., "Lately, I've been feeling miserable every day and can't concentrate on anything.")

[0328] Output: Signal on which the consultation content is transmitted

[0329] Step 2: Send the consultation details to the server

[0330] The terminal sends the user's inputted consultation details to the server. During this process, the consultation details are transferred to the server using a secure communication protocol (HTTPS).

[0331] Input: The consultation content retrieved from the text field when the submit button was pressed.

[0332] Output: Data of the consultation content sent to the server

[0333] Step 3: Analysis using natural language processing

[0334] The server passes the received consultation data to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the text data and extracts important keywords and the intent of the sentences.

[0335] Input: Text data of the submitted consultation content

[0336] Output: Analysis results (e.g., extracted keywords and intent)

[0337] Step 4: Generate and send the first response.

[0338] The server generates a preliminary response based on the results of natural language processing analysis and provides it to the user. This response includes specific advice and solutions. The preliminary response is automatically generated using a generative AI model.

[0339] Input: Natural language processing analysis results

[0340] Output: Text data of the generated primary response (e.g., "I understand how you're feeling. Please try starting with something small.")

[0341] Step 5: Emotion recognition by the emotion recognition engine

[0342] The server passes the consultation content to an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotions. The emotion recognition engine analyzes the consultation content and provides a numerical representation of the user's emotions.

[0343] Input: Text data of the user's inquiry.

[0344] Output: Emotional analysis results (e.g., a numerical value representing the degree of sadness)

[0345] Step 6: Determining the severity

[0346] The server determines the severity of the consultation based on the results of the emotion recognition engine. The severity level is used as a criterion for determining whether professional intervention is necessary.

[0347] Input: Analysis results of emotion recognition

[0348] Output: Severity assessment result (e.g., Severity = High)

[0349] Step 7: Provide a list of specialists and appointment times.

[0350] If the severity is determined to be high, the server will provide the user with a list of specialists and information on available appointment times. This allows the user to select the most suitable specialist.

[0351] Input: Severity assessment result

[0352] Output: List of specialists and available time slots

[0353] Step 8: Recommendation of the best expert

[0354] Based on the results of the emotion recognition engine, the server recommends the most suitable expert to the user. This allows the user to choose the expert best suited to their situation.

[0355] Input: Analysis results of emotion recognition

[0356] Output: Recommendations from the best experts

[0357] Step 9: Select a specialist and confirm your appointment

[0358] The user selects the appropriate expert from the presented list and confirms the reservation. This information is then sent back to the server, and the reservation is confirmed.

[0359] Input: User-selected expert and appointment time

[0360] Output: Confirmed reservation information

[0361] Step 10: Fee collection based on payment information

[0362] Once a reservation is confirmed, the server collects the fee based on the user's registered payment information. A secure payment system (e.g., online payment service) is used in this process.

[0363] Input: User's payment information and confirmed reservation information

[0364] Output: Confirmation information for fee collection

[0365] Step 11: Conducting consultations and providing support

[0366] At the scheduled time, the expert will conduct a consultation with the user, either online or offline. For online consultations, a video call tool will be used.

[0367] Input: Appointed time and consultation details

[0368] Output: Consultation content and support from experts

[0369] Step 12: Gathering and evaluating feedback

[0370] After the consultation ends, the server sends the user a feedback form to collect information about the consultation and satisfaction level. This feedback is used to improve the system and evaluate the experts.

[0371] Input: Access link to the feedback form after the consultation.

[0372] Output: User-filled feedback information

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0375] This invention relates to a system that provides rapid and appropriate support to users when they are experiencing psychological crises or emotional stress. Conventional systems have faced challenges in accurately understanding the user's emotional state and providing empathetic support. Furthermore, seamlessly handling a series of processes such as quickly referring users to specialists, managing appointments, and collecting fees has also been difficult.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing emotions from the consultation content and adjusting the severity assessment based on the results, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, and means for managing reservations for consultations with experts and collecting fees when reservations are confirmed. This makes it possible to accurately grasp the user's emotional state and quickly provide support that is sensitive to those emotions.

[0378] "Consultation content" refers to a detailed description of the problems or concerns that the user is facing.

[0379] "Natural language processing" is a technology that allows computers to analyze and understand natural human language.

[0380] "Initial response" refers to the specific advice or solutions provided first in response to a user's inquiry.

[0381] "Severity" is an indicator used to judge the importance and urgency of the matter being discussed.

[0382] "Recognizing emotions" is the process of identifying a user's emotional state based on the information they provide.

[0383] "Expert information" refers to detailed information about experts who possess knowledge and experience in a specific field.

[0384] "Managing appointments" refers to the process of coordinating and confirming consultation schedules between users and experts.

[0385] "Collecting fees" refers to the procedure of receiving payment from users for the services provided.

[0386] "Feedback" refers to the opinions and evaluations that users provide after consulting with us.

[0387] "Recommending the most suitable expert" means suggesting the expert who is best suited to the user's needs based on their consultation.

[0388] This invention provides a system that offers prompt and appropriate support to users experiencing psychological crises or emotional stress. This system seamlessly executes a series of processes, including receiving and analyzing consultation requests, generating initial responses, determining the severity of the situation, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[0389] The overall system flow is as follows: the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal sends this consultation content to the server. The server passes the received consultation content to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[0390] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as joy, sadness, anger, and fear from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong anxiety or despair, the severity will be set high. If the severity is high and it is determined that professional intervention is necessary, the server provides the user with a list of specialists and information on available appointment times. A function to recommend the most suitable specialist based on the emotions recognized by the emotion engine is also included. This allows the user to select the most appropriate specialist who can empathize with their emotions.

[0391] The user selects a suitable expert from the presented list and confirms the appointment. Upon confirmation, the server collects the fee based on the user's registered payment information. The fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation. At the scheduled time, the expert conducts an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for problem-solving.

[0392] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0393] Hardware and software to use

[0394] Hardware:

[0395] Smartphones (iOS, ANDROID (registered trademark))

[0396] software:

[0397] Transformers library (sentiment analysis and natural language processing)

[0398] Backend system using RESTful API

[0399] As a concrete example, consider a case where a user enters a question such as, "I've been feeling very anxious lately. What should I do?"

[0400] Example of a prompt

[0401] "I've been feeling incredibly anxious lately. What should I do?"

[0402] The system receives this input, performs emotional analysis, and provides initial advice such as, "Let's start by creating a daily routine. It's important to set small, gradual goals and feel a sense of accomplishment." Furthermore, if the emotional engine identifies strong anxiety, it sets the severity level higher and provides information on the most readily available specialist. In this way, the system can provide users with quick and appropriate support.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] Users access the system's consultation form using their device and enter their problems or concerns.

[0406] Input: Text input of the user's problem or concern.

[0407] Specific action: The user launches the application on their smartphone and enters text into the consultation form.

[0408] Step 2:

[0409] The device sends this consultation information to the server.

[0410] Input: User's submitted inquiry details

[0411] Output: Data to send to the server

[0412] Specific operation: The terminal sends the entered text to the server via an HTTP request.

[0413] Step 3:

[0414] The server passes the received inquiry details to a natural language processing engine for analysis.

[0415] Input: User's inquiry

[0416] Output: Analysis results

[0417] Specific operation: The server uses a natural language processing library (such as transformers) to analyze the consultation content and perform calculations to understand its content.

[0418] Step 4:

[0419] Based on the analysis results, a primary response is generated and provided to the user.

[0420] Input: Analysis results

[0421] Output: Primary response (advice and solutions)

[0422] Specific operation: The server generates specific advice and solutions from the analysis results and creates text data to provide to the user.

[0423] Step 5:

[0424] The server determines the severity of the consultation.

[0425] Input: Analysis results of the consultation content

[0426] Output: Severity judgment result

[0427] Specific operation: The server uses an emotion engine to perform an emotional analysis of the consultation content and calculates the severity level.

[0428] Step 6:

[0429] The system recognizes emotions from the content of the consultation and adjusts the severity assessment based on those findings.

[0430] Input: Sentiment analysis results

[0431] Output: Adjusted severity assessment result

[0432] Specific operation: The emotion engine identifies emotions from user input and adjusts the severity level determination based on those emotions.

[0433] Step 7:

[0434] If it is determined that expert assistance is required, we will provide information on experts and guide the user to the appropriate specialist.

[0435] Input: Adjusted severity rating result

[0436] Output: Expert information and guidance

[0437] Specific operation: If the severity level is high, the server retrieves appropriate expert information from the database and prepares the data to display to the user.

[0438] Step 8:

[0439] Manage appointments for consultations with experts and collect fees upon appointment confirmation.

[0440] Input: User's reservation selection data, payment information

[0441] Output: Booking confirmation data, Payment confirmation data

[0442] Specific operation: The server processes the reservation selection and payment information, confirms the reservation, processes the payment, and saves the confirmation data.

[0443] Step 9:

[0444] After consulting with experts, we will collect feedback from users.

[0445] Input: User feedback data

[0446] Output: Feedback saved data

[0447] Specific operation: The server receives the feedback provided by the user after the consultation and saves it to the database.

[0448] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0449] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0450] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0451] [Second Embodiment]

[0452] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0453] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0454] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0455] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0456] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0458] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0459] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0460] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0461] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0462] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0463] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0464] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The program's processing is described in detail below.

[0465] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[0466] The server analyzes the received inquiry using natural language processing technology and generates an initial response. This initial response is provided to the user in the form of specific advice and solutions. This allows the user to obtain initial information to resolve the issue themselves.

[0467] Furthermore, the server determines the severity of the consultation. If the severity is high and professional assistance is deemed necessary, the server provides the user with a list of specialists and information on available appointment times. The user can then select an appropriate specialist from the provided list and confirm the appointment.

[0468] The server collects a monthly fee from the user once the reservation is confirmed. This fee collection process is based on payment methods such as credit card information. Expert fees are distributed appropriately after the consultation has taken place.

[0469] At the scheduled time, the expert will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This will allow the user to receive concrete support for resolving their problems.

[0470] After the consultation ends, the server sends the user a feedback form. Through this form, the user submits their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0471] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." However, if the user replies, "I still think that's difficult," the server determines that professional help is needed and directs the user to a specialist.

[0472] Next, let's consider the case of someone contemplating suicide. If a user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It provides information on the most readily available specialists, and the user can quickly schedule a consultation with one of them.

[0473] As a result, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] User:

[0477] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[0478] Step 2:

[0479] Terminal:

[0480] The user's inputted consultation details are sent to the server.

[0481] Step 3:

[0482] server:

[0483] The received consultation content is passed to a natural language processing engine for analysis.

[0484] Step 4:

[0485] server:

[0486] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[0487] Step 5:

[0488] server:

[0489] The generated primary response is formatted and sent to the terminal for the user to receive.

[0490] Step 6:

[0491] User:

[0492] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[0493] Step 7:

[0494] server:

[0495] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[0496] Step 8:

[0497] server:

[0498] If the situation is deemed serious and requires professional assistance, the user will be provided with a list of available specialists and information on their available appointment times.

[0499] Step 9:

[0500] User:

[0501] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[0502] Step 10:

[0503] server:

[0504] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[0505] Step 11:

[0506] server:

[0507] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[0508] Step 12:

[0509] server:

[0510] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[0511] Step 13:

[0512] Experts:

[0513] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[0514] Step 14:

[0515] User:

[0516] You will receive a consultation from an expert at the scheduled time.

[0517] Step 15:

[0518] server:

[0519] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[0520] Step 16:

[0521] User:

[0522] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[0523] Step 17:

[0524] server:

[0525] Based on the feedback, we will improve the system and conduct expert evaluations.

[0526] The above outlines the specific processing steps and the actions performed at each step. Through these steps, users can receive efficient and appropriate support.

[0527] (Example 1)

[0528] Next, we will describe Example 1. 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."

[0529] The present invention aims to provide a system that offers appropriate and prompt support to users when they face major social problems. In particular, it aims to provide a seamless process for appropriately assessing the severity of the consultation, promptly referring users to experts when necessary, and conducting online or offline consultations.

[0530] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0531] In this invention, the server includes means for the user to input and transmit consultation content using a terminal, means for analyzing the received consultation content using natural language processing technology, and means for generating an initial response based on the analysis results and providing it to the user. This makes it possible to quickly provide initial support to the problems faced by the user, appropriately assess the severity of the problem, and provide expert support in a timely manner.

[0532] A "user" refers to an individual who accesses the system and enters their problems or concerns into the consultation form.

[0533] A "terminal" refers to an electronic device, such as a computer or smartphone, that a user uses to access a system.

[0534] "Consultation content" refers to the text data of the user's problems or concerns that they input and submit to the system.

[0535] A "server" refers to a remote computer system that receives user inquiries, analyzes them using natural language processing technology, and executes various functions.

[0536] "Natural language processing technology" is a general term for algorithms and programs that enable computers to understand human language, and is used for analysis and generating first-line responses.

[0537] "Initial response" refers to the initial response provided by the server to the user after analyzing the inquiry, including the first advice and solutions.

[0538] "Severity" refers to the criteria used to determine the importance and urgency of the matter being discussed.

[0539] A "specialist" refers to a professional who possesses the qualifications and knowledge to provide support and advice based on the user's inquiries.

[0540] "Guidance" refers to the process by which a server provides a user with information about the appropriate expert and encourages the user to consult with that expert.

[0541] "Reservation" refers to the procedure for users to confirm the date and time of a consultation with an expert, as well as the information confirming that reservation.

[0542] "Fee collection" refers to the process of requiring users to pay a fee for consultations with experts.

[0543] "Feedback" refers to the opinions and evaluations collected from users after a consultation has taken place.

[0544] Modes for carrying out the invention

[0545] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The specific configuration and operation of the system will be described below.

[0546] System Configuration

[0547] The system mainly consists of the following elements:

[0548] 1. The device the user will be using (e.g., PC, smartphone)

[0549] 2. Server

[0550] 3. Natural language processing technologies (specifically, OpenAI's GPT-4)

[0551] 4. Payment services (e.g., PayPal, Stripe)

[0552] 5. Video conferencing systems (e.g., Zoom, Microsoft Teams)

[0553] Hardware and software usage

[0554] terminal

[0555] A terminal is a device used by a user to access the system, and is not dependent on any particular type as long as it has an internet connection. The terminal has a web browser or a dedicated application installed, through which the user accesses the system's consultation form.

[0556] server

[0557] The server is the central computing resource for receiving, analyzing, and generating responses to inquiries submitted by users. The server has the following main functions:

[0558] Data reception: Receives consultation content sent by the user from their device.

[0559] Natural Language Processing: Received text data is analyzed using natural language processing techniques (such as GPT-4).

[0560] Primary response generation: Generates a primary response based on the analysis results and provides it to the user.

[0561] Severity Assessment: Evaluate the severity of the consultation content.

[0562] Expert Information Provision: If the severity is determined to be high, the user will be provided with a list of experts and an appointment will be scheduled with them.

[0563] Payment Collection: After the reservation is confirmed, the fee will be collected based on the user's payment information.

[0564] Feedback Collection: Collect feedback from users after the consultation is complete.

[0565] Natural Language Processing Technology

[0566] The server uses natural language processing technology to analyze the user's inquiry and generate an appropriate initial response. Specifically, OpenAI's GPT-4 is used. This allows for the automatic generation of appropriate responses to user input.

[0567] Payment services

[0568] External payment services (e.g., PayPal, Stripe) are used for fee collection, providing a secure payment environment.

[0569] Video conferencing system

[0570] When experts provide online consultations to users, video conferencing systems (e.g., Zoom, Microsoft Teams) are used. This allows for real-time consultations with experts located in remote areas.

[0571] Specific example

[0572] Example 1: Consultation regarding social withdrawal

[0573] A user submits a question saying, "I've been a hikikomori (social recluse) for the past year. How can I get myself to go outside?" The server uses natural language processing technology to analyze the question and generates an initial response: "Let's start with small steps. Try taking a walk around your neighborhood." If the user replies, "I still think that's difficult," the server determines the situation is serious and provides a list of experts.

[0574] Example 2: If you are considering suicide

[0575] If a user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with them.

[0576] Example of a prompt

[0577] "I've been a recluse for the past year. How can I get myself to go outside?"

[0578] "I'm thinking of committing suicide. Please help me."

[0579] In this way, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[0580] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0581] Processing steps:

[0582] Step 1:

[0583] Users access the system's consultation form using their device, enter their problems or concerns, and submit it. The input includes the user's text data, and the output is the device sending this text data to the server.

[0584] Step 2:

[0585] The server receives the consultation content sent by the user. The input includes the user's text data, and the received text data is stored on the server as output.

[0586] Step 3:

[0587] The server analyzes the received consultation content using natural language processing technology. Specifically, a natural language processing model (e.g., GPT-4) is used for the analysis. The input includes the user's text data, and the output is a data structure generated based on the analysis results.

[0588] Step 4:

[0589] The server generates a primary response based on the analysis results and provides it to the user. The primary response is generated using a generative AI model. The input includes the analysis results, and the output is the text data of the primary response. The server sends this text data to the user's terminal.

[0590] Step 5:

[0591] The server determines the severity of the consultation. Specifically, a natural language processing model evaluates the severity. The input includes the analysis results of the consultation content, and the output generates the severity determination result.

[0592] Step 6:

[0593] If the server determines the situation is serious, it will provide expert information and guide the user to a specialist. The input includes the severity assessment result, and the output generates a list of specialists and available appointment times. The server then sends this information to the user's terminal.

[0594] Step 7:

[0595] The user selects the appropriate expert from the presented list and confirms the reservation. The input includes the expert list and the user's selection, and the output generates reservation information. The user then sends this information to the server.

[0596] Step 8:

[0597] The server collects a monthly fee from the user once the reservation is confirmed. Input includes reservation information and the user's payment information, and output is a confirmation of the fee collection. An external payment service is used for fee collection.

[0598] Step 9:

[0599] Experts conduct online or offline consultations with users. Input includes appointment information, and output is a consultation report. The expert then sends this report to the server.

[0600] Step 10:

[0601] After the consultation ends, the server sends a feedback form to the user. The input includes the consultation report, and the output is the result of submitting the feedback form. The user fills out the feedback and submits it to the server.

[0602] Step 11:

[0603] The server collects user feedback to help improve the system and for expert evaluation. Input includes user feedback data, and output generates improvement suggestions and evaluation results.

[0604] (Application Example 1)

[0605] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0606] When users face urgent security issues, it is difficult for them to receive prompt and appropriate assistance. Existing systems take time to immediately find the right expert based on the severity of the problem and to schedule a consultation, increasing user anxiety and risk. Therefore, there is a need for a system that can respond quickly to security issues and ensure user safety.

[0607] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0608] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for determining the severity of the consultation content, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, means for managing reservations for consultations with experts and collecting fees when reservations are confirmed, and means for immediately providing expert information and facilitating a rapid response in the case of security-related emergencies. As a result, the user can receive prompt and appropriate support even in emergencies.

[0609] "Consultation details" refer to information describing the problems or concerns that the user is facing.

[0610] A "means of receiving information" refers to an interface that allows users to input their inquiries into the system and retrieve that information.

[0611] "Natural language processing" is a technology that analyzes text data entered by users to understand its meaning and intent.

[0612] "Initial response" refers to a response that includes initial advice or solutions, and is information provided to the user.

[0613] A "means for determining severity" refers to an algorithm that evaluates the urgency and importance of the consultation content and determines the need for appropriate action.

[0614] "Expert information" refers to data regarding the contact information and available times of experts who possess the skills and knowledge to address a specific problem.

[0615] "Means of guidance" refers to functions that guide users to appropriate specialists and establish a process for receiving support.

[0616] "Methods for managing reservations" refers to a system that sets and adjusts consultation dates and times with experts, and retains that information once the time is confirmed.

[0617] "Means of collecting fees" refers to the process of collecting appropriate fees from users once a consultation is confirmed.

[0618] "Means for collecting feedback" refers to a function that obtains opinions and impressions from users after a consultation and uses them to improve the system and evaluate experts.

[0619] "A method for recommending the most suitable expert from multiple experts" refers to an algorithm that selects and recommends the most appropriate expert based on the user's consultation content.

[0620] "Security-related urgent issues" refer to safety-related problems that require a swift response, such as intruder intrusions or stalking incidents.

[0621] "Means to facilitate rapid response" refers to a function that provides immediate access to expert information regarding urgent security issues, enabling a swift response.

[0622] To implement this invention, the process begins with the user launching the "Emergency Support Help" application on their smartphone. The user first enters their problem into the consultation form within the application. This problem is entered in text format and sent to the server.

[0623] The server receives this input and analyzes its content using natural language processing (NLP) techniques. It utilizes SpaCy software and a Japanese NLP model. Based on the analysis, it generates a preliminary response and sends it back to the user. This preliminary response provides quick initial advice and simple solutions.

[0624] Next, the server executes an algorithm to determine the severity of the consultation. This severity assessment analyzes the keywords and context of the entered consultation and scores its importance. Based on the results, if the severity is high, expert information is provided and the user is guided to a specialist.

[0625] Expert information includes the expert's contact details and available times. Users select an expert they wish to consult from the presented list and make a consultation appointment. The server manages the appointments and collects fees using the user's credit card information once the appointment is confirmed. This ensures that experts receive timely and appropriate compensation, and users receive prompt and professional assistance.

[0626] Furthermore, in the event of a security-related emergency (such as an intruder's visit or stalking), this system immediately provides expert information to facilitate a rapid response. This allows users to deal with problems with peace of mind even in emergencies.

[0627] After the consultation ends, the server sends the user a feedback form. The user uses this form to provide feedback on the consultation and opinions on application features. This feedback helps improve the system and contributes to a better user experience.

[0628] As a concrete example, consider a scenario where a user enters a message stating, "An intruder has entered my home." The server instantly analyzes this message and determines its severity. As a result, the server responds with, "Please call 110 immediately. The following specialists are available to assist you," and provides a list of specialists. This entire process allows the user to receive quick and appropriate assistance.

[0629] An example of a prompt sentence to input into the generating AI model is, "How should an emergency message regarding an intruder be handled, and what advice should be provided?"

[0630] Based on the above, this invention provides a system that enables a rapid and appropriate response to urgent security-related issues.

[0631] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0632] Step 1:

[0633] The user launches the "Emergency Support Help" application on their smartphone and enters their problem into the consultation form. The entered information is sent from the device to the server in text format. Specifically, when the user enters their problem and presses the submit button, the information is sent to the server via an HTTP request.

[0634] Step 2:

[0635] The server analyzes the received consultation content using natural language processing technology. Here, natural language processing libraries such as SpaCy are used to analyze the input text data. The input is the text data of the user's consultation, and the output is the analysis results, including the meaning and importance of that content.

[0636] Step 3:

[0637] Based on the analysis results, the server generates an initial response and provides it to the user. Using a generation AI model, it outputs appropriate advice and solutions. The input is the analysis results obtained in the previous step, and the output is the text message of the initial response provided to the user. Specifically, the generated response message is sent to the terminal in JSON format and displayed on the terminal.

[0638] Step 4:

[0639] The server executes an algorithm to determine the severity of the consultation. For example, it calculates a severity score based on keyword frequency and context. The input is the analysis result, and the output is the severity score. Specifically, it uses a numerical engine that determines urgency based on whether a particular keyword is included and from the context.

[0640] Step 5:

[0641] If the severity level is high, the server provides expert information and directs the user to an expert. Expert information includes contact details and available time slots. The input is the severity score, and the output is a list of experts provided to the user. Specifically, it queries the database for available experts and retrieves information about the relevant experts.

[0642] Step 6:

[0643] The user selects a specialist from a presented list and makes a consultation appointment. The terminal displays the options and accepts the user's selection. The input is the user's selection, and the output is the appointment information. Specifically, when the user selects a specialist, the selection is sent to the server and the appointment is recorded.

[0644] Step 7:

[0645] The server collects payment using the user's credit card information once the reservation is confirmed. Payment is made using an online payment gateway. Inputs are reservation information and payment information, and output is a notification of payment completion. Specifically, it calls a payment API to execute the payment process and notifies the user of the result.

[0646] Step 8:

[0647] After the consultation ends, the server sends a feedback form to the user. The terminal displays the form and accepts the user's feedback. The input is the consultation completion event, and the output is the feedback information. Specifically, the feedback form is sent to the user's terminal in JSON format, and the user's input is returned to the server.

[0648] Step 9:

[0649] The collected feedback is analyzed on the server and used to improve the system. The input is feedback data, and the output is improvement suggestions and evaluation reports. Specifically, the feedback data is statistically analyzed to identify areas for system improvement.

[0650] This series of steps ensures that users receive prompt and appropriate assistance even in emergencies.

[0651] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0652] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with an emotion engine that recognizes the user's emotions, a more personalized response can be achieved.

[0653] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[0654] The server passes the received inquiry to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[0655] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is also used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong sadness or despair, the severity will be set high.

[0656] If the situation is deemed serious and requires professional intervention, the server provides the user with a list of specialists and information on available appointment times. A feature is also included that recommends the most suitable specialist based on emotions recognized by an emotion engine. This allows users to select the most appropriate specialist who is emotionally supportive.

[0657] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects a monthly fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is conducted.

[0658] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for resolving their problems.

[0659] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0660] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[0661] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[0662] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] User:

[0666] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[0667] Step 2:

[0668] Terminal:

[0669] The user's inputted consultation details are sent to the server.

[0670] Step 3:

[0671] server:

[0672] The received consultation content is passed to a natural language processing engine for analysis.

[0673] Step 4:

[0674] server:

[0675] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[0676] Step 5:

[0677] server:

[0678] The generated primary response is formatted and sent to the terminal for the user to receive.

[0679] Step 6:

[0680] User:

[0681] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[0682] Step 7:

[0683] server:

[0684] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[0685] Step 8:

[0686] server:

[0687] Furthermore, the consultation content is passed to an emotion engine to recognize the user's emotions.

[0688] Step 9:

[0689] server:

[0690] The severity level is adjusted based on the results of the emotion engine. For example, if the user is showing strong sadness or despair, the severity level is set higher.

[0691] Step 10:

[0692] server:

[0693] If the situation is deemed serious and requires professional intervention, the user will be provided with a list of available specialists and information on their available appointment times. Furthermore, based on the results of the emotion engine, the most suitable specialist will be recommended.

[0694] Step 11:

[0695] User:

[0696] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[0697] Step 12:

[0698] server:

[0699] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[0700] Step 13:

[0701] server:

[0702] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[0703] Step 14:

[0704] server:

[0705] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[0706] Step 15:

[0707] Experts:

[0708] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[0709] Step 16:

[0710] User:

[0711] You will receive a consultation from an expert at the scheduled time.

[0712] Step 17:

[0713] server:

[0714] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[0715] Step 18:

[0716] User:

[0717] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[0718] Step 19:

[0719] server:

[0720] Based on the feedback, we will improve the system and conduct expert evaluations.

[0721] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[0722] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[0723] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[0724] (Example 2)

[0725] Next, we will describe Example 2. 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".

[0726] There is a challenge in providing prompt and appropriate support for the various social problems faced by those seeking help. Conventional systems often lack sufficient analysis of consultation content and assessment of severity, making personalized responses difficult. Furthermore, they lack the ability to recognize and respond to emotions, making it difficult to provide empathetic support. Therefore, there is a need for a system that can quickly match individuals with appropriate professionals and provide effective support.

[0727] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0728] In this invention, the server includes means for receiving consultation content from a user, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing the emotions in the consultation content, means for determining the severity of the consultation content based on the recognized emotions, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary according to the severity, and means for managing reservations for consultations with experts and collecting fees when the reservation is confirmed. This enables prompt and appropriate support that is sensitive to the emotions of the person seeking advice.

[0729] A "user" refers to an individual or legal entity that uses the system to input their consultation details and seek support.

[0730] "Consultation content" refers to the written text and information about the user's worries and problems that they input through the system.

[0731] "Natural language processing" is a technology that analyzes the content of inquiries entered by users and extracts their intentions and important information.

[0732] "Initial response" refers to the initial advice or solution generated by the server based on the results of natural language processing.

[0733] "Emotion recognition" is a technology that identifies emotions such as anger, sadness, fear, surprise, and happiness from the content of a user's consultation.

[0734] "Severity" refers to the degree of importance of a problem, determined based on the user's inquiry and perceived emotions.

[0735] An "expert" refers to an individual or organization with specialized knowledge and experience relevant to the topic of consultation, who provides specific support and advice to the user.

[0736] "Expert information" refers to information such as the expert's name, qualifications, contact information, and available consultation times.

[0737] A "reservation" is the process of securing a designated date and time for a user to consult with an expert.

[0738] "Fee collection" refers to the process of receiving payment from the user once a consultation with an expert has been confirmed, and generally involves using payment methods such as credit cards.

[0739] "Feedback" refers to the opinions and impressions that users provide after a consultation, and this information is used to improve the system and evaluate experts.

[0740] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with a function that recognizes the user's emotions, it achieves a more personalized response.

[0741] First, the user accesses the system's consultation form using their device and enters their problem or concern. The device then sends this consultation information to the server. The device can be a typical personal computer, smartphone, or tablet.

[0742] The server passes the received inquiry to a natural language processing engine (for example, the Google Cloud Natural Language API as a common API) for analysis. Based on the analysis results, the server generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[0743] Furthermore, the server determines the severity of the consultation. In this process, it utilizes an emotion recognition engine (for example, IBM Watson Tone Analyzer, a common API) to recognize emotions from the user's consultation. The emotion recognition engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and uses the results to determine the severity. For example, if the user expresses strong sadness or despair, the severity level will be set high.

[0744] If the situation is deemed serious and requires professional intervention, the server will provide the user with a list of specialists and information on available appointment times. The system also incorporates a feature that recommends the most suitable specialist based on emotions recognized by an emotion recognition engine. This allows users to select the most appropriate specialist who can empathize with their situation.

[0745] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects the fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is completed.

[0746] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. For online consultations, common video conferencing tools (such as Zoom) can be used. This allows the user to receive concrete support for problem-solving.

[0747] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0748] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation request saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the request and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion recognition engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[0749] Furthermore, let's consider the case of someone contemplating suicide. If the user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion recognition engine highly values ​​the user's sense of despair. The server then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with one of them.

[0750] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] The processing flow of this system's program

[0753] Step 1: User inquiry input

[0754] The user accesses the system's consultation form using their device and enters their concerns or problems. This input is entered into the text box on the device, and the user proceeds to the next step when the submit button is pressed.

[0755] Input: The content of the consultation entered by the user (e.g., "Lately, I've been feeling miserable every day and can't concentrate on anything.")

[0756] Output: Signal on which the consultation content is transmitted

[0757] Step 2: Send the consultation details to the server

[0758] The terminal sends the user's inputted consultation details to the server. During this process, the consultation details are transferred to the server using a secure communication protocol (HTTPS).

[0759] Input: The consultation content retrieved from the text field when the submit button was pressed.

[0760] Output: Data of the consultation content sent to the server

[0761] Step 3: Analysis using natural language processing

[0762] The server passes the received consultation data to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the text data and extracts important keywords and the intent of the sentences.

[0763] Input: Text data of the submitted consultation content

[0764] Output: Analysis results (e.g., extracted keywords and intent)

[0765] Step 4: Generate and send the first response.

[0766] The server generates a preliminary response based on the results of natural language processing analysis and provides it to the user. This response includes specific advice and solutions. The preliminary response is automatically generated using a generative AI model.

[0767] Input: Natural language processing analysis results

[0768] Output: Text data of the generated primary response (e.g., "I understand how you're feeling. Please try starting with something small.")

[0769] Step 5: Emotion recognition by the emotion recognition engine

[0770] The server passes the consultation content to an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotions. The emotion recognition engine analyzes the consultation content and provides a numerical representation of the user's emotions.

[0771] Input: Text data of the user's inquiry.

[0772] Output: Emotional analysis results (e.g., a numerical value representing the degree of sadness)

[0773] Step 6: Determining the severity

[0774] The server determines the severity of the consultation based on the results of the emotion recognition engine. The severity level is used as a criterion for determining whether professional intervention is necessary.

[0775] Input: Analysis results of emotion recognition

[0776] Output: Severity assessment result (e.g., Severity = High)

[0777] Step 7: Provide a list of specialists and appointment times.

[0778] If the severity is determined to be high, the server will provide the user with a list of specialists and information on available appointment times. This allows the user to select the most suitable specialist.

[0779] Input: Severity assessment result

[0780] Output: List of specialists and available time slots

[0781] Step 8: Recommendation of the best expert

[0782] Based on the results of the emotion recognition engine, the server recommends the most suitable expert to the user. This allows the user to choose the expert best suited to their situation.

[0783] Input: Analysis results of emotion recognition

[0784] Output: Recommendations from the best experts

[0785] Step 9: Select a specialist and confirm your appointment

[0786] The user selects the appropriate expert from the presented list and confirms the reservation. This information is then sent back to the server, and the reservation is confirmed.

[0787] Input: User-selected expert and appointment time

[0788] Output: Confirmed reservation information

[0789] Step 10: Fee collection based on payment information

[0790] Once a reservation is confirmed, the server collects the fee based on the user's registered payment information. A secure payment system (e.g., online payment service) is used in this process.

[0791] Input: User's payment information and confirmed reservation information

[0792] Output: Confirmation information for fee collection

[0793] Step 11: Conducting consultations and providing support

[0794] At the scheduled time, the expert will conduct a consultation with the user, either online or offline. For online consultations, a video call tool will be used.

[0795] Input: Appointed time and consultation details

[0796] Output: Consultation content and support from experts

[0797] Step 12: Gathering and evaluating feedback

[0798] After the consultation ends, the server sends the user a feedback form to collect information about the consultation and satisfaction level. This feedback is used to improve the system and evaluate the experts.

[0799] Input: Access link to the feedback form after the consultation.

[0800] Output: User-filled feedback information

[0801] (Application Example 2)

[0802] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0803] This invention relates to a system that provides rapid and appropriate support to users when they are experiencing psychological crises or emotional stress. Conventional systems have faced challenges in accurately understanding the user's emotional state and providing empathetic support. Furthermore, seamlessly handling a series of processes such as quickly referring users to specialists, managing appointments, and collecting fees has also been difficult.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0805] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing emotions from the consultation content and adjusting the severity assessment based on the results, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, and means for managing reservations for consultations with experts and collecting fees when reservations are confirmed. This makes it possible to accurately grasp the user's emotional state and quickly provide support that is sensitive to those emotions.

[0806] "Consultation content" refers to a detailed description of the problems or concerns that the user is facing.

[0807] "Natural language processing" is a technology that allows computers to analyze and understand natural human language.

[0808] "Initial response" refers to the specific advice or solutions provided first in response to a user's inquiry.

[0809] "Severity" is an indicator used to judge the importance and urgency of the matter being discussed.

[0810] "Recognizing emotions" is the process of identifying a user's emotional state based on the information they provide.

[0811] "Expert information" refers to detailed information about experts who possess knowledge and experience in a specific field.

[0812] "Managing appointments" refers to the process of coordinating and confirming consultation schedules between users and experts.

[0813] "Collecting fees" refers to the procedure of receiving payment from users for the services provided.

[0814] "Feedback" refers to the opinions and evaluations that users provide after consulting with us.

[0815] "Recommending the most suitable expert" means suggesting the expert who is best suited to the user's needs based on their consultation.

[0816] This invention provides a system that offers prompt and appropriate support to users experiencing psychological crises or emotional stress. This system seamlessly executes a series of processes, including receiving and analyzing consultation requests, generating initial responses, determining the severity of the situation, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[0817] The overall system flow is as follows: the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal sends this consultation content to the server. The server passes the received consultation content to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[0818] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as joy, sadness, anger, and fear from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong anxiety or despair, the severity will be set high. If the severity is high and it is determined that professional intervention is necessary, the server provides the user with a list of specialists and information on available appointment times. A function to recommend the most suitable specialist based on the emotions recognized by the emotion engine is also included. This allows the user to select the most appropriate specialist who can empathize with their emotions.

[0819] The user selects a suitable expert from the presented list and confirms the appointment. Upon confirmation, the server collects the fee based on the user's registered payment information. The fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation. At the scheduled time, the expert conducts an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for problem-solving.

[0820] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0821] Hardware and software to use

[0822] Hardware:

[0823] Smartphones (iOS, Android)

[0824] software:

[0825] Transformers library (sentiment analysis and natural language processing)

[0826] Backend system using RESTful API

[0827] As a concrete example, consider a case where a user enters a question such as, "I've been feeling very anxious lately. What should I do?"

[0828] Example of a prompt

[0829] "I've been feeling incredibly anxious lately. What should I do?"

[0830] The system receives this input, performs emotional analysis, and provides initial advice such as, "Let's start by creating a daily routine. It's important to set small, gradual goals and feel a sense of accomplishment." Furthermore, if the emotional engine identifies strong anxiety, it sets the severity level higher and provides information on the most readily available specialist. In this way, the system can provide users with quick and appropriate support.

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1:

[0833] Users access the system's consultation form using their device and enter their problems or concerns.

[0834] Input: Text input of the user's problem or concern.

[0835] Specific action: The user launches the application on their smartphone and enters text into the consultation form.

[0836] Step 2:

[0837] The device sends this consultation information to the server.

[0838] Input: User's submitted inquiry details

[0839] Output: Data to send to the server

[0840] Specific operation: The terminal sends the entered text to the server via an HTTP request.

[0841] Step 3:

[0842] The server passes the received inquiry details to a natural language processing engine for analysis.

[0843] Input: User's inquiry

[0844] Output: Analysis results

[0845] Specific operation: The server uses a natural language processing library (such as transformers) to analyze the consultation content and perform calculations to understand its content.

[0846] Step 4:

[0847] Based on the analysis results, a primary response is generated and provided to the user.

[0848] Input: Analysis results

[0849] Output: Primary response (advice and solutions)

[0850] Specific operation: The server generates specific advice and solutions from the analysis results and creates text data to provide to the user.

[0851] Step 5:

[0852] The server determines the severity of the consultation.

[0853] Input: Analysis results of the consultation content

[0854] Output: Severity judgment result

[0855] Specific operation: The server uses an emotion engine to perform an emotional analysis of the consultation content and calculates the severity level.

[0856] Step 6:

[0857] The system recognizes emotions from the content of the consultation and adjusts the severity assessment based on those findings.

[0858] Input: Sentiment analysis results

[0859] Output: Adjusted severity assessment result

[0860] Specific operation: The emotion engine identifies emotions from user input and adjusts the severity level determination based on those emotions.

[0861] Step 7:

[0862] If it is determined that expert assistance is required, we will provide information on experts and guide the user to the appropriate specialist.

[0863] Input: Adjusted severity rating result

[0864] Output: Expert information and guidance

[0865] Specific operation: If the severity level is high, the server retrieves appropriate expert information from the database and prepares the data to display to the user.

[0866] Step 8:

[0867] Manage appointments for consultations with experts and collect fees upon appointment confirmation.

[0868] Input: User's reservation selection data, payment information

[0869] Output: Booking confirmation data, Payment confirmation data

[0870] Specific operation: The server processes the reservation selection and payment information, confirms the reservation, processes the payment, and saves the confirmation data.

[0871] Step 9:

[0872] After consulting with experts, we will collect feedback from users.

[0873] Input: User feedback data

[0874] Output: Feedback saved data

[0875] Specific operation: The server receives the feedback provided by the user after the consultation and saves it to the database.

[0876] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0877] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0878] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0879] [Third Embodiment]

[0880] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0881] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0882] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0883] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0884] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0885] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0886] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0887] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0888] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0889] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0890] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0891] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0892] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The program's processing is described in detail below.

[0893] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[0894] The server analyzes the received inquiry using natural language processing technology and generates an initial response. This initial response is provided to the user in the form of specific advice and solutions. This allows the user to obtain initial information to resolve the issue themselves.

[0895] Furthermore, the server determines the severity of the consultation. If the severity is high and professional assistance is deemed necessary, the server provides the user with a list of specialists and information on available appointment times. The user can then select an appropriate specialist from the provided list and confirm the appointment.

[0896] The server collects a monthly fee from the user once the reservation is confirmed. This fee collection process is based on payment methods such as credit card information. Expert fees are distributed appropriately after the consultation has taken place.

[0897] At the scheduled time, the expert will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This will allow the user to receive concrete support for resolving their problems.

[0898] After the consultation ends, the server sends the user a feedback form. Through this form, the user submits their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[0899] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." However, if the user replies, "I still think that's difficult," the server determines that professional help is needed and directs the user to a specialist.

[0900] Next, let's consider the case of someone contemplating suicide. If a user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It provides information on the most readily available specialists, and the user can quickly schedule a consultation with one of them.

[0901] As a result, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback.

[0902] The following describes the processing flow.

[0903] Step 1:

[0904] User:

[0905] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[0906] Step 2:

[0907] Terminal:

[0908] The user's inputted consultation details are sent to the server.

[0909] Step 3:

[0910] server:

[0911] The received consultation content is passed to a natural language processing engine for analysis.

[0912] Step 4:

[0913] server:

[0914] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[0915] Step 5:

[0916] server:

[0917] The generated primary response is formatted and sent to the terminal for the user to receive.

[0918] Step 6:

[0919] User:

[0920] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[0921] Step 7:

[0922] server:

[0923] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[0924] Step 8:

[0925] server:

[0926] If the situation is deemed serious and requires professional assistance, the user will be provided with a list of available specialists and information on their available appointment times.

[0927] Step 9:

[0928] User:

[0929] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[0930] Step 10:

[0931] server:

[0932] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[0933] Step 11:

[0934] server:

[0935] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[0936] Step 12:

[0937] server:

[0938] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[0939] Step 13:

[0940] Experts:

[0941] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[0942] Step 14:

[0943] User:

[0944] You will receive a consultation from an expert at the scheduled time.

[0945] Step 15:

[0946] server:

[0947] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[0948] Step 16:

[0949] User:

[0950] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[0951] Step 17:

[0952] server:

[0953] Based on the feedback, we will improve the system and conduct expert evaluations.

[0954] The above outlines the specific processing steps and the actions performed at each step. Through these steps, users can receive efficient and appropriate support.

[0955] (Example 1)

[0956] Next, we will describe Example 1. 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."

[0957] The present invention aims to provide a system that offers appropriate and prompt support to users when they face major social problems. In particular, it aims to provide a seamless process for appropriately assessing the severity of the consultation, promptly referring users to experts when necessary, and conducting online or offline consultations.

[0958] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0959] In this invention, the server includes means for the user to input and transmit consultation content using a terminal, means for analyzing the received consultation content using natural language processing technology, and means for generating an initial response based on the analysis results and providing it to the user. This makes it possible to quickly provide initial support to the problems faced by the user, appropriately assess the severity of the problem, and provide expert support in a timely manner.

[0960] A "user" refers to an individual who accesses the system and enters their problems or concerns into the consultation form.

[0961] A "terminal" refers to an electronic device, such as a computer or smartphone, that a user uses to access a system.

[0962] "Consultation content" refers to the text data of the user's problems or concerns that they input and submit to the system.

[0963] A "server" refers to a remote computer system that receives user inquiries, analyzes them using natural language processing technology, and executes various functions.

[0964] "Natural language processing technology" is a general term for algorithms and programs that enable computers to understand human language, and is used for analysis and generating first-line responses.

[0965] "Initial response" refers to the initial response provided by the server to the user after analyzing the inquiry, including the first advice and solutions.

[0966] "Severity" refers to the criteria used to determine the importance and urgency of the matter being discussed.

[0967] A "specialist" refers to a professional who possesses the qualifications and knowledge to provide support and advice based on the user's inquiries.

[0968] "Guidance" refers to the process by which a server provides a user with information about the appropriate expert and encourages the user to consult with that expert.

[0969] "Reservation" refers to the procedure for users to confirm the date and time of a consultation with an expert, as well as the information confirming that reservation.

[0970] "Fee collection" refers to the process of requiring users to pay a fee for consultations with experts.

[0971] "Feedback" refers to the opinions and evaluations collected from users after a consultation has taken place.

[0972] Modes for carrying out the invention

[0973] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The specific configuration and operation of the system will be described below.

[0974] System Configuration

[0975] The system mainly consists of the following elements:

[0976] 1. The device the user will be using (e.g., PC, smartphone)

[0977] 2. Server

[0978] 3. Natural language processing technologies (specifically, OpenAI's GPT-4)

[0979] 4. Payment services (e.g., PayPal, Stripe)

[0980] 5. Video conferencing systems (e.g., Zoom, Microsoft Teams)

[0981] Hardware and software usage

[0982] terminal

[0983] A terminal is a device used by a user to access the system, and is not dependent on any particular type as long as it has an internet connection. The terminal has a web browser or a dedicated application installed, through which the user accesses the system's consultation form.

[0984] server

[0985] The server is the central computing resource for receiving, analyzing, and generating responses to inquiries submitted by users. The server has the following main functions:

[0986] Data reception: Receives consultation content sent by the user from their device.

[0987] Natural Language Processing: Received text data is analyzed using natural language processing techniques (such as GPT-4).

[0988] Primary response generation: Generates a primary response based on the analysis results and provides it to the user.

[0989] Severity Assessment: Evaluate the severity of the consultation content.

[0990] Expert Information Provision: If the severity is determined to be high, the user will be provided with a list of experts and an appointment will be scheduled with them.

[0991] Payment Collection: After the reservation is confirmed, the fee will be collected based on the user's payment information.

[0992] Feedback Collection: Collect feedback from users after the consultation is complete.

[0993] Natural Language Processing Technology

[0994] The server uses natural language processing technology to analyze the user's inquiry and generate an appropriate initial response. Specifically, OpenAI's GPT-4 is used. This allows for the automatic generation of appropriate responses to user input.

[0995] Payment services

[0996] External payment services (e.g., PayPal, Stripe) are used for fee collection, providing a secure payment environment.

[0997] Video conferencing system

[0998] When experts provide online consultations to users, video conferencing systems (e.g., Zoom, Microsoft Teams) are used. This allows for real-time consultations with experts located in remote areas.

[0999] Specific example

[1000] Example 1: Consultation regarding social withdrawal

[1001] A user submits a question saying, "I've been a hikikomori (social recluse) for the past year. How can I get myself to go outside?" The server uses natural language processing technology to analyze the question and generates an initial response: "Let's start with small steps. Try taking a walk around your neighborhood." If the user replies, "I still think that's difficult," the server determines the situation is serious and provides a list of experts.

[1002] Example 2: If you are considering suicide

[1003] If a user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with them.

[1004] Example of a prompt

[1005] "I've been a recluse for the past year. How can I get myself to go outside?"

[1006] "I'm thinking of committing suicide. Please help me."

[1007] In this way, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[1008] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1009] Processing steps:

[1010] Step 1:

[1011] Users access the system's consultation form using their device, enter their problems or concerns, and submit it. The input includes the user's text data, and the output is the device sending this text data to the server.

[1012] Step 2:

[1013] The server receives the consultation content sent by the user. The input includes the user's text data, and the received text data is stored on the server as output.

[1014] Step 3:

[1015] The server analyzes the received consultation content using natural language processing technology. Specifically, a natural language processing model (e.g., GPT-4) is used for the analysis. The input includes the user's text data, and the output is a data structure generated based on the analysis results.

[1016] Step 4:

[1017] The server generates a primary response based on the analysis results and provides it to the user. The primary response is generated using a generative AI model. The input includes the analysis results, and the output is the text data of the primary response. The server sends this text data to the user's terminal.

[1018] Step 5:

[1019] The server determines the severity of the consultation. Specifically, a natural language processing model evaluates the severity. The input includes the analysis results of the consultation content, and the output generates the severity determination result.

[1020] Step 6:

[1021] If the server determines the situation is serious, it will provide expert information and guide the user to a specialist. The input includes the severity assessment result, and the output generates a list of specialists and available appointment times. The server then sends this information to the user's terminal.

[1022] Step 7:

[1023] The user selects the appropriate expert from the presented list and confirms the reservation. The input includes the expert list and the user's selection, and the output generates reservation information. The user then sends this information to the server.

[1024] Step 8:

[1025] The server collects a monthly fee from the user once the reservation is confirmed. Input includes reservation information and the user's payment information, and output is a confirmation of the fee collection. An external payment service is used for fee collection.

[1026] Step 9:

[1027] Experts conduct online or offline consultations with users. Input includes appointment information, and output is a consultation report. The expert then sends this report to the server.

[1028] Step 10:

[1029] After the consultation ends, the server sends a feedback form to the user. The input includes the consultation report, and the output is the result of submitting the feedback form. The user fills out the feedback and submits it to the server.

[1030] Step 11:

[1031] The server collects user feedback to help improve the system and for expert evaluation. Input includes user feedback data, and output generates improvement suggestions and evaluation results.

[1032] (Application Example 1)

[1033] Next, we will explain Application Example 1. In the following explanation, 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."

[1034] When users face urgent security issues, it is difficult for them to receive prompt and appropriate assistance. Existing systems take time to immediately find the right expert based on the severity of the problem and to schedule a consultation, increasing user anxiety and risk. Therefore, there is a need for a system that can respond quickly to security issues and ensure user safety.

[1035] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1036] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for determining the severity of the consultation content, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, means for managing reservations for consultations with experts and collecting fees when reservations are confirmed, and means for immediately providing expert information and facilitating a rapid response in the case of security-related emergencies. As a result, the user can receive prompt and appropriate support even in emergencies.

[1037] "Consultation details" refer to information describing the problems or concerns that the user is facing.

[1038] A "means of receiving information" refers to an interface that allows users to input their inquiries into the system and retrieve that information.

[1039] "Natural language processing" is a technology that analyzes text data entered by users to understand its meaning and intent.

[1040] "Initial response" refers to a response that includes initial advice or solutions, and is information provided to the user.

[1041] A "means for determining severity" refers to an algorithm that evaluates the urgency and importance of the consultation content and determines the need for appropriate action.

[1042] "Expert information" refers to data regarding the contact information and available times of experts who possess the skills and knowledge to address a specific problem.

[1043] "Means of guidance" refers to functions that guide users to appropriate specialists and establish a process for receiving support.

[1044] "Methods for managing reservations" refers to a system that sets and adjusts consultation dates and times with experts, and retains that information once the time is confirmed.

[1045] "Means of collecting fees" refers to the process of collecting appropriate fees from users once a consultation is confirmed.

[1046] "Means for collecting feedback" refers to a function that obtains opinions and impressions from users after a consultation and uses them to improve the system and evaluate experts.

[1047] "A method for recommending the most suitable expert from multiple experts" refers to an algorithm that selects and recommends the most appropriate expert based on the user's consultation content.

[1048] "Security-related urgent issues" refer to safety-related problems that require a swift response, such as intruder intrusions or stalking incidents.

[1049] "Means to facilitate rapid response" refers to a function that provides immediate access to expert information regarding urgent security issues, enabling a swift response.

[1050] To implement this invention, the process begins with the user launching the "Emergency Support Help" application on their smartphone. The user first enters their problem into the consultation form within the application. This problem is entered in text format and sent to the server.

[1051] The server receives this input and analyzes its content using natural language processing (NLP) techniques. It utilizes SpaCy software and a Japanese NLP model. Based on the analysis, it generates a preliminary response and sends it back to the user. This preliminary response provides quick initial advice and simple solutions.

[1052] Next, the server executes an algorithm to determine the severity of the consultation. This severity assessment analyzes the keywords and context of the entered consultation and scores its importance. Based on the results, if the severity is high, expert information is provided and the user is guided to a specialist.

[1053] Expert information includes the expert's contact details and available times. Users select an expert they wish to consult from the presented list and make a consultation appointment. The server manages the appointments and collects fees using the user's credit card information once the appointment is confirmed. This ensures that experts receive timely and appropriate compensation, and users receive prompt and professional assistance.

[1054] Furthermore, in the event of a security-related emergency (such as an intruder's visit or stalking), this system immediately provides expert information to facilitate a rapid response. This allows users to deal with problems with peace of mind even in emergencies.

[1055] After the consultation ends, the server sends the user a feedback form. The user uses this form to provide feedback on the consultation and opinions on application features. This feedback helps improve the system and contributes to a better user experience.

[1056] As a concrete example, consider a scenario where a user enters a message stating, "An intruder has entered my home." The server instantly analyzes this message and determines its severity. As a result, the server responds with, "Please call 110 immediately. The following specialists are available to assist you," and provides a list of specialists. This entire process allows the user to receive quick and appropriate assistance.

[1057] An example of a prompt sentence to input into the generating AI model is, "How should an emergency message regarding an intruder be handled, and what advice should be provided?"

[1058] Based on the above, this invention provides a system that enables a rapid and appropriate response to urgent security-related issues.

[1059] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1060] Step 1:

[1061] The user launches the "Emergency Support Help" application on their smartphone and enters their problem into the consultation form. The entered information is sent from the device to the server in text format. Specifically, when the user enters their problem and presses the submit button, the information is sent to the server via an HTTP request.

[1062] Step 2:

[1063] The server analyzes the received consultation content using natural language processing technology. Here, natural language processing libraries such as SpaCy are used to analyze the input text data. The input is the text data of the user's consultation, and the output is the analysis results, including the meaning and importance of that content.

[1064] Step 3:

[1065] Based on the analysis results, the server generates an initial response and provides it to the user. Using a generation AI model, it outputs appropriate advice and solutions. The input is the analysis results obtained in the previous step, and the output is the text message of the initial response provided to the user. Specifically, the generated response message is sent to the terminal in JSON format and displayed on the terminal.

[1066] Step 4:

[1067] The server executes an algorithm to determine the severity of the consultation. For example, it calculates a severity score based on keyword frequency and context. The input is the analysis result, and the output is the severity score. Specifically, it uses a numerical engine that determines urgency based on whether a particular keyword is included and from the context.

[1068] Step 5:

[1069] If the severity level is high, the server provides expert information and directs the user to an expert. Expert information includes contact details and available time slots. The input is the severity score, and the output is a list of experts provided to the user. Specifically, it queries the database for available experts and retrieves information about the relevant experts.

[1070] Step 6:

[1071] The user selects a specialist from a presented list and makes a consultation appointment. The terminal displays the options and accepts the user's selection. The input is the user's selection, and the output is the appointment information. Specifically, when the user selects a specialist, the selection is sent to the server and the appointment is recorded.

[1072] Step 7:

[1073] The server collects payment using the user's credit card information once the reservation is confirmed. Payment is made using an online payment gateway. Inputs are reservation information and payment information, and output is a notification of payment completion. Specifically, it calls a payment API to execute the payment process and notifies the user of the result.

[1074] Step 8:

[1075] After the consultation ends, the server sends a feedback form to the user. The terminal displays the form and accepts the user's feedback. The input is the consultation completion event, and the output is the feedback information. Specifically, the feedback form is sent to the user's terminal in JSON format, and the user's input is returned to the server.

[1076] Step 9:

[1077] The collected feedback is analyzed on the server and used to improve the system. The input is feedback data, and the output is improvement suggestions and evaluation reports. Specifically, the feedback data is statistically analyzed to identify areas for system improvement.

[1078] This series of steps ensures that users receive prompt and appropriate assistance even in emergencies.

[1079] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1080] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with an emotion engine that recognizes the user's emotions, a more personalized response can be achieved.

[1081] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[1082] The server passes the received inquiry to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[1083] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is also used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong sadness or despair, the severity will be set high.

[1084] If the situation is deemed serious and requires professional intervention, the server provides the user with a list of specialists and information on available appointment times. A feature is also included that recommends the most suitable specialist based on emotions recognized by an emotion engine. This allows users to select the most appropriate specialist who is emotionally supportive.

[1085] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects a monthly fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is conducted.

[1086] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for resolving their problems.

[1087] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1088] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[1089] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[1090] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[1091] The following describes the processing flow.

[1092] Step 1:

[1093] User:

[1094] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[1095] Step 2:

[1096] Terminal:

[1097] The user's inputted consultation details are sent to the server.

[1098] Step 3:

[1099] server:

[1100] The received consultation content is passed to a natural language processing engine for analysis.

[1101] Step 4:

[1102] server:

[1103] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[1104] Step 5:

[1105] server:

[1106] The generated primary response is formatted and sent to the terminal for the user to receive.

[1107] Step 6:

[1108] User:

[1109] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[1110] Step 7:

[1111] server:

[1112] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[1113] Step 8:

[1114] server:

[1115] Furthermore, the consultation content is passed to an emotion engine to recognize the user's emotions.

[1116] Step 9:

[1117] server:

[1118] The severity level is adjusted based on the results of the emotion engine. For example, if the user is showing strong sadness or despair, the severity level is set higher.

[1119] Step 10:

[1120] server:

[1121] If the situation is deemed serious and requires professional intervention, the user will be provided with a list of available specialists and information on their available appointment times. Furthermore, based on the results of the emotion engine, the most suitable specialist will be recommended.

[1122] Step 11:

[1123] User:

[1124] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[1125] Step 12:

[1126] server:

[1127] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[1128] Step 13:

[1129] server:

[1130] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[1131] Step 14:

[1132] server:

[1133] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[1134] Step 15:

[1135] Experts:

[1136] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[1137] Step 16:

[1138] User:

[1139] You will receive a consultation from an expert at the scheduled time.

[1140] Step 17:

[1141] server:

[1142] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[1143] Step 18:

[1144] User:

[1145] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[1146] Step 19:

[1147] server:

[1148] Based on the feedback, we will improve the system and conduct expert evaluations.

[1149] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[1150] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[1151] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[1152] (Example 2)

[1153] Next, we will describe Example 2. 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."

[1154] There is a challenge in providing prompt and appropriate support for the various social problems faced by those seeking help. Conventional systems often lack sufficient analysis of consultation content and assessment of severity, making personalized responses difficult. Furthermore, they lack the ability to recognize and respond to emotions, making it difficult to provide empathetic support. Therefore, there is a need for a system that can quickly match individuals with appropriate professionals and provide effective support.

[1155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1156] In this invention, the server includes means for receiving consultation content from a user, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing the emotions in the consultation content, means for determining the severity of the consultation content based on the recognized emotions, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary according to the severity, and means for managing reservations for consultations with experts and collecting fees when the reservation is confirmed. This enables prompt and appropriate support that is sensitive to the emotions of the person seeking advice.

[1157] A "user" refers to an individual or legal entity that uses the system to input their consultation details and seek support.

[1158] "Consultation content" refers to the written text and information about the user's worries and problems that they input through the system.

[1159] "Natural language processing" is a technology that analyzes the content of inquiries entered by users and extracts their intentions and important information.

[1160] "Initial response" refers to the initial advice or solution generated by the server based on the results of natural language processing.

[1161] "Emotion recognition" is a technology that identifies emotions such as anger, sadness, fear, surprise, and happiness from the content of a user's consultation.

[1162] "Severity" refers to the degree of importance of a problem, determined based on the user's inquiry and perceived emotions.

[1163] An "expert" refers to an individual or organization with specialized knowledge and experience relevant to the topic of consultation, who provides specific support and advice to the user.

[1164] "Expert information" refers to information such as the expert's name, qualifications, contact information, and available consultation times.

[1165] A "reservation" is the process of securing a designated date and time for a user to consult with an expert.

[1166] "Fee collection" refers to the process of receiving payment from the user once a consultation with an expert has been confirmed, and generally involves using payment methods such as credit cards.

[1167] "Feedback" refers to the opinions and impressions that users provide after a consultation, and this information is used to improve the system and evaluate experts.

[1168] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with a function that recognizes the user's emotions, it achieves a more personalized response.

[1169] First, the user accesses the system's consultation form using their device and enters their problem or concern. The device then sends this consultation information to the server. The device can be a typical personal computer, smartphone, or tablet.

[1170] The server passes the received inquiry to a natural language processing engine (for example, the Google Cloud Natural Language API as a common API) for analysis. Based on the analysis results, the server generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[1171] Furthermore, the server determines the severity of the consultation. In this process, it utilizes an emotion recognition engine (for example, IBM Watson Tone Analyzer, a common API) to recognize emotions from the user's consultation. The emotion recognition engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and uses the results to determine the severity. For example, if the user expresses strong sadness or despair, the severity level will be set high.

[1172] If the situation is deemed serious and requires professional intervention, the server will provide the user with a list of specialists and information on available appointment times. The system also incorporates a feature that recommends the most suitable specialist based on emotions recognized by an emotion recognition engine. This allows users to select the most appropriate specialist who can empathize with their situation.

[1173] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects the fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is completed.

[1174] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. For online consultations, common video conferencing tools (such as Zoom) can be used. This allows the user to receive concrete support for problem-solving.

[1175] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1176] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation request saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the request and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion recognition engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[1177] Furthermore, let's consider the case of someone contemplating suicide. If the user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion recognition engine highly values ​​the user's sense of despair. The server then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with one of them.

[1178] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[1179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1180] The processing flow of this system's program

[1181] Step 1: User inquiry input

[1182] The user accesses the system's consultation form using their device and enters their concerns or problems. This input is entered into the text box on the device, and the user proceeds to the next step when the submit button is pressed.

[1183] Input: The content of the consultation entered by the user (e.g., "Lately, I've been feeling miserable every day and can't concentrate on anything.")

[1184] Output: Signal on which the consultation content is transmitted

[1185] Step 2: Send the consultation details to the server

[1186] The terminal sends the user's inputted consultation details to the server. During this process, the consultation details are transferred to the server using a secure communication protocol (HTTPS).

[1187] Input: The consultation content retrieved from the text field when the submit button was pressed.

[1188] Output: Data of the consultation content sent to the server

[1189] Step 3: Analysis using natural language processing

[1190] The server passes the received consultation data to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the text data and extracts important keywords and the intent of the sentences.

[1191] Input: Text data of the submitted consultation content

[1192] Output: Analysis results (e.g., extracted keywords and intent)

[1193] Step 4: Generate and send the first response.

[1194] The server generates a preliminary response based on the results of natural language processing analysis and provides it to the user. This response includes specific advice and solutions. The preliminary response is automatically generated using a generative AI model.

[1195] Input: Natural language processing analysis results

[1196] Output: Text data of the generated primary response (e.g., "I understand how you're feeling. Please try starting with something small.")

[1197] Step 5: Emotion recognition by the emotion recognition engine

[1198] The server passes the consultation content to an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotions. The emotion recognition engine analyzes the consultation content and provides a numerical representation of the user's emotions.

[1199] Input: Text data of the user's inquiry.

[1200] Output: Emotional analysis results (e.g., a numerical value representing the degree of sadness)

[1201] Step 6: Determining the severity

[1202] The server determines the severity of the consultation based on the results of the emotion recognition engine. The severity level is used as a criterion for determining whether professional intervention is necessary.

[1203] Input: Analysis results of emotion recognition

[1204] Output: Severity assessment result (e.g., Severity = High)

[1205] Step 7: Provide a list of specialists and appointment times.

[1206] If the severity is determined to be high, the server will provide the user with a list of specialists and information on available appointment times. This allows the user to select the most suitable specialist.

[1207] Input: Severity assessment result

[1208] Output: List of specialists and available time slots

[1209] Step 8: Recommendation of the best expert

[1210] Based on the results of the emotion recognition engine, the server recommends the most suitable expert to the user. This allows the user to choose the expert best suited to their situation.

[1211] Input: Analysis results of emotion recognition

[1212] Output: Recommendations from the best experts

[1213] Step 9: Select a specialist and confirm your appointment

[1214] The user selects the appropriate expert from the presented list and confirms the reservation. This information is then sent back to the server, and the reservation is confirmed.

[1215] Input: User-selected expert and appointment time

[1216] Output: Confirmed reservation information

[1217] Step 10: Fee collection based on payment information

[1218] Once a reservation is confirmed, the server collects the fee based on the user's registered payment information. A secure payment system (e.g., online payment service) is used in this process.

[1219] Input: User's payment information and confirmed reservation information

[1220] Output: Confirmation information for fee collection

[1221] Step 11: Conducting consultations and providing support

[1222] At the scheduled time, the expert will conduct a consultation with the user, either online or offline. For online consultations, a video call tool will be used.

[1223] Input: Appointed time and consultation details

[1224] Output: Consultation content and support from experts

[1225] Step 12: Gathering and evaluating feedback

[1226] After the consultation ends, the server sends the user a feedback form to collect information about the consultation and satisfaction level. This feedback is used to improve the system and evaluate the experts.

[1227] Input: Access link to the feedback form after the consultation.

[1228] Output: User-filled feedback information

[1229] (Application Example 2)

[1230] Next, we will explain application example 2. In the following explanation, 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."

[1231] This invention relates to a system that provides rapid and appropriate support to users when they are experiencing psychological crises or emotional stress. Conventional systems have faced challenges in accurately understanding the user's emotional state and providing empathetic support. Furthermore, seamlessly handling a series of processes such as quickly referring users to specialists, managing appointments, and collecting fees has also been difficult.

[1232] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1233] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing emotions from the consultation content and adjusting the severity assessment based on the results, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, and means for managing reservations for consultations with experts and collecting fees when reservations are confirmed. This makes it possible to accurately grasp the user's emotional state and quickly provide support that is sensitive to those emotions.

[1234] "Consultation content" refers to a detailed description of the problems or concerns that the user is facing.

[1235] "Natural language processing" is a technology that allows computers to analyze and understand natural human language.

[1236] "Initial response" refers to the specific advice or solutions provided first in response to a user's inquiry.

[1237] "Severity" is an indicator used to judge the importance and urgency of the matter being discussed.

[1238] "Recognizing emotions" is the process of identifying a user's emotional state based on the information they provide.

[1239] "Expert information" refers to detailed information about experts who possess knowledge and experience in a specific field.

[1240] "Managing appointments" refers to the process of coordinating and confirming consultation schedules between users and experts.

[1241] "Collecting fees" refers to the procedure of receiving payment from users for the services provided.

[1242] "Feedback" refers to the opinions and evaluations that users provide after consulting with us.

[1243] "Recommending the most suitable expert" means suggesting the expert who is best suited to the user's needs based on their consultation.

[1244] This invention provides a system that offers prompt and appropriate support to users experiencing psychological crises or emotional stress. This system seamlessly executes a series of processes, including receiving and analyzing consultation requests, generating initial responses, determining the severity of the situation, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[1245] The overall system flow is as follows: the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal sends this consultation content to the server. The server passes the received consultation content to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[1246] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as joy, sadness, anger, and fear from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong anxiety or despair, the severity will be set high. If the severity is high and it is determined that professional intervention is necessary, the server provides the user with a list of specialists and information on available appointment times. A function to recommend the most suitable specialist based on the emotions recognized by the emotion engine is also included. This allows the user to select the most appropriate specialist who can empathize with their emotions.

[1247] The user selects a suitable expert from the presented list and confirms the appointment. Upon confirmation, the server collects the fee based on the user's registered payment information. The fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation. At the scheduled time, the expert conducts an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for problem-solving.

[1248] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1249] Hardware and software to use

[1250] Hardware:

[1251] Smartphones (iOS, Android)

[1252] software:

[1253] Transformers library (sentiment analysis and natural language processing)

[1254] Backend system using RESTful API

[1255] As a concrete example, consider a case where a user enters a question such as, "I've been feeling very anxious lately. What should I do?"

[1256] Example of a prompt

[1257] "I've been feeling incredibly anxious lately. What should I do?"

[1258] The system receives this input, performs emotional analysis, and provides initial advice such as, "Let's start by creating a daily routine. It's important to set small, gradual goals and feel a sense of accomplishment." Furthermore, if the emotional engine identifies strong anxiety, it sets the severity level higher and provides information on the most readily available specialist. In this way, the system can provide users with quick and appropriate support.

[1259] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1260] Step 1:

[1261] Users access the system's consultation form using their device and enter their problems or concerns.

[1262] Input: Text input of the user's problem or concern.

[1263] Specific action: The user launches the application on their smartphone and enters text into the consultation form.

[1264] Step 2:

[1265] The device sends this consultation information to the server.

[1266] Input: User's submitted inquiry details

[1267] Output: Data to send to the server

[1268] Specific operation: The terminal sends the entered text to the server via an HTTP request.

[1269] Step 3:

[1270] The server passes the received inquiry details to a natural language processing engine for analysis.

[1271] Input: User's inquiry

[1272] Output: Analysis results

[1273] Specific operation: The server uses a natural language processing library (such as transformers) to analyze the consultation content and perform calculations to understand its content.

[1274] Step 4:

[1275] Based on the analysis results, a primary response is generated and provided to the user.

[1276] Input: Analysis results

[1277] Output: Primary response (advice and solutions)

[1278] Specific operation: The server generates specific advice and solutions from the analysis results and creates text data to provide to the user.

[1279] Step 5:

[1280] The server determines the severity of the consultation.

[1281] Input: Analysis results of the consultation content

[1282] Output: Severity judgment result

[1283] Specific operation: The server uses an emotion engine to perform an emotional analysis of the consultation content and calculates the severity level.

[1284] Step 6:

[1285] The system recognizes emotions from the content of the consultation and adjusts the severity assessment based on those findings.

[1286] Input: Sentiment analysis results

[1287] Output: Adjusted severity assessment result

[1288] Specific operation: The emotion engine identifies emotions from user input and adjusts the severity level determination based on those emotions.

[1289] Step 7:

[1290] If it is determined that expert assistance is required, we will provide information on experts and guide the user to the appropriate specialist.

[1291] Input: Adjusted severity rating result

[1292] Output: Expert information and guidance

[1293] Specific operation: If the severity level is high, the server retrieves appropriate expert information from the database and prepares the data to display to the user.

[1294] Step 8:

[1295] Manage appointments for consultations with experts and collect fees upon appointment confirmation.

[1296] Input: User's reservation selection data, payment information

[1297] Output: Booking confirmation data, Payment confirmation data

[1298] Specific operation: The server processes the reservation selection and payment information, confirms the reservation, processes the payment, and saves the confirmation data.

[1299] Step 9:

[1300] After consulting with experts, we will collect feedback from users.

[1301] Input: User feedback data

[1302] Output: Feedback saved data

[1303] Specific operation: The server receives the feedback provided by the user after the consultation and saves it to the database.

[1304] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1305] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1306] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1307] [Fourth Embodiment]

[1308] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1309] As shown in Figure 7, the 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.

[1310] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1311] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1312] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1313] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1314] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1315] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1316] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1317] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1318] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1319] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1320] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1321] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The program's processing is described in detail below.

[1322] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[1323] The server analyzes the received inquiry using natural language processing technology and generates an initial response. This initial response is provided to the user in the form of specific advice and solutions. This allows the user to obtain initial information to resolve the issue themselves.

[1324] Furthermore, the server determines the severity of the consultation. If the severity is high and professional assistance is deemed necessary, the server provides the user with a list of specialists and information on available appointment times. The user can then select an appropriate specialist from the provided list and confirm the appointment.

[1325] The server collects a monthly fee from the user once the reservation is confirmed. This fee collection process is based on payment methods such as credit card information. Expert fees are distributed appropriately after the consultation has taken place.

[1326] At the scheduled time, the expert will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This will allow the user to receive concrete support for resolving their problems.

[1327] After the consultation ends, the server sends the user a feedback form. Through this form, the user submits their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1328] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." However, if the user replies, "I still think that's difficult," the server determines that professional help is needed and directs the user to a specialist.

[1329] Next, let's consider the case of someone contemplating suicide. If a user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It provides information on the most readily available specialists, and the user can quickly schedule a consultation with one of them.

[1330] As a result, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback.

[1331] The following describes the processing flow.

[1332] Step 1:

[1333] User:

[1334] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[1335] Step 2:

[1336] Terminal:

[1337] The user's inputted consultation details are sent to the server.

[1338] Step 3:

[1339] server:

[1340] The received consultation content is passed to a natural language processing engine for analysis.

[1341] Step 4:

[1342] server:

[1343] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[1344] Step 5:

[1345] server:

[1346] The generated primary response is formatted and sent to the terminal for the user to receive.

[1347] Step 6:

[1348] User:

[1349] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[1350] Step 7:

[1351] server:

[1352] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[1353] Step 8:

[1354] server:

[1355] If the situation is deemed serious and requires professional assistance, the user will be provided with a list of available specialists and information on their available appointment times.

[1356] Step 9:

[1357] User:

[1358] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[1359] Step 10:

[1360] server:

[1361] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[1362] Step 11:

[1363] server:

[1364] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[1365] Step 12:

[1366] server:

[1367] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[1368] Step 13:

[1369] Experts:

[1370] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[1371] Step 14:

[1372] User:

[1373] You will receive a consultation from an expert at the scheduled time.

[1374] Step 15:

[1375] server:

[1376] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[1377] Step 16:

[1378] User:

[1379] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[1380] Step 17:

[1381] server:

[1382] Based on the feedback, we will improve the system and conduct expert evaluations.

[1383] The above outlines the specific processing steps and the actions performed at each step. Through these steps, users can receive efficient and appropriate support.

[1384] (Example 1)

[1385] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1386] The present invention aims to provide a system that offers appropriate and prompt support to users when they face major social problems. In particular, it aims to provide a seamless process for appropriately assessing the severity of the consultation, promptly referring users to experts when necessary, and conducting online or offline consultations.

[1387] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1388] In this invention, the server includes means for the user to input and transmit consultation content using a terminal, means for analyzing the received consultation content using natural language processing technology, and means for generating an initial response based on the analysis results and providing it to the user. This makes it possible to quickly provide initial support to the problems faced by the user, appropriately assess the severity of the problem, and provide expert support in a timely manner.

[1389] A "user" refers to an individual who accesses the system and enters their problems or concerns into the consultation form.

[1390] A "terminal" refers to an electronic device, such as a computer or smartphone, that a user uses to access a system.

[1391] "Consultation content" refers to the text data of the user's problems or concerns that they input and submit to the system.

[1392] A "server" refers to a remote computer system that receives user inquiries, analyzes them using natural language processing technology, and executes various functions.

[1393] "Natural language processing technology" is a general term for algorithms and programs that enable computers to understand human language, and is used for analysis and generating first-line responses.

[1394] "Initial response" refers to the initial response provided by the server to the user after analyzing the inquiry, including the first advice and solutions.

[1395] "Severity" refers to the criteria used to determine the importance and urgency of the matter being discussed.

[1396] A "specialist" refers to a professional who possesses the qualifications and knowledge to provide support and advice based on the user's inquiries.

[1397] "Guidance" refers to the process by which a server provides a user with information about the appropriate expert and encourages the user to consult with that expert.

[1398] "Reservation" refers to the procedure for users to confirm the date and time of a consultation with an expert, as well as the information confirming that reservation.

[1399] "Fee collection" refers to the process of requiring users to pay a fee for consultations with experts.

[1400] "Feedback" refers to the opinions and evaluations collected from users after a consultation has taken place.

[1401] Modes for carrying out the invention

[1402] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. The specific configuration and operation of the system will be described below.

[1403] System Configuration

[1404] The system mainly consists of the following elements:

[1405] 1. The device the user will be using (e.g., PC, smartphone)

[1406] 2. Server

[1407] 3. Natural language processing technologies (specifically, OpenAI's GPT-4)

[1408] 4. Payment services (e.g., PayPal, Stripe)

[1409] 5. Video conferencing systems (e.g., Zoom, Microsoft Teams)

[1410] Hardware and software usage

[1411] terminal

[1412] A terminal is a device used by a user to access the system, and is not dependent on any particular type as long as it has an internet connection. The terminal has a web browser or a dedicated application installed, through which the user accesses the system's consultation form.

[1413] server

[1414] The server is the central computing resource for receiving, analyzing, and generating responses to inquiries submitted by users. The server has the following main functions:

[1415] Data reception: Receives consultation content sent by the user from their device.

[1416] Natural Language Processing: Received text data is analyzed using natural language processing techniques (such as GPT-4).

[1417] Primary response generation: Generates a primary response based on the analysis results and provides it to the user.

[1418] Severity Assessment: Evaluate the severity of the consultation content.

[1419] Expert Information Provision: If the severity is determined to be high, the user will be provided with a list of experts and an appointment will be scheduled with them.

[1420] Payment Collection: After the reservation is confirmed, the fee will be collected based on the user's payment information.

[1421] Feedback Collection: Collect feedback from users after the consultation is complete.

[1422] Natural Language Processing Technology

[1423] The server uses natural language processing technology to analyze the user's inquiry and generate an appropriate initial response. Specifically, OpenAI's GPT-4 is used. This allows for the automatic generation of appropriate responses to user input.

[1424] Payment services

[1425] External payment services (e.g., PayPal, Stripe) are used for fee collection, providing a secure payment environment.

[1426] Video conferencing system

[1427] When experts provide online consultations to users, video conferencing systems (e.g., Zoom, Microsoft Teams) are used. This allows for real-time consultations with experts located in remote areas.

[1428] Specific example

[1429] Example 1: Consultation regarding social withdrawal

[1430] A user submits a question saying, "I've been a hikikomori (social recluse) for the past year. How can I get myself to go outside?" The server uses natural language processing technology to analyze the question and generates an initial response: "Let's start with small steps. Try taking a walk around your neighborhood." If the user replies, "I still think that's difficult," the server determines the situation is serious and provides a list of experts.

[1431] Example 2: If you are considering suicide

[1432] If a user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity and determines that emergency action is needed. It then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with them.

[1433] Example of a prompt

[1434] "I've been a recluse for the past year. How can I get myself to go outside?"

[1435] "I'm thinking of committing suicide. Please help me."

[1436] In this way, the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It can seamlessly handle everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[1437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1438] Processing steps:

[1439] Step 1:

[1440] Users access the system's consultation form using their device, enter their problems or concerns, and submit it. The input includes the user's text data, and the output is the device sending this text data to the server.

[1441] Step 2:

[1442] The server receives the consultation content sent by the user. The input includes the user's text data, and the received text data is stored on the server as output.

[1443] Step 3:

[1444] The server analyzes the received consultation content using natural language processing technology. Specifically, a natural language processing model (e.g., GPT-4) is used for the analysis. The input includes the user's text data, and the output is a data structure generated based on the analysis results.

[1445] Step 4:

[1446] The server generates a primary response based on the analysis results and provides it to the user. The primary response is generated using a generative AI model. The input includes the analysis results, and the output is the text data of the primary response. The server sends this text data to the user's terminal.

[1447] Step 5:

[1448] The server determines the severity of the consultation. Specifically, a natural language processing model evaluates the severity. The input includes the analysis results of the consultation content, and the output generates the severity determination result.

[1449] Step 6:

[1450] If the server determines the situation is serious, it will provide expert information and guide the user to a specialist. The input includes the severity assessment result, and the output generates a list of specialists and available appointment times. The server then sends this information to the user's terminal.

[1451] Step 7:

[1452] The user selects the appropriate expert from the presented list and confirms the reservation. The input includes the expert list and the user's selection, and the output generates reservation information. The user then sends this information to the server.

[1453] Step 8:

[1454] The server collects a monthly fee from the user once the reservation is confirmed. Input includes reservation information and the user's payment information, and output is a confirmation of the fee collection. An external payment service is used for fee collection.

[1455] Step 9:

[1456] Experts conduct online or offline consultations with users. Input includes appointment information, and output is a consultation report. The expert then sends this report to the server.

[1457] Step 10:

[1458] After the consultation ends, the server sends a feedback form to the user. The input includes the consultation report, and the output is the result of submitting the feedback form. The user fills out the feedback and submits it to the server.

[1459] Step 11:

[1460] The server collects user feedback to help improve the system and for expert evaluation. Input includes user feedback data, and output generates improvement suggestions and evaluation results.

[1461] (Application Example 1)

[1462] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1463] When users face urgent security issues, it is difficult for them to receive prompt and appropriate assistance. Existing systems take time to immediately find the right expert based on the severity of the problem and to schedule a consultation, increasing user anxiety and risk. Therefore, there is a need for a system that can respond quickly to security issues and ensure user safety.

[1464] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1465] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for determining the severity of the consultation content, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, means for managing reservations for consultations with experts and collecting fees when reservations are confirmed, and means for immediately providing expert information and facilitating a rapid response in the case of security-related emergencies. As a result, the user can receive prompt and appropriate support even in emergencies.

[1466] "Consultation details" refer to information describing the problems or concerns that the user is facing.

[1467] A "means of receiving information" refers to an interface that allows users to input their inquiries into the system and retrieve that information.

[1468] "Natural language processing" is a technology that analyzes text data entered by users to understand its meaning and intent.

[1469] "Initial response" refers to a response that includes initial advice or solutions, and is information provided to the user.

[1470] A "means for determining severity" refers to an algorithm that evaluates the urgency and importance of the consultation content and determines the need for appropriate action.

[1471] "Expert information" refers to data regarding the contact information and available times of experts who possess the skills and knowledge to address a specific problem.

[1472] "Means of guidance" refers to functions that guide users to appropriate specialists and establish a process for receiving support.

[1473] "Methods for managing reservations" refers to a system that sets and adjusts consultation dates and times with experts, and retains that information once the time is confirmed.

[1474] "Means of collecting fees" refers to the process of collecting appropriate fees from users once a consultation is confirmed.

[1475] "Means for collecting feedback" refers to a function that obtains opinions and impressions from users after a consultation and uses them to improve the system and evaluate experts.

[1476] "A method for recommending the most suitable expert from multiple experts" refers to an algorithm that selects and recommends the most appropriate expert based on the user's consultation content.

[1477] "Security-related urgent issues" refer to safety-related problems that require a swift response, such as intruder intrusions or stalking incidents.

[1478] "Means to facilitate rapid response" refers to a function that provides immediate access to expert information regarding urgent security issues, enabling a swift response.

[1479] To implement this invention, the process begins with the user launching the "Emergency Support Help" application on their smartphone. The user first enters their problem into the consultation form within the application. This problem is entered in text format and sent to the server.

[1480] The server receives this input and analyzes its content using natural language processing (NLP) techniques. It utilizes SpaCy software and a Japanese NLP model. Based on the analysis, it generates a preliminary response and sends it back to the user. This preliminary response provides quick initial advice and simple solutions.

[1481] Next, the server executes an algorithm to determine the severity of the consultation. This severity assessment analyzes the keywords and context of the entered consultation and scores its importance. Based on the results, if the severity is high, expert information is provided and the user is guided to a specialist.

[1482] Expert information includes the expert's contact details and available times. Users select an expert they wish to consult from the presented list and make a consultation appointment. The server manages the appointments and collects fees using the user's credit card information once the appointment is confirmed. This ensures that experts receive timely and appropriate compensation, and users receive prompt and professional assistance.

[1483] Furthermore, in the event of a security-related emergency (such as an intruder's visit or stalking), this system immediately provides expert information to facilitate a rapid response. This allows users to deal with problems with peace of mind even in emergencies.

[1484] After the consultation ends, the server sends the user a feedback form. The user uses this form to provide feedback on the consultation and opinions on application features. This feedback helps improve the system and contributes to a better user experience.

[1485] As a concrete example, consider a scenario where a user enters a message stating, "An intruder has entered my home." The server instantly analyzes this message and determines its severity. As a result, the server responds with, "Please call 110 immediately. The following specialists are available to assist you," and provides a list of specialists. This entire process allows the user to receive quick and appropriate assistance.

[1486] An example of a prompt sentence to input into the generating AI model is, "How should an emergency message regarding an intruder be handled, and what advice should be provided?"

[1487] Based on the above, this invention provides a system that enables a rapid and appropriate response to urgent security-related issues.

[1488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1489] Step 1:

[1490] The user launches the "Emergency Support Help" application on their smartphone and enters their problem into the consultation form. The entered information is sent from the device to the server in text format. Specifically, when the user enters their problem and presses the submit button, the information is sent to the server via an HTTP request.

[1491] Step 2:

[1492] The server analyzes the received consultation content using natural language processing technology. Here, natural language processing libraries such as SpaCy are used to analyze the input text data. The input is the text data of the user's consultation, and the output is the analysis results, including the meaning and importance of that content.

[1493] Step 3:

[1494] Based on the analysis results, the server generates an initial response and provides it to the user. Using a generation AI model, it outputs appropriate advice and solutions. The input is the analysis results obtained in the previous step, and the output is the text message of the initial response provided to the user. Specifically, the generated response message is sent to the terminal in JSON format and displayed on the terminal.

[1495] Step 4:

[1496] The server executes an algorithm to determine the severity of the consultation. For example, it calculates a severity score based on keyword frequency and context. The input is the analysis result, and the output is the severity score. Specifically, it uses a numerical engine that determines urgency based on whether a particular keyword is included and from the context.

[1497] Step 5:

[1498] If the severity level is high, the server provides expert information and directs the user to an expert. Expert information includes contact details and available time slots. The input is the severity score, and the output is a list of experts provided to the user. Specifically, it queries the database for available experts and retrieves information about the relevant experts.

[1499] Step 6:

[1500] The user selects a specialist from a presented list and makes a consultation appointment. The terminal displays the options and accepts the user's selection. The input is the user's selection, and the output is the appointment information. Specifically, when the user selects a specialist, the selection is sent to the server and the appointment is recorded.

[1501] Step 7:

[1502] The server collects payment using the user's credit card information once the reservation is confirmed. Payment is made using an online payment gateway. Inputs are reservation information and payment information, and output is a notification of payment completion. Specifically, it calls a payment API to execute the payment process and notifies the user of the result.

[1503] Step 8:

[1504] After the consultation ends, the server sends a feedback form to the user. The terminal displays the form and accepts the user's feedback. The input is the consultation completion event, and the output is the feedback information. Specifically, the feedback form is sent to the user's terminal in JSON format, and the user's input is returned to the server.

[1505] Step 9:

[1506] The collected feedback is analyzed on the server and used to improve the system. The input is feedback data, and the output is improvement suggestions and evaluation reports. Specifically, the feedback data is statistically analyzed to identify areas for system improvement.

[1507] This series of steps ensures that users receive prompt and appropriate assistance even in emergencies.

[1508] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1509] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with an emotion engine that recognizes the user's emotions, a more personalized response can be achieved.

[1510] The overall system works as follows: First, the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal then sends this consultation information to the server.

[1511] The server passes the received inquiry to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[1512] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is also used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong sadness or despair, the severity will be set high.

[1513] If the situation is deemed serious and requires professional intervention, the server provides the user with a list of specialists and information on available appointment times. A feature is also included that recommends the most suitable specialist based on emotions recognized by an emotion engine. This allows users to select the most appropriate specialist who is emotionally supportive.

[1514] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects a monthly fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is conducted.

[1515] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for resolving their problems.

[1516] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1517] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[1518] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[1519] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[1520] The following describes the processing flow.

[1521] Step 1:

[1522] User:

[1523] Access the system's consultation form from your device's browser or dedicated application and enter your problems or concerns.

[1524] Step 2:

[1525] Terminal:

[1526] The user's inputted consultation details are sent to the server.

[1527] Step 3:

[1528] server:

[1529] The received consultation content is passed to a natural language processing engine for analysis.

[1530] Step 4:

[1531] server:

[1532] The analysis results from the natural language processing engine are obtained, and a primary response is generated.

[1533] Step 5:

[1534] server:

[1535] The generated primary response is formatted and sent to the terminal for the user to receive.

[1536] Step 6:

[1537] User:

[1538] Check the initial response from the server on your terminal to obtain initial advice and solutions.

[1539] Step 7:

[1540] server:

[1541] The severity of the consultation will be re-analyzed to determine whether professional intervention is necessary.

[1542] Step 8:

[1543] server:

[1544] Furthermore, the consultation content is passed to an emotion engine to recognize the user's emotions.

[1545] Step 9:

[1546] server:

[1547] The severity level is adjusted based on the results of the emotion engine. For example, if the user is showing strong sadness or despair, the severity level is set higher.

[1548] Step 10:

[1549] server:

[1550] If the situation is deemed serious and requires professional intervention, the user will be provided with a list of available specialists and information on their available appointment times. Furthermore, based on the results of the emotion engine, the most suitable specialist will be recommended.

[1551] Step 11:

[1552] User:

[1553] Select a specialist from the provided list, enter the necessary information for booking, and confirm your reservation.

[1554] Step 12:

[1555] server:

[1556] Receive user expert selection and booking confirmation, and notify the expert of the booking details.

[1557] Step 13:

[1558] server:

[1559] Once a reservation is confirmed, the monthly fee will be collected based on the user's registered payment information.

[1560] Step 14:

[1561] server:

[1562] Manage the fees paid to experts and distribute them appropriately after consultations have been conducted.

[1563] Step 15:

[1564] Experts:

[1565] We provide consultations to users online or offline according to their scheduled time, offering necessary advice and treatment plans.

[1566] Step 16:

[1567] User:

[1568] You will receive a consultation from an expert at the scheduled time.

[1569] Step 17:

[1570] server:

[1571] After the consultation ends, a feedback form will be sent to the user to collect information regarding the consultation content and satisfaction level.

[1572] Step 18:

[1573] User:

[1574] You can submit your thoughts on the consultation and your opinions on the system through the feedback form.

[1575] Step 19:

[1576] server:

[1577] Based on the feedback, we will improve the system and conduct expert evaluations.

[1578] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the message and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[1579] Next, let's consider the case of someone contemplating suicide. If the user types "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion engine highly values ​​the user's sense of despair. It then provides information on the most readily available professionals, allowing the user to quickly schedule a consultation with one of them.

[1580] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[1581] (Example 2)

[1582] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1583] There is a challenge in providing prompt and appropriate support for the various social problems faced by those seeking help. Conventional systems often lack sufficient analysis of consultation content and assessment of severity, making personalized responses difficult. Furthermore, they lack the ability to recognize and respond to emotions, making it difficult to provide empathetic support. Therefore, there is a need for a system that can quickly match individuals with appropriate professionals and provide effective support.

[1584] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1585] In this invention, the server includes means for receiving consultation content from a user, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing the emotions in the consultation content, means for determining the severity of the consultation content based on the recognized emotions, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary according to the severity, and means for managing reservations for consultations with experts and collecting fees when the reservation is confirmed. This enables prompt and appropriate support that is sensitive to the emotions of the person seeking advice.

[1586] A "user" refers to an individual or legal entity that uses the system to input their consultation details and seek support.

[1587] "Consultation content" refers to the written text and information about the user's worries and problems that they input through the system.

[1588] "Natural language processing" is a technology that analyzes the content of inquiries entered by users and extracts their intentions and important information.

[1589] "Initial response" refers to the initial advice or solution generated by the server based on the results of natural language processing.

[1590] "Emotion recognition" is a technology that identifies emotions such as anger, sadness, fear, surprise, and happiness from the content of a user's consultation.

[1591] "Severity" refers to the degree of importance of a problem, determined based on the user's inquiry and perceived emotions.

[1592] An "expert" refers to an individual or organization with specialized knowledge and experience relevant to the topic of consultation, who provides specific support and advice to the user.

[1593] "Expert information" refers to information such as the expert's name, qualifications, contact information, and available consultation times.

[1594] A "reservation" is the process of securing a designated date and time for a user to consult with an expert.

[1595] "Fee collection" refers to the process of receiving payment from the user once a consultation with an expert has been confirmed, and generally involves using payment methods such as credit cards.

[1596] "Feedback" refers to the opinions and impressions that users provide after a consultation, and this information is used to improve the system and evaluate experts.

[1597] This invention provides an embodiment of a system that provides appropriate support quickly when a user is facing a major social problem. In particular, by combining it with a function that recognizes the user's emotions, it achieves a more personalized response.

[1598] First, the user accesses the system's consultation form using their device and enters their problem or concern. The device then sends this consultation information to the server. The device can be a typical personal computer, smartphone, or tablet.

[1599] The server passes the received inquiry to a natural language processing engine (for example, the Google Cloud Natural Language API as a common API) for analysis. Based on the analysis results, the server generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[1600] Furthermore, the server determines the severity of the consultation. In this process, it utilizes an emotion recognition engine (for example, IBM Watson Tone Analyzer, a common API) to recognize emotions from the user's consultation. The emotion recognition engine identifies emotions such as anger, sadness, fear, surprise, and happiness from the user's input, and uses the results to determine the severity. For example, if the user expresses strong sadness or despair, the severity level will be set high.

[1601] If the situation is deemed serious and requires professional intervention, the server will provide the user with a list of specialists and information on available appointment times. The system also incorporates a feature that recommends the most suitable specialist based on emotions recognized by an emotion recognition engine. This allows users to select the most appropriate specialist who can empathize with their situation.

[1602] The user selects a suitable expert from the presented list and confirms the reservation. Upon confirmation of the reservation, the server collects the fee based on the user's registered payment information. This fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation is completed.

[1603] At the scheduled time, a specialist will conduct an online or offline consultation with the user, providing necessary advice and treatment plans. For online consultations, common video conferencing tools (such as Zoom) can be used. This allows the user to receive concrete support for problem-solving.

[1604] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1605] As a concrete example, let's consider a consultation about social withdrawal. Suppose a user sends a consultation request saying, "I've been socially withdrawn for the past year. How can I get myself to go outside?" The server uses natural language processing to analyze the request and generates an initial response such as, "Let's start with small steps. Try taking a walk around your neighborhood." On the other hand, if the emotion recognition engine detects strong anxiety or fear from the user's input, it will rate the severity higher and prioritize directing the user to a professional.

[1606] Furthermore, let's consider the case of someone contemplating suicide. If the user enters "I'm thinking of suicide. Please help me," the server immediately assesses the severity, and the emotion recognition engine highly values ​​the user's sense of despair. The server then provides information on the most readily available specialists, allowing the user to quickly schedule a consultation with one of them.

[1607] This means the system of the present invention provides a series of processes for users to receive prompt and appropriate support. It seamlessly handles everything from receiving and analyzing inquiries, providing initial responses, determining the severity of the issue, referring and managing appointments with experts, collecting fees, and gathering feedback, enabling appropriate responses tailored to the user's emotions.

[1608] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1609] The processing flow of this system's program

[1610] Step 1: User inquiry input

[1611] The user accesses the system's consultation form using their device and enters their concerns or problems. This input is entered into the text box on the device, and the user proceeds to the next step when the submit button is pressed.

[1612] Input: The content of the consultation entered by the user (e.g., "Lately, I've been feeling miserable every day and can't concentrate on anything.")

[1613] Output: Signal on which the consultation content is transmitted

[1614] Step 2: Send the consultation details to the server

[1615] The terminal sends the user's inputted consultation details to the server. During this process, the consultation details are transferred to the server using a secure communication protocol (HTTPS).

[1616] Input: The consultation content retrieved from the text field when the submit button was pressed.

[1617] Output: Data of the consultation content sent to the server

[1618] Step 3: Analysis using natural language processing

[1619] The server passes the received consultation data to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the text data and extracts important keywords and the intent of the sentences.

[1620] Input: Text data of the submitted consultation content

[1621] Output: Analysis results (e.g., extracted keywords and intent)

[1622] Step 4: Generate and send the first response.

[1623] The server generates a preliminary response based on the results of natural language processing analysis and provides it to the user. This response includes specific advice and solutions. The preliminary response is automatically generated using a generative AI model.

[1624] Input: Natural language processing analysis results

[1625] Output: Text data of the generated primary response (e.g., "I understand how you're feeling. Please try starting with something small.")

[1626] Step 5: Emotion recognition by the emotion recognition engine

[1627] The server passes the consultation content to an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to identify the user's emotions. The emotion recognition engine analyzes the consultation content and provides a numerical representation of the user's emotions.

[1628] Input: Text data of the user's inquiry.

[1629] Output: Emotional analysis results (e.g., a numerical value representing the degree of sadness)

[1630] Step 6: Determining the severity

[1631] The server determines the severity of the consultation based on the results of the emotion recognition engine. The severity level is used as a criterion for determining whether professional intervention is necessary.

[1632] Input: Analysis results of emotion recognition

[1633] Output: Severity assessment result (e.g., Severity = High)

[1634] Step 7: Provide a list of specialists and appointment times.

[1635] If the severity is determined to be high, the server will provide the user with a list of specialists and information on available appointment times. This allows the user to select the most suitable specialist.

[1636] Input: Severity assessment result

[1637] Output: List of specialists and available time slots

[1638] Step 8: Recommendation of the best expert

[1639] Based on the results of the emotion recognition engine, the server recommends the most suitable expert to the user. This allows the user to choose the expert best suited to their situation.

[1640] Input: Analysis results of emotion recognition

[1641] Output: Recommendations from the best experts

[1642] Step 9: Select a specialist and confirm your appointment

[1643] The user selects the appropriate expert from the presented list and confirms the reservation. This information is then sent back to the server, and the reservation is confirmed.

[1644] Input: User-selected expert and appointment time

[1645] Output: Confirmed reservation information

[1646] Step 10: Fee collection based on payment information

[1647] Once a reservation is confirmed, the server collects the fee based on the user's registered payment information. A secure payment system (e.g., online payment service) is used in this process.

[1648] Input: User's payment information and confirmed reservation information

[1649] Output: Confirmation information for fee collection

[1650] Step 11: Conducting consultations and providing support

[1651] At the scheduled time, the expert will conduct a consultation with the user, either online or offline. For online consultations, a video call tool will be used.

[1652] Input: Appointed time and consultation details

[1653] Output: Consultation content and support from experts

[1654] Step 12: Gathering and evaluating feedback

[1655] After the consultation ends, the server sends the user a feedback form to collect information about the consultation and satisfaction level. This feedback is used to improve the system and evaluate the experts.

[1656] Input: Access link to the feedback form after the consultation.

[1657] Output: User-filled feedback information

[1658] (Application Example 2)

[1659] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1660] This invention relates to a system that provides rapid and appropriate support to users when they are experiencing psychological crises or emotional stress. Conventional systems have faced challenges in accurately understanding the user's emotional state and providing empathetic support. Furthermore, seamlessly handling a series of processes such as quickly referring users to specialists, managing appointments, and collecting fees has also been difficult.

[1661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1662] In this invention, the server includes means for receiving consultation content, means for analyzing the received consultation content using natural language processing, means for generating and providing a primary response to the user based on the analysis results, means for recognizing emotions from the consultation content and adjusting the severity assessment based on the results, means for providing expert information and guiding the user to an expert if it is determined that expert assistance is necessary, and means for managing reservations for consultations with experts and collecting fees when reservations are confirmed. This makes it possible to accurately grasp the user's emotional state and quickly provide support that is sensitive to those emotions.

[1663] "Consultation content" refers to a detailed description of the problems or concerns that the user is facing.

[1664] "Natural language processing" is a technology that allows computers to analyze and understand natural human language.

[1665] "Initial response" refers to the specific advice or solutions provided first in response to a user's inquiry.

[1666] "Severity" is an indicator used to judge the importance and urgency of the matter being discussed.

[1667] "Recognizing emotions" is the process of identifying a user's emotional state based on the information they provide.

[1668] "Expert information" refers to detailed information about experts who possess knowledge and experience in a specific field.

[1669] "Managing appointments" refers to the process of coordinating and confirming consultation schedules between users and experts.

[1670] "Collecting fees" refers to the procedure of receiving payment from users for the services provided.

[1671] "Feedback" refers to the opinions and evaluations that users provide after consulting with us.

[1672] "Recommending the most suitable expert" means suggesting the expert who is best suited to the user's needs based on their consultation.

[1673] This invention provides a system that offers prompt and appropriate support to users experiencing psychological crises or emotional stress. This system seamlessly executes a series of processes, including receiving and analyzing consultation requests, generating initial responses, determining the severity of the situation, referring and managing appointments with specialists, collecting fees, and gathering feedback.

[1674] The overall system flow is as follows: the user accesses the system's consultation form using a terminal and enters their problem or concern. The terminal sends this consultation content to the server. The server passes the received consultation content to a natural language processing engine for analysis. Based on the analysis results, it generates an initial response and provides it to the user. The initial response is provided to the user in the form of specific advice and solutions, allowing the user to obtain initial information.

[1675] Furthermore, the server determines the severity of the consultation. In this process, an emotion engine is used to recognize emotions from the user's consultation. The emotion engine identifies emotions such as joy, sadness, anger, and fear from the user's input, and adjusts the severity determination based on the results. For example, if the user expresses strong anxiety or despair, the severity will be set high. If the severity is high and it is determined that professional intervention is necessary, the server provides the user with a list of specialists and information on available appointment times. A function to recommend the most suitable specialist based on the emotions recognized by the emotion engine is also included. This allows the user to select the most appropriate specialist who can empathize with their emotions.

[1676] The user selects a suitable expert from the presented list and confirms the appointment. Upon confirmation, the server collects the fee based on the user's registered payment information. The fee collection process is based on payment methods such as credit card information, and the expert's compensation is appropriately distributed after the consultation. At the scheduled time, the expert conducts an online or offline consultation with the user, providing necessary advice and treatment plans. This allows the user to receive concrete support for problem-solving.

[1677] After the consultation ends, the server sends the user a feedback form to collect information about the consultation content and satisfaction level. Through this form, users can submit their thoughts on the consultation and their opinions on the system. This feedback is used to improve the system and evaluate the experts.

[1678] Hardware and software to use

[1679] Hardware:

[1680] Smartphones (iOS, Android)

[1681] software:

[1682] Transformers library (sentiment analysis and natural language processing)

[1683] Backend system using RESTful API

[1684] As a concrete example, consider a case where a user enters a question such as, "I've been feeling very anxious lately. What should I do?"

[1685] Example of a prompt

[1686] "I've been feeling incredibly anxious lately. What should I do?"

[1687] The system receives this input, performs emotional analysis, and provides initial advice such as, "Let's start by creating a daily routine. It's important to set small, gradual goals and feel a sense of accomplishment." Furthermore, if the emotional engine identifies strong anxiety, it sets the severity level higher and provides information on the most readily available specialist. In this way, the system can provide users with quick and appropriate support.

[1688] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1689] Step 1:

[1690] Users access the system's consultation form using their device and enter their problems or concerns.

[1691] Input: Text input of the user's problem or concern.

[1692] Specific action: The user launches the application on their smartphone and enters text into the consultation form.

[1693] Step 2:

[1694] The device sends this consultation information to the server.

[1695] Input: User's submitted inquiry details

[1696] Output: Data to send to the server

[1697] Specific operation: The terminal sends the entered text to the server via an HTTP request.

[1698] Step 3:

[1699] The server passes the received inquiry details to a natural language processing engine for analysis.

[1700] Input: User's inquiry

[1701] Output: Analysis results

[1702] Specific operation: The server uses a natural language processing library (such as transformers) to analyze the consultation content and perform calculations to understand its content.

[1703] Step 4:

[1704] Based on the analysis results, a primary response is generated and provided to the user.

[1705] Input: Analysis results

[1706] Output: Primary response (advice and solutions)

[1707] Specific operation: The server generates specific advice and solutions from the analysis results and creates text data to provide to the user.

[1708] Step 5:

[1709] The server determines the severity of the consultation.

[1710] Input: Analysis results of the consultation content

[1711] Output: Severity judgment result

[1712] Specific operation: The server uses an emotion engine to perform an emotional analysis of the consultation content and calculates the severity level.

[1713] Step 6:

[1714] The system recognizes emotions from the content of the consultation and adjusts the severity assessment based on those findings.

[1715] Input: Sentiment analysis results

[1716] Output: Adjusted severity assessment result

[1717] Specific operation: The emotion engine identifies emotions from user input and adjusts the severity level determination based on those emotions.

[1718] Step 7:

[1719] If it is determined that expert assistance is required, we will provide information on experts and guide the user to the appropriate specialist.

[1720] Input: Adjusted severity rating result

[1721] Output: Expert information and guidance

[1722] Specific operation: If the severity level is high, the server retrieves appropriate expert information from the database and prepares the data to display to the user.

[1723] Step 8:

[1724] Manage appointments for consultations with experts and collect fees upon appointment confirmation.

[1725] Input: User's reservation selection data, payment information

[1726] Output: Booking confirmation data, Payment confirmation data

[1727] Specific operation: The server processes the reservation selection and payment information, confirms the reservation, processes the payment, and saves the confirmation data.

[1728] Step 9:

[1729] After consulting with experts, we will collect feedback from users.

[1730] Input: User feedback data

[1731] Output: Feedback saved data

[1732] Specific operation: The server receives the feedback provided by the user after the consultation and saves it to the database.

[1733] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1734] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1735] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1736] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1737] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1738] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1739] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1740] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1741] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1742] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1743] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1744] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1745] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1746] 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.

[1747] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1748] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1749] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1750] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1751] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1752] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1753] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1754] The following is further disclosed regarding the embodiments described above.

[1755] (Claim 1)

[1756] A means of receiving inquiries,

[1757] A means of analyzing the received consultation content using natural language processing,

[1758] A means for generating a primary response based on the analysis results and providing it to the user,

[1759] A means of determining the severity of the consultation,

[1760] When it is determined that expert assistance is necessary, a means of providing expert information and guiding the user to the appropriate expert is provided.

[1761] A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation,

[1762] A system that includes this.

[1763] (Claim 2)

[1764] The system according to claim 1, further comprising means for collecting user feedback after consultation with an expert.

[1765] (Claim 3)

[1766] The system according to claim 1, further comprising means for recommending the most suitable expert from among multiple experts based on the content of the consultation received.

[1767] "Example 1"

[1768] (Claim 1)

[1769] A means for users to input and send consultation details using a device,

[1770] A means of analyzing the received consultation content using natural language processing technology,

[1771] A means for generating a primary response based on the analysis results and providing it to the user,

[1772] A means of determining the severity of the consultation,

[1773] In cases of high severity, a means of providing expert information and guiding users to experts,

[1774] A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation,

[1775] A means by which experts provide online or offline consultations to users,

[1776] A system that includes this.

[1777] (Claim 2)

[1778] The system according to claim 1, further comprising means for collecting user feedback after consultation with an expert.

[1779] (Claim 3)

[1780] The system according to claim 1, further comprising means for recommending the most suitable expert from among multiple experts based on the content of the consultation received.

[1781] "Application Example 1"

[1782] (Claim 1)

[1783] A means of receiving inquiries,

[1784] A means of analyzing the received consultation content using natural language processing,

[1785] A means for generating a primary response based on the analysis results and providing it to the user,

[1786] A means of determining the severity of the consultation,

[1787] When it is determined that expert assistance is necessary, a means of providing expert information and guiding the user to the appropriate expert is provided.

[1788] A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation,

[1789] In the event of a security-related emergency, a means to immediately provide expert information and facilitate a rapid response is needed.

[1790] A system that includes this.

[1791] (Claim 2)

[1792] The system according to claim 1, further comprising means for collecting user feedback after the consultation has taken place.

[1793] (Claim 3)

[1794] The system according to claim 1, further comprising means for recommending the most suitable expert from among multiple experts based on the content of the consultation received.

[1795] "Example 2 of combining an emotion engine"

[1796] (Claim 1)

[1797] A means of receiving inquiries from users,

[1798] A means of analyzing the received consultation content using natural language processing,

[1799] A means for generating a primary response based on the analysis results and providing it to the user,

[1800] A means of recognizing emotions in the content of a consultation,

[1801] A means of determining the severity of the consultation based on recognized emotions,

[1802] When it is determined that professional intervention is necessary depending on the severity of the situation, the system provides information on experts and guides users to those experts.

[1803] A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation,

[1804] A system that includes this.

[1805] (Claim 2)

[1806] The system according to claim 1, further comprising means for collecting user feedback after consultation with an expert.

[1807] (Claim 3)

[1808] The system according to claim 1, further comprising means for recommending the most suitable expert from among multiple experts based on the content of the consultation received and the emotions recognized.

[1809] "Application example 2 when combining with an emotional engine"

[1810] (Claim 1)

[1811] A means of receiving inquiries,

[1812] A means of analyzing the received consultation content using natural language processing,

[1813] A means for generating a primary response based on the analysis results and providing it to the user,

[1814] A means of determining the severity of the consultation,

[1815] A means of recognizing emotions from the content of the consultation and adjusting the severity assessment based on the results,

[1816] When it is determined that expert assistance is necessary, a means of providing expert information and guiding the user to the appropriate expert is provided.

[1817] A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation,

[1818] A system that includes this.

[1819] (Claim 2)

[1820] The system according to claim 1, further comprising means for collecting user feedback after consultation with an expert.

[1821] (Claim 3)

[1822] The system according to claim 1, further comprising means for recommending the most suitable expert from among multiple experts based on the content of the consultation received. [Explanation of symbols]

[1823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving inquiries, A means of analyzing the received consultation content using natural language processing, A means for generating a primary response based on the analysis results and providing it to the user, A means of determining the severity of the consultation, When it is determined that expert assistance is necessary, a means of providing expert information and guiding the user to the appropriate expert is provided. A system for managing appointments for consultations with experts and collecting fees upon appointment confirmation, A system that includes this.

2. The system according to claim 1, further comprising means for collecting user feedback after consultation with an expert.

3. The system according to claim 1, further comprising means for recommending the most suitable expert from among multiple experts based on the content of the consultation received.

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